Many conventional incremental conductance (INC) methods are applied for maximum power point tracking (MPPT) of photovoltaic arrays. Where, the optimization step size determines the speed of MPPT. Fast tracking could be achieved with bigger increments but the system migh More
Many conventional incremental conductance (INC) methods are applied for maximum power point tracking (MPPT) of photovoltaic arrays. Where, the optimization step size determines the speed of MPPT. Fast tracking could be achieved with bigger increments but the system might not operate properly at the MPP and might become oscillated at this point; therefore, there is a trade-off between the time needed to reach the MPP and the oscillation error. This article is to present an adaptive optimization step size in the INC to improve solar array performance. To adjust the MPP in the photovoltaic (PV) operation point, brain emotional learning based intelligent controller (BELBIC) is applied as an adaptive optimization step size in the INC. This would considerably increase the system's accuracy. The effectiveness of this proposed method is verified by comparing its simulation and experimental results with the conventional methods in different operating conditions.
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