Hybrid Metaheuristic-Neural Algorithms for Maximum Power Point Tracking in IoT-Monitored Solar Arrays Under Partial Shading and Spectral Irradiance Variability
Keywords:
Maximum Power Point Tracking, Partial Shading, Spectral Irradiance, Hybrid Metaheuristic, Neural Network, IoT Monitoring, Photovoltaic Systems, Grey Wolf Optimizer, GRU NetworkAbstract
The operational efficiency of photovoltaic (PV) systems under non-uniform irradiance conditions remains a critical challenge in renewable energy deployment. Conventional maximum power point tracking (MPPT) algorithms frequently converge to local maxima under partial shading and spectral irradiance fluctuations, resulting in substantial energy yield losses. This study introduces a novel hybrid architecture integrating metaheuristic optimization with adaptive neural networks, deployed within an Internet of Things (IoT)-enabled real-time monitoring framework. The proposed algorithm termed H-MNMPPT (Hybrid Metaheuristic-Neural MPPT) synergistically combines the exploratory robustness of the Enhanced Grey Wolf Optimizer (EGWO) with the predictive adaptability of a gated recurrent unit (GRU)-based neural estimator. Experimental validation was conducted on a 2.4 kW rooftop PV array instrumented with distributed IoT sensors capturing irradiance, temperature, spectral distribution, and module-level current-voltage characteristics. Under dynamically shifting partial shading patterns and spectrally variant irradiance (AM 1.0 to AM 2.5), H-MNMPPT demonstrated a 98.7% tracking efficiency, outperforming Perturb and Observe (P&O) by 21.3%, PSO-based MPPT by 14.1%, and conventional ANN-MPPT by 9.8%. Furthermore, convergence time was reduced by 63% compared to standard GWO, and the system maintained sub-second response latency under IoT-triggered environmental transitions. This architecture establishes a new benchmark for intelligent, resilient MPPT in next-generation smart solar farms.
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