Bagging-based neural network ensemble for load identification with parameter sensitivity considered
Extensive installation of measuring devices in power systems promotes the application of the artificial intelligence (AI) in load identification. However, the convergence problems of training and the relatively low accuracy hinder the AI method from further development. In this study, a neural netwo...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2022-11-01
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| Series: | Energy Reports |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2352484722015001 |
| _version_ | 1828010125754892288 |
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| author | Xinyuan Hu Yuan Zeng Chao Qin Dezhuang Meng |
| author_facet | Xinyuan Hu Yuan Zeng Chao Qin Dezhuang Meng |
| author_sort | Xinyuan Hu |
| collection | DOAJ |
| description | Extensive installation of measuring devices in power systems promotes the application of the artificial intelligence (AI) in load identification. However, the convergence problems of training and the relatively low accuracy hinder the AI method from further development. In this study, a neural network ensemble method considering parameter sensitivity is proposed to solve these problems. In this method, with distributed generation considered, the parameters of the load model are classified according to the response uniqueness, and identified separately by multiple base learners according to its features. Additionally, ensemble algorithm is introduced for the higher accuracy, and Bagging strategy is used to ensure the diversity of learners through sampling the train set. Numerical simulations on a real power grid system validate the applicability of the proposed method in the field of parameter identification. |
| first_indexed | 2024-04-10T08:49:19Z |
| format | Article |
| id | doaj.art-a65ef4b65fbc4331b718f9ecba108894 |
| institution | Directory Open Access Journal |
| issn | 2352-4847 |
| language | English |
| last_indexed | 2024-04-10T08:49:19Z |
| publishDate | 2022-11-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Energy Reports |
| spelling | doaj.art-a65ef4b65fbc4331b718f9ecba1088942023-02-22T04:31:11ZengElsevierEnergy Reports2352-48472022-11-018199205Bagging-based neural network ensemble for load identification with parameter sensitivity consideredXinyuan Hu0Yuan Zeng1Chao Qin2Dezhuang Meng3Key Laboratory of Smart Grid of Ministry of Education, Tianjin University, Tianjin 300072, China; State Grid Tianjin Chengnan Electric Supply Co., ltd., Tianjin 300201, ChinaKey Laboratory of Smart Grid of Ministry of Education, Tianjin University, Tianjin 300072, China; Corresponding author.Key Laboratory of Smart Grid of Ministry of Education, Tianjin University, Tianjin 300072, ChinaKey Laboratory of Smart Grid of Ministry of Education, Tianjin University, Tianjin 300072, ChinaExtensive installation of measuring devices in power systems promotes the application of the artificial intelligence (AI) in load identification. However, the convergence problems of training and the relatively low accuracy hinder the AI method from further development. In this study, a neural network ensemble method considering parameter sensitivity is proposed to solve these problems. In this method, with distributed generation considered, the parameters of the load model are classified according to the response uniqueness, and identified separately by multiple base learners according to its features. Additionally, ensemble algorithm is introduced for the higher accuracy, and Bagging strategy is used to ensure the diversity of learners through sampling the train set. Numerical simulations on a real power grid system validate the applicability of the proposed method in the field of parameter identification.http://www.sciencedirect.com/science/article/pii/S2352484722015001Parameter identificationEnsemble learningBaggingParameter sensitivityGeneralized composite load model |
| spellingShingle | Xinyuan Hu Yuan Zeng Chao Qin Dezhuang Meng Bagging-based neural network ensemble for load identification with parameter sensitivity considered Energy Reports Parameter identification Ensemble learning Bagging Parameter sensitivity Generalized composite load model |
| title | Bagging-based neural network ensemble for load identification with parameter sensitivity considered |
| title_full | Bagging-based neural network ensemble for load identification with parameter sensitivity considered |
| title_fullStr | Bagging-based neural network ensemble for load identification with parameter sensitivity considered |
| title_full_unstemmed | Bagging-based neural network ensemble for load identification with parameter sensitivity considered |
| title_short | Bagging-based neural network ensemble for load identification with parameter sensitivity considered |
| title_sort | bagging based neural network ensemble for load identification with parameter sensitivity considered |
| topic | Parameter identification Ensemble learning Bagging Parameter sensitivity Generalized composite load model |
| url | http://www.sciencedirect.com/science/article/pii/S2352484722015001 |
| work_keys_str_mv | AT xinyuanhu baggingbasedneuralnetworkensembleforloadidentificationwithparametersensitivityconsidered AT yuanzeng baggingbasedneuralnetworkensembleforloadidentificationwithparametersensitivityconsidered AT chaoqin baggingbasedneuralnetworkensembleforloadidentificationwithparametersensitivityconsidered AT dezhuangmeng baggingbasedneuralnetworkensembleforloadidentificationwithparametersensitivityconsidered |