Electrical and Electronic Engineering, Energy, Renewable Energy, Sustainability and the Environment, Control and Systems Engineering
95
Scopus Publications
Scopus Publications
Optimal Distributed Generation Placement and Real-Time Load Forecasting Using Machine Learning for Voltage Stability Enhancement V. Suryanarayana Reddy, Busireddy Hemanth Kumar Journal of Circuits Systems and Computers, 2026 Modern power systems demand robust voltage stability and accurate real-time load forecasting, especially with the rising integration of Distributed Generation (DG) sources such as wind and solar energy. This paper presents a machine learning-based framework for optimal placement and sizing of DG units, designed to minimize the Voltage Stability Index (VSI) and improve grid resilience. The study evaluates three DG capacity scenarios (100%, 75% and 125%) on the IEEE 14-bus test system to examine their impact on voltage profiles. To enable real-time grid operations, a Bagged Regression model is employed, trained in both historical and simulated data for short-term load forecasting. Comparative analysis against traditional regression methods and advanced algorithms demonstrates superior accuracy, computational efficiency and reliability of the proposed framework. The main contributions include the integration of DG placement with real-time forecasting, the application of Bagged Regression for adaptive load prediction, performance benchmarking against boosting methods and extensive validation using the IEEE 14-bus system. The results confirm that the framework effectively enhances voltage profiles, reduces VSI and delivers scalable, adaptive and efficient solutions aligned with the operational needs of modern smart grids.
Adaptive deep reinforcement learning-based control strategy for high-performance permanent magnet synchronous motor drive systems S. Dukkipati, S. S. Nagendra, B. H. Kumar, E. Parimalasundar Electrical Engineering and Electromechanics, 2026 Introduction. In recent days, electric vehicles, robotics and in many control system applications, permanent magnet synchronous motors (PMSMs) are widely utilized. Problem. Due to non-linear behavior of system, external interferences and frequent changes in parameters, conventional control techniques like direct torque control, field-oriented control and PI control, frequently experience decline in performance. Goal. This paper presents a new deep learning based reinforcement learning (RL) PMSM control approach that makes use of the twin delayed deep deterministic policy gradient (TD3) and deep deterministic policy gradient (DDPG) algorithms. These algorithms utilize actor-critic architectures to learn optimal control policies in a model-free manner, enabling adaptive and intelligent motor control. Methodology. A MATLAB/Simulink-based simulation framework is developed to train and evaluate the proposed deep reinforcement learning (DRL) based controllers against conventional PI controllers. Performance metrics, including speed tracking accuracy, torque ripple minimization are analyzed. Results. The results demonstrate that DRL-based controllers exhibit superior adaptability, robustness, and dynamic performance under varying load and speed conditions in contrast to traditional control methods. Notably, the comparative analysis reveals that the TD3 algorithm outperforms DDPG by mitigating overestimation bias, resulting in smoother torque output and more stable control actions. Scientific novelty. This paper illustrates the capability of DRL for advanced PMSM control. Practical value. Paving the way for real-time implementation in modern electric drive systems. References 25, tables 3, figures 12.
Assessment of recent metaheuristic algorithms for support vector regression-based building energy consumption prediction in net-zero energy buildings Rajasekar Thota, Pydi Bala Krishna, Dasari Sreeman, Busireddy Hemanth Kumar, Arvind R. Singh, Mohit Bajaj, Viktoriia Bereznychenko Energy Exploration and Exploitation, 2026 The rapid rise in building construction creates the energy demand for nearly half of the world's energy demand. To minimize energy consumption in buildings, a concept called net-zero energy building (NZEB) is gaining popularity in developing countries and is being implemented in India as well. The NZEB aims to match the on-site renewable energy generation available at the building location with the building energy consumption (BEC) without relying on grid energy. To attain this concept in a real-time scenario, it requires information about the energy generation at the building and the energy consumption at each instant. It is necessary to predict the dynamically varying building loads to easily manage the available sources without the involvement of the grid. This can be achieved by designing an accurate prediction model. This article presents a comparative assessment of recent metaheuristic algorithms for hyperparameter optimization of a support vector regression (SVR) model to enhance the prediction performance of BEC. The analysis was conducted using hourly campus-scale energy consumption data collected from the National Institute of Technology Silchar, Assam, India, from 1 March 2018 to 29 February 2020, comprising 17,544 samples. The Polar Fox Optimization algorithm, Flood Algorithm, and Hiking Optimization Algorithm (HOA) were comparatively evaluated for SVR hyperparameter tuning in this application. The mean absolute error (MAE), mean absolute percentage error (MAPE), root mean squared error (RMSE), R 2 , percentage BIAS (PBIAS), and Willmott's Index (WI) error metrics are used to evaluate the performance of the optimized SVR models. The recently developed HOA algorithm exhibits better prediction accuracy with an MAE of 8.3099 kWh, RMSE of 11.1283 kWh, R 2 of 0.9986, MAPE of 2.7820%, PBIAS of −0.0759%, and WI of 0.9996 when compared to other models. The comparative results for different models show that the recent metaheuristic optimization methods can improve the performance of SVR model for accurate BEC prediction in NZEB applications.
