{"id":"https://openalex.org/W2999392543","doi":"https://doi.org/10.1109/iccp48234.2019.8959649","title":"Detection of Sources of Instability in Smart Grids Using Machine Learning Techniques","display_name":"Detection of Sources of Instability in Smart Grids Using Machine Learning Techniques","publication_year":2019,"publication_date":"2019-09-01","ids":{"openalex":"https://openalex.org/W2999392543","doi":"https://doi.org/10.1109/iccp48234.2019.8959649","mag":"2999392543"},"language":"en","primary_location":{"id":"doi:10.1109/iccp48234.2019.8959649","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iccp48234.2019.8959649","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 IEEE 15th International Conference on Intelligent Computer Communication and Processing (ICCP)","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/A5073669892","display_name":"Dorin Moldovan","orcid":"https://orcid.org/0000-0002-7559-3862"},"institutions":[{"id":"https://openalex.org/I158333966","display_name":"Technical University of Cluj-Napoca","ror":"https://ror.org/03r8nwp71","country_code":"RO","type":"education","lineage":["https://openalex.org/I158333966"]}],"countries":["RO"],"is_corresponding":true,"raw_author_name":"Dorin Moldovan","raw_affiliation_strings":["Technical University of Cluj-Napoca,Computer Science Department,Cluj-Napoca,Romania","Computer Science Department, Technical University of Cluj-Napoca, Cluj-Napoca, Romania"],"affiliations":[{"raw_affiliation_string":"Technical University of Cluj-Napoca,Computer Science Department,Cluj-Napoca,Romania","institution_ids":["https://openalex.org/I158333966"]},{"raw_affiliation_string":"Computer Science Department, Technical University of Cluj-Napoca, Cluj-Napoca, Romania","institution_ids":["https://openalex.org/I158333966"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5052747708","display_name":"Ioan Salomie","orcid":"https://orcid.org/0000-0002-7437-8300"},"institutions":[{"id":"https://openalex.org/I158333966","display_name":"Technical University of Cluj-Napoca","ror":"https://ror.org/03r8nwp71","country_code":"RO","type":"education","lineage":["https://openalex.org/I158333966"]}],"countries":["RO"],"is_corresponding":false,"raw_author_name":"Ioan Salomie","raw_affiliation_strings":["Technical University of Cluj-Napoca,Computer Science Department,Cluj-Napoca,Romania","Computer Science Department, Technical University of Cluj-Napoca, Cluj-Napoca, Romania"],"affiliations":[{"raw_affiliation_string":"Technical University of Cluj-Napoca,Computer Science Department,Cluj-Napoca,Romania","institution_ids":["https://openalex.org/I158333966"]},{"raw_affiliation_string":"Computer Science Department, Technical University of Cluj-Napoca, Cluj-Napoca, Romania","institution_ids":["https://openalex.org/I158333966"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5073669892"],"corresponding_institution_ids":["https://openalex.org/I158333966"],"apc_list":null,"apc_paid":null,"fwci":1.6692,"has_fulltext":false,"cited_by_count":35,"citation_normalized_percentile":{"value":0.8493382,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":96,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"175","last_page":"182"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11052","display_name":"Energy Load and Power Forecasting","score":0.9983999729156494,"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"}},"topics":[{"id":"https://openalex.org/T11052","display_name":"Energy Load and Power Forecasting","score":0.9983999729156494,"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":0.995199978351593,"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/T10603","display_name":"Smart Grid Energy Management","score":0.9912999868392944,"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/artificial-intelligence","display_name":"Artificial intelligence","score":0.7281323671340942},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7172264456748962},{"id":"https://openalex.org/keywords/python","display_name":"Python (programming language)","score":0.673712432384491},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.6437991857528687},{"id":"https://openalex.org/keywords/multivariate-adaptive-regression-splines","display_name":"Multivariate