Question: Earth Mover's Distance pooling with Siamese LSTMs for automated short answer grading.

This paper introduces a novel adaptation of the Long Short-Term Memory (LSTM) network, referred to as the Siamese LSTM, for analyzing datasets featuring pairs of sequences of varying lengths. Specifically, we apply this model to evaluate the semantic similarity between sentences, achieving superior results compared to existing state-of-the-art methods. Our approach outperforms intricate systems and manually engineered features, demonstrating its efficacy. We augment word embedding vectors with synonymic information and feed these into our Siamese LSTMs, which then encode the core meaning of sentences into fixed-size vectors, regardless of syntax or specific word choice. To ensure the learned sentence representations form a highly structured space, we constrain subsequent operations to utilize a Manhattan distance metric. This strategy compels the model to learn a space where the geometry accurately reflects intricate semantic relationships. Our findings add to a growing body of evidence that highlights the capability of LSTMs as sophisticated language models, adept at handling tasks that demand a deep understanding of language.

Machine comprehension plays an essential role in NLP and has been widely explored with dataset like MCTest. However, this dataset is too simple and too small for learning true reasoning abilities. (Hermann et al., 2015) therefore release a large scale news article dataset and propose a deep LSTM reader system for machine comprehension. However, the training process is expensive. We therefore try feature-engineered approach with semantics on the new dataset to see how traditional machine learning technique and semantics can help with machine comprehension. Meanwhile, our proposed L2R reader system achieves good performance with efficiency and less training data.

How should one apply deep learning to tasks such as morphological reinflection, which stochastically edit one string to get another? A recent approach to such sequence-to-sequence tasks is to compress the input string into a vector that is then used to generate the output string, using recurrent neural networks. In contrast, we propose to keep the traditional architecture, which uses a finite-state transducer to score all possible output strings, but to augment the scoring function with the help of recurrent networks. A stack of bidirectional LSTMs reads the input string from leftto-right and right-to-left, in order to summarize the input context in which a transducer arc is applied. We combine these learned features with the transducer to define a probability distribution over aligned output strings, in the form of a weighted finite-state automaton. This reduces hand-engineering of features, allows learned features to examine unbounded context in the input string, and still permits exact inference through dynamic programming. We illustrate our method on the tasks of morphological reinflection and lemmatization.

Sarcasm detection has been modeled as a binary document classification task, with rich features being defined manually over input documents. Traditional models employ discrete manual features to address the task, with much research effect being devoted to the design of effective feature templates. We investigate the use of neural network for tweet sarcasm detection, and compare the effects of the continuous automatic features with discrete manual features. In particular, we use a bi-directional gated recurrent neural network to capture syntactic and semantic information over tweets locally, and a pooling neural network to extract contextual features automatically from history tweets. Results show that neural features give improved accuracies for sarcasm detection, with different error distributions compared with discrete manual features.

Nowadays, it is a heated topic for many industries to build automatic question-answering (QA) systems. A key solution to these QA systems is to retrieve from a QA knowledge base the most similar question of a given question, which can be reformulated as a paraphrase identification (PI) or a natural language inference (NLI) problem. However, most existing models for PI and NLI have at least two problems: They rely on a large amount of labeled data, which is not always available in real scenarios, and they may not be efficient for industrial applications. In this paper, we study transfer learning for the PI and NLI problems, aiming to propose a general framework, which can effectively and efficiently adapt the shared knowledge learned from a resource-rich source domain to a resource-poor target domain. Specifically, since most existing transfer learning methods only focus on learning a shared feature space across domains while ignoring the relationship between the source and target domains, we propose to simultaneously learn shared representations and domain relationships in a unified framework. Furthermore, we propose an efficient and effective hybrid model by combining a sentence encoding-based method and a sentence interaction-based method as our base model. Extensive experiments on both paraphrase identification and natural language inference demonstrate that our base model is efficient and has promising performance compared to the competing models, and our transfer learning method can help to significantly boost the performance. Further analysis shows that the inter-domain and intra-domain relationship captured by our model are insightful. Last but not least, we deploy our transfer learning model for PI into our online chatbot system, which can bring in significant improvements over our existing system. Finally, we launch our new system on the chatbot platform Eva in our E-commerce site AliExpress.

In this paper, we investigate the usage of autoencoders in modeling textual data. Traditional autoencoders suffer from at least two aspects: scalability with the high dimensionality of vocabulary size and dealing with task-irrelevant words. We address this problem by introducing supervision via the loss function of autoencoders. In particular, we first train a linear classifier on the labeled data, then define a loss for the autoencoder with the weights learned from the linear classifier. To reduce the bias brought by one single classifier, we define a posterior probability distribution on the weights of the classifier, and derive the marginalized loss of the autoencoder with Laplace approximation. We show that our choice of loss function can be rationalized from the perspective of Bregman Divergence, which justifies the soundness of our model. We evaluate the effectiveness of our model on six sentiment analysis datasets, and show that our model significantly outperforms all the competing methods with respect to classification accuracy. We also show that our model is able to take advantage of unlabeled dataset and get improved performance. We further show that our model successfully learns highly discriminative feature maps, which explains its superior performance.

Neural network architectures such as long short-term memory (LSTM) have been proven to be powerful models for processing sequences including text, audio and video. On the basis of vanilla LSTM, multi-modal attention mechanisms are proposed in this paper to synthesize the time and semantic information of input sequences. First, we reconstruct the forget and input gates of the LSTM unit from the perspective of attention model in the temporal dimension. Then the memory content of the LSTM unit is recalculated using a cluster-based attention mechanism in semantic space. Experiments on acoustic scene classification tasks show performance improvements of the proposed methods when compared with vanilla LSTM. The classification errors on LITIS ROUEN dataset and DCASE2016 dataset are reduced by 16.5% and 7.7% relatively. We get a second place in the Kaggle’s YouTube-8M video understanding challenge, and multi-modal attention based LSTM model is one of our bestperforming single systems.

Summary form only given. The paper describes a novel automatic procedure for the generation of multiple-choice tests from electronic documents. In addition to employing various NLP techniques including term extraction and shallow parsing, the system makes use of language resources such as corpora and ontologies. The system operates in a fully automatic mode and also a semiautomatic environment where the user is offered the option to post-edit the generated test items. The results from the conducted evaluation suggest that the new procedure is very effective saving time and labour considerably and that the test items produced with the help of the program are not of inferior quality to those produced manually.

Long short-term memory (LSTM) is normally used in recurrent neural network (RNN) as basic recurrent unit. However, conventional LSTM assumes that the state at current time step depends on previous time step. This assumption constraints the time dependency modeling capability. In this study, we propose a new variation of LSTM, advanced LSTM (A-LSTM), for better temporal context modeling. We employ A-LSTM in weighted pooling RNN for emotion recognition. The A-LSTM outperforms the conventional LSTM by 5.5% relatively. The A-LSTM based weighted pooling RNN can also complement the state-of-the-art emotion classification framework. This shows the advantage of A-LSTM.

In this paper, we describe our system submitted for the semantic textual similarity (STS) task at SemEval 2012. We implemented two approaches to calculate the degree of similarity between two sentences. First approach combines corpus-based semantic relatedness measure over the whole sentence with the knowledge-based semantic similarity scores obtained for the words falling under the same syntactic roles in both the sentences. We fed all these scores as features to machine learning models to obtain a single score giving the degree of similarity of the sentences. Linear Regression and Bagging models were used for this purpose. We used Explicit Semantic Analysis (ESA) as the corpus-based semantic relatedness measure. For the knowledgebased semantic similarity between words, a modified WordNet based Lin measure was used. Second approach uses a bipartite based method over the WordNet based Lin measure, without any modification. This paper shows a significant improvement in calculating the semantic similarity between sentences by the fusion of the knowledge-based similarity measure and the corpus-based relatedness measure against corpus based measure taken alone.

The reading comprehension task, that asks questions about a given evidence document, is a central problem in natural language understanding. Recent formulations of this task have typically focused on answer selection from a set of candidates pre-defined manually or through the use of an external NLP pipeline. However, Rajpurkar et al. (2016) recently released the SQUAD dataset in which the answers can be arbitrary strings from the supplied text. In this paper, we focus on this answer extraction task, presenting a novel model architecture that efficiently builds fixed length representations of all spans in the evidence document with a recurrent network. We show that scoring explicit span representations significantly improves performance over other approaches that factor the prediction into separate predictions about words or start and end markers. Our approach improves upon the best published results of Wang & Jiang (2016) by 5% and decreases the error of Rajpurkar et al.’s baseline by > 50%.


