Self-Tuning Current Control via ANN for Enhanced Harmonic Mitigation in Hybrid PV–Battery Storage Systems Utilizing the 3L-HANPC Inverter

The accelerated integration of photovoltaic (PV) systems, particularly within Hybrid PV–Battery Storage Systems (PV-BSS), establishes a compelling need for advanced control strategies that are fundamental to achieving effective Energy Saving Management. However, conventional proportional–integral (PI) controllers demonstrate limited adaptability and necessitate tedious, manual parameter tuning, frequently resulting in suboptimal dynamic performance, especially under load transients. To specifically address these constraints within the domain of high-power electronics, this paper introduces a novel Artificial Neural Network (ANN)-based current controller tailored for the 1500 VDC Three-Level Hybrid Active Neutral Point Clamped (3L-HANPC) inverter, which is a widely accepted PV-BSS topology. The optimal Multi-Layer Perceptron (MLP) architecture was identified using a multi-criteria methodology, which strategically balanced Total Harmonic Distortion (THD) performance against training efficiency. Simulation results affirm that the proposed ANN controller achieves superior harmonic mitigation and demonstrates faster dynamic responses compared to the PI counterpart. Moreover, the controller fundamentally ensures stable operation while eliminating the necessity for complex PI parameter tuning. Its dependable performance across both trained and unseen operating points strongly validates its robust adaptability. This self-tuning ANN approach thus provides a viable pathway for enhancing the reliability of future hybrid energy storage systems.