Developing a new SVPWM control strategy for open-winding brushless doubly fed reluctance generators

In this paper, a new open-winding control strategy is proposed for a brushless doubly fed reluctance generator (BDFRG) used for stand-alone wind turbine or ship generators. The BDFRG is characterized with two windings on the stator: a power winding and a control winding. The control winding is fed with dual two-level three-phase converters, and a vector control scheme based on space vector pulsewidth modulation is designed. Compared with traditional three-level inverter systems, the dc-link voltage and the voltage rating of power devices in the proposed system are reduced by 50% while still greatly improving the reliability, redundancy, and fault tolerance of the proposed system by increasing the switching modes. Its performance is evaluated by simulation in MATLAB/Simulink and an experimental study on a 42-kW prototype machine.

Constant V/Hz control based on DSP for open winding doubly-fed wind power generator

A DSP implementation of Space Vector PWM (SVPWM) using constant V/Hz control for the open winding doubly-fed generator is proposed. This control of SVPWM modulation mode and open winding structure combination has the high voltage utilization ratio, greatly improves the control precision of the system, and reduces the stator winding output current distortion rate, though the complexity of the system is increased. This paper describes the basic principle of SVPWM and discusses the particularity of SVPWM waveform generated by hybrid vector under the condition of open winding. This method is applied to a state of doubly-fed wind power generator. The experimental verification shows that this control method can make the output voltage amplitude of the doubly-fed induction generator be 380V and the frequency be 50Hz by using of TMS32028335 chip based on constant V/Hz control of symmetric SVPWM modulation wave.

LRD : Latent Relation Discovery for vector space expansion and information retrieval

In this paper, we propose a text mining method called LRD (latent relation discovery), which extends the traditional vector space model of document representation in order to improve information retrieval (IR) on documents and document clustering. Our LRD method extracts terms and entities, such as person, organization, or project names, and discovers relationships between them by taking into account their co-occurrence in textual corpora. Given a target entity, LRD discovers other entities closely related to the target effectively and efficiently. With respect to such relatedness, a measure of relation strength between entities is defined. LRD uses relation strength to enhance the vector space model, and uses the enhanced vector space model for query based IR on documents and clustering documents in order to discover complex relationships among terms and entities. Our experiments on a standard dataset for query based IR shows that our LRD method performed significantly better than traditional vector space model and other five standard statistical methods for vector expansion.

Statistical mechanics of support vector networks

Using methods of Statistical Physics, we investigate the generalization performance of support vector machines (SVMs), which have been recently introduced as a general alternative to neural networks. For nonlinear classification rules, the generalization error saturates on a plateau, when the number of examples is too small to properly estimate the coefficients of the nonlinear part. When trained on simple rules, we find that SVMs overfit only weakly. The performance of SVMs is strongly enhanced, when the distribution of the inputs has a gap in feature space.