Optimization Techniques on Quantum and Classical Systems: A Comprehensive Comparative Study Hussain Shaik, Morampudi Rajitha, K. Priyamvada, R. Naveena Bhargavi E3s Web of Conferences, 2025 Quantum optimization is a promising field revolutionizing problem-solving across domains. This study compares Particle Swarm Optimization (PSO), Moth Flame Optimization (MFO), and Genetic Algorithm (GA) on three platforms : a local computer, a local computer with quantum integration, and an IBM quantum machine. Results indicate PSO’s consistent performance across all setups, with the IBM quantum machine having a longer elapsed time. For MFO, the optimal solution is found using the IBM quantum machine, despite its longer execution time. Similarly, GA achieves the best results on the IBM quantum machine. These findings suggest that while quantum computers excel in solving complex problems, their execution time for simpler tasks remains higher than classical setups. Future research should address challenges like noise, limited qubits, and high material costs to improve quantum computers’ efficiency and availability.
Artificial Intelligence-Enabled Techno-Economic Analysis and Optimization of Grid-Tied Solar PV-Fuel Cell Hybrid Power Systems for Enhanced Performance Pooja Soni, R. Naveena Bhargavi, Vikramaditya Dave, Hemani Paliwal E3s Web of Conferences, 2024 The incorporation of energy from renewable sources into the power grid is crucial for achieving sustainable and environmentally friendly power generation. This study proposes an artificial intelligence (AI)-enabled methodology for the analysis & optimization of “grid-tied solar photovoltaic (PV)-fuel cell hybrid power systems.” The research aims to demonstrate how AI techniques can assist in decision-making, improve system performance, and achieve higher levels of energy efficiency and financial viability. The study presents the results of a project focusing on a renewable energy system that feeds into the grid and powers a university building. The hybrid power system’s performance and cost were evaluated using unified approaches to modeling, simulation, optimization, and control. The findings indicate that the AI-optimized “solar PV-fuel cell hybrid system connected to the grid” offers excellent performance, meeting 74% of the building’s energy needs through renewable sources. The system also achieved a low levelled price for energy and minimise CO2 emissions, further enhancing its environmental sustainability. The proposed AI-enabled approach proves to be a promising solution for creating grid-connected renewable energy systems with significant benefits for energy efficiency, cost-effectiveness, and environmental impact.
Review: Relay coordination in DGs with Electric Vehicle Srikant ganji, J. Namratha Manohar, G. Yesuratnam, R. Naveena Bhargavi E3s Web of Conferences, 2024 This research offers a comprehensive examination of the most effective approach to synchronize overcurrent relays within a protective system alongside protective relay. Every effort has been made to encompass all potential strategies for achieving optimal coordination among overcurrent relays. Both modern approaches, like Harris hawk optimisation, and more traditional ones, such ground-based circuit breakers, are included in this category of overcurrent protection with Electric Vehicle. The operation of a Smart Grid [1] is characterized by varying load demands, generation levels from DGs, and charging/discharging behaviours of EVs. These factors can lead to different fault currents and altered fault impedance profiles at various times. An adaptive coordination scheme can dynamically adjust relay settings based on these changing conditions to optimize sensitivity and minimize coordination time. In this paper briefly discuss traditional approaches but focus mostly on using Harris Hawk Optimisation to enhance the coordination of overcurrent relays. This document compiles citations for all relevant works and offers a concise overview of the study conducted. Furthermore, the outcomes of these methods are documented in their corresponding references.