Question: Earth Mover's Distance pooling in Siamese LSTMs for automated short answer grading.

This paper delves into the integration of effective features for the automation of short answer scoring in educational contexts. A significant area where Natural Language Processing (NLP) can make a tangible difference is in assisting educators with the scoring of student-written responses, particularly those that require an assessment of content. A key obstacle in this domain is the scarcity of labeled data, specifically scored answers to a given question. To address this, supplementary information from human scoring criteria, including exemplars for various score tiers and descriptions of pivotal concepts, can be leveraged. In our study, we investigate techniques for the amalgamation of scoring guidelines with labeled responses. Our findings reveal that the application of stacked generalization, a concept introduced by Wolpert in 1992, significantly boosts performance, particularly when training datasets are relatively small. This approach allows for the effective utilization of available resources to enhance the accuracy and reliability of automated short answer scoring systems.

Modeling interactions between two sentences is crucial for a number of natural language processing tasks including Answer Selection, Dialogue Act Analysis, etc. While deep learning methods like Recurrent Neural Network or Convolutional Neural Network have been proved to be powerful for sentence modeling, prior studies paid less attention on interactions between sentences. In this work, we propose a Sentence Interaction Network (SIN) for modeling the complex interactions between two sentences. By introducing “interaction states” for word and phrase pairs, SIN is powerful and flexible in capturing sentence interactions for different tasks. We obtain significant improvements on Answer Selection and Dialogue Act Analysis without any feature engineering.

This paper presents the results of systematic and comparative experimentation with major types of methodologies for automatic duplicate question detection when these are applied to datasets of progressively larger sizes, thus allowing to study the learning profiles of this task under these different approaches and evaluate their merits. This study was made possible by resorting to the recent release for research purposes, by the Quora online question answering engine, of a new dataset with over 400,000 pairs labeled with respect to their elements being duplicate interrogative segments.

Modern teaching paradigms promote active student participation, encouraging teachers to adapt the teaching process to involve more practical work. In the introductory programming course at the Faculty of Computer and Information Science, University of Ljubljana, Slovenia, homework assignments contribute approximately one half to the total grade, requiring a significant investment of time and human resources in the assessment process. This problem was alleviated by the automated assessment of homework assignments. In this paper, we introduce an automated assessment system for programming assignments that includes dynamic testing of student programs, plagiarism detection, and a proper presentation of the results. We share our experience and compare the introduced system with the manual assessment approach used before.

The long short-term memory (LSTM) network trained by gradient descent solves difficult problems which traditional recurrent neural networks in general cannot. We have recently observed that the decoupled extended Kalman filter training algorithm allows for even better performance, reducing significantly the number of training steps when compared to the original gradient descent training algorithm. In this paper we present a set of experiments which are unsolvable by classical recurrent networks but which are solved elegantly and robustly and quickly by LSTM combined with Kalman filters.

We propose a fully automatic technique for evaluating text summaries without the need to prepare the gold standard summaries manually. A standard and popular summary evaluation techniques or tools are not fully automatic; they all need some manual process or manual reference summary. Using recognizing textual entailment (TE), automatically generated summaries can be evaluated completely automatically without any manual preparation process. We use a TE system based on a combination of lexical entailment module, lexical distance module, Chunk module, Named Entity module and syntactic text entailment (TE) module. The documents are used as text (T) and summary of these documents are taken as hypothesis (H). Therefore, the more information of the document is entailed by its summary the better the summary. Comparing with the ROUGE 1.5.5 evaluation scores over TAC 2008 (formerly DUC, conducted by NIST) dataset, the proposed evaluation technique predicts the ROUGE scores with a accuracy of 98.25% with respect to ROUGE-2 and 95.65% with respect to ROUGE-SU4.

As part of evaluating a summary automatically, it is usual to determine how much of the contents of one or more human-produced ‘ideal’ summaries it contains. Past automated methods such as ROUGE compare using fixed word ngrams, which are not ideal for a variety of reasons. In this paper we describe a framework in which summary evaluation measures can be instantiated and compared, and we implement a specific evaluation method using very small units of content, called Basic Elements, that address some of the shortcomings of ngrams. This method is tested on DUC 2003, 2004, and 2005 systems and produces very good correlations with human judgments.

We describe a Deep Learning approach to modeling the relevance of a document’s text to a query, applied to biomedical literature. Instead of mapping each document and query to a common semantic space, we compute a variable-length difference vector between the query and document which is then passed through a deep convolution stage followed by a deep regression network to produce the estimated probability of the document’s relevance to the query. Despite the small amount of training data, this approach produces a more robust predictor than computing similarities between semantic vector representations of the query and document, and also results in significant improvements over traditional IR text factors. In the future, we plan to explore its application in improving PubMed search.

Traditional convolutional neural network (CNN) based query classification uses linear feature mapping in its convolution operation. The recurrent neural network (RNN), differs from a CNN in representing word sequence with their ordering information kept explicitly. We propose using a deep long-short-term-memory (DLSTM) based feature mapping to learn feature representation for CNN. The DLSTM, which is a stack of LSTM units, has different order of feature representations at different depth of LSTM unit. The bottom LSTM unit equipped with input and output gates, extracts the first order feature representation from current word. To extract higher order nonlinear feature representation, the LSTM unit at higher position gets input from two parts. First part is the lower LSTM unit’s memory cell from previous word. Second part is the lower LSTM unit’s hidden output from current word. In this way, the DLSTM captures the nonlinear nonconsecutive interaction within n-grams. Using an architecture that combines a stack of the DLSTM layers with a tradition CNN layer, we have observed new state-of-the-art query classification accuracy on benchmark data sets for query classification.

Despite the success of deep learning on many fronts especially image and speech, its application in text classification often is still not as good as a simple linear SVM on n-gram TF-IDF representation especially for smaller datasets. Deep learning tends to emphasize on sentence level semantics when learning a representation with models like recurrent neural network or recursive neural network, however from the success of TF-IDF representation, it seems a bag-of-words type of representation has its strength. Taking advantage of both representions, we present a model known as TDSM (Top Down Semantic Model) for extracting a sentence representation that considers both the word-level semantics by linearly combining the words with attention weights and the sentence-level semantics with BiLSTM and use it on text classification. We apply the model on characters and our results show that our model is better than all the other characterbased and word-based convolutional neural network models by (Zhang et al., 2015) across seven different datasets with only 1% of their parameters. We also demonstrate that this model beats traditional linear models on TF-IDF vectors on small and polished datasets like news article in which typically deep learning models surrender.

Prediction without justification has limited applicability. As a remedy, we learn to extract pieces of input text as justifications – rationales – that are tailored to be short and coherent, yet sufficient for making the same prediction. Our approach combines two modular components, generator and encoder, which are trained to operate well together. The generator specifies a distribution over text fragments as candidate rationales and these are passed through the encoder for prediction. Rationales are never given during training. Instead, the model is regularized by desiderata for rationales. We evaluate the approach on multi-aspect sentiment analysis against manually annotated test cases. Our approach outperforms attention-based baseline by a significant margin. We also successfully illustrate the method on the question retrieval task.1


Question: Earth Mover's Distance pooling with Siamese LSTMs for automated short answer grading.

This tutorial offers a comprehensive guide to the fundamental concepts behind Support Vector (SV) machines in the context of function estimation. It delves into the core principles that underpin SV machines and their application in predicting continuous values. Moreover, the tutorial compiles a range of algorithms currently employed for training SV machines. These methods encompass both the quadratic (or convex) programming aspect, essential for solving optimization problems, and sophisticated techniques designed to handle large-scale datasets efficiently. The tutorial also explores various adaptations and enhancements to the standard SV algorithm, showcasing how these modifications have been integrated to improve performance. Additionally, it delves into the regularization perspective within the SV framework, highlighting how regularization techniques are utilized to prevent overfitting and enhance model generalization. This detailed exploration is aimed at providing readers with a solid foundation in SV machines for function estimation, equipping them with the knowledge to apply and extend these models in various predictive analytics tasks.

