Performance evaluation of MPPT algorithms for PV system under real operating conditions

The efficiency of photovoltaic systems is greatly dependent on operating conditions such as solar irradiance and temperature variations. To maximize power extraction, Maximum Power Point Tracking (MPPT) techniques ensure operation at the most efficient power output. This study compares three MPPT methods: the traditional Perturb and Observe (P&O), Incremental Conductance (INC), and a novel enhanced version proposed in this work, the Improved Incremental Conductance (ImprovedINC) algorithm. Unlike traditional approaches, ImprovedINC introduces moving average filtering, historical data buffers, and adaptive duty cycle adjustment with anti-windup protection to improve noise resilience, tracking precision, and convergence speed. Simulations are performed under varying irradiance profiles using controlled test scenarios and real meteorological data from the NASA POWER database. The analysis focuses on power output stability, tracking accuracy, and responsiveness to environmental fluctuations. Results show that while INC outperforms P&O in terms of stability and speed, the ImprovedINC algorithm demonstrates superior performance, maintaining a high average efficiency of 99.43%, minimal steady-state oscillations, and the fastest dynamic response. This contribution demonstrates the effectiveness of integrating intelligent control enhancements into conventional MPPT frameworks, offering a promising solution for maximizing photovoltaic system performance in real-world applications.