[1]张 耀,姚 瑶,陈 卓,等.基于粒子群差分进化极限学习机的电力系统故障诊断模型[J].机械与电子,2024,42(03):60-64.
　ZHANG Yao,YAO Yao,CHEN Zhuo,et al.Power System Fault Diagnosis Model Via Particle Swarm Differential Evolution-based Extreme Learning Machine[J].Machinery & Electronics,2024,42(03):60-64.
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基于粒子群差分进化极限学习机的电力系统故障诊断模型()
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《机械与电子》[ISSN:1001-2257/CN:52-1052/TH]

卷:
42
期数:
2024年03期
页码:
60-64
栏目:
智能制造
出版日期:
2024-03-25

文章信息/Info

Title:
Power System Fault Diagnosis Model Via Particle Swarm Differential Evolution-based Extreme Learning Machine
文章编号:
1001-2257 ( 2024 ) 03-0060-05
作者:
张 耀 1 ; 姚 瑶 1 ; 陈 卓 1 ; 袁子霞 2 ; 熊国江 2
1. 贵州电网有限责任公司电力调度控制中心,贵州 贵阳 550002 ;
2. 贵州大学电气工程学院,贵州 贵阳 550025
Author(s):
ZHANG Yao1 ;  YAO Yao1 ;  CHEN Zhuo1 ;  YUAN Zixia2 ;  XIONG Guojiang2
( 1.Guizhou Electric Power Grid Dispatching and Control Center , Guiyang 550002 , China ;
2.College of Electrical Engineering , Guizhou University , Guiyang 550025 , China )
关键词:
故障诊断; 极限学习机; 进化算法; 交叉验证
Keywords:
fault diagnosis ;  extreme learning machine ;  evolutionary algorithm ;  cross-validation
分类号:
TM76
文献标志码:
A
摘要:
针对电力系统发生的故障进行快速诊断,对电网及时恢复供电、降低故障影响具有非同寻常的意义。为了有效处理电力系统故障中存在的保护继电器和断路器运行的不确定性,提出了一种基于多重随机变异粒子群差分进化算法( MRPSODE )的极限学习机故障诊断模型,利用 MRPSODE 算法确定极限学习机最佳的隐含层节点个数,实现高效率的故障诊断。采用交叉验证方法降低噪声对原始样本数据的影响,确保诊断性能。实际故障案例的仿真分析结果表明,所提方法能够成功诊断复杂故障,与其他方法相比具有较强的竞争力。
Abstract:
Rapid diagnosis for faults occurring in power systems is of extraordinary significance for timely restoration of power supply and reduction of fault impact.In order to effectively deal with the uncertainty in the operation of protective relays and circuit breakers during power system faults , this paper proposes an extreme learning machine-based fault diagnosis model based on particle swarm differential evolution algorithm with multiple random variants ( MRPSODE ) .The MRPSODE is used to determine the optimal number of nodes in the hidden layer of extreme learning machine to achieve efficient fault diagnosis.A cross-validation method is used to reduce the influence of noise on the original samples to improve the diagnosis performance.Simulation results of actual fault cases show that the proposed method can successfully diagnose complex faults and is competitive compared with other methods.

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备注/Memo

备注/Memo:
收稿日期: 2023-06-27
基金项目:国家自然科学基金资助项目( 51907035 )
作者简介:张 耀 ( 1988- ),男,贵州铜仁人,硕士,工程师,研究方向为电力系统调度运行。
更新日期/Last Update: 2024-03-25