A research on line loss calculation based on BP neural network with genetic algorithm optimization
In order to realize the calculation of the line loss of the distribution network with complex structure and low-voltage station area, this paper presents a line loss calculation method based on BP neural network with genetic algorithm optimization. The proposed method is based on the actual operatio...
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| Veröffentlicht in: | IOP conference series. Earth and environmental science 2021-02, Vol.675 (1), p.12155 |
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| container_title | IOP conference series. Earth and environmental science |
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| creator | Jin, Yukun Li, Zeng Han, Yipin Li, Xiaopeng Li, Pingting Li, Guangdi Wang, Hao |
| description | In order to realize the calculation of the line loss of the distribution network with complex structure and low-voltage station area, this paper presents a line loss calculation method based on BP neural network with genetic algorithm optimization. The proposed method is based on the actual operation data of the distribution network. Firstly, build an error back propagation (BP) neural network model to compute the theoretical line loss of the distribution network, then use genetic algorithm (GA) to optimize the neural network and establish the GA-BP model. Based on the proposed model, the calculation demonstrates that the neural network line loss rate calculation model with genetic algorithm optimization shows better performance than the single BP neural network model, such as better nonlinear fitting ability and higher calculation accuracy. Therefore, the line loss calculation method proposed in this paper based on the BP neural network with the genetic algorithm optimization can improve the accuracy of the distribution network line loss rate calculation model. |
| doi_str_mv | 10.1088/1755-1315/675/1/012155 |
| format | Article |
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The proposed method is based on the actual operation data of the distribution network. Firstly, build an error back propagation (BP) neural network model to compute the theoretical line loss of the distribution network, then use genetic algorithm (GA) to optimize the neural network and establish the GA-BP model. Based on the proposed model, the calculation demonstrates that the neural network line loss rate calculation model with genetic algorithm optimization shows better performance than the single BP neural network model, such as better nonlinear fitting ability and higher calculation accuracy. Therefore, the line loss calculation method proposed in this paper based on the BP neural network with the genetic algorithm optimization can improve the accuracy of the distribution network line loss rate calculation model.</description><identifier>ISSN: 1755-1307</identifier><identifier>EISSN: 1755-1315</identifier><identifier>DOI: 10.1088/1755-1315/675/1/012155</identifier><language>eng</language><publisher>Bristol: IOP Publishing</publisher><subject>Accuracy ; Algorithms ; Back propagation networks ; Genetic algorithms ; Mathematical models ; Neural networks ; Optimization</subject><ispartof>IOP conference series. Earth and environmental science, 2021-02, Vol.675 (1), p.12155</ispartof><rights>2021. This work is published under http://creativecommons.org/licenses/by/3.0/ (the “License”). 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Earth and environmental science</title><description>In order to realize the calculation of the line loss of the distribution network with complex structure and low-voltage station area, this paper presents a line loss calculation method based on BP neural network with genetic algorithm optimization. The proposed method is based on the actual operation data of the distribution network. Firstly, build an error back propagation (BP) neural network model to compute the theoretical line loss of the distribution network, then use genetic algorithm (GA) to optimize the neural network and establish the GA-BP model. Based on the proposed model, the calculation demonstrates that the neural network line loss rate calculation model with genetic algorithm optimization shows better performance than the single BP neural network model, such as better nonlinear fitting ability and higher calculation accuracy. Therefore, the line loss calculation method proposed in this paper based on the BP neural network with the genetic algorithm optimization can improve the accuracy of the distribution network line loss rate calculation model.