Prediction of power grid fault repair time based on multi-model fusion
There are many types of power grid faults,and the reasons are complicated.The prediction of fault repair time is difficult.Due to the rise of new technologies such as deep learning,it is feasible to accurately mine the faulty worksheet and accurately predict the fault repair time.Taking the historic...
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| Format: | Article |
| Language: | zho |
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Beijing Xintong Media Co., Ltd
2020-01-01
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| Series: | Dianxin kexue |
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| Online Access: | http://www.telecomsci.com/zh/article/doi/10.11959/j.issn.1000-0801.2020016/ |
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| author | Jianyue PAN Yizhen WU Hanlin XU |
| author_facet | Jianyue PAN Yizhen WU Hanlin XU |
| author_sort | Jianyue PAN |
| collection | DOAJ |
| description | There are many types of power grid faults,and the reasons are complicated.The prediction of fault repair time is difficult.Due to the rise of new technologies such as deep learning,it is feasible to accurately mine the faulty worksheet and accurately predict the fault repair time.Taking the historical grid fault repair worksheet as the research object,the multi-model fusion prediction method was proposed,and the prediction results of LightGBM,XGBoost and LSTM were weighted and fused.The experimental results show that the multi-model fusion prediction method can accurately estimate the fault repair time and provide better support for the automation and intelligence of grid fault repair. |
| format | Article |
| id | doaj-art-0e1c09a426f84620bba134353b4be8c2 |
| institution | Kabale University |
| issn | 1000-0801 |
| language | zho |
| publishDate | 2020-01-01 |
| publisher | Beijing Xintong Media Co., Ltd |
| record_format | Article |
| series | Dianxin kexue |
| spelling | doaj-art-0e1c09a426f84620bba134353b4be8c22025-01-15T03:01:23ZzhoBeijing Xintong Media Co., LtdDianxin kexue1000-08012020-01-013614415059585429Prediction of power grid fault repair time based on multi-model fusionJianyue PANYizhen WUHanlin XUThere are many types of power grid faults,and the reasons are complicated.The prediction of fault repair time is difficult.Due to the rise of new technologies such as deep learning,it is feasible to accurately mine the faulty worksheet and accurately predict the fault repair time.Taking the historical grid fault repair worksheet as the research object,the multi-model fusion prediction method was proposed,and the prediction results of LightGBM,XGBoost and LSTM were weighted and fused.The experimental results show that the multi-model fusion prediction method can accurately estimate the fault repair time and provide better support for the automation and intelligence of grid fault repair.http://www.telecomsci.com/zh/article/doi/10.11959/j.issn.1000-0801.2020016/grid faultrepair timeLSTMLightGBMXGBoost |
| spellingShingle | Jianyue PAN Yizhen WU Hanlin XU Prediction of power grid fault repair time based on multi-model fusion Dianxin kexue grid fault repair time LSTM LightGBM XGBoost |
| title | Prediction of power grid fault repair time based on multi-model fusion |
| title_full | Prediction of power grid fault repair time based on multi-model fusion |
| title_fullStr | Prediction of power grid fault repair time based on multi-model fusion |
| title_full_unstemmed | Prediction of power grid fault repair time based on multi-model fusion |
| title_short | Prediction of power grid fault repair time based on multi-model fusion |
| title_sort | prediction of power grid fault repair time based on multi model fusion |
| topic | grid fault repair time LSTM LightGBM XGBoost |
| url | http://www.telecomsci.com/zh/article/doi/10.11959/j.issn.1000-0801.2020016/ |
| work_keys_str_mv | AT jianyuepan predictionofpowergridfaultrepairtimebasedonmultimodelfusion AT yizhenwu predictionofpowergridfaultrepairtimebasedonmultimodelfusion AT hanlinxu predictionofpowergridfaultrepairtimebasedonmultimodelfusion |