计算技术与自动化2023，Vol.42Issue(4)：41-46,6.DOI:10.16339/j.cnki.jsjsyzdh.202304007
基于增强支持向量机的电力隧道多状态全过程监控方法
Multi-state Whole-process Monitoring Method of Power Tunnel Based on Enhanced Support Vector Machine
刘滨 1刘春 1邵必飞 1杨郭明 1苟军 1黄贵武1
作者信息
- 1. 国网兰州供电公司,甘肃兰州 730030
- 折叠
摘要
Abstract
A multi-state whole-process monitoring method of power tunnel based on enhanced support vector machine is proposed to accurately monitor the whole process of multi-state power tunnel and visually display the monitoring results.The low-frequency approximate factors of the dynamic data flow of the power tunnel are extracted by discrete wavelet,the data are recombined and the features are extracted,and the low-frequency factors are fused again to obtain the multi-state mixed characteristics of the power tunnel.In-depth study of support vector machine(SVM),access to the samples of unac-quired categories,through the access points marked with"true"and"false"classification results to optimize the classifier,so as to enhance the support vector machine.Particle swarm optimization(PSO)algorithm was used to optimize and enhance the parameters of support vector machine,and a power tunnel multi-state recognition model based on PSO-SVM was ob-tained.Through the training and testing of the model,the multi-state of power tunnel was identified,and the whole process monitoring of power tunnel multi-state was realized.Experimental results show that this method can monitor various states of various indicators in power tunnel in time and accurately,give early warning to abnormal situations in time,and realize the whole process of multi-state monitoring of power tunnel.关键词
电力隧道/支持向量机/全过程监控/多特征混合/分类器/离散小波分解Key words
power tunnel/support vector machine/whole-process monitoring/multi-feature mixing/classifier/discrete wavelet decomposition分类
信息技术与安全科学引用本文复制引用
刘滨,刘春,邵必飞,杨郭明,苟军,黄贵武..基于增强支持向量机的电力隧道多状态全过程监控方法[J].计算技术与自动化,2023,42(4):41-46,6.