</description><subject>Accuracy</subject><subject>Algorithms</subject><subject>Back propagation networks</subject><subject>Genetic algorithms</subject><subject>Mathematical models</subject><subject>Neural networks</subject><subject>Optimization</subject><issn>1755-1307</issn><issn>1755-1315</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2021</creationdate><recordtype>article</recordtype><sourceid>BENPR</sourceid><recordid>eNo9kMtOwzAQRS0EEqXwC8gS6xCPHTvJslS8JCRYwNpy_Ghd0qTYiSr4ehyKurrzuDOjOQhdA7kFUlU5lJxnwIDnouQ55AQocH6CZsfG6TEm5Tm6iHFDiCgLVs9Qs8DBRquCXuO-w63vLG77GLFWrR5bNfhUbVS0ZmrfveHOjkG1SYZ9Hz7x3g9rvLIp9RqrdtWHVNjifjf4rf_5G79EZ0610V796xx9PNy_L5-yl9fH5-XiJdO0EDwTpTBaKTC6rJ1lrnAVOMWMIbQRtaHEWG6M41bzuiHc1ZYarhkUldPUMc7m6Oawdxf6r9HGQW76MXTppKQcaC0EEEgucXDpkN4M1sld8FsVviUQOfGUEyo5YZOJpwR54Ml-ARO9akU</recordid><startdate>20210201</startdate><enddate>20210201</enddate><creator>Jin, Yukun</creator><creator>Li, Zeng</creator><creator>Han, Yipin</creator><creator>Li, Xiaopeng</creator><creator>Li, Pingting</creator><creator>Li, Guangdi</creator><creator>Wang, Hao</creator><general>IOP Publishing</general><scope>AAYXX</scope><scope>CITATION</scope><scope>ABUWG</scope><scope>AEUYN</scope><scope>AFKRA</scope><scope>ATCPS</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>BHPHI</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>GNUQQ</scope><scope>HCIFZ</scope><scope>PATMY</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>PYCSY</scope></search><sort><creationdate>20210201</creationdate><title>A research on line loss calculation based on BP neural network with genetic algorithm optimization</title><author>Jin, Yukun ; Li, Zeng ; Han, Yipin ; Li, Xiaopeng ; Li, Pingting ; Li, Guangdi ; Wang, Hao</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c2465-676dcaa1dc79fe3f4f81fa3dd02b69d20de5ddf5ec59b05f9e2d5c3148fc2f353</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2021</creationdate><topic>Accuracy</topic><topic>Algorithms</topic><topic>Back propagation networks</topic><topic>Genetic algorithms</topic><topic>Mathematical models</topic><topic>Neural networks</topic><topic>Optimization</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Jin, Yukun</creatorcontrib><creatorcontrib>Li, Zeng</creatorcontrib><creatorcontrib>Han, Yipin</creatorcontrib><creatorcontrib>Li, Xiaopeng</creatorcontrib><creatorcontrib>Li, Pingting</creatorcontrib><creatorcontrib>Li, Guangdi</creatorcontrib><creatorcontrib>Wang, Hao</creatorcontrib><collection>CrossRef</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest One Sustainability</collection><collection>ProQuest Central UK/Ireland</collection><collection>Agricultural & Environmental Science Collection</collection><collection>ProQuest Central Essentials</collection><collection>ProQuest Central</collection><collection>Natural Science Collection</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central Korea</collection><collection>ProQuest Central Student</collection><collection>SciTech Premium Collection</collection><collection>Environmental Science Database</collection><collection>Publicly Available Content Database</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central China</collection><collection>Environmental Science Collection</collection><jtitle>IOP conference series. Earth and environmental science</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Jin, Yukun</au><au>Li, Zeng</au><au>Han, Yipin</au><au>Li, Xiaopeng</au><au>Li, Pingting</au><au>Li, Guangdi</au><au>Wang, Hao</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>A research on line loss calculation based on BP neural network with genetic algorithm optimization</atitle><jtitle>IOP conference series. Earth and environmental science</jtitle><date>2021-02-01</date><risdate>2021</risdate><volume>675</volume><issue>1</issue><spage>12155</spage><pages>12155-</pages><issn>1755-1307</issn><eissn>1755-1315</eissn><abstract>In order to realize the calculation of the line loss of the distribution network with complex structure and low-voltage station area, this paper presents a line loss calculation method based on BP neural network with genetic algorithm optimization. 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| subjects | Accuracy Algorithms Back propagation networks Genetic algorithms Mathematical models Neural networks Optimization |
| title | A research on line loss calculation based on BP neural network with genetic algorithm optimization |
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