Bidirectional Onboard Charger for Hybrid Electric Vehicles using ANN Based Current Controller R. Naveena Bhargavi, M. BhanuPrakash Isml 2024 Intelligent Systems and Machine Learning Conference, 2024 An Artificial Neural Network-based voltage ripple controller and battery charging circuit evaluation are presented in this paper (ANN). A single-phase bidirectional on-board charger (OBC) is linked to a permanent magnet alternator and its inverter to charge the batteries of plug-in Hybrid Electric Vehicles (HEVs). High-efficiency vehicles use intricate power electronics. By linking power relays to the generator drive mechanism, the suggested circuit might potentially charge batteries. By doing this way with conventional OBCs, power density in automobiles may be enhanced. After ANN calculates the ripple in the output voltage, the power converter may modify the system frequency in response to variations in load. Therefore, the system is conditioned to respond to a non-linear change in the load, and the ripple voltage at the output may be maintained within an acceptable range by frequency regulation. The computing performance allows for around a week of parametric simulation time. The ANN model avoids the time consumption and resources required for circuit simulations, relying instead on statistics. The output voltage ripple estimated with the ANN model was confirmed to be accurate by the simulation results.
Fault Tolerant Control for UPQC using ANN Naveena Bhargavi Repalle, G. Yesuratnam, E. Vidyasagar Proceedings 2024 International Conference on Social and Sustainable Innovations in Technology and Engineering Sasi ITE 2024, 2024 UPQC, Unified Power Quality Conditioner is multifunctional device in FACTS family which can provide independent or combined functions of shunt compensator, series compensator, voltage regulator and phase angle regulator. UPQC can compensate not only harmonic currents and unbalances of a non-linear load but also voltage harmonics and unbalances of the power supply which improves the power quality offered for other harmonic sensitive loads. The proposed work is to develop a power quality conditioner with a simple and robust controller to compensate for the voltage sag, voltage unbalance, harmonics, sag with unbalance, unbalance with harmonics. In traditional proportional-integral (PI) or proportional-integral derivative (PID) feedback controllers, careful tuning is required and the residual tracking error due to hysteresis and dynamic effects persists from one cycle to the next. The proposed controller will drive the tracking error asymptotically to zero as the number of operating cycles increases. Since control circuit relies on the availability and quality of sensor measurement, a circuit development for detecting faulty sensors and to restore the missing sensors using ANN and PSO is also proposed. The proposed work is to develop an ANN based fault tolerant control system for controlling of UPQC connected to a 440 V radial distribution network.
Implementation of a Novel Tabu Search Optimization Algorithm to Extract Parasitic Parameters of Solar Panel Naveena Bhargavi Repalle, Pullacheri Sarala, Lucian Mihet-Popa, Shashidhar Reddy Kotha, Nagalingam Rajeswaran Energies, 2022 The aging of PV cells reduces their electrical performance i.e., the parasitic parameters are introduced in the solar panel. The shunt resistance (RSh), series resistance (RS), photo current (IPh), diode current (Id), and diffusion constant (a1) are known as parasitic or extraction parameters. Cracks and hotspots reduce the performance of PV cells and result in poor V–I characteristics. Certain tests are carried out over a long period of time to determine the quality of solar cells; for example, 1000 h of testing is comparable to 20 years of operation. The extraction of solar parameters is important for PV modules. The Tabu Search Optimization (TSO) algorithm is a robust meta-heuristic algorithm that was employed in this study for the extraction of parasitic parameters. Particle Swarm Optimization (PSO) and a Genetic lgorithm (GA), as well as other well-known optimization methods, were used to test the proposed method’s correctness. The other approaches included the lightning search algorithm (LSA), gravitational search algorithm (GSA), and pattern search (PS). It can be concluded that the TSO approach extracts all six parameters in a reasonably short period of time. The work presented in this paper was developed and analyzed using a MATLAB-Simulink software environment.
Transient stability improvement using SSSC and STATCOM International Journal of Advanced Science and Technology, 2020
Effect of Transformer connections in Distributed Generation system R.Naveena Bhargavi, P.Rajesh Kumar, M.Lakshmi Swarupa, Ch. Shravani Proceedings of the 2020 International Conference on Renewable Energy Integration into Smart Grids A Multidisciplinary Approach to Technology Modelling and Simulation Icreisg 2020, 2020
