PSO-based SMC variable step size P&O MPPT controller for PV systems under fast changing atmospheric conditions

This paper proposes an innovative technique for designing sliding mode maximum power point tracking (MPPT) controller for photovoltaic (PV) systems under fast changing atmospheric conditions. The particle swarm optimization (PSO) algorithm is used to find the optimal sliding mode controller (SMC) gains used to drive the variable step of the conventional perturb and observe (P&O) algorithm. The system operates in two modes: offline mode required for testing different set of SMC gains leading to optimum values; and online mode where the SMC optimum gains were used to drive the variable step of the P&O MPPT. The effectiveness of the proposed MPPT has been studied successfully using a Solarex MSX-60 module connected to a boost DC-DC converter powering a resistive load. Comparison study of the proposed MPPT with the classical fixed step P&O is presented showing a good efficiency and improvements of the proposed algorithm in transient, steady-state, and dynamic responses, especially under fast changing atmospheric conditions. Besides using PSO to tune the SMC parameters, our main contribution consists of improving the performance of proposed algorithm to track effectively the maximum power point (MPP) with low oscillation, low ripple, low overshoot, and good rapidity in slow and fast changing atmospheric conditions compared with conventional P&O.