Transformer fault identification method based on self-adaptive extreme learning machine

被引：0

作者：

Wu J. ^{[1
]}

Qin W. ^{[1
]}

Liang H. ^{[1
]}

Jin S. ^{[1
]}

Luo W. ^{[1
]}

机构：

[1] School of Automation, Guangdong University of Technology, Guangzhou

来源：

Dianli Zidonghua Shebei/Electric Power Automation Equipment | 2019年 / 39卷 / 10期

基金：

中国国家自然科学基金;

关键词：

Extreme learning machine; Fault identification; Immune algorithm; Particle swarm optimization algorithm; Power transformers;

D O I：

10.16081/j.epae.201908037

中图分类号：

学科分类号：

摘要：

In view of the problem of limited data processing ability and low accuracy of single intelligent algorithm when the cumulative scale and complexity of transformer state data increase, a transformer fault identification method based on self-adaptive extreme learning machine is proposed. The IA(Immune Algorithm) is used to classify the superior and inferior particle populations due to its diversity adjustment mechanism and storage mechanism, and the superior and inferior particles are evolved in different ways. The PSO(Particle Swarm Optimization) algorithm improved by IA effectively overcomes the shortcoming that the population is prone to premature development and thus leads to evolutionary stagnation, and improves the global optimization ability. On the basis of parameter optimization, the transformer fault identification model is established according to the optimization output results. The experimental results show that the fault identification accuracy of the proposed method is higher than the ELM(Extreme Learning Machine) method, the PSO-ELM(Particle Swarm Optimization-based Extreme Learning Machine) method and the GA-ELM(Genetic Algorithm-based Extreme Learning Machine) method. © 2019, Electric Power Automation Equipment Press. All right reserved.

引用

页码：181 / 186

页数：5

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