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dc.contributor.authorPrusty, B.R.
dc.contributor.authorJena, D.
dc.identifier.citation2016 IEEE Annual India Conference, INDICON 2016, 2017, Vol., , pp.-en_US
dc.description.abstractGaussian mixture approximation (GMA)-based probabilistic load flow (PLF) is an efficacious approach for quantifying the uncertainties associated with non-Gaussian and discrete input random variables (RVs). GMA approximates these input RVs by an equivalent weighted finite sum of Gaussian components. Expectation maximization (EM) algorithm is a well-established approach to estimate the parameters of the mixture components. The critical aspect is to know a priori the optimal number of components approximating the non-Gaussian distributions. The estimation of optimal number of parameters is essential because the parameters with inappropriate components may not evaluate the mixture model accurately. This paper adopts a cluster distortion function-based approach to determine the optimal number of mixture components. The k-means clustering result pertaining to that optimal number is then used for EM initialization. PLF using multivariate-GMA is performed on two IEEE test systems, considering various types of input RVs and their multiple correlations. � 2016 IEEE.en_US
dc.titleEstimation of optimal number of components in Gaussian mixture model-based probabilistic load flow studyen_US
dc.typeBook chapteren_US
Appears in Collections:2. Conference Papers

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