Detection of Faults in Power System Using Wavelet Transform and Independent Component Analysis

Uninterruptible power supply is the main motive of power utility companies that motivate them for identifying and locating the different types of faults as quickly as possible to protect the power system prevent complete power black outs using intelligent techniques. Thus, the present research work presents a novel method for detection of fault disturbances based on Wavelet Transform (WT) and Independent Component Analysis (ICA). The voltage signal is taken offline under fault conditions and is being processed through wavelet and ICA for detection. The time-frequency resolution from WT transform detects the fault initiation instant in the signal. Again, a performance index is calculated from independent component analysis under fault condition which is used to detect the fault disturbance in the voltage signal. The proposed approach is tested to be robust enough under various operating scenarios like without noise, with 20-dB noise and variation in frequency. Further, the detection study is carried out using a performance index, energy content, by applying the existing Fourier transform (FT), short time Fourier transform (STFT) and the proposed wavelet transform. Fault disturbances are detected if the energy calculated in each scenario is greater than the corresponding threshold value. The fault detection study is simulated in MATLAB/Simulink for a typical power system. This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by the authors or by the respective copyright holders. The original citation of this paper is: P. K. Ray, B. K. Panigrahi, P. K. Rout, A. Mohanty, H. Dubey, "Detection of Faults in Power System Using Wavelet Transform and Independent Component Analysis", First International Conference on Advancement of Computer Communication & Electrical Technology, October 2016, Murshidabad, India, DOI: 10.13140/RG.2.2.20394.82882 The techniques used for detection analysis is presented in Section 2 followed by the algorithm of implementation in Section 3. Then, Section 4 explains the result analysis both qualitatively and quantitatively, followed by the conclusions in Section 5. 2 TECHNIQUES FOR FAULT DETECTION This section describes the techniques used for identification of different types of faults in wind system connected to grid power system. The WT and ICA as detection techniques are presented briefly with their mathematical modeling. Different operating scenarios are taken as case studies to test these methods for fault identifications which help in assessing both normal as well as faulty operating conditions. The details of these methods are as follows. 2.1 Wavelet transforms (WT) The wavelet transform is a signal processing algorithm which is useful in detection of abnormal operating conditions based on decomposition of the power signals into different ranges of frequencies by the help of a series of low-pass and high-pass filters. This usually provides us a time-frequency multi-resolution analysis that greatly useful for identifying any short of abrupt variations in the electrical parameters such as voltage, phase, current, frequency etc. Here, Daubechies4 (dB4) is being used as the mother wavelet basis function for the fault detection analysis (Ukil & R. Živanović 2007). Usually, the signal is divided into a set of approximate (a) and detail (d) co-efficient representing the low-frequency and high frequency bands respectively. The decomposition is as presented below in Figure 1. Figure 1: Wavelet decomposition tree Considering a voltage signal of the power system as ( ), the continuous wavelet transform (CWT) is expressed as: * 1 ( , , ) ( ) t N CWT V M N V t dt M a             (1) Where and are called as dilation and translation parameters and is known as the wavelet basis function. Now WT in discrete form as: