Maximum power point tracking strategies for solar PV systems: A review of current methods and future innovations

Abstract

Photovoltaic (PV) systems are critical for solar energy conversion but face performance degradation due to dynamic environmental conditions. Maximum power point tracking (MPPT) algorithms optimize PV operation to ensure maximum power extraction under such variability. This review comprehensively classifies and analyzes MPPT techniques into three categories: classical, adaptive, and hybrid methods. Classical approaches including Perturb and Observe (P&O) and Incremental Conductance (IncCond) remain widely adopted for their simplicity and low-cost implementation yet exhibit limitations under rapid environmental transients. Adaptive methods (e.g., Fuzzy Logic Controllers and Artificial Neural Networks) enhance accuracy and adaptability at the cost of computational resources. Hybrid techniques synergize classical and adaptive strategies to balance stability with responsiveness. The study further examines how temperature, irradiance fluctuations, and partial shading impact PV performance and MPPT efficacy. Critical evaluation reveals strengths and limitations of current methods, highlighting opportunities for reliability and efficiency improvements.