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DC Field | Value | Language |
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dc.contributor.author | Prusty, B.R. | |
dc.contributor.author | Jena, D. | |
dc.date.accessioned | 2020-03-30T10:18:46Z | - |
dc.date.available | 2020-03-30T10:18:46Z | - |
dc.date.issued | 2017 | |
dc.identifier.citation | 2016 IEEE Annual India Conference, INDICON 2016, 2017, Vol., , pp.- | en_US |
dc.identifier.uri | http://idr.nitk.ac.in/jspui/handle/123456789/8467 | - |
dc.description.abstract | Gaussian 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.title | Estimation of optimal number of components in Gaussian mixture model-based probabilistic load flow study | en_US |
dc.type | Book chapter | en_US |
Appears in Collections: | 2. Conference Papers |
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