Advanced Fault Detection, Classification, and Localization in Transmission Lines: A Comparative Study of ANFIS, Neural Networks, and Hybrid Methods

Abstract

Electric systems are getting more complex with time, and primitive protection methods such as traveling wave and impedance-based methods face limitations and shortcomings. This paper incorporates and presents the applications of an adaptive neuro-fuzzy inference system and compares it with a back propagation neural network, self-organizing map, and hybrid method of discrete wavelet with adaptive neuro-fuzzy inference system for fault detections, classification, and localization in transmission lines. These methods, in comparison with primitive methods, could be capable of detecting, identifying, and predicting the location of the faults more accurately. The IEEE 9-bus system is utilized to obtain data from one end of the transmission line to develop an ANFIS-based model. This system is simulated in MATLAB/Simulink for different fault cases at various locations. The three-phase voltage and current at one end of IEEE 9-bus number seven are taken for training. Three ANFIS models are developed for fault detection, classification, and localization and compared with other models. For verification of the models, mean square error, mean absolute error, and regression analysis have been computed and compared for all the models. All four techniques have performed well for fault classification, detection, and location. However, the percentage error for the ANFIS-based fault model is less compared to backpropagation, self-organizing map, and discrete wavelet transform with ANFIS. Therefore, the proposed ANFIS models can be implemented for deploying in real-time-based protection systems.

Transmission Line Fault Classification Using Conformer Convolution-Augmented Transformer Model

“…ML-based methods require substantial data to effectively train classifier models. While most studies rely on simulated data due to the scarcity of realsystem fault data [3,[5][6][7], our approach also incorporates historical event data from the Taiwan power system, comprising 108 events with varied fault scenarios. It is noteworthy that a laboratory physical system developed in [8] generates a relevant dataset.…”

“…For fault classification purposes, most feature extraction processes involve transforming the input time series data into the frequency domain. Previous research has introduced feature extraction methodologies such as wavelet packet transformation (WT) [6,7,14,15], multiwavelet packet transformation (MWT) [13], Hilbert-Huang Transform (HHT) [8], time series imaging [10], and mathematical morphology (MM) [9]. The precise classification of faults in transmission lines through WTs necessitates dynamically adjusting their parameters according to the prevailing power system topology [4].…”

Advancements in Fault Detection Techniques for Transmission Lines: A Literature Review

“…Advanced Fault Detection, Classification, and Localization in Transmission Lines: A Comparative Study of ANFIS, Neural Networks, and Hybrid Methods by Kanwal, S., Jiriwibhakorn, S. (2024)[35] …”