An effective technique for fault detection and classification in distribution system with the aid of DWT and ANFIS Online publication date: Thu, 05-Oct-2017
by T.C. Srinivasa Rao; S.S. Tulasi Ram; J.B.V. Subrahmanyam
International Journal of Automation and Control (IJAAC), Vol. 11, No. 4, 2017
Abstract: In this paper, the location and types of faults are identified and analysed in the distributed system by using wavelet and adaptive neuro-fuzzy inference (ANFIS) technique. When fault occurring in the system, the system behaviours are monitored and signals are measured which can be seen as distorted waveforms. These distorted waveforms are composed of different frequency components and which are needed to be represented in time-frequency domain for fault analysis. For this representation of signal, discrete wavelet transform (DWT) is presented. It extracts the features and forms the datasets which are forwarded to ANFIS classifier for classifying the type of fault occurred in the distributed power systems. The proposed technique is implemented in MATLAB/Simulink platform and this is validated using statistical measures such as accuracy, sensitivity and specificity. The proposed method is compared with the existing techniques DWT-FFNN and DWT-RBFNN methods.
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