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.

中文翻译：

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基于接收信号强度测距的开关柜超高频局部放电精确定位方法

局部放电是气体绝缘开关设备中遇到的主要绝缘缺陷。当它发生在气体绝缘开关设备腔内时，它会降低绝缘性，并且迟早会导致击穿。因此，尽早发现绝缘缺陷，定位放电，并进行缺陷识别和维护非常重要。目前基于超高频的局部放电定位方法主要采用时间延迟。然而，为了获得准确的延迟时间，需要非常高的采样率，这需要昂贵的硬件并极大地限制了其应用。因此，本文提出一种基于接收信号强度指示测距的定位方法，并进行位置估计。选择了一种易于实现且定位精度高的粒子群优化算法来弥补当前接收信号强度指标测距方法定位精度低的问题。为进一步提高定位精度，研究了粒子群优化算法的收敛条件，并考虑其约束条件，提出了一种改进的粒子群优化算法。通过结合超高频无线传感器阵列定位的特点，优化了粒径。仿真结果表明，采用基于接收信号强度指标测距的超高频开关设备局部放电定位方法和改进的粒子群优化算法的定位精度明显更好。