Impact of Light Intensity and Temperature Variations on Photovoltaic Array Output Power with MPPT Algorithm Enhancement

The output power of photovoltaic (PV) arrays is affected by variations in light intensity and temperature, necessitating the widespread application of Maximum Power Point Tracking (MPPT) technology in PV systems. Among all Maximum Power Point (MPP) control strategies, the Perturb and Observe (P&O) MPPT algorithm is extensively used due to its ease of implementation. However, its limitations include energy oscillation losses when the operating point crosses the MPP under steady-state conditions and inferior dynamic performance during abrupt changes in light intensity or temperature. This paper introduces an enhanced variable-step P&O MPPT algorithm that dynamically adjusts the perturbation step size according to the operating point. In comparison to conventional fixed-step methods, the proposed technique effectively improves MPPT tracking speed and power conversion efficiency. Through simulation and experimental analysis, the feasibility of this improved algorithm is verified, with code implementation focusing on real-time duty cycle adjustment and voltage-current sampling routines.

Furthermore, the application of MPPT technology plays a critical role in optimizing the performance of PV systems. Since PV array output power is influenced by light intensity and temperature fluctuations, appropriate control strategies are required to achieve accurate MPP tracking. Currently, the P&O MPPT algorithm remains popular owing to its straightforward implementation structure. Nonetheless, traditional P&O methods cause energy oscillations near the MPP during steady-state operation and exhibit sluggish dynamic response under rapid environmental changes. The algorithm's core function typically involves periodic voltage/current measurements followed by power comparison and directional perturbation, which can be coded using conditional branching and PWM modulation techniques.

To address these issues, this paper proposes an improved variable-step P&O MPPT algorithm. By dynamically modifying the step size, the algorithm enhances both tracking velocity and conversion efficiency. Unlike fixed-step approaches, the proposed method better adapts to variations in light intensity and temperature, achieving more stable power tracking. Simulation and experimental results demonstrate the superiority and practicality of the enhanced algorithm, with key implementation features including adaptive step-size scaling based on power derivatives and hysteresis compensation for noise reduction.

In summary, MPPT technology is vital for PV system efficiency. The improved variable-step P&O MPPT algorithm presented herein reduces energy oscillation losses during steady-state operation and delivers superior dynamic response under sudden light/temperature changes compared to conventional P&O methods. Through further research and practical applications, this approach promises to provide more reliable solutions for PV system performance optimization, potentially incorporating advanced features like fuzzy logic controllers or neural network-based step prediction in future iterations.