Design of a fuzzy current‐sensor less maximum power point tracking algorithm for photovoltaic systems

Abstract

In this study, a fuzzy current‐sensor less maximum power point tracking (MPPT) algorithm is presented. The algorithm proposed to improve the performance and reduce the cost of photovoltaic systems. The proposed algorithm creates the variable step‐size/variable frequency specification in the drift‐free single input voltage sensor (SIVS) method designing a reliable and widely used fuzzy control algorithm. The proposed algorithm will improve the SIVS methods employing the advantages of the fuzzy controllers in the MPPT algorithms based on the real and false error areas analysis concept developed by Tousi et al. (2016). The proposed method will detect exact drift conditions and move to the optimal setpoint at the maximum speed. The method selects the correct direction, appropriate step‐size, and suitable control frequency to increase dynamic efficiency against the drift condition. Also, the steady‐state efficiency of the algorithm will increase because of high‐frequency and low‐power oscillation performances. The proposed method will be simulated and compared in Matlab/Simulink environment and tested in laboratory conditions on the designed solar charge controller.

An improved regression‐based perturb and observation global maximum power point tracker methods

“…The present MPPT methods have limitations, such as difficult implementation and power loss due to rapid duty cycle variations during the tracking. The larger step size of the duty cycle results in more power oscillations, and the small step size affects convergence speed [31]. Most MPPT methods adjust the perturbation step in voltage to overcome the step size drawbacks of P&O.…”