Please use this identifier to cite or link to this item: http://cmuir.cmu.ac.th/jspui/handle/6653943832/63622
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dc.contributor.authorJirasak Laowanitwattanaen_US
dc.contributor.authorSermsak Uatrongjiten_US
dc.date.accessioned2019-03-18T02:22:07Z-
dc.date.available2019-03-18T02:22:07Z-
dc.date.issued2019-01-18en_US
dc.identifier.other2-s2.0-85062243386en_US
dc.identifier.other10.1109/ECTICon.2018.8620035en_US
dc.identifier.urihttps://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85062243386&origin=inwarden_US
dc.identifier.urihttp://cmuir.cmu.ac.th/jspui/handle/6653943832/63622-
dc.description.abstract© 2018 IEEE Increasing of the electricity power generation due to some undetermined renewable energy sources i.e. photovoltaic cell or wind power plants, has affected power system performances. Probabilistic power flow analysis (PPF) based on generalized polynomial chaos (gPC) is a method for analyzing these effects, but it requires exact distribution characteristics of uncertain parameters. This paper proposes an implementation of the arbitrary polynomial chaos (aPC) expansion to solve the PPF solution. With aPC, the set of orthonormal basis polynomial is directly calculated by the available recorded data of uncertain sources. The proposed technique has been applied to the modified IEEE 39-bus system in MATLAB environment. The numerical results showed that the proposed method was not only reliable, but also reduced the computation burden compared to PPF based on Monte Carlo Simulation.en_US
dc.subjectComputer Scienceen_US
dc.subjectEngineeringen_US
dc.subjectMathematicsen_US
dc.subjectPhysics and Astronomyen_US
dc.titleApplication of arbitrary polynomial chaos expansion to probabilistic power flow analysis of power systems with renewable energy sourcesen_US
dc.typeConference Proceedingen_US
article.title.sourcetitleECTI-CON 2018 - 15th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technologyen_US
article.stream.affiliationsChiang Mai Universityen_US
Appears in Collections:CMUL: Journal Articles

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