Faculty Publications
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Item Optimal Phasor Measurement Units Placement in Radial Distribution Networks Using Integer Linear Programming(Springer Science and Business Media Deutschland GmbH, 2021) Tangi, S.; Gaonkar, D.N.The traditional passive distribution networks are evolving into active distribution networks with the integration of dispersed generation (DG) to distribution networks. The conventional network monitoring systems do not monitor and provide the information accurately at a faster rate due to the intermittency nature of sources such as wind and solar. Hence, a real-time accurate and faster monitoring equipment like phasor measurement unit (PMU) is needed. The focal objective of this work is to bring the power distribution network more closely aligned with the smart grid communication technology for better system monitoring conditions. This paper presents the deployment of PMU optimal allocation in the radial distribution network by adopting an integer linear programming (ILP) technique for the system’s full observability at a standard operating condition. Standard radial test feeders such as 12-bus, 15-bus, 28-bus, IEEE 33-bus, IEEE 69-bus, and 119-bus are chosen to study the effect of PMU placement problem. MATLAB programming is used as a simulation software to check the observability of the above test systems. © 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.Item Voltage estimation of active distribution network using PMU technology(Institute of Electrical and Electronics Engineers Inc., 2021) Tangi, S.; Gaonkar, D.N.As the Distributed Generation (DG) is evolving, the distribution network has undergone tremendous change. The DG sources like wind and solar are intermittent in nature. Hence to estimate the Active distribution network's (ADN) parameters (Voltage and Current) accurately with the DG variation, the traditional offline method like long-term smart distribution network planning (with off-line variant) method is not feasible in terms of network's reliability and operation. In this work, an online voltage estimation technique using PMU (Phasor Measurement Unit) technology is proposed to enhance a distribution network's reliable operation and real-time monitoring. The proposed estimation method does not require PMU units at all nodes in the network. The estimation of unknown states information can be achieved from available PMU unit's data. The proposed methodology is economical and feasible for voltage estimation, and system observability as the number of PMU units required is less. The standard IEEE Distribution network systems are considered for checking the feasibility of the recommended technique. For the simulation of the case studies, MATLAB programming is used. A Forward and Backward sweep (FBS) load-flow algorithm is used to authenticate the proposed methodology. © 2013 IEEE.Item Time Series-Based Load Flow Simulation Algorithm for Distributed Generation in Distribution Networks †(Multidisciplinary Digital Publishing Institute (MDPI), 2024) Tangi, S.; Gaonkar, D.N.; Veerendra, A.S.; Shivarudraswamy, R.This paper proposes a load flow model to estimate the actual power output by incorporating time series data for solar irradiance and wind speed at a specific location. The integration of this time series data into the network is carried out in three distinct scenarios: considering only solar output, only wind output, and the combined contribution of solar and wind. These data integration processes are followed by load flow analysis conducted on the standard IEEE 33Bus radial distribution system. The time series simulations are executed using OpenDSS (Open Distribution System Simulator) software, which utilizes a COM (Common Object Model) interface to display results in MATLAB. © 2024 by the authors.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 Multi-Agent-Based Coordinated Voltage Regulation Technique in an Unbalanced Distribution System(Multidisciplinary Digital Publishing Institute (MDPI), 2025) Tangi, S.; Gaonkar, D.N.; Nuvvula, R.S.S.; Ali, A.; Riyaz Ahammed, S.R.Unbalanced active distribution networks must be carefully analyzed to minimize undesirable implications from internal unbalances and the incorporation of intermittent sources, such as DG (Distributed Generation). A coordinated voltage regulation mechanism is being created employing a MAS (Multi-Agent System) control structure to solve the difficulties mentioned earlier. The proposed technique increases coordination between DGs and Shunt capacitors (SCs) to optimize the voltage profile and reduce overall power losses, along with voltage and current unbalanced factors in the proposed unbalanced 3-phase radial distribution network. To ensure improved real-time monitoring, PMUs (Phasor Measurement Units) measure the state parameters of the above-regulated distribution network in realtime. Because it does not necessitate the placement of PMUs at all nodes for total network observability, it is a cost-effective technique for estimating network state. The IEEE standard, a 25-bus unbalanced 3-phase distribution network feeder, is used to assess the viability of the recommended technique. MATLAB R2024a programming is used to simulate the case studies. © 2025 by the authors.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.