Enhanced CPCV algorithm for improving power quality in electric vehicle battery charging Arvind R. Singh, B. Jyothi, Boya Anil Kumar, Mohit Bajaj, B. Hemanth Kumar, Milkias Berhanu Tuka Scientific Reports, 2025 There are several difficulties to overcome when integrating electric vehicles (EVs) into power distribution networks, especially when it comes to preserving power quality (PQ) because of the harmonic distortion produced throughout battery charging. These issues are not sufficiently addressed by conventional charging algorithms like Constant Current Constant Voltage (CCCV), which frequently leads to higher Total Harmonic Distortion (THD), decreased system efficiency, and generally insufficient performance PQ. The Constant Power Constant Voltage (CPCV) charging algorithm, which is a revolutionary approach to addressing these issues, dynamically modifies the charging power according to the battery's state of charge (SoC). Compared to conventional techniques, this creative technology efficiently controls harmonic emissions and enhances power quality. Three distinct EV models-the Tesla Model 3, the BYD ATTO 3, and the Kia EV3 Long Range-were used in simulations to assess the algorithm's performance. The findings show that the CPCV algorithm works noticeably better than the CCCV method with respect to of lowering harmonic distortion; for the 3rd, 5th, 7th harmonics, THD values were lowered to as low as 0.41%. Furthermore, comparing to the greater loss seen in CCCV (3.85 kWh to 5.89 kWh), the CPCV algorithm also demonstrated significant decreases in energy losses, ranging from 2.72 kWh to 3.51 kWh. Furthermore, the CPCV method boosted efficiency by guaranteeing a power factor that was almost constant throughout all charging conditions. These results demonstrate the CPCV algorithm's efficacy in improving power quality, maximizing energy use, and facilitating the smooth integration of EVs into contemporary distribution systems. In order to meet the increasing demand for EV charging infrastructure and guarantee an additional dependable and sustainable power system, CPCV offers an attractive option by reducing harmonic distortion and increasing general charging efficiency.
Optimizing sustainable energy management in grid connected microgrids using quantum particle swarm optimization for cost and emission reduction K. Paul, B. Jyothi, R. Seshu Kumar, Arvind R. Singh, Mohit Bajaj, B. Hemanth Kumar, Ievgen Zaitsev Scientific Reports, 2025 The global shift towards decentralized energy systems, driven by the integration of distributed generation technologies and renewable energy sources, underscores the critical need for effective energy management strategies in microgrids. This study proposes a novel multi-objective optimization framework for grid-connected microgrids using quantum particle swarm optimization (QPSO) to address the dual challenges of minimizing operational costs and reducing environmental emissions. The microgrid configuration analyzed includes renewable energy sources like photovoltaic panels and wind turbines, along with conventional energy sources and battery storage. By incorporating quantum-inspired mechanics, QPSO overcomes limitations such as premature convergence and solution stagnation, often seen in traditional methods. Simulation results demonstrate that QPSO achieves a 9.67% reduction in operational costs, equating to savings of €158.87, and a 13.23% reduction in carbon emissions, lowering emissions to 513.70 kg of CO 2 equivalent in the economic scheduling scenario. In the environmentally constrained economic scheduling scenario, the method delivers a balanced solution with operational costs of €174.11 and emissions of 401.63 kg of CO 2 . The algorithm’s performance is validated across various microgrid configurations, including standard low-voltage setups. These results highlight QPSO’s potential as an efficient tool for optimizing microgrid energy management, promoting both economic and environmental sustainability. This study provides a robust framework for achieving practical solutions in real-world applications, emphasizing the role of advanced optimization techniques in sustainable energy systems.
