GRD Journals- Global Research and Development Journal for Engineering | Volume 1 | Issue 6 | May 2016 ISSN: 2455-5703
Wavelet Feature Based Fault Detection and Classification Technique for Transmission line Protection Manish Kurre PG Student Department of Electrical and Electronics Engineering Disha Institute of Management and Technology, Raipur, CG, India
Shailesh. M. Deshmukh Assistant Professor & Head of Department Department of Electrical and Electronics Engineering Disha Institute of Management and Technology, Raipur, CG, India
Abstract In the present scenario, the efficiency of a power system depends on how a fault is accurately detected and classified, so that quick restoration and maintenance of power is accomplished. Fault detection, fault classification, needs to be performed using a fast and responsive algorithm at different levels of a power system. Effect of factors such as fault impedance, fault inception angle (FIA), and fault distance, which cause disturbances in power line can be analyzed by Wavelet based multi resolution analysis (MRA). This paper proposed, a fault detection and classification technique using MRA based on wavelet transform. The present paper also deals with the exploration of advantages and problems related with the proposed fault detection and classification technique. The method of fault detection and classification proposed in this work is based on the three-phase current and voltage waveforms measured during the occurrence of fault in the power transmission-line. The technique proposed in this paper, is verified using MATLAB/Simulink software and the obtained results shows that the wavelet based MRA is a good tool for detection and classification of faults. However it is also shown that the most critical problem related to this technique is the selection of appropriate threshold values for all the three phases. It has been also shown that this technique requires expert hands and knowledge of the system for the selection of proper threshold value. Keywords- Transmission line, fault detection and classification, wavelet feature, multi-resolution analysis
I. INTRODUCTION The two most important tasks involved in transmission-line relaying are fault detection and classification [19]. These two tasks must be accomplished in fast and precise manner as much as possible to protect the system from the harmful faults. The higher accuracy is also important to restore the system efficiently. Interruption of power flow in a power line is mainly due to the occurrence of faults at various levels. Meanwhile, the reliability and economical aspects of power transfer is highly affected by the occurrence of the faults which in turn affects the profit from the system. However the performance of the system can be enhanced by accurate and fast fault detection. Wavelet Transform (WT) [18] conducts both time and frequency domain analysis of current signals and transients in voltage waveforms and hence provides a tool for detection and classification of faults. It is usually observed that, the overhead lines are affected by transients, due to the travelling wave phenomenon after the inception of fault. With the analysis of the faults due to induced transients one can gather handy information which covers a wide range of aspects like location and detection of fault. Ideally, the identification of mother wavelet signifies the preciseness of wavelet analysis. The choice of the appropriate mother wavelet depends on the nature of the signal and on the type of information to be extracted from the signal. In this paper Wavelet multi resolution analysis is used as the solution for information extraction from transient signals caused by faults. Wavelet family, dB4 (Daubechies) is used as the mother wavelet. By applying wavelet MRA technique [6], extraction of third level detailed coefficient from the current signal after summation is performed. The existence of a fault is identified based on the detail coefficients summation magnitude, a generalized algorithm has been implemented for the transmission line faults classification.
II. DISCRETE WAVELET TRANSFORM Filter bank theory [1] is the foundation for the development of the discrete wavelet transform (DWT). At level (k), the wavelet transform coefficients of a signal are determined by using a high pass and a low pass filters. The obtained coefficients of low pass filter from the earlier stage are then down sampled by a factor two to reduce the dimension. The high pass filter is obtained from the mother wavelet function and further measures the details coefficients for the input signal. Similarly, the low pass filter delivers a smoothed version of the input signal and is derived from a scaling function, which is associated to the mother wavelet function [2] & [3]. For a function s(t), its continuous wavelet transform (WT) can be calculated from the following equation:
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