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In this paper, an Artificial Neural Network (ANN) MPPT controller has been proposed. The data required to generate the ANN model are obtained from the principle of Perturbation and Observation (P&O) method. The neural network MPPT controller is developed in two modes: the offline mode required for testing different set of neural network parameters to find the optimal neural network controller (structure, activation function, and training algorithm) and the online mode which the optimal ANN MPPT controller is used in PV system. The inputs variables for ANN are the output power derivate (dP) and voltage derivate (dV) corresponding to a given insolation and operating cell temperature conditions, which they have significant influence on the ANN response ; the output variable of ANN is the corresponding normalized increasing or decreasing duty cycle (+1 or -1). The proposed neural network MPPT is tested and validated using Matlab/Simulink model for different atmospheric conditions. Results and analysis are presented, many contribution have been demonstrated (response time, MPPT tracking, Overshoot).

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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. KEYWORDS fixed step size, MPPT, particle swarm optimization, photovoltaic system, sliding mode controller, variable step size

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