An Approach for Detecting Faulty Lines in a Small-Current, Grounded System Using Learning Spiking Neural P Systems with NLMS

Yangheng Hu, Yijin Wu, Qiang Yang, Yang Liu, Shunli Wang, Jianping Dong (), Xiaohua Zeng and Dapeng Zhang
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Yangheng Hu: Sichuan Shugong Highway Engineering Test Co., Ltd., Chengdu 610101, China
Yijin Wu: School of Automation, Chengdu University of Information Technology, Chengdu 610225, China
Qiang Yang: School of Automation, Chengdu University of Information Technology, Chengdu 610225, China
Yang Liu: Sichuan Shugong Highway Engineering Test Co., Ltd., Chengdu 610101, China
Shunli Wang: Electric Power College, Inner Mongolia University of Technology, Hohhot 010000, China
Jianping Dong: School of Automation, Chengdu University of Information Technology, Chengdu 610225, China
Xiaohua Zeng: Sichuan Shugong Highway Engineering Test Co., Ltd., Chengdu 610101, China
Dapeng Zhang: School of Automation, Chengdu University of Information Technology, Chengdu 610225, China

Energies, 2024, vol. 17, issue 22, 1-23

Abstract: Detecting faulty lines in small-current, grounded systems is a crucial yet challenging task in power system protection. Existing methods often struggle with the accurate identification of faults due to the complex and dynamic nature of current and voltage signals in these systems. This gap in reliable fault detection necessitates more advanced methodologies to improve system stability and safety. Here, a novel approach, using learning spiking neural P systems combined with a normalized least mean squares (NLMS) algorithm to enhance faulty line detection in small-current, grounded systems, is proposed. The proposed method analyzes the features of current and voltage signals, as well as active and reactive power, by separately considering their transient and steady-state components. To improve fault detection accuracy, we quantified the likelihood of a fault occurrence based on feature changes and expanded the feature space to higher dimensions using an ascending dimension structure. An adaptive learning mechanism was introduced to optimize the convergence and precision of the detection model. Simulation scheduling datasets and real-world data were used to validate the effectiveness of the proposed approach, demonstrating significant improvements over traditional methods. These findings provide a robust framework for faulty-line detection in small-current, grounded systems, contributing to enhanced reliability and safety in power system operations. This approach has the potential to be widely applied in power system protection and maintenance, advancing the broader field of intelligent fault diagnosis.

Keywords: faulty line detection; membrane computing; spiking neural P systems; learning spiking neural P systems (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2024
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