{"corpus_id":115511693,"paper_sha":"343ce75308263b9595f3fff05aaffe68ffa16534","doi":"10.1016/J.APENERGY.2018.07.084","arxiv_id":null,"pmid":null,"pmcid":null,"mag_id":2883867434,"dblp_id":null,"acl_id":null,"title":"A survey of artificial neural network in wind energy systems","year":2018,"publication_date":"2018-10-01","venue":"Applied Energy","journal":{"name":"Applied Energy","pages":null,"volume":null},"journal_issn":null,"journal_title":null,"publication_types":["Review"],"pubmed_pub_types":null,"s2_fields_of_study":["Computer Science","Engineering","Environmental Science"],"reference_count":153,"citation_count":387,"influential_citation_count":6,"is_open_access":true,"arxiv_categories":null,"arxiv_license":null,"arxiv_journal_ref":null,"mesh_headings":null,"chemicals":null,"comments_corrections":null,"source_flags":1,"s2_open_access_pdf_url":"http://eprints.whiterose.ac.uk/137289/1/201701_v53.pdf","s2_open_access_landing_url":"https://www.semanticscholar.org/paper/343ce75308263b9595f3fff05aaffe68ffa16534","s2_open_access_license":"CCBYNCND","s2_open_access_status":"GREEN","pmc_open_access_pdf_url":null,"pmc_open_access_landing_url":null,"pmc_open_access_license":null,"pmc_open_access_status":null,"unpaywall_open_access_pdf_url":null,"unpaywall_open_access_landing_url":null,"unpaywall_open_access_license":null,"unpaywall_open_access_status":null,"abstract":"Abstract Wind energy has become one of the most important forms of renewable energy. Wind energy conversion systems are more sophisticated and new approaches are required based on advance analytics. This paper presents an exhaustive review of artificial neural networks used in wind energy systems, identifying the methods most employed for different applications and demonstrating that Artificial Neural Networks can be an alternative to conventional methods in many cases. More than 85% of the 190 references employed in this paper have been published in the last 5 years. The methods are classified and analysed into four groups according to the application: forecasting and predictions; design optimization; fault detection and diagnosis; and optimal control. A statistical analysis of the current state and future trends in this field is carried out. An analysis of each application group about the strengths and weaknesses of each ANN structure is carried out. A quantitative analysis of the main references is carried out showing new statistical results of the current state and future trends of the topic. The paper describes the main challenges and technological gaps concerning the application of ANN to wind turbines, according to the literature review. An overall table is provided to summarize the most important references according to the application groups and case studies.","claims":[{"public_id":"cl_ba1dc7dbf1829f47bd1ea9a243d9a520","status":"active","text":"A statistical analysis and quantitative analysis of the literature provide the current state and future trends of artificial neural networks in wind energy systems.","confidence":0.93,"contributors":[{"id":1,"public_id":"12632b8b5f","public_label":"Anonymous (12632b8b5f)","roles":["extraction"],"url":"https://sah.borca.ai/u/12632b8b5f"}],"url":"https://sah.borca.ai/claims/cl_ba1dc7dbf1829f47bd1ea9a243d9a520"},{"public_id":"cl_9388b69dd86d532a0d5abe843b3438cc","status":"active","text":"Artificial neural networks can be an alternative to conventional methods in many wind energy system applications.","confidence":0.91,"contributors":[{"id":1,"public_id":"12632b8b5f","public_label":"Anonymous (12632b8b5f)","roles":["extraction"],"url":"https://sah.borca.ai/u/12632b8b5f"}],"url":"https://sah.borca.ai/claims/cl_9388b69dd86d532a0d5abe843b3438cc"},{"public_id":"cl_1704d2f3b0ca9a39f296046e3f61629e","status":"active","text":"More than 85% of the 190 references were published in the last 5 years.","confidence":0.98,"contributors":[{"id":1,"public_id":"12632b8b5f","public_label":"Anonymous (12632b8b5f)","roles":["extraction"],"url":"https://sah.borca.ai/u/12632b8b5f"}],"url":"https://sah.borca.ai/claims/cl_1704d2f3b0ca9a39f296046e3f61629e"},{"public_id":"cl_2658220816112757cf8c491e0bfed4c4","status":"active","text":"The literature review identifies the main challenges and technological gaps in applying artificial neural networks to wind turbines.","confidence":0.95,"contributors":[{"id":1,"public_id":"12632b8b5f","public_label":"Anonymous (12632b8b5f)","roles":["extraction"],"url":"https://sah.borca.ai/u/12632b8b5f"}],"url":"https://sah.borca.ai/claims/cl_2658220816112757cf8c491e0bfed4c4"},{"public_id":"cl_603c6c2925bff348616baae09045a098","status":"active","text":"The