西安科技大学学报2024，Vol.44Issue(6)：1205-1217,13.DOI:10.13800/j.cnki.xakjdxxb.2024.0618
基于IDT-SAE-ELM的煤矿电缆短路故障识别方法
Identification method for short-circuit fault in coal mine cable based on IDT-SAE-ELM
摘要
Abstract
A short-circuit fault recognition method based on IDT-SAE-LM was proposed to address the problem of low accuracy in fault identification and type determination due to the inability of existing methods to effectively extract deep features of coal mine cable short-circuit faults.Firstly,the traditional SAE model was improved by IDT technology to enhance its ability to efficiently capture the deep fea-tures of fault samples.Then,the Adam algorithm was used to optimize the IDT-SAE model parameters,and the short-circuit fault feature quantity was automatically obtained from the original current signal.Finally,the ELM model was used to replace Softmax to construct the fault classifier,so as to improve the ability of SAE model to identify the fault type with small feature difference,and realize the identifi-cation and type intelligent judgment of coal mine cable short-circuit fault.The short-circuit fault simu-lation was carried out with the actual parameters of the coal mine power grid.The Loss curve and the T-distributed random neighbor embedding algorithm were used to visually analyze the anti-overfitting abil-ity and the deep feature mining ability of the short-circuit fault of the proposed method.The accuracy and precision were used to evaluate the proposed method.The results show that:Compared with the traditional SAE model,the proposed method has better fault feature extraction ability and anti-overfit-ting ability;Compared with artificial intelligence methods such as RF,BPNN,and ELM,the accuracy is improved by7.47%,5.82%,and 5.42%,respectively.Compared with the traditional SAE method,the accuracy is improved by about 11%.Under severe noise interference,the accuracy of short-circuit fault i-dentification in this method is always above 98.75%,which effectively improves the accuracy of short-circuit fault identification and type determination of coal mine cables,and can provide an important basis for the identification of override trip causes and the analysis and treatment of short-circuit accidents.关键词
煤矿/短路故障/堆栈自编码器/极限学习机/Dropout集成技术Key words
coal mine/short circuit fault/stack auto-encoder/extreme learning machine/integrated dropout technology分类
信息技术与安全科学引用本文复制引用
王清亮,李泓朴,李书超,王伟峰..基于IDT-SAE-ELM的煤矿电缆短路故障识别方法[J].西安科技大学学报,2024,44(6):1205-1217,13.基金项目
国家自然科学基金项目(52074213) （52074213）
