Electrical and Electronic Engineering, Energy Engineering and Power Technology, Renewable Energy, Sustainability and the Environment, Control and Systems Engineering
17
Scopus Publications
Scopus Publications
Quantum-driven frequency stability in Indian prospect smart grid with electric vehicle charging station integration and real-time hardware validation M. Kaleeswari, P. Sivakumar, A. Aswini Scientific Reports, 2026 In India, the growth of the Electric Vehicle(EV) is gradually increasing due to the high price of fossil fuel and global warming. But in India, the Electric Vehicle Charging Station(EVCS) infrastructure is inadequate due to the limited number of charging points, insufficient grid support for integration with RES. The integration of Electric Vehicle Charging Station(EVCS) along with RES in the Distributed Generation(DG) system confronts frequency fluctuations, variations in tie-line power, and instability. Thus, the employment of a Load Frequency Controller (LFC) with the DG diminishes the issues due to EVCS. In the proposed work,146 Indian Utility Bus(IUB) System is considered a future smart DG system with several EVCS, and the frequency stability of the system is achieved by Quantum-Inspired optimization methodology. This paper signifies the Quantum-Inspired Enhanced Gorilla Troop Optimization(QIEGTO), and is tested with other quantum-inspired optimization approaches by classical computers. The proposed smart DG system gives better stability enhancement with the QIEGTO method and provides superior performance in peak time, settling time(T s ), and Integral Time Absolute Error(ITAE) for load patterns and penetration level of EV and RES. For different percentages of load pattern variations and intermittent source variations, QIEGTO attains a faster settling time with reduced steady-state error against benchmark algorithms. The real-time validation of the proposed model is verified with Hardware-In-Loop using dSPACE.
Digital twin assisted of frequency stability enhancement in Indian smart grids with EV charging infrastructure along with quantum optimization M. Kaleeswari, P. Sivakumar, A. Aswini Proceedings of the Institution of Mechanical Engineers Part A Journal of Power and Energy, 2026 The amalgamation of Electric Vehicle Charging Station (EVCS) along with high penetration of green energy in Distributed Generation (DG) system affects the frequency stability, tie-line power fluctuations, and instability due to the load pattern variations of industries and different charging patterns at EVCS, and the intermittent sources variations like different wind velocity, and solar irradiance levels. Due to this, there will be a lack of coordination between the load and power demand. Implementation of Load Frequency Controller (LFC) with the DG system will reduce earlier stated problems. However, in this proposed research, LFC is tuned using a quantum-inspired optimization methodology for fast-varying load patterns and source variations in a complex DG system. This paper presents Quantum Enhanced Gorilla Troop Optimization (QEGTO), and performance is compared with other quantum-inspired optimization methodologies. In these assessments, the proposed work with the QIEGTO method gives superior stability in terms of settling time (T s ) and Integral Time Absolute Error (ITAE) for step and random load variations. For different percentages of load pattern variations and intermittent source variations, QIEGTO attains a 20% faster settling time with 40% decrease in steady-state error. Digital Twin model framework analysis satisfies the virtual real-time replica and predicts the frequency variations with neural network along with QIEGTO.
Design and Implementation of a Real-Time Digital Twin for Power Transformer Protection Systems P. Sivakumar, D. Lochana, V. Meenaroshini, M. Kaleeswari 2026 6th International Conference on Multimedia Signal Processing and Communication Technologies Impact 2026, 2026 Protection of power transformers plays a vital role in the reliability of the grid, but conventional differential relays tend to take a pounding from current transformer (CT) saturation, noise, and incipient fault sensitivity. This article introduces a Real-Time Digital Twin (DT)-driven transformer protection system that combines physics-based modelling with an Unscented Kalman Filter (UKF) for precise state estimation and noise-resistant fault detection. The DT platform integrates differential current, negative sequence, and impedance trajectory analyses to find interturn faults early in real time, making conventional offline diagnostics an active protective measure. Simulation of a 100 MVA, 220/33 kV transformer model in MATLAB/Simulink proved 7-10x times sensitivity improvement in interturn faults (1-2% vs. 10-15%), 85% lower misoperations, and 18 dB signal-to-noise improvement via UKFbased reconstruction. The system had 180 us latency and 25 % CPU usage, validating real-time feasibility. This work lays a basis for self-aware, intelligent protection systems that learn dynamically based on transformer health, which will lead to next-generation smart grid protection architectures.
