Faculty Publications

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Author: search.filters.author.Moger, T.Subject: search.filters.subject.Power

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    Security-constrained optimal placement of PMUs using Crow Search Algorithm
    (Elsevier Ltd, 2022) Johnson, T.; Moger, T.
    For precise power system monitoring, a major focus is on involvement of the latest technology based on phasor measurement units (PMUs). As the sole system monitor, state estimator plays an important role in the security of power system operations. Optimal placement of PMUs (OPP) with numerical observability ensures reliable state estimation. For economical and efficient utilization, there is a need to optimize the placement of PMUs in the power system network. A new approach called Crow Search Algorithm (CSA) devised by others, has been used to solve an OPP problem. The performance of this new approach is compared to the dominant method for an optimization problem — binary integer linear programming (BILP). Comparison studies have also been carried out with particle swarm optimization (PSO) method. The major constraints such as topological, numerical observability conditions with and without zero-injection buses (ZIBs) are considered. Contingencies and limitation of measurement channels in a PMU device are also incorporated as constraints. The main advantage of using the CSA is that it provides multiple location sets for same optimal number of PMUs (optimal number same as obtained by BILP). While BILP method provides only one set of locations for the optimal number of PMUs obtained. This becomes advantageous in planning stage for power engineers for placing PMUs. Test systems considered for the case studies are of varied sizes such as IEEE 14-bus, IEEE 30-bus, IEEE 57-bus and 72-bus practical equivalent system of Indian southern region power grid. © 2022 Elsevier B.V.
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    A hybrid model of convolutional neural network and an extreme gradient boosting for reliability evaluation in composite power systems integrated with renewable energy resources
    (Springer Science and Business Media Deutschland GmbH, 2025) Yarramsetty, C.; Moger, T.; Jena, D.
    This paper introduces an approach that enhances the computational efficiency of reliability assessment for composite power systems by integrating machine learning (ML) techniques with sequential monte carlo simulation (SMCS). Integration of renewable energy resources (RERs) into power systems is increasing at a rapid pace. Evaluating the reliability of composite power systems is helpful in identifying any deficiencies in their operation. As power systems operation becomes more fluctuating and stochastic, it is necessary to update the tools used to analyse reliability. In this paper, SMCS is used as a conventional method, as it provides results by taking chronological nature of RERs. However, SMCS is highly computational. ML models fit for solving complex problems that require computational power. ML techniques, such as convolutional neural network (CNN) and hybrib models of Convolutional and Extreme Gradient Boosting (ConXGB), and Convolutional and Random Forest (ConRF) are proposed to determine the expectation of load curtailment and minimum amount of load curtailments. The proposed technique is applied on test system IEEE RTS-79. Results indicate the ConvXGB method is fast and accurate in computing composite reliability indices. For instance, it achieved a Loss of Load Probability (LOLP) of 0.0025 and an Expected Demand Not Supplied (EDNS) of 0.1850 MW, compared to SMCS’s LOLP of 0.0021 and EDNS of 0.1794 MW while reducing computational time from 12900 to 5414 s. These results confirm the proposed method’s speed and accuracy, making it a robust solution for modern power system reliability evaluation. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.
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    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.