Doubly fed induction generator (DFIG) is one of the most popular generators recently used in wind turbine systems (WTSs). This machine has some interesting advantages especially in variables speed applications. In the DFIG-based WTS, the rotor side of the machine is normally fed by a two-level power inverter controlled by the usual pulse width modulation (PWM) technique which has a principal disadvantage; the high level of harmonic distortion. In this paper, a novel technique based on space vector modulation (SVM) and fuzzy logic is proposed to perform the power, provided by the DFIG, to the grid. Simulation results show the efficiency of the proposed technique especially on the quality of the provided power comparatively to the usual PWM.

This article presents an improved direct vector command (DVC) based on intelligent space vector modulation (SVM) for a doubly fed induction generator (DFIG) integrated in a wind turbine system (WTS). The major disadvantages that is usually associated with DVC scheme is the power ripples and harmonic current. To overcome this disadvantages an advanced SVM technique based on fuzzy regulator (FSVM) is proposed. The proposed regulator is shown to be able to reduce the active and reactive powers ripples and to improve the performances of the DVC method. Simulation results are shown by using Matlab/Simulink.

This paper is concerned with the development of a novel classifier for automatic mass detection of mammograms, based on contourlet feature extraction in conjunction with statistical and fuzzy classifiers. In this method, mammograms are segmented into regions of interest (ROI) in order to extract features including geometrical and contourlet coefficients. The extracted features benefit from the superiority of the contourlet method to the state of the art multi-scale techniques. A genetic algorithm is applied for feature weighting with the objective of increasing classification accuracy. Although fuzzy classifiers are interpretable, the majority is order sensitive and suffers from the lack of generalization. In this study, a kernel SVM is integrated with a nerofuzzy rule-based classifier to form a support vector based fuzzy neural network (SVFNN). This classifier benefits from the superior classification power of SVM in high dimensional data spaces and also from the efficient human-like reasoning of fuzzy and neural networks in handling uncertainty information. We use the Mammographic Image Analysis Society (MIAS) standard data set and the features extracted of the digital mammograms are applied to the fuzzy-SVM classifiers to assess the performance. Our experiments resulted in 95.6%,91.52%,89.02%, 85.31% classification accuracy for the subclass FSVM, SVFNN, fuzzy rule based and kernel SVM classifiers respectively and we conclude that the subclass fuzzy-SVM is superior to the other classifiers.