Despite the success of deep learning on many fronts especially image and speech, its application in text classification often is still not as good as a simple linear SVM on n-gram TF-IDF representation especially for smaller datasets. Deep learning tends to emphasize on sentence level semantics when learning a representation with models like recurrent neural network or recursive neural network, however from the success of TF-IDF representation, it seems a bag-of-words type of representation has its strength. Taking advantage of both representions, we present a model known as TDSM (Top Down Semantic Model) for extracting a sentence representation that considers both the word-level semantics by linearly combining the words with attention weights and the sentence-level semantics with BiLSTM and use it on text classification. We apply the model on characters and our results show that our model is better than all the other characterbased and word-based convolutional neural network models by (Zhang et al., 2015) across seven different datasets with only 1% of their parameters. We also demonstrate that this model beats traditional linear models on TF-IDF vectors on small and polished datasets like news article in which typically deep learning models surrender.

In this paper we focus on the problem of question ranking in community question answering (cQA) forums in Arabic. We address the task with machine learning algorithms using advanced Arabic text representations. The latter are obtained by applying tree kernels to constituency parse trees combined with textual similarities, including word embeddings. Our two main contributions are: (i) an Arabic language processing pipeline based on UIMA —from segmentation to constituency parsing— built on top of Farasa, a state-of-the-art Arabic language processing toolkit; and (ii) the application of long short-term memory neural networks to identify the best text fragments in questions to be used in our tree-kernel-based ranker. Our thorough experimentation on a recently released cQA dataset shows that the Arabic linguistic processing provided by Farasa produces strong results and that neural networks combined with tree kernels further boost the performance in terms of both efficiency and accuracy. Our approach also enables an implicit comparison between different processing pipelines as our tests on Farasa and Stanford parsers demonstrate.

Summary form only given. The paper describes a novel automatic procedure for the generation of multiple-choice tests from electronic documents. In addition to employing various NLP techniques including term extraction and shallow parsing, the system makes use of language resources such as corpora and ontologies. The system operates in a fully automatic mode and also a semiautomatic environment where the user is offered the option to post-edit the generated test items. The results from the conducted evaluation suggest that the new procedure is very effective saving time and labour considerably and that the test items produced with the help of the program are not of inferior quality to those produced manually.

We present the Stanford Question Answering Dataset (SQuAD), a new reading comprehension dataset consisting of 100,000+ questions posed by crowdworkers on a set of Wikipedia articles, where the answer to each question is a segment of text from the corresponding reading passage. We analyze the dataset to understand the types of reasoning required to answer the questions, leaning heavily on dependency and constituency trees. We build a strong logistic regression model, which achieves an F1 score of 51.0%, a significant improvement over a simple baseline (20%). However, human performance (86.8%) is much higher, indicating that the dataset presents a good challenge problem for future research. The dataset is freely available at https://stanford-qa.com.

The problem of identifying approximately duplicate records in databases is an essential step for data cleaning and data integration processes. Most existing approaches have relied on generic or manually tuned distance metrics for estimating the similarity of potential duplicates. In this paper, we present a framework for improving duplicate detection using trainable measures of textual similarity. We propose to employ learnable text distance functions for each database field, and show that such measures are capable of adapting to the specific notion of similarity that is appropriate for the field's domain. We present two learnable text similarity measures suitable for this task: an extended variant of learnable string edit distance, and a novel vector-space based measure that employs a Support Vector Machine (SVM) for training. Experimental results on a range of datasets show that our framework can improve duplicate detection accuracy over traditional techniques.

As the amount of unstructured text data that humanity produces overall and on the Internet grows, so does the need to intelligently to process it and extract different types of knowledge from it. Convolutional neural networks (CNNs) and recurrent neural networks (RNNs) have been applied to natural language processing systems with comparative, remarkable results. The CNN is a noble approach to extract higher level features that are invariant to local translation. However, it requires stacking multiple convolutional layers in order to capture long-term dependencies, due to the locality of the convolutional and pooling layers. In this paper, we describe a joint CNN and RNN framework to overcome this problem. Briefly, we use an unsupervised neural language model to train initial word embeddings that are further tuned by our deep learning network, then, the pre-trained parameters of the network are used to initialize the model. At a final stage, the proposed framework combines former information with a set of feature maps learned by a convolutional layer with long-term dependencies learned via long-short-term memory. Empirically, we show that our approach, with slight hyperparameter tuning and static vectors, achieves outstanding results on multiple sentiment analysis benchmarks. Our approach outperforms several existing approaches in term of accuracy; our results are also competitive with the state-of-the-art results on the Stanford Large Movie Review data set with 93.3% accuracy, and the Stanford Sentiment Treebank data set with 48.8% fine-grained and 89.2% binary accuracy, respectively. Our approach has a significant role in reducing the number of parameters and constructing the convolutional layer followed by the recurrent layer as a substitute for the pooling layer. Our results show that we were able to reduce the loss of detailed, local information and capture long-term dependencies with an efficient framework that has fewer parameters and a high level of performance.

We describe a Deep Learning approach to modeling the relevance of a document’s text to a query, applied to biomedical literature. Instead of mapping each document and query to a common semantic space, we compute a variable-length difference vector between the query and document which is then passed through a deep convolution stage followed by a deep regression network to produce the estimated probability of the document’s relevance to the query. Despite the small amount of training data, this approach produces a more robust predictor than computing similarities between semantic vector representations of the query and document, and also results in significant improvements over traditional IR text factors. In the future, we plan to explore its application in improving PubMed search.

The reading comprehension task, that asks questions about a given evidence document, is a central problem in natural language understanding. Recent formulations of this task have typically focused on answer selection from a set of candidates pre-defined manually or through the use of an external NLP pipeline. However, Rajpurkar et al. (2016) recently released the SQUAD dataset in which the answers can be arbitrary strings from the supplied text. In this paper, we focus on this answer extraction task, presenting a novel model architecture that efficiently builds fixed length representations of all spans in the evidence document with a recurrent network. We show that scoring explicit span representations significantly improves performance over other approaches that factor the prediction into separate predictions about words or start and end markers. Our approach improves upon the best published results of Wang & Jiang (2016) by 5% and decreases the error of Rajpurkar et al.’s baseline by > 50%.

This paper proposes a framework for automatically engineering features for two important tasks of question answering: answer sentence selection and answer extraction. We represent question and answer sentence pairs with linguistic structures enriched by semantic information, where the latter is produced by automatic classifiers, e.g., question classifier and Named Entity Recognizer. Tree kernels applied to such structures enable a simple way to generate highly discriminative structural features that combine syntactic and semantic information encoded in the input trees. We conduct experiments on a public benchmark from TREC to compare with previous systems for answer sentence selection and answer extraction. The results show that our models greatly improve on the state of the art, e.g., up to 22% on F1 (relative improvement) for answer extraction, while using no additional resources and no manual feature engineering.

Sentence similarity calculation plays an important role in text processing-related research. Many unsupervised techniques such as knowledge-based techniques, corpus-based techniques, string similarity based techniques, and graph alignment techniques are available to measure sentence similarity. However, none of these techniques have been experimented with Tamil. In this paper, we present the first-ever system to measure semantic similarity for Tamil short phrases using a hybrid approach that makes use of knowledge-based and corpus-based techniques. We tested this system with 2000 general sentence pairs and 100 mathematical sentence pairs. For the dataset of 2000 sentence pairs, this approach achieved a Mean Squared Error of 0.195 and a Pearson Correlation factor of 0.815. For the 100 mathematical sentence pairs, this approach achieved an 85% of accuracy.


Question: Design considerations and trade-offs for high-speed applications in electrical machines.

Exploring Megaspeed Drive Systems: Achieving Rotational Speeds Beyond 1 Million r/min
The cutting-edge research in mesoscale drive systems is advancing towards achieving rotational speeds in excess of 1 million revolutions per minute (r/min) for power ranges spanning from 1 to 1000 kW. The envisioned applications of megaspeed drives (MegaNdrives) encompass future turbo compressor systems for fuel cells and heat pumps, generators/starters for portable microscale gas turbines, high-precision printed circuit board drilling and machining, and electricity generation from pressurized gas flow. This paper delves into the selection criteria for machine type and the intricate design challenges for operating at megaspeeds, including winding design considerations, mechanical rotor construction capable of handling 1,000,000 r/min, choice of magnetic materials for the stator, and optimization strategies for mitigating high-frequency losses and enhancing torque density. Additionally, it evaluates the inverter topologies best suited for these systems, factoring in the exceptionally low stator inductance and the suitability of various high-speed bearing types such as ball bearings, air bearings, foil bearings, and magnetic bearings. The document also presents prototypes and experimental outcomes from the MegaNdrive research conducted at the Swiss Federal Institute of Technology Zurich, and identifies extreme temperature operation and power microelectromechanical systems (MEMS) as key areas for future research and development.

