AUTHOR=Varghese P Rini , Subathra M. S. P. , George S. Thomas , Kumar Nallapaneni Manoj , Suviseshamuthu Easter Selvan , Deb Sanchari TITLE=Application of signal processing techniques and intelligent classifiers for high-impedance fault detection in ensuring the reliable operation of power distribution systems JOURNAL=Frontiers in Energy Research VOLUME=11 YEAR=2023 URL=https://www.frontiersin.org/journals/energy-research/articles/10.3389/fenrg.2023.1114230 DOI=10.3389/fenrg.2023.1114230 ISSN=2296-598X ABSTRACT=

High-impedance fault (HIF) is always a threat and the biggest challenge in the power transmission and distribution system (PTDS). For a PTDS to operate effectively, HIF diagnosis is essential. However, given the HIF’s nature and the involved complexity, detection, identification, and fault location are difficult. This will be even more complicated in conventional PTDSs as they are inefficient and highly vulnerable. Given the importance and urgent need for HIF diagnosis in PTDS, this study reviews state-of-the-art HIF phenomenon and detection techniques and proposes the use of “various signal processing techniques for fault feature extraction” and “ different classifiers for identifying HIF.” First, HIF current/voltage signals are analyzed using signal processing techniques, which include the discrete wavelet transform (DWT), pattern recognition, Kalman filtering, TT transform, mathematical morphology (MM), S transform (ST), fast Fourier transform (FFT), principal component analysis (PCA), linear discriminant analysis (LDA), and wavelet transforms, such as dual-tree, maximum overlap discrete wavelet transform (MODWT), and lifting wavelet transform (LWT). Second, the various HIF and non-HIF faults are classified using intelligent classifiers. The intelligent classifiers include artificial neural networks (ANNs), probabilistic neural networks (PNNs), genetic algorithms (GAs), fuzzy logic, adaptive neuro-fuzzy interface system, support vector machine (SVM), extreme learning machine (ELM), adaptive resonance theory, random forests (RFs), decision trees (DTs), and convolution neural networks (CNNs). In addition to the comparative discussion of various classifier techniques, their evaluation criterion and performance are prioritized. Third, this review also studied different test systems, such as radial distribution network, mesh distribution network, IEEE 4 node, IEEE 13 node feeder, IEEE 34 node feeder, IEEE 39 node feeder, IEEE 123 node feeder, Palash feeder, and test microgrid systems, to assess the pertinence of various HIF detection schemes and the behavior along with methods to locate the HIF. Overall, we believe this review would serve as a comprehensive compendium of advanced techniques for HIF diagnosis in different test systems.