A blockchain consortium-based framework to enhance interoperability, standardization, and secure demand response management in smart grid applications Arvind R. Singh, R. Seshu Kumar, Mohit Bajaj, B. Hemanth Kumar, Vojtech Blazek Results in Engineering, 2025 The rapid advancement of smart grids, propelled by the integration of distributed energy resources (DERs) and renewable energy technologies, has exposed key challenges such as interoperability, standardization, and data security. These issues impede the efficient operation and scalability of smart grid applications, particularly in enabling decentralized, real-time energy trading and demand response management. Effective management of DERs and demand response systems relies heavily on seamless information exchange, data-driven decision-making, and robust digital communication frameworks to maintain system stability and operational efficiency. To address these challenges, this study presents the Blockchain Consortium-Based Demand Energy Trading System (BC-DETS), a blockchain-powered framework designed to enhance interoperability, security, and standardized energy trading mechanisms within smart grid ecosystems. Comprehensive simulations have validated the effectiveness of BC-DETS using Key Performance Indicators (KPIs) such as transaction latency, demand response participation rate, operational cost reductions, and overall grid efficiency under diverse scenarios. Findings reveal a 35% boost in grid efficiency through optimized energy distribution and minimized energy losses, alongside a 15% decrease in operational costs due to reduced transaction overhead and improved energy allocation. Moreover, demand response participation rates increased by 40%, facilitated by secure and transparent blockchain-enabled real-time energy transactions. These numerical findings underscore blockchain’s transformative potential in enhancing the scalability, security, and inclusivity of smart grids, establishing a foundational platform for future advancements in blockchain standardization and sustainable energy management solutions.
Z-source-based multilevel inverter topologies for photovoltaic applications Recent Advancement and Emerging Technologies in Multi Level Inverters, 2025
Different MPPT Algorithms for DC-DC Converter Boddepalli Hemanth Kumar, Arnab Ghosh 2025 IEEE International Students Conference on Electrical Electronics and Computer Science Sceecs 2025, 2025
Machine Learning-Based Prediction of Distributed Solar Adoption Rangu Seshu Kumar, Busireddy Hemanth Kumar, Mejanrao Sushma, Pratyusha Bhasam, E. Parimalasundar, B. Obulapathi 2025 IEEE North East India International Energy Conversion Conference and Exhibition Ne Iecce 2025, 2025
Addressing Ethical Challenges in AI-Driven Health Predictions Busireddy Hemanth Kumar, Sai Teja Nuka, Mahesh Recharla, Chaitran Chakilam, Sambasiva Rao Suura, Chandrashekar Pandugula 2025 2nd International Conference on Computing and Data Science Iccds 2025, 2025
A New Single Switch Fault Tolerant Multilevel Inverter for RES Applications Busireddy Hemanth Kumar, A. Immanuel, Moduboina Sreenivasulu, Chinthala Chaithanya, V. Suryanarayanareddy, E. Parimalasundar 2025 IEEE 4th International Conference on Smart Technologies for Power Energy and Control Stpec 2025 Conference Report, 2025
A Quasi-Z-Source Boost DC-DC Converter Coupled Inductor for PV Applications Busireddy Hemanth Kumar, K. Ravi Teja, Bala Sundar Kammari, Vundela Jitendhar Reddy, B. Obulapathi, E Parimalasundar 2024 3rd International Conference on Smart Technologies and Systems for Next Generation Computing Icstsn 2024, 2024
A Study of High Gain DC-DC Converter Topologies Busireddy Hemanth Kumar, E Parimalasundar, Bala Sundar Kammari, Naveen Reddy Bandi, Jagan Mohan Reddy Eragala, Venkata Nikhil Kandula 2024 International Conference on Cognitive Robotics and Intelligent Systems Icc Robins 2024, 2024
A Review of Non-Isolated DC-DC Converter Topologies Busireddy Hemanth Kumar, E Parimalasundar, Theshmitha Muga, Kranthi Kumar Reddy Reddi Vari, Sai Sravanthi Singaramma Gari, Ravi Kumar Reddy Potlapadu 2024 International Conference on Recent Innovation in Smart and Sustainable Technology Icrisst 2024, 2024
Design of Efficient Power source for Electric Vehicle using Battery and Ultracapacitor K. Sarada, G. G. Rajasekhar, B. Hemanth Kumar, Yernagu Satheesh Kumar Reddy, A K Vijayalakshmi, Abhishek Yadav IEEE International Conference on Advances in Electronics Communication Computing and Intelligent Information Systems Icaecis 2023 Proceedings, 2023
Control of Modified Switched Reluctance Motor for EV Applications B. Hemanth Kumar, P. Kalyan Kumar Reddy, N. Rambabu Naik, M. Tharun, K. Nagaraj, N. Mahesh Babu International Conference on Trends in Electrical Electronics Computer Engineering Teeccon 2022, 2022
Integration of RES with MPPT by SVPWM scheme Busireddy Hemanth Kumar, Vivekanandan Subburaj Intelligent Renewable Energy Systems Integrating Artificial Intelligence Techniques and Optimization Algorithms, 2021
