MPPT algorithms for grid-connected solar systems including deep learning approaches

Photovoltaic (PV) systems, which are the most abundant renewable resources, convert solar radiation into electricity through solar cells but cannot consistently operate at the Maximum Power Point (MPP). Therefore, an external controller using Maximum Power Point Tracking (MPPT) is required. The accuracy and efficiency of this control directly influence system performance, and optimised algorithms can significantly improve results. This study presents a comparative analysis of MPPT algorithms based on efficiency, total harmonic distortion (THD), oscillation behaviour, computational complexity, relative power loss, and relative power gain. The MPPT methods include conventional Perturb and Observe (P&O) and Incremental Conductance (INC); meta-heuristic techniques such as Grey Wolf Optimisation (GWO), Fuzzy Logic (FL), and Particle Swarm Optimisation (PSO); and learning based approaches including Artificial Neural Network (ANN), Long Short-Term Memory (LSTM), Bidirectional LSTM (BiLSTM), and Adaptive Neuro-Fuzzy Inference System (ANFIS). Results reveal that GWO, PSO, and learning-based approaches offer the highest performance, offering around 99% efficiency, low oscillations, favourable THD, and rapid decision-making. While P&O and INC reach nearly 98.5% efficiency, their effectiveness is limited by stronger oscillatory behaviour. FL causes the highest THD, and its high computational complexity and reduced efficiency limit suitability under rapidly changing operating conditions.