电源学报

基于改进粒子群优化算法优化SVM的断路器故障诊断

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陈国平 ¹ , 高慧民中国电源学会学生会员 ² , 陈蒙蒙中国电源学会学生会员 ² , 荣灿灿中国电源学会会员 ² , 夏晨阳中国电源学会会员 ²

电源学报 | 故障诊断与可靠性 2026,24(8): 328-338

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电源学报 |故障诊断与可靠性 2026 , 24 (8) : 328 -338

基于改进粒子群优化算法优化SVM的断路器故障诊断

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陈国平¹, 高慧民中国电源学会学生会员², 陈蒙蒙中国电源学会学生会员², 荣灿灿中国电源学会会员², 夏晨阳中国电源学会会员²

作者信息

^1.国网宿州供电公司，宿州　234000

^2.中国矿业大学电气工程学院，徐州　221116

通讯作者:

夏晨阳（1982—　），男，中国电源学会会员，通信作者，博士，教授。研究方向：无线电能传输技术，高性能电力电子变换器与先进控制技术。E-mail：bluesky198210@163.com。

作者简介:

陈国平（1991—　），男，硕士，工程师。研究方向：电力系统继电保护及其自动化。E-mail：253766475@qq.com。

高慧民（2001—　），男，中国电源学会学生会员，硕士研究生。研究方向：无线电能传输技术和电力设备的状态检测与故障判断。E-mail：ts23230015a31ld@cumt.edu.cn。

陈蒙蒙（2000—　），男，中国电源学会学生会员，硕士研究生。研究方向：中距离无线电能传输，优化算法和电力设备的状态检测与故障判断。E-mail：17184992@cumt.edu.cn。

荣灿灿（1991—　），男，中国电源学会会员，博士，副教授。研究方向：无线电能传输技术与电磁超材料。E-mail：ccrong@cumt.edu.cn。

Optimization of SVM for Circuit Breaker Fault Diagnosis Based on Improved Particle Swarm Optimization Algorithm

Guoping CHEN¹, Huimin GAO Student Member, CPSS², Mengmeng CHEN Student Member, CPSS², Cancan RONG Member, CPSS², Chenyang XIA Member, CPSS²

Affiliations

^1.State Grid Suzhou Power Supply Company, Suzhou 234000, China

^2.School of Electrical Engineering, China University of Mining and Technology, Xuzhou 221116, China

出版时间: 2026-08-30 doi: 10.13234/j.issn.2095-2805.2026.8.328

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摘要

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为了提高断路器故障诊断精度，提出通过改进粒子群优化IPSO（improved particle swarm optimization）算法对支持向量机SVM（support vector machine）相关参数进行优化，进而建立一种新的IPSO-SVM断路器故障诊断模型。其中，IPSO算法通过混沌映射、动态调整惯性系数和更新最优粒子策略等方法对粒子群优化PSO（particle swarm optimization）算法进行改进，提高了算法的收敛速度和精度。进一步地，结合几种典型的高压断路器故障类型和某种断路器的实际运行数据，构建IPSO-SVM故障诊断模型。最后，对比相同训练集下的其他断路器故障诊断模型，实验结果表明，IPSO-SVM模型具有更高的诊断精度，降低了人为干预程度，实现了对故障类型的有效识别，保障了分合闸线圈的可靠运行。

关键词

支持向量机 / 改进粒子群优化算法 / 高压断路器 / 故障诊断 / 混沌映射

Abstract

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To enhance the diagnostic accuracy of circuit breaker faults, an improved particle swarm optimization (IPSO) algorithm is used to optimize the relevant parameters of support vector machine (SVM). Then, a novel IPSO-SVM circuit breaker fault diagnosis model is established, in which the IPSO algorithm enhances the traditional particle swarm optimization (PSO) algorithm by incorporating chaotic mapping, dynamically adjusting the inertia coefficient and updating the optimal particle strategy. Therefore, the convergence speed and accuracy of the algorithm are improved. Furthermore, the IPSO-SVM fault diagnosis model is constructed based on several typical high-voltage circuit breaker fault types and the actual operation data of one specific circuit breaker. Finally, a comparison with other circuit breaker fault diagnosis models using the same training dataset is performed. Experimental results show that the IPSO-SVM model outperformed other models in terms of diagnostic accuracy and reduced the need for manual intervention, achieving effective fault identification of fault types and ensuring the reliable operation of the circuit breaker’s opening and closing coils.

Key words

Support vector machine (SVM) / improved particle swarm optimization (IPSO) algorithm / high-voltage circuit breaker / fault diagnosis / chaotic mapping

引用本文

陈国平, 高慧民中国电源学会学生会员, 陈蒙蒙中国电源学会学生会员, 荣灿灿中国电源学会会员, 夏晨阳中国电源学会会员. 基于改进粒子群优化算法优化SVM的断路器故障诊断. 电源学报, 2026 , 24 (8) : 328 -338 . DOI: 10.13234/j.issn.2095-2805.2026.8.328

Guoping CHEN, Huimin GAO, Mengmeng CHEN, Cancan RONG, Chenyang XIA. Optimization of SVM for Circuit Breaker Fault Diagnosis Based on Improved Particle Swarm Optimization Algorithm[J]. Journal of Power Supply, 2026 , 24 (8) : 328 -338 . DOI: 10.13234/j.issn.2095-2805.2026.8.328

基金

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国网宿州供电公司科技资助项目(B312Q024000B)

参考文献

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参考文献引证文献

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2026年第24卷第8期

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文章信息

doi: 10.13234/j.issn.2095-2805.2026.8.328

接收时间：2024-11-19
首发时间：2026-10-29
出版时间：2026-08-30

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收稿日期：2024-11-19
修回日期：2025-01-11
录用日期：2025-02-05

基金

State Grid Suzhou Power Supply Company Technology Project(B312Q024000B)

国网宿州供电公司科技资助项目(B312Q024000B)

作者信息

^1.国网宿州供电公司，宿州　234000

^2.中国矿业大学电气工程学院，徐州　221116

通讯作者:

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2种不同金属材料的力学参数

科 Family	属数 Number of genus	种数 Number of species	占总种数比例 Percentage of total species (%)	属 Genus	种数 Number of species	占总种数比例 Percentage of total species (%)
鹅膏菌科Amanitaceae	2	11	5.26	鹅膏菌属 Amanita	10	4.78
小菇科 Mycenaceae	2	12	5.74	丝盖伞属 Inocybe	5	2.39
多孔菌科 Polyporaceae	8	14	6.70	蜡蘑属 Laccaria	5	2.39
红菇科 Russulaceae	3	23	11.00	小皮伞属 Marasmius	6	2.87
				小菇属 Mycena	11	5.26
				光柄菇属 Pluteus	5	2.39
				红菇属 Russula	17	8.13
				栓菌属 Trametes	5	2.39

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