adaptive regression splines","score":0.6393020153045654},{"id":"https://openalex.org/keywords/particle-swarm-optimization","display_name":"Particle swarm optimization","score":0.6046613454818726},{"id":"https://openalex.org/keywords/perceptron","display_name":"Perceptron","score":0.5875595211982727},{"id":"https://openalex.org/keywords/random-forest","display_name":"Random forest","score":0.5167690515518188},{"id":"https://openalex.org/keywords/binary-classification","display_name":"Binary classification","score":0.44158199429512024},{"id":"https://openalex.org/keywords/stability","display_name":"Stability (learning theory)","score":0.4361754357814789},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.4237176477909088},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.3402736783027649},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.3392358422279358},{"id":"https://openalex.org/keywords/regression-analysis","display_name":"Regression analysis","score":0.22291085124015808},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.2051880955696106},{"id":"https://openalex.org/keywords/bayesian-multivariate-linear-regression","display_name":"Bayesian multivariate linear regression","score":0.14629268646240234}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7281323671340942},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7172264456748962},{"id":"https://openalex.org/C519991488","wikidata":"https://www.wikidata.org/wiki/Q28865","display_name":"Python (programming language)","level":2,"score":0.673712432384491},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.6437991857528687},{"id":"https://openalex.org/C44882253","wikidata":"https://www.wikidata.org/wiki/Q3455882","display_name":"Multivariate adaptive regression splines","level":4,"score":0.6393020153045654},{"id":"https://openalex.org/C85617194","wikidata":"https://www.wikidata.org/wiki/Q2072794","display_name":"Particle swarm optimization","level":2,"score":0.6046613454818726},{"id":"https://openalex.org/C60908668","wikidata":"https://www.wikidata.org/wiki/Q690207","display_name":"Perceptron","level":3,"score":0.5875595211982727},{"id":"https://openalex.org/C169258074","wikidata":"https://www.wikidata.org/wiki/Q245748","display_name":"Random forest","level":2,"score":0.5167690515518188},{"id":"https://openalex.org/C66905080","wikidata":"https://www.wikidata.org/wiki/Q17005494","display_name":"Binary classification","level":3,"score":0.44158199429512024},{"id":"https://openalex.org/C112972136","wikidata":"https://www.wikidata.org/wiki/Q7595718","display_name":"Stability (learning theory)","level":2,"score":0.4361754357814789},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.4237176477909088},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.3402736783027649},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3392358422279358},{"id":"https://openalex.org/C152877465","wikidata":"https://www.wikidata.org/wiki/Q208042","display_name":"Regression analysis","level":2,"score":0.22291085124015808},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.2051880955696106},{"id":"https://openalex.org/C64946054","wikidata":"https://www.wikidata.org/wiki/Q4874476","display_name":"Bayesian multivariate linear regression","level":3,"score":0.14629268646240234},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/iccp48234.2019.8959649","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iccp48234.2019.8959649","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 IEEE 15th International Conference on Intelligent Computer Communication and Processing (ICCP)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/15","score":0.46000000834465027,"display_name":"Life in