Introduction. This article deals with the optimization of the energy conversion of a grid-connected photovoltaic system. The novelty is to develop an intelligent maximum power point tracking technique using artificial neural network algorithms. Purpose. Intelligent maximum power point tracking technique is developed in order to improve the photovoltaic system performances under the variations of the temperature and irradiation. Methods. This work is to calculate and follow the maximum power point for a photovoltaic system operating according to the artificial intelligence mechanism is and the latter is used an adaptive modified perturbation and observation maximum power point tracking algorithm based on function sign to generate an specify duty cycle applied to DC-DC converter, where we use the feed forward artificial neural network type trained by Levenberg-Marquardt backpropagation. Results. The photovoltaic system that we chose to simulate and apply this intelligent technique on it is a stand-alone photovoltaic system. According to the results obtained from simulation of the photovoltaic system using adaptive modified perturbation and observation artificial neural network the efficiency and the quality of the production of energy from photovoltaic is increased. Practical value. The proposed algorithm is validated by a dSPACE DS1104 for different operating conditions. All practice results confirm the effectiveness of our proposed algorithm.

In this paper, we propose an implementation of a new technique of power maximization using a photovoltaic system emulator. The PV system design and its performance evaluation test before installation would be both costly and time-consuming. To overcome this problem the use of an emulator adds more performance and efficiency in the laboratory. Also, by measuring the voltage and current from the PV emulator the characteristic I-V and P-V are extract.The need to consider the measure power state is strongly nonlinear distribution curve with noise. For that reason, to establish and to detect the power value, measurement equations and dynamic equations proposed MPPT control strategy based on Kalman filter algorithm. The correctness and effectiveness of the strategy is verified by simulation and experiment. This algorithm was experimentally implemented. Data acquisition and control system were implemented using dSPACE1103. The results show that the Kalman filter MPPT work accurately and successfully under the change of solar irradiation.

This paper focuses on the efficient control of a photovoltaic device's voltage. Under irradiation variation and constant load, a robust controller is proposed. The second-order sliding mode controller for buck converter based on super twisting algorithm is designed to ensure both the reliability and robustness of the global system. The proposed control strategy's reliability is demonstrated by experimental results using dSpace 1104.

In this work, a modified perturbs and observes (P&O) technique is used in the hybrid power generation system with power management. There are several algorithms for extracting the maximum power point (MPP) provided from the PV generator; P & O algorithm has a good performance, ease of implementation by analog and digital electronics. However, this algorithm has disadvantages because it oscillates at the point of maximum power and has a relatively long convergence time; therefore, a modification is made to the P & O algorithm in order to reduce setup time and oscillations in the MPP. The proposed system allows optimal use of the photovoltaic (PV) system and the DFIG wind proves its efficiency under variable load conditions. The model is implemented in the MATLAB / Simulink platform.