{"?xml":{"@version":"1.0"},"edm:RDF":{"@xmlns:dc":"http://purl.org/dc/elements/1.1/","@xmlns:edm":"http://www.europeana.eu/schemas/edm/","@xmlns:wgs84_pos":"http://www.w3.org/2003/01/geo/wgs84_pos","@xmlns:foaf":"http://xmlns.com/foaf/0.1/","@xmlns:rdaGr2":"http://rdvocab.info/ElementsGr2","@xmlns:oai":"http://www.openarchives.org/OAI/2.0/","@xmlns:owl":"http://www.w3.org/2002/07/owl#","@xmlns:rdf":"http://www.w3.org/1999/02/22-rdf-syntax-ns#","@xmlns:ore":"http://www.openarchives.org/ore/terms/","@xmlns:skos":"http://www.w3.org/2004/02/skos/core#","@xmlns:dcterms":"http://purl.org/dc/terms/","edm:WebResource":[{"@rdf:about":"http://www.dlib.si/stream/URN:NBN:SI:DOC-D1632K96/4f5d8a1a-9039-4a64-93bb-52447ca0b227/PDF","dcterms:extent":"938 KB"},{"@rdf:about":"http://www.dlib.si/stream/URN:NBN:SI:DOC-D1632K96/fb725a38-5e0c-4ac1-ac6a-7f2728adf948/TEXT","dcterms:extent":"34 KB"}],"edm:TimeSpan":{"@rdf:about":"2013-2025","edm:begin":{"@xml:lang":"en","#text":"2013"},"edm:end":{"@xml:lang":"en","#text":"2025"}},"edm:ProvidedCHO":{"@rdf:about":"URN:NBN:SI:DOC-D1632K96","dcterms:isPartOf":[{"@rdf:resource":"https://www.dlib.si/details/URN:NBN:SI:SPR-XAYCFMST"},{"@xml:lang":"sl","#text":"JET on-line"},{"@xml:lang":"sl","#text":"Journal of energy technology"}],"dcterms:issued":"2013","dc:creator":["Hederić, Željko","Marić, Predrag","Vukobratović, Marko"],"dc:format":[{"@xml:lang":"sl","#text":"letnik:6"},{"@xml:lang":"sl","#text":"številka:iss. 4"},{"@xml:lang":"sl","#text":"str. 11-30"}],"dc:identifier":["COBISSID:1024165212","ISSN:1855-5748","URN:URN:NBN:SI:doc-D1632K96"],"dc:language":"en","dc:publisher":{"@xml:lang":"sl","#text":"Fakulteta za energetiko"},"dc:subject":[{"@xml:lang":"en","#text":"artificial neural network"},{"@xml:lang":"en","#text":"distributed generation"},{"@xml:lang":"en","#text":"genetic algorithm"},{"@xml:lang":"sl","#text":"genetski algoritmi"},{"@xml:lang":"sl","#text":"napetostni nivoji"},{"@xml:lang":"sl","#text":"nevronske mreže"},{"@xml:lang":"sl","#text":"regulacija"},{"@xml:lang":"en","#text":"voltage control"}],"dcterms:temporal":{"@rdf:resource":"2013-2025"},"dc:title":{"@xml:lang":"sl","#text":"Optimization method for control of voltage level and active power losses based on optimal distributed generation placement using artificial neural networks and genetic algorithms| Optimizacijska metoda za nadzor napetostnih nivojev in izgub z upoštevanjem optimalne implementacije razpršene proizvodnje s pomočjo nevronskih mrež in genetskih algoritmov|"},"dc:description":[{"@xml:lang":"sl","#text":"This paper presents a method for reducing active power system losses and voltage level regulation by implementing adequate distributed generation capacity on the appropriate terminal in a distribution system. Active power losses are determined using an Artificial Neural Network (ANN) using simultaneous formulation for the determination process based on voltage level control and injected power. Adequate installed power of distributed generation and the appropriate terminal for distributed generation utilization are selected by means of a genetic algorithm (GA), performed in a distinct manner that fits the type of decision-making assignment. The training data for Artificial Neural Network (ANN) is obtained by means of load flow simulation performed in DIgSILENT PowerFactory software on a part of the Croatian distribution network. The active power losses and voltage conditions are simulated for various operation scenarios in which the back propagation artificial neural network model has been tested to predict the power losses and voltage levels for each system terminal, and GA is used to determine the optimal terminal for distributed generation placement"},{"@xml:lang":"sl","#text":"V članku je predstavljena metoda za zmanjšanje izgub v sistemu in regulacijo napetostnih nivojev z implementacijo razpršenih proizvodnih kapacitet na primernih terminalih distribucijskega sistema. Izgube delovne moči so določene z uporabo Umetne Nevronske Mreže (UNM), kjer je uporabljena sočasna formulacija v procesu odločanja na osnovi nadzora napetostnih nivojev in injiciranih moči. Ustrezne inštalirane moči razpršene proizvodnje in primerni terminali za izkoriščanje razpršene proizvodnje so izbrani na osnovi GenetskihAlgoritmov (GA) izvedenih na poseben način, ki ustreza nalogam v procesu odločanja. Podatki za Umetno Nevronsko Mrežo so pridobljeni na osnovi simulacije pretoka energij v programskem paketu ''DIgSILENT PowerFactory'' na delu Hrvaškega distibucijskega omrežja. Simulacije izgub delovne moči in napetostnih razmer so izvedene za različne obratovalne scenarije, v katerih je testiran model ''vzratnega učenja'' umetne nevronske mreže za predvidevanje izgub moči in napetostnih nivojev za vsak sistemski terminal. Genetski algoritem je uporabljen za določitev optimalnega terminala za umestitev razpršene proizvodnje"}],"edm:type":"TEXT","dc:type":[{"@xml:lang":"sl","#text":"znanstveno časopisje"},{"@xml:lang":"en","#text":"journals"},{"@rdf:resource":"http://www.wikidata.org/entity/Q361785"}]},"ore:Aggregation":{"@rdf:about":"http://www.dlib.si/?URN=URN:NBN:SI:DOC-D1632K96","edm:aggregatedCHO":{"@rdf:resource":"URN:NBN:SI:DOC-D1632K96"},"edm:isShownBy":{"@rdf:resource":"http://www.dlib.si/stream/URN:NBN:SI:DOC-D1632K96/4f5d8a1a-9039-4a64-93bb-52447ca0b227/PDF"},"edm:rights":{"@rdf:resource":"http://rightsstatements.org/vocab/InC/1.0/"},"edm:provider":"Slovenian National E-content Aggregator","edm:intermediateProvider":{"@xml:lang":"en","#text":"National and University Library of Slovenia"},"edm:dataProvider":{"@xml:lang":"sl","#text":"Univerza v Mariboru, Fakulteta za energetiko"},"edm:object":{"@rdf:resource":"http://www.dlib.si/streamdb/URN:NBN:SI:DOC-D1632K96/maxi/edm"},"edm:isShownAt":{"@rdf:resource":"http://www.dlib.si/details/URN:NBN:SI:DOC-D1632K96"}}}}