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Photovoltaic panels are semi-conductor structures that can convert solar energy directly to direct current (DC) electricity power depending on the solar irradiance and ambient temperature. They have a non-linear current-voltage (I-V) characteristic due to their structure. The maximum power obtained from a photovoltaic panel is directly related to solar irradiance and panel temperature. Photovoltaic panels offers a single maximum power point under certain atmospheric conditions (solar irradiance and ambient temperature). Therefore, it is of vital importance to use photovoltaic panels along with maximum power point tracking systems for a more efficient solar power system. Since constantly changing atmospheric conditions (solar irradiance and temperature) and variable load cause changes in maximum power point, the maximum amount of power generated by a photovoltaic panel needs to be tracked continuously. There are various tracking methods such as classical and modern methods for maximum power point tracking. Perturb and observe (P&O), hill climbing (HC), incremental conductance (INC) are among conventional methods, while modern methods include artificial neural networks, fuzzy logic and optimization algorithms. Modern control structures such as fuzzy logic and artificial neural networks are widely used to perform many applications. Using fuzzy logic and artificial neural networks together creates an adaptive control structure, which adjusts changes in the operating point of the control system in a better way. In the present study, a two-loop tracking system consisting of incremental conductance and neuro-fuzzy control is proposed for maximum power point tracking of a photovoltaic system under variable load and solar irradiance. The dynamic performance of the proposed two-loop tracking system was compared with the single-loop incremental conductance method under variable load and solar irradiance. It was observed that the proposed tracking structure better adapted to the changes in the maximum power operating point of the photovoltaic system compared to the incremental conductance method in both cases.

Maximum Power Point Tracking with Neuro-Fuzzy Controller Under Variable Load and Solar Irradiance in Photovoltaic Systems