Meng Cao, Chi Zhang, Q. Tang, Jin He, Xuliang Zhu, Qi Zhao
{"title":"Short-term prediction study on the development trend of free particle defects within GIS based on ARMA model","authors":"Meng Cao, Chi Zhang, Q. Tang, Jin He, Xuliang Zhu, Qi Zhao","doi":"10.1109/AEERO52475.2021.9708119","DOIUrl":null,"url":null,"abstract":"The prediction of the development trend of metal free particle defects is crucial to the maintenance and operation of GIS equipment, and an accurate prediction can effectively reduce the probability of GIS equipment failure. This paper proposes a short-term prediction method for the development trend of metal own particle defects based on ARMA model. Compared with the experimental data, it is found that this method can accurately achieve the prediction of the development trend of the partial discharge characteristic parameter of step mutability, and it is more difficult to predict the partial discharge characteristic parameter of nonlinear change, but the general trend of change can be basically predicted.","PeriodicalId":6828,"journal":{"name":"2021 International Conference on Advanced Electrical Equipment and Reliable Operation (AEERO)","volume":"30 1","pages":"1-4"},"PeriodicalIF":0.0000,"publicationDate":"2021-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2021 International Conference on Advanced Electrical Equipment and Reliable Operation (AEERO)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/AEERO52475.2021.9708119","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
The prediction of the development trend of metal free particle defects is crucial to the maintenance and operation of GIS equipment, and an accurate prediction can effectively reduce the probability of GIS equipment failure. This paper proposes a short-term prediction method for the development trend of metal own particle defects based on ARMA model. Compared with the experimental data, it is found that this method can accurately achieve the prediction of the development trend of the partial discharge characteristic parameter of step mutability, and it is more difficult to predict the partial discharge characteristic parameter of nonlinear change, but the general trend of change can be basically predicted.