Please use this identifier to cite or link to this item: http://cmuir.cmu.ac.th/jspui/handle/6653943832/58517
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dc.contributor.authorSattawat Buranaen_US
dc.contributor.authorPanida Thararaken_US
dc.contributor.authorPeerapol Jirapongen_US
dc.contributor.authorKannathat Mansuwanen_US
dc.date.accessioned2018-09-05T04:25:50Z-
dc.date.available2018-09-05T04:25:50Z-
dc.date.issued2018-01-08en_US
dc.identifier.other2-s2.0-85049433357en_US
dc.identifier.other10.1109/ICITEED.2017.8250468en_US
dc.identifier.urihttps://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85049433357&origin=inwarden_US
dc.identifier.urihttp://cmuir.cmu.ac.th/jspui/handle/6653943832/58517-
dc.description.abstract© 2017 IEEE. Distributed generation (DG) and flexible alternating current transmission systems (FACTS) can be used to increase the efficiency and enhance power generation of distribution systems. However, the installation of DG with FACTS at inappropriate allocations can result in negative impacts. In this paper, a new DG with FACTS allocation planning tool is proposed for determining the optimal location and sizing of DG with FACTS to reduce power losses. The optimal power flow (OPF) with DG and FACTS is formulated as a minimization problem of system power losses subjected to system constraints such as the grid code from Provincial Electricity Authority (PEA) of Thailand, loading limits, generation limits, and voltage limits. DG and FACTS used in this experiment are a synchronous generator and static var compensator (SVC), respectively. Genetic algorithm (GA) implemented by an m-file script in MATLAB is used for the optimization technique. Consequently, evaluation of load flow solutions and objective functions in each generation of GA are determined using DIgSILENT Programing Language (DPL) script in DIgSILENT software. An existing 22 kV distribution system from PEA is used as a test system. The practical system data from a geographic information system (GIS) database are imported for the planning tool. The obtained simulation results show that the optimal allocation of DG with FACTS using proposed tool results in system power loss reduction, line loading and voltage profile improvement.en_US
dc.subjectComputer Scienceen_US
dc.subjectEnergyen_US
dc.subjectEngineeringen_US
dc.subjectMathematicsen_US
dc.titleOptimal allocation of distributed generation with FACTS controller for electrical power loss reduction using genetic algorithmen_US
dc.typeConference Proceedingen_US
article.title.sourcetitle2017 9th International Conference on Information Technology and Electrical Engineering, ICITEE 2017en_US
article.volume2018-Januaryen_US
article.stream.affiliationsChiang Mai Universityen_US
Appears in Collections:CMUL: Journal Articles

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