A precise ultra high frequency partial discharge location method for switchgear based on received signal strength ranging

Jiajia Song, Jinbo Zhang and Xinnan Fan

International Journal of Distributed Sensor Networks, 2020, vol. 16, issue 5, 1550147720903634

Abstract: Partial discharges are the main insulation defects encountered in gas-insulated switchgears. When it occurs inside the gas-insulated switchgear cavity, it degrades insulation, and, sooner or later, causes a breakdown. Therefore, it is important to discover insulation defects as early as possible, locate the discharge, and perform both defect identification and maintenance. Current ultra high frequency-based partial discharge location methods mainly use time delay. To obtain accurate delay times, however, a very high sampling rate is needed, which requires expensive hardware and greatly limits its application. Therefore, in this article, a localization method based on received signal strength indicator ranging is proposed, and location estimation is carried out. An easily implementable particle swarm optimization algorithm with high positioning accuracy is selected to compensate for the low positioning accuracy of current received signal strength indicator ranging methods. To further improve positioning accuracy, the convergence conditions of the particle swarm optimization are investigated, and, considering their constraints, an improved particle swarm optimization algorithm is proposed. By combining the characteristics of ultra high frequency wireless sensor array positioning, the particle size is optimized. The simulation results show that the location accuracy using the ultra high frequency switchgear partial discharge location method based on received signal strength indicator ranging with the improved particle swarm optimization algorithm performs significantly better.

Keywords: Partial discharge; received signal strength ranging; ultra high frequency; improved particle swarm optimization location algorithm (search for similar items in EconPapers)
Date: 2020
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Persistent link: https://EconPapers.repec.org/RePEc:sae:intdis:v:16:y:2020:i:5:p:1550147720903634

DOI: 10.1177/1550147720903634

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