Unified Neural Network–Based MPPT Control For Grid-Independent Hybrid PV–Wind Systems With Adaptive Energy Management
DOI:
https://doi.org/10.29284/7f3tdk44Keywords:
Hybrid PV-Wind System, ANN MPPT, Unified Control, Battery Storage, NPC Inverter, DC-AC Load Sharing, MATLAB Simulation.Abstract
The increasing demand for reliable off-grid renewable energy systems necessitates advanced control strategies capable of managing multi-source variability and dynamic load conditions. This paper proposes a unified Artificial Neural Network (ANN)-based Maximum Power Point Tracking (MPPT) strategy for a standalone hybrid photovoltaic (PV)–wind–battery energy system. Unlike conventional hybrid configurations that employ separate MPPT controllers for each renewable source, the proposed approach utilizes a single intelligent ANN framework to simultaneously coordinate and optimize power extraction from both PV and wind subsystems, thereby eliminating control redundancy and enhancing dynamic response.
The hybrid system comprises a 2032 kW PV array, a 2600 kW wind turbine generator, and a lithium-ion battery bank rated at 300 V and 6500 Ah. A dynamic three-phase load of 1500 kW, with an additional 1000 kW step increase at 4.5 s, is considered to evaluate system adaptability. Power exchange between renewable sources and storage is regulated using a bidirectional DC–DC converter, while a three-level Neutral-Point-Clamped (NPC) inverter maintains a regulated 550 V DC-link voltage and delivers high-quality AC output.
MATLAB/Simulink simulation results demonstrate that the unified ANN-MPPT controller achieves rapid convergence to the maximum power point, stable DC-link voltage regulation, inverter efficiency exceeding 96%, and output voltage total harmonic distortion below 2% under varying environmental and load conditions. The coordinated control framework ensures prioritized load supply while dynamically managing battery charging and discharging to maximize renewable energy utilization.
The proposed unified architecture offers a simplified yet high-performance solution for standalone hybrid renewable systems, improving energy conversion efficiency, system stability, and scalability for remote and off-grid applications.Although validated through detailed simulations, experimental verification will be required to further confirm real-time performance.
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This work is licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
