Abstract: Transmission line faults are the main causes of interruption in power supply system. The fault on transmission lines affects the reliability and stability of the system. Fault identification, location using Synchronized Phasor Measurements has become highly significant, as they were capable of locating faults with its proven higher amount of accuracy. However, Synchronized Phasor Measurements have revealed some of its drawbacks in the conventional fault identification, location system. To improve the accuracy of fault detection, localization and classification of transmission line, a method called Morlet Discrete Neuro Fuzzy logic Wavelet Transform (MDFWT) is proposed. Initially, Morlet Envelope Spectrum (MES) Power wavelet transform is applied to detect faults on the measured frequencies and time of the modulating components. MES power wavelet transform in MDFWT method consists of complex exponential carriers with a multiplied Gaussian window to detect the faults. Detected faults are localized using the Discrete Wavelet Transform (DWT). DWT with Boltzmann entropy theory locates accurately the position of the fault on the transmission line. Finally the classification of transmission line faults in the MDFWT power system is carried out using the Neuro Fuzzy logic wavelet transform. The neuro fuzzy logic wavelet transform uses the if-then rules to classify the faults in an efficient manner with the highest reliability. Neuro fuzzy logic set in MDFWT method highlights the acquired knowledge for easy diagnosis of faults on the transmission line. The experiments are conducted on the factors such as fault detected error rate, fault localization accuracy and fault classification rate.
P. Balakrishnan and K. Sathiyasekar, 2016. Efficient Fault Detection Based on Localization and Classification of Transmission Line Using Morlet Discrete Neuro Fuzzy Logic Wavelet Transform. Asian Journal of Information Technology, 15: 4325-4332.