Land"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":52,"referenced_works":["https://openalex.org/W1485347932","https://openalex.org/W1982783231","https://openalex.org/W1983404251","https://openalex.org/W1994386714","https://openalex.org/W2004929978","https://openalex.org/W2022679230","https://openalex.org/W2050955352","https://openalex.org/W2053640926","https://openalex.org/W2069045230","https://openalex.org/W2072298344","https://openalex.org/W2084594947","https://openalex.org/W2109046263","https://openalex.org/W2125213524","https://openalex.org/W2126644230","https://openalex.org/W2137293424","https://openalex.org/W2142958031","https://openalex.org/W2147601317","https://openalex.org/W2163791372","https://openalex.org/W2175142925","https://openalex.org/W2213201020","https://openalex.org/W2286489929","https://openalex.org/W2293387016","https://openalex.org/W2344480120","https://openalex.org/W2360613746","https://openalex.org/W2552006868","https://openalex.org/W2579583823","https://openalex.org/W2585298065","https://openalex.org/W2598525681","https://openalex.org/W2647099413","https://openalex.org/W2732959465","https://openalex.org/W2734035852","https://openalex.org/W2766957338","https://openalex.org/W2772362066","https://openalex.org/W2786886927","https://openalex.org/W2802314367","https://openalex.org/W2811207049","https://openalex.org/W2814079182","https://openalex.org/W2832574251","https://openalex.org/W2888477829","https://openalex.org/W2889868267","https://openalex.org/W2900501106","https://openalex.org/W2900633867","https://openalex.org/W2906800119","https://openalex.org/W2907560147","https://openalex.org/W2914918035","https://openalex.org/W2922483297","https://openalex.org/W3105901970","https://openalex.org/W3120740533","https://openalex.org/W6740195263","https://openalex.org/W6746533170","https://openalex.org/W6755841512","https://openalex.org/W7057443994"],"related_works":["https://openalex.org/W10576317","https://openalex.org/W4256152544","https://openalex.org/W1481829876","https://openalex.org/W2341492732","https://openalex.org/W3187193180","https://openalex.org/W106542691","https://openalex.org/W4287027380","https://openalex.org/W1699080303","https://openalex.org/W3011239835","https://openalex.org/W4200234001"],"abstract_inverted_index":{"The":[0,52,157],"prediction":[1,117,159],"of":[2,21,55,63,89,118,145],"smart":[3,71],"grid":[4,72],"stability":[5,73,121],"represents":[6],"a":[7,64,149],"challenging":[8],"research":[9,57],"problem":[10],"because":[11],"this":[12,56],"information":[13],"can":[14],"be":[15],"very":[16,35],"useful":[17,36],"for":[18,68],"the":[19,22,29,42,61,70,81,87,116,119,143,170],"identification":[20],"participants":[23,49],"that":[24],"lead":[25],"to":[26,37],"instabilities":[27],"in":[28,40],"system":[30,43],"and":[31,109,136,141,155,168],"consequently":[32],"it":[33],"is":[34,44,75,165],"determine":[38],"configurations":[39],"which":[41,74],"stable":[45],"even":[46],"if":[47],"some":[48],"present":[50],"abnormalities.":[51],"main":[53],"contributions":[54],"article":[58],"are:":[59],"(1)":[60],"presentation":[62],"machine":[65,153],"learning":[66,154],"methodology":[67],"predicting":[69],"based":[76,151],"on":[77,152],"features":[78,90,171],"extraction":[79],"using":[80,91,122,148,174],"tsfresh":[82],"package":[83],"from":[84],"Python,":[85],"(2)":[86],"selection":[88],"three":[92],"methods":[93],"namely,":[94,125],"Binary":[95,102],"Particle":[96],"Swarm":[97],"optimization":[98,105],"Features":[99,106],"Selection":[100,107],"(BPSOFS),":[101],"Kangaroo":[103],"Mob":[104],"(BKMOFS)":[108],"Multivariate":[110],"Adaptive":[111],"Regression":[112,127],"Splines":[113],"(MARS),":[114],"(3)":[115],"system's":[120],"four":[123],"classifiers":[124],"Logistic":[126],"(LR),":[128],"Random":[129],"Forest":[130],"(RF),":[131],"Gradient":[132],"Boosted":[133],"Trees":[134],"(GBT)":[135],"Multilayer":[137],"Perceptron":[138],"Classifier":[139],"(MPC)":[140],"(4)":[142],"detection":[144],"instability":[146],"sources":[147],"method":[150],"statistics.":[156],"best":[158],"results":[160],"are":[161,172],"obtained":[162],"when":[163,169],"MPC":[164],"applied":[166],"(93.8%)":[167],"selected":[173],"BPSOFS.":[175]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":4},{"year":2024,"cited_by_count":9},{"year":2023,"cited_by_count":7},{"year":2022,"cited_by_count":7},{"year":2021,"cited_by_count":4},{"year":2020,"cited_by_count":3}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}