Ultrasonic Localization Method of Transformer Partial Discharge by Time Difference Screening and ABC Secondary Optimization

SHE Changjia; ZHENG Jianyong; HE Jiahong; CHEN Hao; MA Zhaoxing

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Ultrasonic Localization Method of Transformer Partial Discharge by Time Difference Screening and ABC Secondary Optimization

更新时间：2025-12-08

- Ultrasonic Localization Method of Transformer Partial Discharge by Time Difference Screening and ABC Secondary Optimization
- Vol. 47, Issue 8, Pages: 2820-2827(2021)
- 作者机构：
  
  1. 国网江苏省电力有限公司检修分公司
  2. 东南大学电气工程学院
  3. 青岛理工大学信息与控制工程学院
- 作者简介：
- 基金信息：
- DOI：
  CLC：
- Published Online：31 August 2021，
  
  Published：2021
- 稿件说明：
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SHE Changjia, ZHENG Jianyong, HE Jiahong, et al. Ultrasonic Localization Method of Transformer Partial Discharge by Time Difference Screening and ABC Secondary Optimization[J]. 2021, 47(8): 2820-2827.
DOI：

SHE Changjia, ZHENG Jianyong, HE Jiahong, et al. Ultrasonic Localization Method of Transformer Partial Discharge by Time Difference Screening and ABC Secondary Optimization[J]. 2021, 47(8): 2820-2827. DOI：

摘要

针对变压器中局部放电源的位置

提出了一种时差筛选(time difference screening

TDS)和人工蜂群(artificial beecolony

ABC)二次寻优的超声定位方法

首先采用广义互相关法估计各传感器接收信号的时差

在此基础上增加基于球面方程组的时差筛选环节剔除掉误差较大的时差。接着根据有效时差组建立空间定位的约束最优化模型并采用ABC算法进行求解

将求解出的若干定位结果进行K-Means聚类分析

最后再次使用ABC算法对距离聚类中心之和最小的点进行寻优

校正误差大的定位结果。仿真结果表明:该文方法相较于传统方法定位误差更小

且现场试验验证定位误差小于5 cm。

Abstract

To locate the partial discharge（PD） source in a transformer

an ultrasonic localization method by time difference screening（TDS） and artificial bee colony（ABC） secondary optimization is proposed. Firstly

the time difference of received signals is estimated by the generalized cross-correlation method. On this basis

the time-difference screening link based on spherical equations is added to eliminate the time-difference with large errors. Secondly

the constrained optimization model of spatial localization is set up according to the effective time-difference and solved by ABC algorithm. The K-Means clustering analysis is carried out for some localization results. Finally

the ABC algorithm is used to optimize the minimum point of the sum of distance clustering centers

and the localization result with large error is corrected. The simulation results show that the localization error of the method in this paper is more negligible than that of the traditional method

and the localization error is less than 5 cm in the field experiment.

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