Saeed Azimi - Department of electrical engineering, faculty of engineering University of Isfahan, Isfahan, Iran
Behzad Behzad Mirzaeian dehkordi
Mehdi Niroomand

Many conventional incremental conductance (INC) methods are used for maximum power point tracking (MPPT) of photovoltaic (PV) arrays. In these methods the step sizedetermines the speed of MPPT. Fast tracking can be achieved with bigger increments but the system might not operate exactlyat 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 anadaptive step size in the INC to improve solar array performance. Conventional proportional integral (PI) controlleris 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, theeffectiveness of the proposed method is verified by simulation results at different operating conditions and comparing themwith simulation results of conventional method.

Maximum power point tracking (MPPT), Incremental conductance (INC), Adaptive incremental conductance(AINC), Brain emotional learning based intelligent controller(BELBIC)