Adaptive Control of Hybrid PV/Wind Energy System with Battery Storage for Grid Applications

Authors:

T. Dhanumjaya, N. Hemanth Kumar, N. Nishanth Yadav, P. Devi, CH. Karthikeya, Y. Naveen Kumar

Abstract

This paper presents an Artificial Neural Network (ANN)-based adaptive control strategy for a grid-connected hybrid photovoltaic (PV) and wind energy system integrated with Battery Energy Storage System (BESS). The system eliminates fuel cells and electrolyzers, reducing complexity, cost, and maintenance. The ANN controller manages power flow, regulates DC-link voltage, and ensures maximum power extraction from the PV system through intelligent MPPT. The BESS compensates for power fluctuations from intermittent solar and wind sources, ensuring reliable continuous grid supply. MATLAB/Simulink simulation demonstrates that the ANN controller achieves 98.2% DC-link voltage regulation accuracy, 4.8% THD in grid current (within IEEE 519 limits), and 96.5% overall system efficiency, significantly outperforming conventional PI controllers in dynamic response and power quality.

Keywords: Hybrid PV/Wind, ANN Controller, Battery Storage, Grid Integration, DC-Link Voltage, MPPT, Renewable Energy