Hot spot temperature prediction model of transformer based on GP-NLMS
An accurate estimation model of winding hot spot temperature is the key to assess the thermal state and insulation life of oil-immersed transformers. Based on the hot spot temperature and load current monitored by the substation,the genetic programming algorithm is applied to train the basic structu...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | zho |
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Editorial Department of Electric Power Engineering Technology
2022-09-01
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| Series: | 电力工程技术 |
| Subjects: | |
| Online Access: | https://www.epet-info.com/dlgcjs/article/html/210519688 |
| _version_ | 1811204705676689408 |
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| author | ZHANG Jun CHEN Xiao ZHANG Wang ZHU Delyu CHEN Yinghua |
| author_facet | ZHANG Jun CHEN Xiao ZHANG Wang ZHU Delyu CHEN Yinghua |
| author_sort | ZHANG Jun |
| collection | DOAJ |
| description | An accurate estimation model of winding hot spot temperature is the key to assess the thermal state and insulation life of oil-immersed transformers. Based on the hot spot temperature and load current monitored by the substation,the genetic programming algorithm is applied to train the basic structure of the hot spot temperature estimation model. Then,the parameter identification of the hot spot temperature estimation model is performed by the normalized least square mean (NLMS) algorithm. Finally,an explicit prediction model of the hot spot temperature is established for oil-immersed transformers. The explicit winding hot spot temperature estimation model can effectively reflect the relationship between the load factor and the winding hot spot temperature. Moreover,the goodness of fit of the model under the test set is 0.998 8,and the maximum absolute error is only 1.36 ℃,which verify the correctness and effectiveness of the model. Furthermore,the strong generalization performance of the proposed model is proved by estimating the winding hot spot temperature for oil-immersed transformers with the same capacity and model in the same area. |
| first_indexed | 2024-04-12T03:18:12Z |
| format | Article |
| id | doaj.art-147709b74a1043d2888fcb6a8d85d108 |
| institution | Directory Open Access Journal |
| issn | 2096-3203 |
| language | zho |
| last_indexed | 2024-04-12T03:18:12Z |
| publishDate | 2022-09-01 |
| publisher | Editorial Department of Electric Power Engineering Technology |
| record_format | Article |
| series | 电力工程技术 |
| spelling | doaj.art-147709b74a1043d2888fcb6a8d85d1082022-12-22T03:50:00ZzhoEditorial Department of Electric Power Engineering Technology电力工程技术2096-32032022-09-0141516517110.12158/j.2096-3203.2022.05.020Hot spot temperature prediction model of transformer based on GP-NLMSZHANG Jun0CHEN Xiao1ZHANG Wang2ZHU Delyu3CHEN Yinghua4State Grid Jiangsu Electric Power Co., Ltd., Nanjing 210024, ChinaState Grid Jiangsu Electric Power Co., Ltd., Nanjing 210024, ChinaState Grid Jiangsu Electric Power Co., Ltd. Economic and Technical Research Institute, Nanjing 210008, ChinaState Grid Jiangsu Electric Power Co., Ltd. Economic and Technical Research Institute, Nanjing 210008, ChinaState Grid Economic and Technical Research Institute Co., Ltd., Beijing 102200, ChinaAn accurate estimation model of winding hot spot temperature is the key to assess the thermal state and insulation life of oil-immersed transformers. Based on the hot spot temperature and load current monitored by the substation,the genetic programming algorithm is applied to train the basic structure of the hot spot temperature estimation model. Then,the parameter identification of the hot spot temperature estimation model is performed by the normalized least square mean (NLMS) algorithm. Finally,an explicit prediction model of the hot spot temperature is established for oil-immersed transformers. The explicit winding hot spot temperature estimation model can effectively reflect the relationship between the load factor and the winding hot spot temperature. Moreover,the goodness of fit of the model under the test set is 0.998 8,and the maximum absolute error is only 1.36 ℃,which verify the correctness and effectiveness of the model. Furthermore,the strong generalization performance of the proposed model is proved by estimating the winding hot spot temperature for oil-immersed transformers with the same capacity and model in the same area.https://www.epet-info.com/dlgcjs/article/html/210519688oil-immersed transformerhot spot temperaturegenetic programmingnormalized least mean square (nlms) algorithminsulation lifeexplicit prediction model |
| spellingShingle | ZHANG Jun CHEN Xiao ZHANG Wang ZHU Delyu CHEN Yinghua Hot spot temperature prediction model of transformer based on GP-NLMS 电力工程技术 oil-immersed transformer hot spot temperature genetic programming normalized least mean square (nlms) algorithm insulation life explicit prediction model |
| title | Hot spot temperature prediction model of transformer based on GP-NLMS |
| title_full | Hot spot temperature prediction model of transformer based on GP-NLMS |
| title_fullStr | Hot spot temperature prediction model of transformer based on GP-NLMS |
| title_full_unstemmed | Hot spot temperature prediction model of transformer based on GP-NLMS |
| title_short | Hot spot temperature prediction model of transformer based on GP-NLMS |
| title_sort | hot spot temperature prediction model of transformer based on gp nlms |
| topic | oil-immersed transformer hot spot temperature genetic programming normalized least mean square (nlms) algorithm insulation life explicit prediction model |
| url | https://www.epet-info.com/dlgcjs/article/html/210519688 |
| work_keys_str_mv | AT zhangjun hotspottemperaturepredictionmodeloftransformerbasedongpnlms AT chenxiao hotspottemperaturepredictionmodeloftransformerbasedongpnlms AT zhangwang hotspottemperaturepredictionmodeloftransformerbasedongpnlms AT zhudelyu hotspottemperaturepredictionmodeloftransformerbasedongpnlms AT chenyinghua hotspottemperaturepredictionmodeloftransformerbasedongpnlms |