In order to simplify the manufacturing process of variable reluctance (VR) resolvers for hybrid electric vehicle/electric vehicle (HEV/EV) applications, a novel VR resolver with nonoverlapping tooth-coil windings is proposed in this paper. A comparison of the winding configurations is first carried out between the existing and the proposed designs, followed by the description of the operating principle. Furthermore, the influence of actual application conditions is investigated by finite-element (FE) analyses, including operating speed and assembling eccentricity. In addition, identical stator and windings of the novel design can be employed in three resolvers of different rotor saliencies. The voltage difference among the three rotor combinations, as well as the detecting accuracy, is further investigated. Finally, prototypes are fabricated and tested to verify the analyses.

This paper proposes a novel scheme for the identification of the whole flux linkage map of permanent magnet synchronous machines, by which the map of dq-axis flux linkages at different load or saturation conditions can be identified by the minimization of a proposed estimation model. The proposed method works on a conventional three-phase inverter based vector control system and the immune clonal based quantum genetic algorithm is employed for the global searching of minimal point. Besides, it is also noteworthy that the influence of uncertain inverter nonlinearity and circuit resistance are cancelled during the modeling process. The proposed method is subsequently tested on two PMSMs and shows quite good performance compared with the finite element prediction results.

With fast power semiconductor devices based on GaN and SiC becoming more common, there is a need for improved driving circuits. Transformers with smaller inter-winding capacitance in the isolated gate drive power supply helps in reducing the conducted EMI emission from the power converter to auxiliary sources. This paper presents a transformer with a small volume, a low power loss and a small inter-capacitance in a gate drive power supply to fast switching devices, such as GaN HEMT and SiC MOSFET. The transformer core is embedded into PCB to increase the integration density. Two different transformer designs, the coplanar-winding PCB embedded transformer and the toroidal PCB embedded transformer, are presented and compared. The former has a 0.8 pF inter-capacitance and the latter has 85% efficiency with 73 W/in3 power density. Both designs are dedicated to a 2 W gate drive power supply for wide-band-gap device, which can operate at 200 °C ambient temperature.

This paper reviews the convective heat transfer within the air gap of both cylindrical and disk geometry rotating electrical machines, including worked examples relevant to fractional horsepower electrical machines. Thermal analysis of electrical machines is important because torque density is limited by maximum temperature. Knowledge of surface convective heat transfer coefficients is necessary for accurate thermal modeling, for example, using lumped parameter models. There exists a wide body of relevant literature, but much of it has traditionally been in other application areas, dominated by mechanical engineers, such as gas turbine design. Particular attention is therefore given to the explanation of the relevant nondimensional parameters and to the presentation of measured convective heat transfer correlations for a wide variety of situations from laminar to turbulent flow at small and large gap sizes for both radial-flux and axial-flux electrical machines.

This paper presents a torque-maximizing design of a double-stator, axial-flux PM machine using a simple non-linear magnetic analysis. The proposed analysis consists of a geometric- flux-tube-based equivalent-magnetic-circuit model. The model can take into account not only the magnetic saturation but also the three-dimensional(3D) flux distribution. The proposed analysis has realized a dramatic reduction in the computation time compared to 3D-FEA, keeping reasonable analytical accuracy. At first, the validity of the proposed analysis is examined for the various design parameters. After verifying the accuracy of the torque computation of the proposed analysis through the comparisons with 3D-FEA, an 8-pole axial-flux PM machine with double-stator-single-rotor is optimally designed. The performance of the optimized motor are finally verified by 3D-FEA.

Although motors that use rare-earth permanent magnets (PMs) typically exhibit high performance, high costs and concerns about the stability of raw material supplies are leading to their decreased production. However, the performance of such motors is not easily matched without the use of rare-earth PMs. This paper proposes and examines a PM-assisted synchronous reluctance motor with ferrite magnets that has the same power density as rare-earth PM synchronous motors. A suitable rotor structure for high torque and high power is discussed with respect to the demagnetization of ferrite magnets and the mechanical strength.

The design process of a double-sided slotted TORUS axialflux permanent-magnet (AFPM) motor suitable for direct drive of electric vehicle (EV) is presented. It used sizing equation and Finite Element Analysis (FEA). AFPM motor is a high-torque-density motor easily mounted compactly onto a vehicle wheel, fitting the wheel rim perfectly. A preliminary design is a double-sided slotted AFPM motor with 6 rotor poles for high torque-density and stable rotation. In determining the design requirements, a simple vehicle-dynamics model that evaluates vehicle performance through the typical cruising trip of an automobile was considered. To obtain, with the highest possible torque, the initial design parameters of the motor, AFPM’s fundamental theory and sizing equation were applied. Vector Field Opera-3D 14.0 commercial software ran the FEA of the motor design, evaluating and enhancing accuracy of the design parameters. Results of the FEA simulation were compared with those obtained from the sizing equation; at no-load condition, the flux density at every part of the motor agreed. The motor’s design meets all the requirements and limits of EV, and fits the shape and size of a classical-vehicle wheel rim. The design process is comprehensive and can be used for an arbitrary EV with an arbitrary cruising scenario.

 This paper presents multilevel inverters as an application for all-electric vehicle (EV) and hybrid-electric vehicle (HEV) motor drives. Diode-clamped inverters and cascaded H-bridge inverters, (1) can generate near-sinusoidal voltages with only fundamental frequency switching; (2) have almost no electromagnetic interference (EMI) and commonmode voltage; and (3) make an EV more accessible/safer and open wiring possible for most of an EV’s power system. This paper explores the benefits and discusses control schemes of the cascade inverter for use as an EV motor drive or a parallel HEV drive and the diode-clamped inverter as a series HEV motor drive. Analytical, simulated, and experimental results show the superiority of these multilevel inverters for this new niche.

This paper compares the many fault tolerant three-phase ac motor drive topologies that have been proposed to provide output capacity for the inverter faults of switch short or open-circuits, phase-leg short-circuits, and single-phase open-circuits. Also included is a review of the respective control methods for fault tolerant inverters including two-phase and unipolar control methods. The output voltage and current space in terms of dq components is identified for each topology and fault. These quantities are then used to normalize the power capacity of each system during a fault to a standard inverter during normal operation. A silicon overrating cost factor is adopted as a metric to compare the relative switching device costs of the topologies compared to a standard three-phase inverter.

Permanent magnet (PM) type transverse flux linear motors (TFLMs) are electromagnetic devices, which can develop directly powerful linear motion. These motors have been developed to apply to high power system, such as railway traction, electrodynamics vibrator, free-piston generator, etc. This paper presents an optimum design of a PM-type TFLM to reduce the weight of motor with constraints of thrust and detent force using response surface methodology (RSM) and genetic algorithms (GAs). RSM is well adapted to make analytical model of motor weight with constraints of thrust and detent forces, and enable objective function to be easily created and a great computational time to be saved. Finite element computations have been used for numerical experiments on geometrical design variables in order to determine the coefficients of a second-order analytical model for the RSM. GAs are used as a searching tool for design optimization of TFLM to reduce the weight of motor and improve the motor performance.


Question: Design considerations and trade-offs in electrical machines for high-speed applications.

Exploring the Capabilities and Constraints of High-Speed Permanent-Magnet Motors

This study delves into the capabilities and constraints associated with high-speed permanent-magnet (PM) motors. It emphasizes the critical role of material selection for the PM, stator core, and retaining sleeve in influencing motor performance. Both slotted and slotless topologies are scrutinized and contrasted, with detailed analyses of magnetic, electrical, mechanical, and thermal characteristics. These analyses are conducted using a combination of analytical methods and finite-element modeling. The study evaluates the thermal limitations and the risk of PM demagnetization, along with rotor losses. An optimization strategy for motor structure is proposed, focusing on the diameter ratio and iron flux density as pivotal design parameters. This approach aims to strike a balance between performance enhancement and practical engineering constraints in high-speed PM motor design.

