Multi phases stator short-circuits faults diagnosis & classification in DFIG using wavelet & fuzzy based technique

In this article we present new results using hybrid method carried out three Dimensions (3-D) Continuous Wavelet Transform (CWT) and fuzzy inference system (FIS) to investigate the detectability and classification of of multi phases stator inter turn short circuit faults in proposed dynamic model of DFIG developed by [1], and to overcome the limitation of classical Fourier Transform (FT). This approach is successfully used with Motor Current Signature Analysis (MCSA) and suitable developed model of DFIG in healthy and faulty mode using Matlab environment. As first step we performed new results using 3-D plot CWT to extract the discriminating features. The features extracted from the wavelet transformed signal are the second most predominant frequency, the time range at which it occurs and the corresponding wavelet coefficients. Then as second and last step a fuzzy Inference system is designed and implemented using Matlab software with these three features extracted from the wavelet transformed signal as inputs and generates an output that classifies the fault and no fault conditions. It is observed that the results are particularly powerful.