Maximum Power Point Tracking Control for Photovoltaic System Using Adaptive Neuro- Fuzzy "ANFIS"

International audienceDue to scarcity of fossil fuel and increasing demand of power supply, we are forced to utilize the renewable energy resources. Considering easy availability and vast potential, world has turned to solar photovoltaic energy to meet out its ever increasing energy demand. The mathematical modeling and simulation of the photovoltaic system is implemented in the MAT LAB/Simulink environment and the same thing is tested and validated using Artificial Intelligent (Al) Iike AN FIS. This paper presents Maximum Power Point Tracking Control for Photovoltaic System Using Adaptive Neuro- Fuzzy "ANFIS". The PV array has an optimum operating point to generate maximum power at some particular point called maximum power point (MPP). To track this maximum power point and to draw maximum power from PV arrays, MPPT controller is required in a stand-alone PV system. Due to the nonlinearity in the output characteristics of PV array, it is very much essential to track the MPPT of the PV array for varying maximum power point due to the insolation variation. In order to track the MPPT conventional controller like Adaptive Neuro-Fuzzy "ANFIS" and fuzzy logic controller is proposed and simulated. The output of the controller, pulse generated from PWM can switch MOSFET to change the duty cycle of boost DC-DC converter. The result reveals that the maximum power point is tracked satisfactorily for varying insolation condition

Fuzzy super twisting algorithm dual direct torque control of doubly fed induction machine

This paper proposes the fundamental aspects of hybrid nonlinear control which is composed of the super twisting algorithm (STA) based second order sliding mode control applying fuzzy logic method (FSOSMC), with pertinent simulation results for a doubly fed induction machine (DFIM) drive. To minimize chattering effect phenomenon due to Signum function employed in sliding mode algorithm, a new method is proposed. This technique consists in replacing the signum function by fuzzy switching function in the SOSMC to minimize flux and torque ripples. This FSOSMC is associated to the double direct torque control DDTC of the doubly fed induction machine (DFIM) by combining the advantages of fuzzy logic (FL) and the advantages of super-twisting sliding mode. The FSOSMC-DDTC strategy is compared with a PI-DDTC and SOSMC-DDTC. Simulation results demonstrate good efficiency and excellent robustness of the hybrid nonlinear controller

Experimental control of photovoltaic system using neuro - Kalman filter maximum power point tracking (MPPT) technique

International audienceThis paper proposes a new maximum power point tracking (MPPT) technique of photovoltaic system based on Kalman filter (KF) and associate to Artificial Neural Networks (ANN). The design process of photovoltaic (PV) modules can be greatly enhanced by using advanced and accurate models. Furthermore, the use of a neural model especially for accuracy improvement of the electrical equivalent circuit parameters, where the analytic equation of the model cannot be easily expressed, because the relationship between parameters is nonlinear. The proposed neural network is trained once by using some measured I-V and P-V curves and to keep in account the change of all the parameters at different operating conditions. For that reason, to get the fast tracking performance on this noisy conditions, and to maximize the power of photovoltaic system a KF method have been used. The performance analysis of perturb and observe (P&O) and KF MPPT techniques has been simulated in MATLAB/Simulink software and their model and control schemes has been analyzed and validated