基于改进DBO优化的CNN-KELM柔性直流换流阀故障辅助决策方法

Fault-assisted Decision-making Method for Flexible DC Converter Valve Based on CNN-KELM with Improved DBO Optimization

马群 ¹戈一航 ¹刘黎 ²田蘅¹

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作者信息

1. 国网电力科学研究院有限公司,南京 211106;北京科东电力控制系统有限责任公司,北京 100092
2. 国网浙江省电力有限公司舟山供电公司,浙江舟山 316000
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摘要

为提高柔性直流换流阀故障分类正确率,提出基于改进蜣螂优化算法(IDBO)的卷积神经网络融合-核极限学习机(CNN-KELM)柔性直流换流阀故障分类方法.对柔性直流换流阀故障特征库归一化处理,利用CNN进行故障特征提取,采用IDBO优化KELM的核参数和惩罚因子.将IDBO-CNN-KELM作为分类器对提取到的柔性直流换流阀故障库进行分类.通过实验证明,IDBO-CNN-KELM模型在故障测试集中的分类正确率达到97.727％,相较传统KELM、PSO(粒子群算法)-CNN-KELM提升了1.136％、0.577％,证明了IDBO-CNN-KELM模型的精确性.该方法有效提高了柔性直流换流阀故障分类的准确性和效率,增强了电网直流输电可靠性.

Abstract

In order to improve the correct rate of flexible DC converter valve fault classification,a flexible DC converter valve fault classification method based on the convolutional neural network fused kernel-extreme learning machine(CNN-KELM)optimized by the Improved dung beetle optimization algorithm(IDBO)is proposed.The flexible DC converter valve fault feature library is normalized,the CNN network is used for fault feature extraction,and IDBO is used to optimize the kernel parameters and penalty factors of KELM.IDBO-CNN-KELM is used as a classifier to classify the extracted flexible DC converter valve fault library.Through experiments,the IDBO-CNN-KELM model achieves a classification correctness of 97.727％in the fault test set,which improves 1.136％and 0.577％compared with the traditional KELM and PSO-CNN-KELM,proving the accuracy of the IDBO-CNN-KELM model.The method effectively improves the accuracy and efficiency of fault classification of flexible DC converter valves,and enhances the reliability of DC transmission in power grids.

关键词

柔性直流换流阀/蜣螂优化算法/卷积神经网络/核极限学习机/故障辅助决策

Key words

flexible DC converter valve/dung beetle optimisation algorithm/convolutional neural network/nuclear limit learning machine/fault-assisted decision making

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出版年

2024

科技和产业

中国技术经济学会

科技和产业

影响因子：0.361

ISSN：1671-1807

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