Nonlinear transient and steady state analysis for self-excited single-phase synchronous reluctance generator

With today\u27s trend for distributed generation and the need for alternative and renewable energy sources, self-excited induction and synchronous reluctance generators have attracted more attention for wind, tidal and hydro power generation applications. Compared to synchronous and DC generators, they have the advantages: they are brushless, they are robust, they do not need DC excitation and they are relatively low cost.;Compared with SEIG, the self-excited reluctance generator (SERG) not only has the advantages of simplicity and ruggedness, but can also have enhanced steady-state characteristics and high efficiency over a wide range of operation. Moreover, its output frequency is determined only by the prime mover speed, rather than by both the load and the prime mover speed as in an induction generator, so SERG can be easily integrated with power electronic devices to implement a control scheme.;Most of the current analyses deal with three-phase reluctance generators, but insufficient attention has been paid to single-phase self-excited reluctance generators (SPSERG). Their unbalanced loads make their analysis more difficult. This research is motivated by the fact that SPSERG provides a good alternative to single-phase induction generators used in stand-alone generation applications. A general methodology is suggested for transient response prediction and steady state performance analysis for the SPSERG type of electric machine.;To establish a design environment, finite element method is an effective tool, which can be integrated in machine modeling to obtain good performance prediction. In this work, an off-line FEM approach is proposed to obtain the saturation characteristics for state space simulation. During the process, transformation between instantaneous inductance and average inductance is investigated. Off-line FEM + SS approach is proved to be a simple and economic method and can fit the experimental results in good accuracy.;Moreover, a steady state model has to be built to reveal the parametric dependence and provide good design guidance. However, because of the unbalanced load and nonlinear feature of the machine, existing models are not suitable for analysis. In this dissertation, a novel inductance-oriented steady state model based on the harmonic balance technique is introduced. The idea is that starting from the inductance determination under certain load, the fluxes can be attained by a nonlinear relationship, after that, the machine variables can be solved according to the fluxes. Comparison between simulation and experiment validates this approach

Speech Acquisition and Automatic Speech Recognition for Integrated Spacesuit Audio Systems

A voice-command human-machine interface system has been developed for spacesuit extravehicular activity (EVA) missions. A multichannel acoustic signal processing method has been created for distant speech acquisition in noisy and reverberant environments. This technology reduces noise by exploiting differences in the statistical nature of signal (i.e., speech) and noise that exists in the spatial and temporal domains. As a result, the automatic speech recognition (ASR) accuracy can be improved to the level at which crewmembers would find the speech interface useful. The developed speech human/machine interface will enable both crewmember usability and operational efficiency. It can enjoy a fast rate of data/text entry, small overall size, and can be lightweight. In addition, this design will free the hands and eyes of a suited crewmember. The system components and steps include beam forming/multi-channel noise reduction, single-channel noise reduction, speech feature extraction, feature transformation and normalization, feature compression, model adaption, ASR HMM (Hidden Markov Model) training, and ASR decoding. A state-of-the-art phoneme recognizer can obtain an accuracy rate of 65 percent when the training and testing data are free of noise. When it is used in spacesuits, the rate drops to about 33 percent. With the developed microphone array speech-processing technologies, the performance is improved and the phoneme recognition accuracy rate rises to 44 percent. The recognizer can be further improved by combining the microphone array and HMM model adaptation techniques and using speech samples collected from inside spacesuits. In addition, arithmetic complexity models for the major HMMbased ASR components were developed. They can help real-time ASR system designers select proper tasks when in the face of constraints in computational resources

Robust Manifold Nonnegative Tucker Factorization for Tensor Data Representation

Nonnegative Tucker Factorization (NTF) minimizes the euclidean distance or Kullback-Leibler divergence between the original data and its low-rank approximation which often suffers from grossly corruptions or outliers and the neglect of manifold structures of data. In particular, NTF suffers from rotational ambiguity, whose solutions with and without rotation transformations are equally in the sense of yielding the maximum likelihood. In this paper, we propose three Robust Manifold NTF algorithms to handle outliers by incorporating structural knowledge about the outliers. They first applies a half-quadratic optimization algorithm to transform the problem into a general weighted NTF where the weights are influenced by the outliers. Then, we introduce the correntropy induced metric, Huber function and Cauchy function for weights respectively, to handle the outliers. Finally, we introduce a manifold regularization to overcome the rotational ambiguity of NTF. We have compared the proposed method with a number of representative references covering major branches of NTF on a variety of real-world image databases. Experimental results illustrate the effectiveness of the proposed method under two evaluation metrics (accuracy and nmi)