We present an analytical method to study magnetic fields in permanent-magnet brushless motors, taking into consideration the effect of stator slotting. Our attention concentrates particularly on the instantaneous field distribution in the slot regions where the magnet pole transition passes over the slot opening. The accuracy in the flux density vector distribution in such regions plays a critical role in the prediction of the magnetic forces, i.e., the cogging torque and unbalanced magnetic pull. However, the currently available analytical solutions for calculating air-gap fields in permanent magnet motors can estimate only the distribution of the flux density component in the radial direction. Magnetic field and forces computed by the new analytical method agree well with those obtained by the finite-element method. The analytical method provides a useful tool for design and optimization of permanent-magnet motors.

In this paper, the authors investigate the iron loss of interior permanent magnet motors driven by pulsewidth modulation (PWM) inverters from both results of the experiments and the finite-element analysis. In the analysis, the iron loss of the motor is decomposed into several components due to their origins, for instance, the fundamental field, carrier of the PWM inverter, slot ripples, and harmonic magnetomotive forces of the permanent magnet in order to clarify the main loss factors. The Fourier transformation and the finite-element method considering the carrier harmonics are applied to this calculation. The calculated iron loss is compared with the measurement at each driving condition. The measured and the calculated results agree well. It is clarified that the iron loss caused by the carrier of the PWM inverter is the largest component at low-speed condition under the maximum torque control, whereas the loss caused by the harmonic magnetomotive forces of the permanent magnet remarkably increase at high-speed condition under the flux-weakening control

Axial Field Permanent Magnet Motor (AFPM) can be easily integrated into wheel-hub motor of Electrical Vehicle (EV) as impact structure and light weight. Improved hexagonal asymmetry coil is presented in Printed Circuit Board (PCB) stator of coreless AFPM. Geometry parameters of coil shape is optimized. According to the structure characteristics of coreless AFPM, dimension formulas of PCB coil are derived. Analytical calculation models of back-EMF and winding co-efficiency are built up. To maximize ratio of torque to copper loss, coil parameters such as radial length, inclined angle of active conductors etc. are optimized. Finally comparison and verification are performed by means of 3D finite element method and prototype test. The ratio of torque to copper loss is increased 30%. As a result, motor rated torque is also increased at certain motor size, heating load and dissipation condition.

The combined star-delta winding has been attracting the attention of industry because of its potential to improve the efficiency of electrical machines. In order to show the efficiency benefits of this type of winding, many authors have used methods to determine the improvements in winding factors, published in different papers, which have not been validated yet. Some of these methods contradict each other. Furthermore, as a demerit of this winding, a unidirectional rotation has been reported. In this paper, a set of new formulas to calculate the winding factors for combined star-delta windings is proposed. In order to check the validity of the proposed formulas, a 2-D FEM and experimental tests on a salient pole synchronous machine are presented, which allows different combined star-delta winding connections. In contrast to other publications, the measurements show that combined windings can be applied for both directions of rotation without any problems.

This paper demonstrates the performance of a 900 V silicon carbide (SiC) power MOSFET operating at 13.56 MHz in a 2 kW resonant inverter targeted for wireless power transfer (WPT) systems. Operating at multi-MHz switching frequency leads to smaller passive components and has the potential to improve power density and reduce weight for weight-sensitive applications. In addition, the advent of wide band gap (WBG) power semiconductors with higher breakdown voltages than silicon (Si) allows the design of high-power (2 kW or more) inverters even when operating at MHz frequencies. Previous work described the design and implementation of a class Φ2 inverter using an enhancement mode gallium nitride (eGaN) FET at output power of 1.3 kW and switching at 13.56 MHz [1]. While eGaN FETs have promising electrical characteristics for high-frequency operation, SiC MOSFETs are becoming available with higher temperature ratings, higher breakdown voltages and higher avalanche ratings to operate at tens of MHz switching frequency. In this work, we study and evaluate the electrical characteristics of a 900 V, 7L D2PAK SiC MOSFET and demonstrate the design of a 2 kW single-ended resonant inverter operating at 13.56 MHz.

A method of estimating the speed of an induction motor is presented. This method is based on the adaptive control theory. Experimental results of a direct field oriented induction motor control without speed sensors are presented.<<ETX>>

A pulse-width-modulation (PWM) scheme that uses the converter switching frequency to minimize unwanted load current harmonics is described. This results in the reduction of the number of switch communications per cycle. The method is suitable for high-performance variable-speed AC-motor drives that require high-output switching frequencies for near-silent operation. It is also applicable, without change, to voltage or current-source inverters and to two and four-quadrant three-phase PWM rectifiers. Key predicted results have been verified experimentally on a 5 kVA inverter breadboard.<<ETX>>

A numerical study of PMSM based on finite element method for estimating the airgap magnetic field distribution considering stator slots effect is presented both for traditional radial magnet structure and Halbach magnet array topology. Magnetic field, flux, torque and the induction in the air gap are presented. The two machines structures performances were compared which put in view the modifications brought by these structures.. According to numerical calculations, and harmonic analysis, the PMSM with Halbach magnet array provides a higher torque and better performance than the classical radial PMSM.

A power electronic inverter is developed for a high frequency induction heating application. This equipment is available for the implementation of ZVS LCL resonance method was used. Using IGBT 20MVA, 100kHz LCL resonant induction heating equipment during the development process, 100kHz IGBT module development process and understanding gained from the design of LCL resonant Zero Crossing through the PLL control over operation of the IGBT Soft Switching, and focused on minimizing losses. In addition, through the actual design and implementation of devices using the area to test drive the development of large inverter technology based on the test

Line-Start Synchronous Reluctance (LS-SyR) Motors are a viable solution to reach IE4 efficiency class with the frame size of induction motors having standard efficiency. The proposed paper reviews the guidelines for the design of LS-SyR machines and introduces a new technique for testing their capability of load synchronization. Design wise, the efficiency at synchronous speed and the load pull-in capability must be traded off. The key design principles of LS-SyR machines are reviewed in this sense, and two manufacturing solutions are proposed for comparison. A new experimental method for the evaluation of the pull-in characteristic is proposed. The test fixture consists of a torque-controlled ac drive load, equipped with a shaft accelerometer. The load applied to the line-start machine under test is augmented with a component proportional to the acceleration measurement, to emulate additional inertia.


Question: Design considerations and trade-offs for high-speed applications in electrical machines.

This paper details the development, assembly, and experimental validation of an integrated flywheel energy storage system, which incorporates a homopolar inductor motor/generator and a high-frequency drive mechanism. The project is approached as a holistic design, encompassing the flywheel system, the motor, the drive, and the control system. The motor design is characterized by minimized rotor losses, a slotless stator, and the use of durable, cost-effective materials. Notably, the rotor serves a dual purpose, functioning as both the motor's rotor and the energy storage component for the flywheel system. To accommodate the high electrical frequencies, a high-frequency six-step drive strategy is implemented, replacing traditional pulsewidth modulation methods. Additionally, a speed-sensorless controller, which operates without the need for state estimation, is introduced. A functional prototype of the flywheel system has been successfully tested at a 9.4 kW power level, achieving an average system efficiency of 83% across a wide speed range from 30,000 to 60,000 revolutions per minute.

**Question**: Comparative Analysis of Different Control Strategies for Grid-Connected Wind Energy Conversion Systems

**Documents**: A Novel Control Strategy for Grid-Connected Wind Energy Conversion Systems Using a Permanent Magnet Synchronous Generator
This paper presents a novel control strategy for grid-connected wind energy conversion systems (WECS) using a permanent magnet synchronous generator (PMSG). The proposed control strategy is based on the use of a back-to-back voltage source converter (VSC) system. The VSC on the generator side is controlled to achieve maximum power point tracking (MPPT), while the VSC on the grid side is controlled to regulate the dc-link voltage and to ensure the power quality at the point of common coupling (PCC). The proposed control strategy is implemented on a 2 MW WECS using a PMSG connected to the grid through a back-to-back VSC system. The simulation results show that the proposed control strategy is effective in achieving maximum power extraction from the wind turbine and in maintaining the power quality at the PCC.

