Faculty Publications
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Item Comparative Evaluation of Basic Probabilistic Load Flow Methods with Wind Power Integration(Institute of Electrical and Electronics Engineers Inc., 2021) Singh, V.; Moger, T.; Jena, D.The unprecedented penetration of distributed energy resources (DERs) such as wind power generations (WPGs) poses tremendous challenges for for the planning and maintenance of power systems due to their intermittent and uncertain nature. This paper mainly focuses on comparing basic probabilistic load flow (PLF) techniques when WPGs are integrated into the existing power grid. Considering loads and WPGs as random inputs, the performance of the cumulant method (CM) and point estimation method (PEM) are analyzed with respect to Monte-Carlo method for higher precision and less computational time. Case-studies are carried out on sample 10-bus and SR 72-bus equivalent systems. Simulation results demonstrated that 2n+1 PEM provides the best performance when dealing with high level of uncertainty associated with input variables. © 2021 IEEE.Item Modified Cumulant based Probabilistic Load Flow Considering Correlation between Loads and Wind Power Generations(Institute of Electrical and Electronics Engineers Inc., 2022) Singh, V.; Moger, T.; Jena, D.With the growing use of wind sources, power system analysis should consider the variation of wind power and the correlation among wind farms. In this paper, the Cumulant method (CM) for performing probabilistic load flow (PLF) analysis is modified to account for the correlation between random input variables. Considering the dependence between loads and wind power generations (WPGs), the modified CM models the dependent variables as a function of many independent ones using the Nataf transformation. The effectiveness of the suggested method is verified by performing case studies on a 24-bus equivalent system of the Indian southern region power grid. Furthermore, relative error values in reference with the Monte-Carlo simulation (MCS) method are analyzed. © 2022 IEEE.Item Probabilistic Load Flow for Wind Integrated Power System Considering Node Power Uncertainties and Random Branch Outages(Institute of Electrical and Electronics Engineers Inc., 2023) Singh, V.; Moger, T.; Jena, D.This paper proposes an analytical probabilistic load flow (PLF) approach that considers conventional generator outages, load variability, and random branch outages. The branch outages are modeled as 0-1 distributions of fictitious power injections at the appropriate nodes. The distribution of state variables and line power flows is then obtained using a combined Cumulant and Gram-Charlier series expansion approach. The proposed PLF performs contingency sequencing with fuzzy logic to eliminate random line checking and avoid masking mistakes faced by performance index-based algorithms. The Jacobian inverse calculation in the traditional Cumulant method is eliminated to conserve storage space and speed up the computation using the Gauss-Jordan method. The correlations among loads and wind power generations has been modeled using the Nataf transformation process. Results of 24-bus and 259-bus equivalent systems of the Indian southern and western power grids are analyzed and validated with those obtained using the Monte Carlo simulation method. The suggested method's efficacy is justified by its accuracy and low computational burden. © 2010-2012 IEEE.Item Probabilistic Load Flow Approach Combining Cumulant Method and K-Means Clustering to Handle Large Fluctuations of Stochastic Variables(Institute of Electrical and Electronics Engineers Inc., 2023) Singh, V.; Moger, T.; Jena, D.The modern electrical power system faces various uncertainties, including load fluctuations, forced outages of conventional generators, network branches. Furthermore, the rising penetration of wind power generation introduces additional uncertainty, causing difficulties in power system planning, operation. This paper uses an analytical probabilistic load flow approach to account for all such uncertainties. The random branch outages are simulated using the fictional powers injections into the relevant nodes. A fuzzy method is used to perform contingency sequencing to avoid masking mistakes that might occur when utilizing performance index-based sequencing methods. The sparse Jacobian inverse is eliminated to preserve storage space, accelerate the computation. A modified Cumulant method is used in conjunction with the K-means clustering process to deal with the substantial fluctuations of the input variables. In the proposed approach, the correlated samples are generated using inverse Nataf transformation. These correlated samples are clustered using K-means clustering. The Cumulant method is applied within each cluster, total probability law is used to integrate each cluster's findings. The proposed PLF is tested on 24-bus, 259-bus wind integrated equivalent systems. Compared with the Monte-Carlo simulation, the proposed PLF yields computationally efficient, more accurate findings. © 1972-2012 IEEE.Item Maximum entropy based probabilistic load flow for assessing input uncertainties and line outages in wind-integrated power systems(Elsevier Ltd, 2025) Singh, V.; Moger, T.; Jena, D.The swift expansion of distributed generation, particularly from photovoltaics and wind turbines, poses a formidable challenge to conventional probabilistic load flow (PLF) methods. This paper addresses the urgent need for a robust and efficient PLF approach by investigating a maximum entropy (ME) based probabilistic density function (PDF) approximation, utilizing advanced cumulant arithmetic from linearized power flow formulation. The ME-PLF method notably enhances the accuracy of output PDFs under extensive uncertainties, such as load demand fluctuations and disturbances in network branches. Unlike the Gram–Charlier expansion (GCE) reconstruction method, ME-PLF effectively eliminates the issue of erroneously obtaining negative values in the tail regions of the PDFs. Additionally, the fundamental cumulant method (CM) is refined to better model dependencies between wind power generators (WPGs) and loads. The simulations are conducted using the MATLAB programming software. Results from practical test systems have been validated against those obtained using the Monte Carlo simulation method. The suggested method has been proven to be highly effective due to its preciseness and reduced computational effort. © 2025 Elsevier B.V.Item A modified point estimate-based probabilistic load flow approach for improving tail accuracy in wind-integrated power systems(Elsevier Ltd, 2025) Singh, V.; Moger, T.; Jena, D.Modern power systems confront risks, including demand variations and forced outages of traditional generators. Moreover, the extensive grid integration of new energy generation has exacerbated the uncertainty because of its intermittent nature. The Hong's three-point estimation method (3PEM) for performing probabilistic load flow (PLF) is commonly used to cope with power system uncertainties; however, it has poor tail accuracy. To overcome this issue, the basic 3PEM is modified by adding a new pair of tail points. This modified 3PEM (MH3PEM) is equivalent to 5PEM but utilize reduced order moments. Also, a hybrid Hong-Harr PEM approach is proposed to efficiently deal with a mixture of independent and correlated input variables. The input variables’ correlation is modeled using the Nataf transformation. The proposed approaches are tested on wind farm-integrated 24-bus and 72-bus equivalent systems, and their findings are compared with the fundamental PEM schemes. Utilizing the Monte-Carlo simulation as a reference, the MH3PEM provides the most accurate results with a low computational burden. © 2025 Elsevier B.V.