Deep neural network based digital twin framework for per-day energy extraction in partially illuminated photovoltaic systems Aswini A, Sivakumar P, Kaleeswari M Proceedings of the Institution of Mechanical Engineers Part A Journal of Power and Energy, 2025 Digital Twins (DTs) in digitalization have become a potent device in real-time optimization of the photovoltaic (PV) system. Partial shading in PV systems is a serious issue resulting in significant energy wastage. The methodology discussed in this paper is to maximize the per-day energy extraction (PDEE) in the case of shading through the utilization of a supplementary PV source. The methodology combines two important features: (1) Digital Twin Framework design for PV Systems Using Calibrated Voltage, Current, Temperature, and Solar insolation Data to facilitate reliable online power assessment with reduced dependence on hardware sensors, thereby improving scalability and maintainability. (2) Incorporating external biasing units in series to mitigate partial shading effects in PV arrays to maintain V m ref across all PV arrays to optimize the overall power retrieval. A PV system’s digital twin is a fusion of analytical Formulation of the Photovoltaic Model and deep neural networks (DNNs) optimized using the improved Harris Hawks Optimization (IHHO) algorithm. Moreover, a machine learning model can be applied to the Digital Twin features to predict the Global maximum reference voltage. Simulations and experimental findings indicate an increase in power extraction by 15.4%, which points to a viable approach to reduce the effect of shading in a realistic PV system.
LFC Enhancement in Seashore Side Distributed Generation with Electric Vehicle Integration by using Deep Q-Learning Network P. Sivakumar, M. Kaleeswari, A. Aswini, P. Manikandan, M. Manimalar, K. Sarenyadhevi Proceedings 3rd International Conference on Self Sustainable Artificial Intelligence Systems Icssas 2025, 2025 Nowadays, energy from green energies is the most essential for all communities due to the increasing global warming and preceding problems related to such situation leading to extensive heat levels, an increase in sea level, loss of sea rise, irregulated rainfall or summer. Because of that, most countries are facing more economic and climatic issues. Adapting to green energy is crucial to ride out such conditions. The emission of CO2 is one of the biggest influences for Green House Gas(GHG) emissions. So, Electric Vehicle usage demand is increasing among all countries for the general improvement of the environment and reduction of the overall global GHG crisis. As water is of major source of the earth, the generation of power from the sea tides has become popular and significant in most countries. Due to the high penetration of hybrid tidal, PV-wind energy in Distributed Generation(DG) with the integration of EVCS can be done for the satisfaction of load demand in the future. Due to variations in tidal waves, solar irradiance, and wind fluctuations, it is not easy to achieve stability in the system's performance. With the proper tuning of the Load Frequency Controller with Deep Q-Q-Network (DQN), the required different optimization methods, and frequency regulation will be achieved. The settling time, Integral Time Absolute Error(ITAE), and Integral Squared Error(ISE) are analyzed using different methods. The settling time decreases by 15%, and the ISE and ITAE decrease by about 25% from the conventional control methods.
Unobtrusive Optimization: Passive Cell Balancing for Enhanced Battery Efficiency A. Aswini, P. Siva Kumar, M. Kaleeswari, V.J Agadeesh Proceedings of the 3rd IEEE International Conference on Power Electronics Intelligent Control and Energy Systems Icpeices 2024, 2024 Passive cell balancing is a widely employed technique in battery management systems (BMS) aimed at equalizing the state of charge (SoC) or voltage among individual cells within a battery pack. Passive balancing relies on dissipating excess energy from cells with higher voltage as heat or redirecting it to cells with lower voltage through passive components like resistors. This paper provides an overview of passive cell balancing methodologies, including circuit configurations and operation principles. Various factors affect the effectiveness of passive balancing, such as cell characteristics, pack configuration, and balancing circuit design. The paper accomplishes with a discussion on the practical applications of passive cell balancing in battery management systems, emphasizing its role in enhancing battery performance, extending lifespan, and ensuring safety in diverse applications ranging from portable electronics to electric vehicles and grid energy storage systems. Passive balancing of the battery is simulated in Matlab.
IoT based Solar Powered Induction Heating for Hyperthermia Treatment P. Siva Kumar, A. Aswini, M. Kaleeswari, P.Arockia Prasad, R. Manickavasagam, P.Rathi Devi 2023 4th International Conference on Electronics and Sustainable Communication Systems Icesc 2023 Proceedings, 2023