**Similar Documents**: This article introduces an innovative control methodology for grid-connected wind energy conversion systems (WECS) that utilize a permanent magnet synchronous generator (PMSG). The control strategy is centered around the employment of a back-to-back voltage source converter (VSC) system. Specifically, the VSC on the generator side is meticulously controlled to execute maximum power point tracking (MPPT), ensuring optimal power extraction from the wind. Meanwhile,

This paper investigates the performance of a three designs of tubular linear permanent magnet motors (TLPMMs) with quasi-Halbach magnetized moving-magnet armature and slotted stator with a single-slot. The motors were developed for reciprocating compressor applications such as household refrigerators. The three designs have different magnet shape structures, and the stator core of all designs have employed a soft magnetic composite (SMC) material, Somaloy 700. Based on finite element analysis (FEA) an extensively analysis on the opencircuit magnetic field distribution, back-EMF, thrust force, fluxlinkage, winding inductance and cogging force in the three designs have been established. The simulation results indicate the effectiveness of the designs to drive a linear reciprocating compressor in household refrigerators, and also the simulation results have shown the superiority of the two designs with T-shape and trapezoidal magnet configurations, over the TLPMM with conventional rectangular magnet. Moreover, the permanent magnets (PMs) array arrangement has significant effects on the motor performance.

Mesoscale gas turbine generator systems are a promising solution for high energy and power density portable devices. This paper focuses on the design of a 100 W, 500000 rpm generator suitable for use with a gas turbine. The design procedure selects the suitable machine type and bearing technology, and determines the electromagnetic characteristics. The losses caused by the high frequency operation are minimized by optimizing the winding and the stator core material. The final design is a permanent-magnet machine with a volume of 3.5 cm/sup 3/ and experimental measurements from a test bench are presented.

In this paper, a new medium voltage power converter topology using a diode rectifier, three-level boost (TLB) converter, and neutral-point-clamped (NPC) inverter is proposed for a high-power permanent magnet synchronous generator-based wind energy conversion system. The generator-side TLB converter performs the maximum power point tracking and balancing of dc-link capacitor voltages, while the grid-side NPC inverter regulates the net dc-bus voltage and reactive power to the grid. A significant improvement in the grid power quality is accomplished as the NPC inverter no longer controls the dc-link neutral point voltage. A model predictive strategy is proposed to control the complete system where the discrete-time models of the proposed power electronic converters are used to predict the future behavior of control variables. These predictions are evaluated using two independent cost functions, and the switching states which minimize these cost functions are selected and applied to the generator- and grid-side converters directly. In order to comply with the high-power application, the switching frequencies of the TLB converter and NPC inverter are minimized and maintained below 1.5 and 1 kHz, respectively. The proposed topology and control strategy are verified through MATLAB simulations on a 3-MW/3000-V/577-A system and dSPACE DS1103-based experiments on 3.6-kW/208-V/10-A prototype.

This paper proposes an axial-flux-modulation permanent magnet (PM) machine and its sensorless control strategy for in-wheel application in electrical vehicles. A Vernier structure is integrated with the axial-flux PM machine to include the magnetic gear effect and improve output torque. The sensorless control strategy of the proposed machine, including initial rotor position estimation and rotating position estimation, is proposed for flux-modulation motors in this paper. The initial rotor position estimation is based on the technique of rectangular pulse voltage injection and the rotating rotor position estimation is based on the sliding mode observer (SMO). The saturation effect on inductances, which is the theoretical basis of the rectangular pulse voltage injection, makes the stator parameter variation in different loads and affects the SMO estimation. To overcome this problem, a novel online parameter self-adjustment procedure for the SMO is introduced. The machine design and its sensorless control performance are verified by simulation and prototype experiments.

Variable reluctance resolvers are needed to regulate electrical machines with inverter control. Due to several issues, e.g. discrete windings in the stator and geometric tolerances in the assembly of the electrical machine, the rotors angular position cannot be acquired properly. The subject of this paper is a variable reluctance resolver model to investigate manufacturing induced geometrical tolerances of the electrical machine in means of rotor position. For this purpose, a block diagram of a stator segment is built, connecting 24 segments to one stator core using PLECS. The rotor can be set freely in shape and position. By varying a permeance block the rotation, i.e. the position dependent airgap, is mirrored. The proposed model allows a sensitivity analysis of the sensor signal according to geometrical changes and the absolute position in the electrical machine system. To evaluate the investigations, the results are verified by measurements on a sensor test bench. The simulation results show a similar behavior as the measurement. Although the values differ by a factor of five, this absolute difference is discussed by means of neglected boundary conditions.

Synchronous Reluctance motors can replace the more commonly used, induction, switched reluctance and permanent magnet motors. They have been shown to outperform similarly dimensioned induction motors in efficiency, torque and power density. They are simple and cheap to construct and require simple control. Such a motor would be ideal for wide spread use in Africa, which currently faces a power deficit, considering that motors account for about 43% of global electric power consumption. The use of synchronous reluctance motors in Electric Vehicles may contribute to the production of more efficient, heavier load and longer range vehicles. This would accelerate adoption of Electric Vehicles and it would help wean the world off fossil fuels. The synchronous reluctance motor has disadvantages of high torque ripple and poor power factor. A radical redesign of the rotor geometry and simultaneous optimisation of the rotor and the stator may significantly improve its performance.

This paper presents a simple and effective procedure for the determination of the Gorges polygon, suitable for all possible winding configurations in electrical machines. This methodology takes into account the determination of a Winding Distribution Table (WDT), in which all the information about the distribution of the currents along the stator periphery is computed and from which the Görges polygon are easily derived. The proposed method can be applied to both symmetrical and asymmetrical multi-phase windings, including concentrated, fractional, reduced and dead-coil ones. The examples provided in this paper demonstrate the versatility of the proposed method.

Traditional two-level high-frequency pulse width modulation (PWM) inverters for motor drives have several problems associated with their high frequency switching which produces common-mode voltage and high voltage change (dV/dt) rates to the motor windings. Multilevel inverters solve these problems because their devices can switch at a much lower frequency. Two different multilevel topologies are identified for use as a converter for electric drives, a cascade inverter with separate dc sources and a back-to-back diode clamped converter. The cascade inverter is a natural fit for large automotive allelectric drives because of the high VA ratings possible and because it uses several levels of dc voltage sources which would be available from batteries or fuel cells. The back-to-back diode clamped converter is ideal where a source of ac voltage is available such as a hybrid electric vehicle. Simulation and experimental results show the superiority of these two converters over PWM based drives.

It has recently been shown that the most commonly used methods for calculating high-frequency eddy-current loss in round-wire windings can have substantial error, exceeding 60%. Previous work includes a formula based on a parametric set of finite-element analysis (FEA) simulations that gives proximity-effect loss for a large range of frequencies, using the parameters from a lookup table based on winding geometry. We improve the formula by decreasing the number of parameters in the formula and also, more importantly, by using simple functions to get the parameters from winding geometry such that a large lookup table is not needed. The function we present is exact in the low frequency limit (diameter much smaller than skin depth) and has error less than 4% at higher frequencies. We make our new model complete by examining the field expression needed to get the total proximity-effect loss and by including the skin-effect loss. We also present experimental results confirming the validity of the model and its superiority to standard methods.

The development of ship propulsion synchronous motors with high temperature superconductor (HTS) field windings for Naval electric ship applications has progressed to the point where a full scale motor is now under construction. A 5 MW, 230-rpm prototype ship propulsion motor was built and tested by the Center for Advanced Power Systems (CAPS) on behalf of U.S. Office of Naval Research (ONR). It met or exceeded all its design goals. Currently, a 36.5 MW, 120-rpm ship propulsion motor is being built for delivery to ONR at the end of 2006. This paper presents test results of the 5 MW motor and the status of the 36.5 MW motor


Question: Design considerations and trade-offs for high-speed applications in electrical machines.

This study delves into the design and evaluation of an ultra-high speed drive system, specifically addressing the demands of novel application areas that necessitate the operation of electrical machines at extraordinary velocities. A major hurdle in this endeavor is the creation of a testing platform capable of enduring operational speeds in excess of 500,000 rpm while accurately measuring minuscule torque values in the millinewton meter range. The paper details the development of a specialized test rig designed to thoroughly assess the performance of such ultra-high speed drive systems. It further explores the computation of electromagnetic losses, air friction, and critical speeds, offering a comparative analysis of theoretical predictions and empirical outcomes. Despite achieving an impressive efficiency of 95%, the investigation reveals that at the upper echelons of speed, frictional losses assume a predominant role, impacting the overall performance and efficiency of the ultra-high speed machine.

