Fault Diagnosis of Data-Driven Photovoltaic Power Generation System Based on Deep Reinforcement Learning
Aiming at the problem of fault diagnosis of the photovoltaic power generation system, this paper proposes a photovoltaic power generation system fault diagnosis method based on deep reinforcement learning. This method takes data-driven as the starting point. Firstly, the compressed sensing algorithm...
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| Veröffentlicht in: | Mathematical problems in engineering 2021-11, Vol.2021, p.1-10 |
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| creator | Dai, Shuang Wang, Dingmei Li, Weijun Zhou, Qiang Tian, Guangke Dong, Haiying |
| description | Aiming at the problem of fault diagnosis of the photovoltaic power generation system, this paper proposes a photovoltaic power generation system fault diagnosis method based on deep reinforcement learning. This method takes data-driven as the starting point. Firstly, the compressed sensing algorithm is used to fill the missing photovoltaic data and then state, action, strategy, and return functions from the environment. Based on the interaction rules and other factors, the fault diagnosis model of the photovoltaic power generation system is established, and the deep neural network is used to approximate the decision network to find the optimal strategy, so as to realize the fault diagnosis of the photovoltaic power generation system. Finally, the effectiveness and accuracy of the method are verified by simulation. The simulation results show that this method can accurately diagnose the fault types of the photovoltaic power generation system, which is of great significance to enhance the security of the photovoltaic power generation system and improve the intelligent operation and maintenance level of the photovoltaic power generation system. |
| doi_str_mv | 10.1155/2021/2506286 |
| format | Article |
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This method takes data-driven as the starting point. Firstly, the compressed sensing algorithm is used to fill the missing photovoltaic data and then state, action, strategy, and return functions from the environment. Based on the interaction rules and other factors, the fault diagnosis model of the photovoltaic power generation system is established, and the deep neural network is used to approximate the decision network to find the optimal strategy, so as to realize the fault diagnosis of the photovoltaic power generation system. Finally, the effectiveness and accuracy of the method are verified by simulation. The simulation results show that this method can accurately diagnose the fault types of the photovoltaic power generation system, which is of great significance to enhance the security of the photovoltaic power generation system and improve the intelligent operation and maintenance level of the photovoltaic power generation system.</description><identifier>ISSN: 1024-123X</identifier><identifier>EISSN: 1563-5147</identifier><identifier>DOI: 10.1155/2021/2506286</identifier><language>eng</language><publisher>New York: Hindawi</publisher><subject>Algorithms ; Artificial intelligence ; Artificial neural networks ; Computer simulation ; Decision making ; Deep learning ; Electric power generation ; Engineering ; Fault diagnosis ; Iterative methods ; Machine learning ; Neural networks ; Pattern recognition ; Photovoltaic cells ; Time series</subject><ispartof>Mathematical problems in engineering, 2021-11, Vol.2021, p.1-10</ispartof><rights>Copyright © 2021 Shuang Dai et al.