In this paper, two maximum power point tracking (MPPT) algorithms in a photovoltaic electrical energy generation system are analyzed and compared. The Matlab/Simulink is used to establish the model of a photovoltaic system with MPPT function. This system is developed by combining the models of established solar module and DC-DC boost converter with the algorithms of hill climbing (HC) and artificial neural network (ANC), respectively. The system is simulated under different atmospheric conditions and MPPT algorithms. According to the comparisons among the simulation results, it can be concluded that the photovoltaic system with ANN MPPT algorithm is simpler: it does not require knowledge of internal system parameters, needs less calculation, is faster and provides a compact solution for multi-variable problems.

In this paper, two main contributions are presented to manage the power flow between a wind turbine and a solar power system. The first one is to use the fuzzy logic controller as an objective to find the maximum power point tracking, applied to a hybrid wind-solar system, at fixed atmospheric conditions. The second one is to respond to real-time control system constraints and to improve the generating system performance. For this, a hardware implementation of the proposed algorithm is performed using the Xilinx System Generator. The experimental simulation results show that the suggested system presents high accuracy and acceptable execution time performances. The proposed model and its control strategy offer a proper tool for optimizing the hybrid power system performance which we can use in smart house applications.

Abstract. The paper make a comparison among two control methods for maximum power point tracking (MPPT) of a photovoltaic (PV) system under varying irradiation and temperature conditions: the fuzzy control method and the neuro-fuzzy control method. Both techniques have been simulated and analyzed by using Matlab/Simulink software. The power transitions at varying irradiation and temperature conditions are observed and the power tracking time realized by the fuzzy logic controller against the neurofuzzy logic controller has been evaluated.

Abstract:In this paper, two main contributions are presented to manage the power flow between a 11 wind turbine and a solar power system. The first one is to use the fuzzy logic controller as an 12 objective to find the maximum power point tracking, applied to a hybrid wind-solar system, at fixed 13 atmospheric conditions. The second one is to response to real-time control system constraints and

In our day, solar energy and wind energy are becoming more and more used as renewable sources by various countries for different uses such as in an isolated home. These energies admit a unique limitation related to the characteristic of energy instability. For this, the objective of this manuscript is to command and synchronize the power flow of a hybrid system using two sources of energy (solar and wind). The first contribution of our work is the utilization of an artificial neural network controller to command, at fixed atmospheric conditions, the maximum power point. The second contribution is the optimization of the system respecting real-time constraints to increase a generating system performance. As a matter of fact, the proposed system and the controller are modeled using MATLAB/Simulink and a Xilinx System Generator is utilized for hardware implementation. The simulation results, compared with other works in the literature, present high performance, efficiency, and precision. The suggested system and its control strategy give the opportunity of optimizing the hybrid power system performance, which is utilized in rural pumping or other smart house applications.