| Issue |
MATEC Web of Conf.
Volume 399, 2024
2024 3rd International Conference on Advanced Electronics, Electrical and Green Energy (AEEGE 2024)
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|
|---|---|---|
| Article Number | 00022 | |
| Number of page(s) | 4 | |
| DOI | https://doi.org/10.1051/matecconf/202439900022 | |
| Published online | 24 June 2024 | |
Power quality disturbance detection method based on optimized kernel extreme learning machine
1 Electric Power Research Institute State Grid Sichuan Electric Power Company, Chengdu, China
2 Wuhan NARI Limited Liability Company State Grid Electric Power Research Institue Liability Corporation, Wuhan, Nanjing, China
In order to improve the accuracy of rapid detection of power quality, a power quality disturbance (PQD) classification method based on kernel-based extreme learning machine (KELM) is proposed, and chaos optimization is used to improve the global optimization performance of the particle swarm algorithm. This method first uses KELM to establish a classification model, and then uses an improved chaotic particle swarm optimization (CPSO) to optimize the parameters of KELM. Comparative analysis of example simulation results shows that the algorithm has higher classification accuracy and improves the reliability of power quality disturbance detection.
© The Authors, published by EDP Sciences, 2024
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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