Power quality disturbance signal classification in microgrid based on kernel extreme learning machine

被引：2

作者：

Jing, Guoxiu ^{[1
]}

Wang, Dengke ^{[2
]}

Xiao, Qi ^{[3
]}

Shen, Qianxiang ^{[1
]}

Huang, Bonan ^{[1
]}

机构：

[1] Northeastern Univ, Coll Informat Sci & Engn, Shenyang, Peoples R China

[2] State Grid Luohe Power Supply Co, Luohe 462000, Peoples R China

[3] ShanghaiTech Univ, Sch Informat Sci & Technol, Shanghai, Peoples R China

来源：

ELECTRONICS LETTERS | 2024年 / 60卷 / 16期

基金：

中国国家自然科学基金;

关键词：

learning (artificial intelligence); power grids; SYSTEM;

D O I：

10.1049/ell2.13312

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

This paper presents a kernel extreme learning machine (KELM) integrated with the improved whale optimization algorithm (IWOA) to address the power quality disturbance (PQD) issue in microgrids. First, an adaptive variational mode decomposition method is employed to extract PQD signals in microgrids. Then, the IWOA is utilized to optimize the penalty factor and kernel function parameters for the KELM classifier model, thereby enhancing the performance of the classifier. Furthermore, the test results indicate that the proposed IWOA-KELM achieves high classification accuracy and rapid convergence for complex PQD signals. This paper presents a kernel extreme learning machine integrated with the improved whale optimization algorithm to address power quality issues in microgrids resulting from distributed power access. In this work, the adaptive variational mode decomposition method is employed to decompose the complex disturbance signals in microgrids. image

引用

页数：5

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