The main aim of this paper is to improve the performance of high current dual active bridge converters when operated over a wide voltage range. A typical application is for fuel cell vehicles where a bi-directional interface between a 12V battery and a high voltage DC bus is required. The battery side voltage ranges from 11V to 16V while the fuel cell is operated between 220V and 447V and the required power is typically 1kW. Careful analysis shows that the high currents on the battery side cause significant design issues in order to obtain a high efficiency. The standard phase shift modulation method can result in high conduction and switching losses. This paper proposes a combined triangular and trapezoidal modulation method to reduce losses over the wide operating range. Approximately, a 2% improvement in efficiency can be expected. An experimental system is used to verify the improved performance of the dual active bridge using the proposed advanced modulation method.

In this paper, a compact rep-rate GW pulsed power generator is developed. First, its three key subsystems are theoretically analyzed, engineering designed, and experimentally investigated, respectively. The emphases are put on these four problems: the theoretical analysis of the voltage distribution across the conical secondary windings of the high-coupling transformer, the investigation of the high energy storage density dielectric used in the pulse forming line, the choice of the gas flow velocity of the gas blowing system, and theoretical analysis of the passive stability of the pulsed power generator operated in rep-rate mode. Second, the developed pulsed power generator is described in detail. It has a 0.2-m diameter, a 1.0-m length, and a 20- Ω wave impedance. Across a 100- Ω resistive dummy load, it can steadily operate at a 300-kV output voltage in 50-Hz rep-rate and 250 kV in 150 Hz without gas blowing system. The average power is ~ 1 kW. Finally, the pulsed power generator is applied to drive a relativistic backward-wave oscillator, generating a high-power microwave with peak output power of 200 MW and duration (full-width at half-maximum) of 5 ns in 150-Hz rep-rate. These efforts set a good foundation for the development of a compact rep-rate pulsed power generator and show a promising application for the future.

Line-Start Synchronous Reluctance (LS-SyR) Motors are a viable solution to reach IE4 efficiency class with the frame size of induction motors having standard efficiency. The proposed paper reviews the guidelines for the design of LS-SyR machines and introduces a new technique for testing their capability of load synchronization. Design wise, the efficiency at synchronous speed and the load pull-in capability must be traded off. The key design principles of LS-SyR machines are reviewed in this sense, and two manufacturing solutions are proposed for comparison. A new experimental method for the evaluation of the pull-in characteristic is proposed. The test fixture consists of a torque-controlled ac drive load, equipped with a shaft accelerometer. The load applied to the line-start machine under test is augmented with a component proportional to the acceleration measurement, to emulate additional inertia.

This paper presents the design process of a slotted TORUS axial-flux permanent-magnet motor suitable for direct drive of an electric vehicle through sizing equation and Finite Element Analysis. AFPM motor is a high torque density motor which can be easily and compactly mounted into the automobile's wheel fitting the rim perfectly. A double-sided slotted AFPM motor with 6 rotor poles for high torque density and stable rotation is the preliminary design. In order to determine the design requirements, a simple vehicle dynamic model evaluating the vehicle performance considering an automobile's typical-trip cursing scenario is considered. Axial-flux permanent-magnet machine's fundamental theory and sizing equation are applied to obtain the initial design parameters of the motor with the highest possible torque. The FEA of designed motor was conducted via commercial Vector Field Opera-3D 14.0 software for evaluation and accuracy enhancement of the design parameters. FEA simulation results are compared with those results obtained from sizing equation showing a good agreement of flux density values in various parts of the designed motor at no-load condition. The motor meets all the requirements and limitations of the electric vehicle fitting the shape and the size of a classical rim of the vehicle wheel.

Multilevel inverter technology has emerged recently as a very important alternative in the area of high-power medium-voltage energy control. This paper presents the most important topologies like diode-clamped inverter (neutral-point clamped), capacitor-clamped (flying capacitor), and cascaded multicell with separate dc sources. Emerging topologies like asymmetric hybrid cells and soft-switched multilevel inverters are also discussed. This paper also presents the most relevant control and modulation methods developed for this family of converters: multilevel sinusoidal pulsewidth modulation, multilevel selective harmonic elimination, and space-vector modulation. Special attention is dedicated to the latest and more relevant applications of these converters such as laminators, conveyor belts, and unified power-flow controllers. The need of an active front end at the input side for those inverters supplying regenerative loads is also discussed, and the circuit topology options are also presented. Finally, the peripherally developing areas such as high-voltage high-power devices and optical sensors and other opportunities for future development are addressed.

Linear induction motors are used in various industries but they have some specific phenomena which are the causes for some problems. The most important phenomenon is called end effect. End effect decreases efficiency, power factor and output force and unbalances the phase currents. This phenomenon is more important in medium and high speeds machines. In this paper a factor, EEF , is obtained by an accurate equivalent circuit model, to determine the end effect intensity. In this way, all of effective design parameters on end effect is described. Accuracy of this equivalent circuit model is evaluated by two dimensional finite-element analysis using ANSYS. The results show the accuracy of the equivalent circuit model. Keywords—Linear induction motor, end effect, equivalent circuit model, finite-element method.

High voltage high frequency (HF) transformer provides the isolation between high and low dc link voltages in dual active bridge (DAB) converters. Such DAB converters are finding wide applications as an intermediate DC-DC converter in transformerless intelligent power substation (TIPS), which is proposed as an alternative for conventional distribution-transformer connecting 13.8 kV and 480 V grids. The design of HF transformer used in DAB stage of such application is very challenging considering the required isolation, size and cost. In this paper, the specification generation, design, characterization, test and measurement results on a 10kHz HF transformer are presented, highlighting the above challenges.

Torque density of a novel dual-rotor, radial-flux multiphase permanent magnet (PM) machine is enhanced by injecting stator current harmonics without increasing stator yoke thickness. In order to enhance a regular PM machine torque capability by injecting harmonic currents, the no-load air-gap flux density should be designed to be trapezoidal, which increases the flux per pole, and results in a thicker stator yoke. However, this drawback can be avoided in a dual-rotor, radial-flux PM machine. In such a dual-rotor PM machine, the main flux can be designed to go directly from the inner rotor poles, to stator inner and outer teeth, then to the outer rotor poles, or reverse, without through the stator yoke. Therefore, the stator yoke thickness is not affected by air-gap flux at all and can actually be reduced to minimum for mechanical reasons. This paper applies stator current harmonic injection technique to a dual-rotor, radial-flux PM machine. Sizing equations of the multiphase PM machines are deduced and used for initial design and theoretical comparison. The torque performance of the proposed dual-rotor, radial-flux multiphase PM machine with stator harmonic current injection is evaluated using finite element simulation, and compared to a traditional three-phase dual-rotor, radial-flux PM machine. Simulation results validate the feasibility of torque density enhancement. The same technique is proven that it is also suitable in a dual-rotor axial-flux PM machine topology.

This paper develops a magnetic equivalent circuit model suitable to the design and optimization of the synchronous ac homopolar machine. The ac homopolar machine is of particular interest in the application of grid-based flywheel energy storage, where it has the potential to significantly reduce self-discharge associated with magnetic losses. The ac homopolar machine features both axial and radial magnetizing flux paths, which requires finite element analysis to be conducted in 3-D. The computation time associated with 3-D finite element modeling is highly prohibitive in the design process. The magnetic equivalent circuit model developed in this paper is shown to be a viable alternative for calculating several design performance parameters and has a computation time which is orders of magnitude less than that of 3-D finite element analysis. Results obtained from the developed model are shown to be in good agreement with finite element and experimental results for varying levels of saturation.

It has recently been shown that the most commonly used methods for calculating high-frequency eddy-current loss in round-wire windings can have substantial error, exceeding 60%. Previous work includes a formula based on a parametric set of finite-element analysis (FEA) simulations that gives proximity-effect loss for a large range of frequencies, using the parameters from a lookup table based on winding geometry. We improve the formula by decreasing the number of parameters in the formula and also, more importantly, by using simple functions to get the parameters from winding geometry such that a large lookup table is not needed. The function we present is exact in the low frequency limit (diameter much smaller than skin depth) and has error less than 4% at higher frequencies. We make our new model complete by examining the field expression needed to get the total proximity-effect loss and by including the skin-effect loss. We also present experimental results confirming the validity of the model and its superiority to standard methods.


Question: Design considerations and trade-offs for high-speed applications in electrical machines.

