An adaptive robust fuzzy PI controller for maximum power point tracking of photovoltaic system

The transduction of solar energy to electrical energy requires Solar Photovoltaic (SPV) systems, which must be operated at Maximum Power Point (MPP) to extract maximum possible power. Being dependent on environmental factors such as irradiation and temperature, the MPP and, therefore, the SPV system's performance is nonlinear. A Maximum Power Point Tracking (MPPT) controller is usually employed, which guides the SPV systems to work at MPP. For this task, in this paper, an Adaptive Robust Fuzzy Proportional-Integral (ARFPI) controller for MPPT of an SPV system is proposed. The proposed ARFPI controller parameters have been tuned using Particle Swarm Optimization by minimizing an equal-weighted combination of Integral of the Time-Weighted Absolute Error (ITAE) and Integral of the Absolute Error (IAE). In this combination, ITAE penalized long-term errors offering a faster response, while IAE penalized aggregate errors offering lower ripples. The MPPT performance of the proposed controller has been assessed using undershoot and steady-state ripples for several varying irradiance and temperature profiles with real-world data. Further, to assess its relative performance, it has also been benchmarked against traditional MPPT techniques, i.e., perturb and observe, incremental conductance, and PID controller. The presented investigations revealed clear superiority (reduced ripples and under-shoot) of ARFPI controller, and therefore it is concluded to be a potential MPPT controller for the SPV system.