Application of signal processing techniques and intelligent classifiers for high-impedance fault detection in ensuring the reliable operation of power distribution systems

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.

Development of intelligent protection and automation control systems using fuzzy logic elements

“…A literature review has shown that combining multiple functions in one microprocessor terminal dramatically reduces the reliability of protection, since if this terminal fails, too many functions will be lost at once compared to the case when these functions are distributed among several terminals [6]- [9], the need to limit the number of functions implemented in one terminal was also discussed in their reports by scientists at the international conference "modern trends in the development of protection systems and automation of power systems" [10]- [12], a sharp increase in the level of complexity of the work of personnel servicing protection with the transition to the MPD, as the cause of severe accidents in power systems [13], [14], the complexity of the software interface and the need to introduce an excessive number of set points during the programming of the MPR [15]- [17], the unsatisfactory state of the electromagnetic environment at most old substations, which were designed and built for electromechanical protection [18]- [20], and not for microprocessor protection [21], and about the numerous failures in the operation of the MPD arising from this [22], the lack of universal strict requirements for the hardware of the MPD and software, and as a result [23], too many programs and algorithms embedded in the MPD used in one power system, which leads to problems during operation and to increase the probability of false operation of these devices. Most of these devices must be installed in heated switchgear, since at temperatures below -25 degrees, the liquid crystal display with which microprocessor devices are equipped becomes unreadable.…”