An adaptive incremental conductance MPPT based on BELBIC controller in photovoltaic systems

Many conventional incremental conductance (INC) methods are used for maximum power point tracking (MPPT) of photovoltaic (PV) arrays. In these methods the step size determines the speed of MPPT. Fast tracking can be achieved with bigger increments but the system might not operate exactly at the MPP and may oscillate about it instead; so there is a tradeoff between the time needed to reach the MPP and the oscillation error. The main purpose of this paper is to present an adaptive step size in the INC to improve solar array performance. Conventional proportional integral (PI) controller is applied the MPP to the PV output voltage terminals; however, in this paper brain emotional learning based intelligent controller (BELBIC) is used as an adaptive step size in the INC. This controller decrease the oscillation error, so there will be a considerable increase in system accuracy. At the end, the effectiveness of the proposed method is verified by simulation results at different operating conditions and comparing them with simulation results of conventional method.