Faculty Publications
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Item Smart distribution network voltage estimation using PMU technology considering zero injection constraints(Public Library of Science, 2024) Tangi, S.; Gaonkar, D.N.; Nuvvula, R.S.S.; P Kumar, P.P.; Çolak, I.; Tazay, A.F.; Mosaad, M.I.To properly control the network of the power system and ensure its protection, Phasor measurement units (PMUs) must be used to monitor the network's operation. PMUs can provide synchronized real-time measurements. These measurements can be used for state estimation, fault detection and diagnosis, and other grid control applications. Conventional state estimation methods use weighting factors to balance the different types of measurements, and zero injection measurements can lead to large weighting factors that can introduce computational errors. The offered methods are designed to ensure that these zero injection criteria can be strictly satisfied while calculating the voltage profile and observability of the various distribution networks without sacrificing computing efficiency. The proposed method's viability is assessed using standard IEEE distribution networks. MATLAB coding is used to simulate the case analyses. Overall, the study provides a valuable contribution to the field of power distribution system monitoring and control by simplifying the process of determining the optimal locations for PMUs in a distribution network and assessing the impact of ZI buses on the voltage profile of the system. ©: © 2024 Tangi et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.Item Smart Strategies for Improving Electric Vehicle Battery Performance and Efficiency(Nature Research, 2025) Tangi, S.; Vatsa, A.; Opam, A.; Bonthagorla, P.K.; Gaonkar, D.N.The increasing demand for Electric Vehicles (EVs) necessitates accurate range prediction and optimization of driving parameters to address range anxiety and improve user experience. This study proposes a machine learning-based framework for predicting EV range, optimum acceleration, and velocity using a synthetically generated dataset of 2,000 samples designed to reflect real-world driving scenarios. Four models—Random Forest (RF), Extra Trees (ET), Linear Regression (LR), and Long Short-Term Memory (LSTM)—were evaluated individually and in ensemble combinations. To ensure statistical reliability, all models were trained and tested over ten independent runs with randomized data partitions, and the results were reported as average performance with standard deviations. The ensembles consistently outperformed individual models, with the full ensemble (RF + ET + LSTM + LR) achieving the most robust performance across all metrics (MAE, MSE, and R²). Furthermore, a real-time web application was developed using the trained models to dynamically estimate driving parameters. The findings highlight the potential of integrating AI-driven predictive modelling into EV systems to support efficient driving behaviour and energy management. © The Author(s) 2025.