G. Lu, Lulu Zhang, Jing Ma, H. Dai, Yawei Wei, Bin Wang, Chen Shi, Jun Lu
{"title":"Selection method of key stability features of power grid based on improved ant colony algorithm","authors":"G. Lu, Lulu Zhang, Jing Ma, H. Dai, Yawei Wei, Bin Wang, Chen Shi, Jun Lu","doi":"10.1117/12.2680566","DOIUrl":null,"url":null,"abstract":"With the continuous advancement of smart grid and the rapid development of computer and information technology, massive amounts of redundant and noisy information are connected to the power grid. Therefore, this paper proposes a key stable feature selection method based on improved ant colony algorithm. First, use the artificial intelligence model to solve the optimal number of key features. Then use Filter based on the minimum redundancy-maximal relevance (mRMR) algorithm and Wrapper based on the improved ant colony algorithm to select the optimal key feature subset in two stages. Finally, example analysis with IEEE39 nodes system is used to verify the availability and effectiveness of the selection method.","PeriodicalId":201466,"journal":{"name":"Symposium on Advances in Electrical, Electronics and Computer Engineering","volume":"41 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2023-05-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Symposium on Advances in Electrical, Electronics and Computer Engineering","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1117/12.2680566","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
With the continuous advancement of smart grid and the rapid development of computer and information technology, massive amounts of redundant and noisy information are connected to the power grid. Therefore, this paper proposes a key stable feature selection method based on improved ant colony algorithm. First, use the artificial intelligence model to solve the optimal number of key features. Then use Filter based on the minimum redundancy-maximal relevance (mRMR) algorithm and Wrapper based on the improved ant colony algorithm to select the optimal key feature subset in two stages. Finally, example analysis with IEEE39 nodes system is used to verify the availability and effectiveness of the selection method.