{"id":"https://openalex.org/W2735036247","doi":"https://doi.org/10.1109/ijcnn.2017.7966280","title":"Cellular computational extreme learning machine network based frequency predictions in a power system","display_name":"Cellular computational extreme learning machine network based frequency predictions in a power system","publication_year":2017,"publication_date":"2017-05-01","ids":{"openalex":"https://openalex.org/W2735036247","doi":"https://doi.org/10.1109/ijcnn.2017.7966280","mag":"2735036247"},"language":"en","primary_location":{"id":"doi:10.1109/ijcnn.2017.7966280","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn.2017.7966280","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 International Joint Conference on Neural Networks (IJCNN)","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5026188719","display_name":"Iroshani Jayawardene","orcid":"https://orcid.org/0000-0002-8297-9763"},"institutions":[{"id":"https://openalex.org/I8078737","display_name":"Clemson University","ror":"https://ror.org/037s24f05","country_code":"US","type":"education","lineage":["https://openalex.org/I8078737"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Iroshani Jayawardene","raw_affiliation_strings":["Holcombe Department of Electrical and Computer Engineering, Clemson University, Clemson, SC, USA"],"affiliations":[{"raw_affiliation_string":"Holcombe Department of Electrical and Computer Engineering, Clemson University, Clemson, SC, USA","institution_ids":["https://openalex.org/I8078737"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5064203120","display_name":"Ganesh K. Venayagamoorthy","orcid":"https://orcid.org/0000-0003-3154-8119"},"institutions":[{"id":"https://openalex.org/I8078737","display_name":"Clemson University","ror":"https://ror.org/037s24f05","country_code":"US","type":"education","lineage":["https://openalex.org/I8078737"]},{"id":"https://openalex.org/I95023434","display_name":"University of KwaZulu-Natal","ror":"https://ror.org/04qzfn040","country_code":"ZA","type":"education","lineage":["https://openalex.org/I95023434"]}],"countries":["US","ZA"],"is_corresponding":false,"raw_author_name":"Ganesh K. Venayagamoorthy","raw_affiliation_strings":["Holcombe Department of Electrical and Computer Engineering, Clemson University, Clemson, SC, USA","Real-Time Power and Intelligent Systems Laboratory, University of KwaZulu-Natal, Durban, South Africa"],"affiliations":[{"raw_affiliation_string":"Holcombe Department of Electrical and Computer Engineering, Clemson University, Clemson, SC, USA","institution_ids":["https://openalex.org/I8078737"]},{"raw_affiliation_string":"Real-Time Power and Intelligent Systems Laboratory, University of KwaZulu-Natal, Durban, South Africa","institution_ids":["https://openalex.org/I95023434"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5026188719"],"corresponding_institution_ids":["https://openalex.org/I8078737"],"apc_list":null,"apc_paid":null,"fwci":0.39,"has_fulltext":false,"cited_by_count":5,"citation_normalized_percentile":{"value":0.69753747,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":94},"biblio":{"volume":null,"issue":null,"first_page":"3377","last_page":"3384"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12676","display_name":"Machine Learning and ELM","score":0.9998000264167786,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T12676","display_name":"Machine Learning and ELM","score":0.9998000264167786,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10454","display_name":"Optimal Power Flow Distribution","score":0.9972000122070312,"subfield":{"id":"https://openalex.org/subfields/2208","display_name":"Electrical and Electronic Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10305","display_name":"Power System Optimization and Stability","score":0.9962000250816345,"subfield":{"id":"https://openalex.org/subfields/2208","display_name":"Electrical and Electronic Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/extreme-learning-machine","display_name":"Extreme learning machine","score":0.7996558547019958},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7206697463989258},{"id":"https://openalex.org/keywords/electric-power-system","display_name":"Electric power system","score":0.5997617244720459},{"id":"https://openalex.org/keywords/automatic-frequency-control","display_name":"Automatic frequency control","score":0.5740489959716797},{"id":"https://openalex.org/keywords/renewable-energy","display_name":"Renewable energy","score":0.5231356024742126},{"id":"https://openalex.org/keywords/grid","display_name":"Grid","score":0.5165790319442749},{"id":"https://openalex.org/keywords/power-grid","display_name":"Power grid","score":0.43764206767082214},{"id":"https://openalex.org/keywords/power","display_name":"Power (physics)","score":0.3696303367614746},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.3466648459434509},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3369578421115875},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.2313912808895111},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.18170350790023804},{"id":"https://openalex.org/keywords/telecommunications","display_name":"Telecommunications","score":0.1346001923084259},{"id":"https://openalex.org/keywords/electrical-engineering","display_name":"Electrical engineering","score":0.09289783239364624}],"concepts":[{"id":"https://openalex.org/C2780150128","wikidata":"https://www.wikidata.org/wiki/Q21948731","display_name":"Extreme learning machine","level":3,"score":0.7996558547019958},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7206697463989258},{"id":"https://openalex.org/C89227174","wikidata":"https://www.wikidata.org/wiki/Q2388981","display_name":"Electric power system","level":3,"score":0.5997617244720459},{"id":"https://openalex.org/C25915539","wikidata":"https://www.wikidata.org/wiki/Q220786","display_name":"Automatic frequency control","level":2,"score":0.5740489959716797},{"id":"https://openalex.org/C188573790","wikidata":"https://www.wikidata.org/wiki/Q12705","display_name":"Renewable energy","level":2,"score":0.5231356024742126},{"id":"https://openalex.org/C187691185","wikidata":"https://www.wikidata.org/wiki/Q2020720","display_name":"Grid","level":2,"score":0.5165790319442749},{"id":"https://openalex.org/C2983254600","wikidata":"https://www.wikidata.org/wiki/Q1096907","display_name":"Power grid","level":3,"score":0.43764206767082214},{"id":"https://openalex.org/C163258240","wikidata":"https://www.wikidata.org/wiki/Q25342","display_name":"Power (physics)","level":2,"score":0.3696303367614746},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3466648459434509},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3369578421115875},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.2313912808895111},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.18170350790023804},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.1346001923084259},{"id":"https://openalex.org/C119599485","wikidata":"https://www.wikidata.org/wiki/Q43035","display_name":"Electrical engineering","level":1,"score":0.09289783239364624},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ijcnn.2017.7966280","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn.2017.7966280","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 International Joint Conference on Neural Networks (IJCNN)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Affordable and clean energy","score":0.8999999761581421,"id":"https://metadata.un.org/sdg/7"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":14,"referenced_works":["https://openalex.org/W1484104892","https://openalex.org/W1987383100","https://openalex.org/W2068540745","https://openalex.org/W2111072639","https://openalex.org/W2115577730","https://openalex.org/W2124877376","https://openalex.org/W2127723003","https://openalex.org/W2130808067","https://openalex.org/W2148859979","https://openalex.org/W2158054309","https://openalex.org/W2313516183","https://openalex.org/W2334598562","https://openalex.org/W2551410127","https://openalex.org/W6699032245"],"related_works":["https://openalex.org/W2067443264","https://openalex.org/W31566076","https://openalex.org/W4297902562","https://openalex.org/W2741186499","https://openalex.org/W2804652951","https://openalex.org/W2556335056","https://openalex.org/W2989018981","https://openalex.org/W2145753860","https://openalex.org/W1895825885","https://openalex.org/W2380475562"],"abstract_inverted_index":{"Frequency":[0,15],"is":[1,41,83],"one":[2],"of":[3,29],"the":[4],"most":[5],"important":[6],"characteristics":[7],"in":[8,23],"power":[9],"system":[10,56,64],"monitoring,":[11],"control":[12,65],"and":[13,34,66,95,98],"protection.":[14,67],"variations":[16],"can":[17],"be":[18],"observed":[19],"with":[20,88,91],"significant":[21],"changes":[22],"operating":[24],"conditions.":[25],"High":[26],"penetration":[27],"levels":[28],"renewable":[30],"energy":[31],"pose":[32],"variability":[33],"uncertainty":[35],"challenges":[36],"for":[37],"grid":[38],"operation.":[39],"It":[40],"essential":[42],"to":[43,47,51,62,100],"have":[44],"innovative":[45],"methodologies":[46],"take":[48],"necessary":[49],"actions":[50],"overcome":[52],"these":[53],"challenges.":[54],"Power":[55],"frequency":[57,80],"prediction":[58,81],"provides":[59],"an":[60],"insight":[61],"better":[63],"In":[68],"this":[69],"paper,":[70],"a":[71],"cellular":[72],"computational":[73],"extreme":[74],"learning":[75],"machine":[76],"network":[77],"(CCELMN)":[78],"based":[79],"approach":[82],"presented.":[84],"Results":[85],"are":[86],"compared":[87],"those":[89],"obtained":[90],"independent":[92],"ELM":[93],"models":[94],"persistence":[96],"model":[97],"shown":[99],"outperform.":[101]},"counts_by_year":[{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":1},{"year":2021,"cited_by_count":1},{"year":2020,"cited_by_count":1},{"year":2017,"cited_by_count":1}],"updated_date":"2025-11-25T21:42:39.735039","created_date":"2025-10-10T00:00:00"}