Preprocessing of Multi-Time Instant PV Generation Data

dc.contributor.authorPrusty, B.
dc.contributor.authorJena, D.
dc.date.accessioned2026-02-05T09:31:22Z
dc.date.issued2018
dc.description.abstractFor the evaluation of system overlimit risk indices in a PV-integrated power system, PV generation data at specific instants of time (in each day for several years) are required to be collected. Such data have inherent annual periodic variations, which are different at various places. These variations are skewed and/or multimodal, which contributes significantly toward the overall variance of data and is primarily attributable to the Sun's position. This letter proposes a regression model that assumes the observed data as a function of few influencing factors related to the Sun's position and trend in data. Finally, the estimated variations using the developed model are removed from the data to characterize the unpredictable components. © 1969-2012 IEEE.
dc.identifier.citationIEEE Transactions on Power Systems, 2018, 33, 3, pp. 3189-3191
dc.identifier.issn8858950
dc.identifier.urihttps://doi.org/10.1109/TPWRS.2018.2799487
dc.identifier.urihttps://idr.nitk.ac.in/handle/123456789/25170
dc.publisherInstitute of Electrical and Electronics Engineers Inc.
dc.subjectAnalytical models
dc.subjectData structures
dc.subjectProbabilistic logics
dc.subjectProbability distributions
dc.subjectProduction
dc.subjectRegression analysis
dc.subjectArrays
dc.subjectMarket researches
dc.subjectPreprocessing
dc.subjectPV generation
dc.subjectRegression model
dc.subjectUncertainty
dc.subjectunpredictable uncertainty
dc.subjectPhotovoltaic cells
dc.titlePreprocessing of Multi-Time Instant PV Generation Data

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