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All Journal International Journal of Applied Power Engineering (IJAPE)
Nitin Dhote
Nagpur University
Electrical & Electronics Engineering

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Design of an iterative AI enhanced STATCOM-controlled hybrid renewable energy system with multi-agent coordination and predictive stability intelligence sets Bhishan Wadhai; Nitin Dhote; Mohan Lal Kolhe
International Journal of Applied Power Engineering (IJAPE) Vol 15, No 3: September 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijape.v15.i3.pp1147-1156

Abstract

Renewable energy integration causes intermittency, nonlinear dynamics, and grid-code restrictions in modern power systems. Although hybrid renewable energy systems combining wind, photovoltaic, and fuel cell sources increase energy availability, conventional control approaches often fail to maintain voltage stability, power quality, and rapid fault recovery under varying operating conditions. High renewable penetration and noisy conditions worsen these concerns. Existing methods typically address voltage regulation, transient stability, fault resilience, and power sharing independently using fixed or offline-tuned controllers, limiting adaptability during grid disturbances. To overcome these challenges, this study proposes an AI-enhanced STATCOM-controlled hybrid renewable energy system with learning-based control, predictive stability assessment, and multi-agent coordination. Adaptive reactive power support, noise-resilient fault detection, renewable source power sharing, predictive voltage regulation, and physiologically inspired transient stability prediction using hierarchical reinforcement learning. The simulation results maintain system voltage deviation within ±2%, harmonic distortion below 2%, fault detection within 7 ms, and transient stability prediction accuracy above 98% across varied operating conditions. Voltage recovery, overshoot suppression, and resource utilization efficiency improve above benchmark techniques. Thus, findings demonstrate that AI- enhanced STATCOM works as cognitive grid-interfacing agents rather than passive compensators for improving stability, power quality, and operational resilience in various deployment settings.
Intelligent and thermally-conscious on-board EV charging using hybrid genetic optimization and neural-adaptive control process Diksha Khare; Nitin Dhote; Swapna Choudhary
International Journal of Applied Power Engineering (IJAPE) Vol 15, No 3: September 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijape.v15.i3.pp1094-1104

Abstract

The increasing usage of electric vehicles (EVs) has amplified the demand for smart, thermally efficient, and battery-aware onboard charging systems. Conventional charging techniques often do not take into consideration balancing their delivery of energy with thermal stress and battery degradation, which lowers the operational efficiency and useful life of the battery. Current methods primarily focus on an isolated aspect, be it power optimization or thermal optimization; there is no combination of the two to arrive at any adaptive, real-time control based on battery metrics such as health. An integral optimization-control framework is proposed in this work that encapsulates algorithm-driven intelligence and neural adaptation into a single construct for on-board charge EVs. This paper proposes an intelligent and thermally conscious on-board EV charging framework that integrates efficiency-centric optimization using a genetic algorithm (ECO-GA), neural network-based adaptive charging control (NNACC), and metabolic inspired three-stage charging control (MET-C3). The initial phase, known as "ECO-GA" or "efficiency-centric optimization via genetic algorithm", clears a multi-variable fitness function based on charging voltage, current, battery temperature, and cycle life after generating control parameters. Together, this collection greatly improves efficiency in charging, reduces adverse effects caused by heating and lengthens cycles in which batteries are used, paving a strong path towards the charging infrastructure of EVs.
Co-Authors Bhishan Wadhai Diksha Khare Mohan Lal Kolhe Swapna Choudhary