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<p>This article explores and analyses the implementation of an automatic transition mechanism between independent and grid-connected operating modes for a battery and PV array-integrated UPQC. In this system, active filters are coupled in shunt and series inverter. Using a single DC capacitor, it is controlled by an artificial rabbit (ARO) optimized neural network control method. The system deals with the problem of combining clean energy production with power quality enhancement. Also, notwithstanding grid availability, the key loads get an uninterrupted power supply thanks to the automatic changeover. Implementing automatic changeover in a battery and PV-powered UPQC system with the least amount of disruption to the local loads is one of the main difficulties addressed. The system’s performance is validated by subjecting it to various dynamic conditions typically encountered in modern distribution networks. The ARO-optimized ANN control effectively reduces Total Harmonic Distortion (THD) in grid current and enhances voltage stability. Compared to traditional PI, PSO, and GA-based controllers, the proposed method achieves faster convergence, lower THD, and improved grid redundancy. Simulation results validate the superiority of the ARO-optimized control under dynamic conditions, adhering to IEEE-519 standards.</p>

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Artificial Rabbit Optimized Neural Network Control of Battery and PV-Powered UPQC for Micro-Grid Application

  • 期刊论文

  • 发表日期：

    2025年11月14日

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  • Sanam Kouser,
  • G. Raam Dheep,
  • Ramesh C. Bansal

摘要

This article explores and analyses the implementation of an automatic transition mechanism between independent and grid-connected operating modes for a battery and PV array-integrated UPQC. In this system, active filters are coupled in shunt and series inverter. Using a single DC capacitor, it is controlled by an artificial rabbit (ARO) optimized neural network control method. The system deals with the problem of combining clean energy production with power quality enhancement. Also, notwithstanding grid availability, the key loads get an uninterrupted power supply thanks to the automatic changeover. Implementing automatic changeover in a battery and PV-powered UPQC system with the least amount of disruption to the local loads is one of the main difficulties addressed. The system’s performance is validated by subjecting it to various dynamic conditions typically encountered in modern distribution networks. The ARO-optimized ANN control effectively reduces Total Harmonic Distortion (THD) in grid current and enhances voltage stability. Compared to traditional PI, PSO, and GA-based controllers, the proposed method achieves faster convergence, lower THD, and improved grid redundancy. Simulation results validate the superiority of the ARO-optimized control under dynamic conditions, adhering to IEEE-519 standards.