</rights><rights>Copyright © 2021 Shuang Dai et al. This is an open access article distributed under the Creative Commons Attribution License (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. https://creativecommons.org/licenses/by/4.0</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c337t-c4c56601e907cb261cb467d96214a92525d0c5c26d21c8b0bae3f95f54d564d83</citedby><cites>FETCH-LOGICAL-c337t-c4c56601e907cb261cb467d96214a92525d0c5c26d21c8b0bae3f95f54d564d83</cites><orcidid>0000-0002-3909-3246</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,776,780,27903,27904</link.rule.ids></links><search><contributor>Wang, Licheng</contributor><contributor>Licheng Wang</contributor><creatorcontrib>Dai, Shuang</creatorcontrib><creatorcontrib>Wang, Dingmei</creatorcontrib><creatorcontrib>Li, Weijun</creatorcontrib><creatorcontrib>Zhou, Qiang</creatorcontrib><creatorcontrib>Tian, Guangke</creatorcontrib><creatorcontrib>Dong, Haiying</creatorcontrib><title>Fault Diagnosis of Data-Driven Photovoltaic Power Generation System Based on Deep Reinforcement Learning</title><title>Mathematical problems in engineering</title><description>Aiming at the problem of fault diagnosis of the photovoltaic power generation system, this paper proposes a photovoltaic power generation system fault diagnosis method based on deep reinforcement learning. 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The simulation results show that this method can accurately diagnose the fault types of the photovoltaic power generation system, which is of great significance to enhance the security of the photovoltaic power generation system and improve the intelligent operation and maintenance level of the photovoltaic power generation system.</description><subject>Algorithms</subject><subject>Artificial intelligence</subject><subject>Artificial neural networks</subject><subject>Computer simulation</subject><subject>Decision making</subject><subject>Deep learning</subject><subject>Electric power generation</subject><subject>Engineering</subject><subject>Fault diagnosis</subject><subject>Iterative methods</subject><subject>Machine learning</subject><subject>Neural networks</subject><subject>Pattern recognition</subject><subject>Photovoltaic cells</subject><subject>Time series</subject><issn>1024-123X</issn><issn>1563-5147</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2021</creationdate><recordtype>article</recordtype><sourceid>RHX</sourceid><sourceid>BENPR</sourceid><recordid>eNp90E1LAzEQBuAgCtbqzR8Q8Khrk2yS3T1q11ahYPEDvC3Z7Gyb0iY1SVv6793Snj3NDDy8Ay9Ct5Q8UirEgBFGB0wQyXJ5hnpUyDQRlGfn3U4YTyhLfy7RVQgL0klB8x6aj9RmGXFp1My6YAJ2LS5VVEnpzRYsns5ddFu3jMpoPHU78HgMFryKxln8uQ8RVvhZBWhwd5cAa_wBxrbOa1iBjXgCyltjZ9foolXLADen2Uffo5ev4WsyeR-_DZ8miU7TLCaaayEloVCQTNdMUl1zmTWFZJSrggkmGqKFZrJhVOc1qRWkbSFawRsheZOnfXR3zF1797uBEKuF23jbvayYJAUVnKakUw9Hpb0LwUNbrb1ZKb-vKKkOXVaHLqtTlx2_P_K5sY3amf_1H34bcwo</recordid><startdate>20211129</startdate><enddate>20211129</enddate><creator>Dai, Shuang</creator><creator>Wang, Dingmei</creator><creator>Li, Weijun</creator><creator>Zhou, Qiang</creator><creator>Tian, Guangke</creator><creator>Dong, Haiying</creator><general>Hindawi</general><general>Hindawi Limited</general><scope>RHU</scope><scope>RHW</scope><scope>RHX</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7TB</scope><scope>8FD</scope><scope>8FE</scope><scope>8FG</scope><scope>ABJCF</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>ARAPS</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>CCPQU</scope><scope>CWDGH</scope><scope>DWQXO</scope><scope>FR3</scope><scope>GNUQQ</scope><scope>HCIFZ</scope><scope>JQ2</scope><scope>K7-</scope><scope>KR7</scope><scope>L6V</scope><scope>M7S</scope><scope>P5Z</scope><scope>P62</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>PTHSS</scope><orcidid>https://orcid.org/0000-0002-3909-3246</orcidid></search><sort><creationdate>20211129</creationdate><title>Fault Diagnosis of Data-Driven Photovoltaic Power Generation System Based on Deep Reinforcement Learning</title><author>Dai, Shuang ; 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| subjects | Algorithms Artificial intelligence Artificial neural networks Computer simulation Decision making Deep learning Electric power generation Engineering Fault diagnosis Iterative methods Machine learning Neural networks Pattern recognition Photovoltaic cells Time series |
| title | Fault Diagnosis of Data-Driven Photovoltaic Power Generation System Based on Deep Reinforcement Learning |
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