SA-APSO Algorithm and Its Application in Ultrasonic Location of Partial Discharge in Transformer Oil

XU Yanchun; WANG Quan; LI Zhenxing; LI Zhenhua; LYU Mi

doi:10.13296/j.1001-1609.hva.2018.12.022

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SA-APSO Algorithm and Its Application in Ultrasonic Location of Partial Discharge in Transformer Oil

更新时间：2025-04-26

- SA-APSO Algorithm and Its Application in Ultrasonic Location of Partial Discharge in Transformer Oil
- High VoltageApparatus Vol. 54, Issue 12, Pages: 143-149(2018)
- 作者机构：
  
  1. 三峡大学梯级水电站运行与控制湖北省重点实验室
  2. Texas A&M University College of Electrical Engineering and Computer
- 作者简介：
- 基金信息：
- DOI：10.13296/j.1001-1609.hva.2018.12.022
  CLC： TM41
- Published：2018
- 稿件说明：
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[1]徐艳春,王泉,李振兴,李振华,吕密.SA-APSO算法及其在变压器油中局部放电超声定位中的应用[J].高压电器,2018,54(12):143-149.

XU Yanchun, WANG Quan, LI Zhenxing, et al. SA-APSO Algorithm and Its Application in Ultrasonic Location of Partial Discharge in Transformer Oil[J]. High voltageapparatus, 2018, 54(12): 143-149.
[1]徐艳春,王泉,李振兴,李振华,吕密.SA-APSO算法及其在变压器油中局部放电超声定位中的应用[J].高压电器,2018,54(12):143-149. DOI： 10.13296/j.1001-1609.hva.2018.12.022.

XU Yanchun, WANG Quan, LI Zhenxing, et al. SA-APSO Algorithm and Its Application in Ultrasonic Location of Partial Discharge in Transformer Oil[J]. High voltageapparatus, 2018, 54(12): 143-149. DOI： 10.13296/j.1001-1609.hva.2018.12.022.

摘要

针对基本粒子群算法（particle swarm optimization algorithm

PSO）局部寻优能力差及易早熟收敛的情况

提出一种融入模拟退火思路的自适应粒子群混合算法（simulated annealing-adaptive particle swarmoptimization algorithm

SA-APSO）

在惯性权重变化-自适应粒子群基础上融入退火思想

以一定的随机概率接收最优值

能有效提升全局寻优能力并克服早熟收敛现象。利用测试函数进行的仿真结果表明SA-APSO算法在结果精度及稳定度上明显优于基本PSO。并将其应用于变压器油中局部放电的定位计算

将结果与基本PSO及自适应粒子群进行比较

结果表明基于SA-APSO的变压器油中局部放电超声定位方法能够实现全局精确定位

且结果稳定

综合误差小于3.7 mm。

Abstract

In allusion to the basic particle swarm optimization algorithm local search optimization ability is poor and it is easy to premature convergence

an improved PSO

which integrate simulated annealing idea into the adaptive particle swarm optimization algorithm

is proposed.It is named simulated annealing-adaptive particle swarm optimization algorithm（SA-APSO）

based on the adaptive particle swarm optimization

it integrates the simulated annealing idea and accept the optimal value with certain probability.SA-APSO can effectively enhance the global searching ability and overcome the premature convergence phenomenon.The simulation results show that the SA-APSO algorithm is superior to the basic PSO in the accuracy and stability of the results.And its application in location of partial discharge in transformer oil is calculated

the results with the basic PSO and adaptive particle swarm optimization are compared.The results show that based on SA-APSO transformer oil partial discharge-ultrasonic positioning method can realize the accurate global localization and stable result

the synthetic error less than 3.7 mm.

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