reviewed methods are classified into four application groups: forecasting and predictions, design optimization, fault detection and diagnosis, and optimal control.","confidence":0.97,"contributors":[{"id":1,"public_id":"12632b8b5f","public_label":"Anonymous (12632b8b5f)","roles":["extraction"],"url":"https://sah.borca.ai/u/12632b8b5f"}],"url":"https://sah.borca.ai/claims/cl_603c6c2925bff348616baae09045a098"}],"concepts":[{"public_id":"co_1360ef76d9634a651db4faf0e7e6c201","status":"active","name":"last 5 years","description":"The recent publication window used to characterize the recency of the surveyed literature.","types":["time period"],"aliases":["recent 5-year period"],"contributors":[{"id":1,"public_id":"12632b8b5f","public_label":"Anonymous (12632b8b5f)","roles":["extraction"],"url":"https://sah.borca.ai/u/12632b8b5f"}],"url":"https://sah.borca.ai/concepts/co_1360ef76d9634a651db4faf0e7e6c201"},{"public_id":"co_173d5729592ffb3c47da669918ce7f30","status":"active","name":"quantitative analysis","description":"A numerical analysis of the main references and reported trends in the literature.","types":["analysis method"],"aliases":[],"contributors":[{"id":1,"public_id":"12632b8b5f","public_label":"Anonymous (12632b8b5f)","roles":["extraction"],"url":"https://sah.borca.ai/u/12632b8b5f"}],"url":"https://sah.borca.ai/concepts/co_173d5729592ffb3c47da669918ce7f30"},{"public_id":"co_202e99e69948038abfee3dd479b1aa1b","status":"active","name":"forecasting and predictions","description":"Application area focused on predicting wind-related quantities or system behavior.","types":["application area"],"aliases":[],"contributors":[{"id":1,"public_id":"12632b8b5f","public_label":"Anonymous (12632b8b5f)","roles":["extraction"],"url":"https://sah.borca.ai/u/12632b8b5f"}],"url":"https://sah.borca.ai/concepts/co_202e99e69948038abfee3dd479b1aa1b"},{"public_id":"co_227dc5176fc2a492cd4f71676f76cc51","status":"active","name":"optimal control","description":"Application area focused on controlling wind energy systems to achieve desired performance.","types":["application area"],"aliases":[],"contributors":[{"id":1,"public_id":"12632b8b5f","public_label":"Anonymous (12632b8b5f)","roles":["extraction"],"url":"https://sah.borca.ai/u/12632b8b5f"}],"url":"https://sah.borca.ai/concepts/co_227dc5176fc2a492cd4f71676f76cc51"},{"public_id":"co_2e6196078ca95493eaa8ed8acc892d3c","status":"active","name":"190 references","description":"The set of literature references analyzed in the survey.","types":["corpus"],"aliases":["190 reference corpus"],"contributors":[{"id":1,"public_id":"12632b8b5f","public_label":"Anonymous (12632b8b5f)","roles":["extraction"],"url":"https://sah.borca.ai/u/12632b8b5f"}],"url":"https://sah.borca.ai/concepts/co_2e6196078ca95493eaa8ed8acc892d3c"},{"public_id":"co_4b4c4222d5055335e73ead39ea8d3bc8","status":"active","name":"statistical analysis","description":"A quantitative examination of the surveyed literature's publication and application patterns.","types":["analysis method"],"aliases":[],"contributors":[{"id":1,"public_id":"12632b8b5f","public_label":"Anonymous (12632b8b5f)","roles":["extraction"],"url":"https://sah.borca.ai/u/12632b8b5f"}],"url":"https://sah.borca.ai/concepts/co_4b4c4222d5055335e73ead39ea8d3bc8"},{"public_id":"co_62d2eadd6851dd3596fa747b0494f123","status":"active","name":"main challenges","description":"Key difficulties reported in the literature for applying artificial neural networks to wind turbines.","types":["issue"],"aliases":[],"contributors":[{"id":1,"public_id":"12632b8b5f","public_label":"Anonymous (12632b8b5f)","roles":["extraction"],"url":"https://sah.borca.ai/u/12632b8b5f"}],"url":"https://sah.borca.ai/concepts/co_62d2eadd6851dd3596fa747b0494f123"},{"public_id":"co_691b3b3c9845c13a4f39a63d8591cd58","status":"active","name":"wind turbines","description":"Turbine systems that convert wind energy into mechanical or electrical power.","types":["system"],"aliases":[],"contributors":[{"id":1,"public_id":"12632b8b5f","public_label":"Anonymous (12632b8b5f)","roles":["extraction"],"url":"https://sah.borca.ai/u/12632b8b5f"}],"url":"https://sah.borca.ai/concepts/co_691b3b3c9845c13a4f39a63d8591cd58"},{"public_id":"co_6993148b427a930dc434d7a91fef71f0","status":"active","name":"conventional methods","description":"Traditional non-neural approaches used as comparison or alternatives in wind energy applications.","types":["baseline"],"aliases":[],"contributors":[{"id":1,"public_id":"12632b8b5f","public_label":"Anonymous (12632b8b5f)","roles":["extraction"],"url":"https://sah.borca.ai/u/12632b8b5f"}],"url":"https://sah.borca.ai/concepts/co_6993148b427a930dc434d7a91fef71f0"},{"public_id":"co_74627f5c02b6b34a9d2ac2e64b819f8c","status":"active","name":"artificial neural networks in wind energy systems","description":"The use of artificial neural networks across wind energy system applications studied in the survey.","types":["research topic"],"aliases":["ANN in wind energy systems"],"contributors":[{"id":1,"public_id":"12632b8b5f","public_label":"Anonymous (12632b8b5f)","roles":["extraction"],"url":"https://sah.borca.ai/u/12632b8b5f"}],"url":"https://sah.borca.ai/concepts/co_74627f5c02b6b34a9d2ac2e64b819f8c"},{"public_id":"co_a6330145276a84a8ad481366c2a30377","status":"active","name":"fault detection and diagnosis","description":"Application area focused on identifying and diagnosing faults in wind energy systems.","types":["application area"],"aliases":["fault diagnosis"],"contributors":[{"id":1,"public_id":"12632b8b5f","public_label":"Anonymous (12632b8b5f)","roles":["extraction"],"url":"https://sah.borca.ai/u/12632b8b5f"}],"url":"https://sah.borca.ai/concepts/co_a6330145276a84a8ad481366c2a30377"},{"public_id":"co_b05d9b88427f133bae2d286d6d2c6136","status":"active","name":"artificial neural networks","description":"Computational models inspired by biological neural networks and used for learning from data.","types":["method"],"aliases":["ANN","ANNs"],"contributors":[{"id":1,"public_id":"12632b8b5f","public_label":"Anonymous (12632b8b5f)","roles":["extraction"],"url":"https://sah.borca.ai/u/12632b8b5f"}],"url":"https://sah.borca.ai/concepts/co_b05d9b88427f133bae2d286d6d2c6136"},{"public_id":"co_cdda3a63112721bbcbde9062c7b33dc7","status":"active","name":"design optimization","description":"Application area focused on improving the design of wind energy system components or configurations.","types":["application area"],"aliases":[],"contributors":[{"id":1,"public_id":"12632b8b5f","public_label":"Anonymous (12632b8b5f)","roles":["extraction"],"url":"https://sah.borca.ai/u/12632b8b5f"}],"url":"https://sah.borca.ai/concepts/co_cdda3a63112721bbcbde9062c7b33dc7"},{"public_id":"co_dc608f31d5349eae19128b957f35050b","status":"active","name":"technological gaps","description":"Unresolved technical needs or missing capabilities identified for ANN applications in wind turbines.","types":["gap"],"aliases":[],"contributors":[{"id":1,"public_id":"12632b8b5f","public_label":"Anonymous (12632b8b5f)","roles":["extraction"],"url":"https://sah.borca.ai/u/12632b8b5f"}],"url":"https://sah.borca.ai/concepts/co_dc608f31d5349eae19128b957f35050b"},{"public_id":"co_fd17821a55bce002e3b53f8eb2e68dfa","status":"active","name":"wind energy systems","description":"Systems for converting wind energy into usable power.","types":["system"],"aliases":["wind energy conversion systems"],"contributors":[{"id":1,"public_id":"12632b8b5f","public_label":"Anonymous (12632b8b5f)","roles":["extraction"],"url":"https://sah.borca.ai/u/12632b8b5f"}],"url":"https://sah.borca.ai/concepts/co_fd17821a55bce002e3b53f8eb2e68dfa"}],"external_ids":{"DOI":"10.1016/J.APENERGY.2018.07.084","ArXiv":null,"PubMed":null,"PubMedCentral":null,"MAG":2883867434,"DBLP":null,"ACL":null},"open_access":{"is_open_access":true,"pdf_url":"http://eprints.whiterose.ac.uk/137289/1/201701_v53.pdf","landing_url":"https://www.semanticscholar.org/paper/343ce75308263b9595f3fff05aaffe68ffa16534","source":"semantic_scholar","pdf_url_source":"semantic_scholar_open_access_pdf","license":"CCBYNCND","status":"GREEN","reason":null},"reference_availability":{"status":"available","references_indexed":true,"full_text_available":false,"full_text_source":null,"count_basis":"semantic_scholar_metadata","extraction_status":"not_applicable","reason":null},"source":{"provider":"episteme2","base_corpus":"semantic_scholar_dump","freshness_mode":"unknown","basis":["semantic_scholar_metadata","postgres_metadata"],"limits":["paper metadata is based on indexed upstream scholarly datasets","claims and concepts are available only for extracted papers","absence of claims or concepts means no extracted graph data is available in this response"],"status":"available","degraded":false,"degraded_reasons":[],"diagnostics":{"status":"available","degraded":false,"degraded_reasons":[],"metadata_status":"available","graph_status":"available","abstract_status":"available"},"source_flags":1},"paper_id":631499,"paper_uid":"f38aa103-8210-414e-8f92-6e88dad9d795","canonical_identity":{"paper_id":631499,"paper_uid":"f38aa103-8210-414e-8f92-6e88dad9d795","identity_status":"available","lookup_basis":"semantic_scholar_external_id","compatibility_path":"corpus_id"},"url":"https://sah.borca.ai/papers/115511693"}