Authors:
C. Kotteeswaran,Premkumar Ramu,J. Nithya,V. Mohan,Rajeshwari Ramaiah Murugesan,Manjunathan Alagarsamy,Bibhu Prasad Ganthia,DOI NO:
https://doi.org/10.26782/jmcms.2026.04.00002Keywords:
Spiking Neural Networks,Photovoltaic Systems,Active Power Filter,Neuromorphic Control,Harmonic Mitigation,Distributed Control,Power Quality Improvement.,Abstract
The increasing penetration of grid-connected photovoltaic (PV) systems introduces significant power quality challenges due to fast irradiance variations, intermittent generation, and nonlinear load interactions. Conventional control and signal-processing-based power quality enhancement techniques often suffer from limited response speed and reduced effectiveness during high-frequency transients. This paper proposes a Spiking Neural Network (SNN)-based fast transient detection and control framework for power quality improvement in grid-connected PV systems. The proposed approach employs an event-driven neuromorphic SNN to detect voltage and current transients with ultra-low latency, enabling rapid identification of harmonics, voltage sags, swells, and sudden load disturbances. Unlike traditional artificial neural networks, the SNN processes information in the form of discrete spikes, significantly reducing computational complexity and enhancing real-time responsiveness. The detected transient features are directly integrated with a distributed active power filter control strategy to generate adaptive compensating current references. Simulation studies carried out in MATLAB/Simulink under varying irradiance, nonlinear load, and grid disturbance conditions demonstrate that the proposed SNN-based controller achieves faster transient detection, lower total harmonic distortion, and improved power factor compared to conventional PI- and ANN-based controllers. The results confirm the effectiveness of neuromorphic intelligence in enhancing dynamic power quality performance, making the proposed method a promising solution for next-generation smart PV-integrated power systems.Refference:
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