This paper delves into the design of a compact, high-speed permanent-magnet generator specifically tailored for mesoscale gas turbine systems. With a target power output of 100 W at an unprecedented speed of 500000 rpm, this generator aims to support the high energy and power density requirements of portable devices. The design process involves selecting the optimal machine configuration and bearing technology, while meticulously calculating the electromagnetic properties. To address the challenges posed by high-frequency operation, the design emphasizes minimizing losses through the optimization of winding configurations and the choice of stator core material. The end result is a permanent-magnet machine boasting a volume of just 3.5 cm³, showcasing its miniaturization while maintaining high performance. Experimental data obtained from a test bench validates the design's effectiveness and feasibility.

High-voltage-rated solid-state switches such as insulated-gate bipolar transistors (IGBTs) are commercially available up to 6.5 kV. Such voltage ratings are attractive for pulsed power and high-voltage switch-mode converter applications. However, as the IGBT voltage ratings increase, the rate of current rise and fall are generally reduced. This tradeoff is difficult to avoid as IGBTs must maintain a low resistance in the epitaxial or drift region layer. For high-voltage-rated IGBTs with thick drift regions to support the reverse voltage, the required high carrier concentrations are injected at turn on and removed at turn off, which slows the switching speed. An option for faster switching is to series multiple, lower voltage-rated IGBTs. An IGBT-stack prototype with six, 1200 V rated IGBTs in series has been experimentally tested. The six-series IGBT stack consists of individual, optically isolated, gate drivers and aluminum cooling plates for forced air cooling which results in a compact package. Each IGBT is overvoltage protected by transient voltage suppressors. The turn-on current rise time of the six-series IGBT stack and a single 6.5 kV rated IGBT has been experimentally measured in a pulsed resistive-load, capacitor discharge circuit. The IGBT stack has also been compared to two IGBT modules in series, each rated at 3.3 kV, in a boost circuit application switching at 9 kHz and producing an output of 5 kV. The six-series IGBT stack results in improved turn-on switching speed, and significantly higher power boost converter efficiency due to a reduced current tail during turn off. The experimental test parameters and the results of the comparison tests are discussed in the following paper

In this article, the effect of different paralleling strategies, 1D field has been adopted, and a software has been developed to calculate the AC loss and leakage inductance. The problem and causes for unequal current sharing among parallel layers are discussed in this paper.

This paper presents a detailed analysis on the impact of the interleaving angle on EMI filter weight reduction in a paralleled three-phase VSI motor drive system. The EMI noise analysis equivalent circuits are given in frequency domain and EMI filter design methods for a motor drive system are discussed. Based on the analysis, design examples are given for DC and AC common-mode (CM) EMI filters showing that minimum corner frequency in this case cannot ensure minimum EMI filter weight. In effect, the EMI filter weight is also determined by the volt-seconds on the inductor. With this consideration, the impact of interleaving is analyzed based on the equivalent circuit developed. Specifically, it is shown that interleaving can either reduce the volt-second on the inductor or help reduce the inductance needed, which has a different impact on the filter weight when the design condition is different. EMI filters are designed for both AC and DC sides as examples, showing the impact that different interleaving angles can have on filter weight reduction. Verifications are carried out both in simulation and experimentally on a 2 kW converter system.

Although motors that use rare-earth permanent magnets typically exhibit high performance, their cost is high, and there are concerns about the stability of the raw material supply. This paper proposes a permanent-magnet-assisted synchronous reluctance motor (PMASynRM) with a ferrite magnet that does not use rare-earth materials considering productivity. The performance of the proposed PMASynRM is evaluated based on the finite-element method and an experiment using a prototype machine. The analysis results reveal that the proposed PMASynRM has the same power density and an equivalent efficiency as rare-earth permanent-magnet synchronous motors for hybrid electric vehicles (2003 Toyota Prius). Furthermore, some experimental results are presented in order to validate the analytical results. As a result, the proposed PMASynRM was found to achieve high-power-density and high-efficiency performance.

A vernier permanent magnet (VPM) machine using high-temperature superconducting (HTS) winding for dual excitation is proposed for wind power generation. The proposed VPM machine adopts the parallel hybrid excitation configuration, the stator consists of an identical tooth pair connected together by the stator yoke and the rotor contains two sets of consequent-pole permanent magnets joined together by the rotor yoke. The armature winding is located at the stator main slots while the field winding using HTS coils is wound on the stator yoke. With the benefit of the HTS field winding, the air-gap flux can be flexibly adjusted by controlling the magnitude of the dc current. Therefore, the machine performance is improved to cope with different operating conditions.

Traditionally, automotive suspension designs with passive components have been a compromise between the three conflicting demands of road holding, load carrying, and passenger comfort. Linear electromagnetic motor-based active suspension has superior controllability and bandwidth, provides shock load isolation between the vehicle chassis and wheel, and, therefore, has great potential. It also has the ability to recover energy that is dissipated in the shock absorber in the passive systems and results in a much more energy-efficient suspension system. This paper describes the issues pertinent to the design of a high force density tubular permanent-magnet (PM) motor for active suspension in terms of performance optimization, the use of a solid stator core for low-cost production and its impact on thrust force, and the assessment of demagnetization risk.

This paper proposes an online parameter estimation method based on a discrete-time dynamic model for the interior permanent-magnet synchronous motors (IPMSMs). The proposed estimation technique, which takes advantage of the difference in dynamics of motor parameters, consists of two affine projection algorithms. The first one is designed to accurately estimate the stator inductances, whereas the second one is designed to precisely estimate the stator resistance, rotor flux linkage, and load torque. In this paper, the adaptive decoupling proportional-integral (PI) controllers with the maximum torque per ampere operation, which utilize the previously identified parameters in real time, are chosen to verify the effectiveness of the proposed parameter estimation scheme. The simulation results via MATLAB/Simulink and the experimental results via a prototype IPMSM drive system with a TI TMS320F28335 DSP are presented under various conditions. A comparative study with the conventional decoupling PI control method is carried out to demonstrate the better performances (i.e., faster dynamic response, less steady-state error, more robustness, etc.) of the adaptive decoupling PI control scheme based on the proposed online parameter estimation technique.

This paper proposes an EMI filter design and optimization method for both AC and DC side in a DC-fed motor drive system. Based on the noise generation and propagation mechanism, the analysis of common mode (CM) and differential mode (DM) EMI noise equivalent circuits is provided. Based on those equivalent circuits, this paper investigates the system EMI noise between AC and DC side, which shows the interaction between DM and CM noises and the interaction between adding AC and DC side filters. With these considerations, an optimized EMI filter design procedure is proposed to design CM and DM filters for both AC and DC sides. To minimize the impact on the EMI noise of one side caused by adding filter on the other side, certain order must be followed to design AC and DC CM and DM filters. Moreover, the EMI filter weight optimization method is also discussed to get the minimum weight of EMI filter and improve system power density. Simulation and experimental results verify the interaction between AC and DC filters and show that EMI filters can be designed to suppress both AC and DC EMI noise to meet the standard with the proposed EMI filter design method.

A switched reluctance motor (SRM) has been developed as one of the possible candidates of rare-earth-free electric motors. A prototype machine has been built and tested. It has competitive dimensions, torque, power, and efficiency with respect to the 50-kW interior permanent magnet synchronous motor employed in the hybrid electric vehicles (Toyota Prius 2003). It is found that competitive power of 50-kW rating and efficiency of 95% are achieved. The prototype motor provided 85% of the target torque. Except the maximum torque, the most speed-torque region is found to be covered by the test SRM. The cause of discrepancy in the measured and calculated torque values is examined. An improved design is attempted, and a new experimental switched reluctance machine is designed and built for testing. The results are given in this paper.

Permanent magnet (PM) type transverse flux linear motors (TFLMs) are electromagnetic devices, which can develop directly powerful linear motion. These motors have been developed to apply to high power system, such as railway traction, electrodynamics vibrator, free-piston generator, etc. This paper presents an optimum design of a PM-type TFLM to reduce the weight of motor with constraints of thrust and detent force using response surface methodology (RSM) and genetic algorithms (GAs). RSM is well adapted to make analytical model of motor weight with constraints of thrust and detent forces, and enable objective function to be easily created and a great computational time to be saved. Finite element computations have been used for numerical experiments on geometrical design variables in order to determine the coefficients of a second-order analytical model for the RSM. GAs are used as a searching tool for design optimization of TFLM to reduce the weight of motor and improve the motor performance.