Conference Papers
Permanent URI for this collectionhttps://idr.nitk.ac.in/handle/123456789/28506
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Item Modeling of correlated photovoltaic generations and load demands in probabilistic load flow(Institute of Electrical and Electronics Engineers Inc., 2016) Prusty, B.R.; Jena, D.This paper performs probabilistic load flow under the consideration of uncertainty pertaining to conventional generation, photovoltaic (PV) generation and aggregate load demand in power systems. Effect of PV penetration and bus power correlations on distribution of desired random variables (bus voltages and line power flows) is analyzed with the help of an efficient analytical method named modified cumulant method. Generation-generation and load-load correlation cases are considered. Effectiveness of the proposed method has been tested on three test systems such as Ward-Hale 6 bus, IEEE 14 bus and IEEE 30 bus. Results are compared with Monte-Carlo simulation. The effectiveness of the proposed method is justified in terms of accuracy as well as execution time. © 2015 IEEE.Item Estimation of optimal number of components in Gaussian mixture model-based probabilistic load flow study(Institute of Electrical and Electronics Engineers Inc., 2017) Prusty, B.R.; Jena, D.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.Item An efficient hybrid technique for correlated probabilistic load flow study with photovoltaic generations(Institute of Electrical and Electronics Engineers Inc., 2017) Prusty, B.R.; Jena, D.This paper proposes an efficient hybrid technique for probabilistic load flow study. A mixture of correlated Gaussian and non-Gaussian as well as discrete distributions is considered for input random variables. Distributions of desired random variables pertaining to the input random variables are found to be multimodal. Analysis using Gaussian mixture approximation is promising in this context, but computational burden increases significantly with the increase in number of discrete random variables. In contrast, the proposed method precisely obtains distribution of desired random variables in considerably less time without compromising accuracy. Multiple input correlations are effectively incorporated. Accuracy of the proposed method is examined in IEEE 14-bus and 57-bus test systems. Results are compared with combined cumulant-Gaussian mixture approximation method and Monte-Carlo simulation. © 2016 IEEE.