Adaptive Hybrid MPPT Using Artificial Intelligence for an Autonomous PV System

Abstract

This study compares traditional Maximum Power Point Tracking methods, such as Incremental Conductance and Perturb and Observe, with a novel hybrid strategy incorporating Artificial Neural Networks. The hybrid algorithm synergizes the strengths of Incremental Conductance, Perturb and Observe, and Artificial Neural Networks by dynamically adjusting control parameters using historical data. The main objective is to demonstrate the superior performance of the hybrid approach, highlighting its quick adaptation to changes in solar conditions, improved power quality and tracking accuracy, sustained stability, and a significant boost in power extraction compared to established techniques. Real-time simulations are conducted on representative solar energy systems, evaluating performance indicators under various scenarios, including rapid irradiance fluctuations and transient conditions. The results confirm that the hybrid MPPT approach, empowered by Artificial Neural Networks, outperforms traditional methods across key benchmarks such as response time, power quality, tracking precision, stability, and power extraction and even against stand -alone neural network approach. The ultimate aim is to identify the most effective hybrid MPPT technique based on comprehensive performance assessments.