基于粒子群优化的电力变压器局部放电超声检测定位研究

Ri-cheng Luo, Kai Bai, Shao-yu Liu
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引用次数: 7

摘要

粒子群优化算法(PSO)是群体智能的一个分支。该算法本质上是一种随机搜索算法。该算法收敛于全局最小值的概率较高,适合连续函数的寻优。本文讨论了电力变压器局部放电的定位问题,利用粒子群算法研究了电力变压器局部放电的超声测量源定位问题,并基于超声波的传播特性建立了超声定位的信号参数估计模型。实验结果表明,粒子群算法具有较高的计算效率和较快的收敛速度,能有效地防止结果陷入局部最优。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Study on partial discharge localization by ultrasonic measuring in power transformer based on Particle Swarm Optimization
Particle swarm optimization(PSO) algorithm is one of embranchments of swarm intelligence. The algorithm is a random searching algorithm in nature. It can converge to the global minima more probability and be adept in continuous function optimization. In this paper, partial discharge(PD) localization in power transformer is discussed, The partial discharge source localization by ultrasonic measuring in a power transformer is studied by the Particle Swarm optimization, and the signal parameter estimated model of ultrasonic localization has been established based on the propagation properties of the ultrasonic wave. Experiment results have shown that PSO algorithm possesses high calculation efficiency and high convergence speed, and can efficiently prevent the result from falling into the localized optimum.
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