A normalized Haar wavelet transformation based firefly optimization algorithm for power transmission line fault detection problems

Abstract

ABSTRACT Through transmission lines (TLs), an electric power transmission system has been able to transmit power from generating stations to consumers. During transmission, various kinds of malfunctions take place and they are termed as a fault. Although fault is undesirable, it is unavoidable event hampering the smooth functioning of the power system. In power transmission systems, and a large number of voltage and current signal, distortions take place due to faults. Faults occur in power TL causing power supply interruption. Several fault detection techniques have been presented by researchers to detect a fault in TL. However, the time required to locate the fault remained higher and the power loss rate (PLR) was not reduced. To overcome these issues and identify faults in electrical power TL, Haar wavelet feature extraction-based firefly optimized fault detection (HWFE-FFOFD) method has been introduced. The power TL signal sample has been taken as an input. Zero-mean normalization is the pre-processing approach that converts the transmission signal sample into a specified range. To extort features (i.e. voltages and current values) with higher accuracy, the normalized signal has been given to Haar Wavelet Transform. Then, the extracted features at different time instants have been given to the firefly optimized fault detection (FFOFD) algorithm. In the FFOFD algorithm, extracted features have been considered as firefly populations. The FFOFD algorithm functions with the flashing behavior of a firefly. At last, the firefly position has been updated and ranked according to light intensity to detect a fault in electrical power TL. In this manner, the fault detection time (FDT) gets reduced using HWFE-FFOFD method. HWFE-FFOFD method is evaluated in FEA, FDT, and PLR. From the experimental results obtained, it can be confirmed that the HWFE-FFOFD method has been able to enhance accuracy by 14% and minimize time by 26% and PLR by 62% when compared to conventional methods.