Summary Currently, the majority of economic activities depend on energy management and water management. We note, in the case of Tunisia, that the quantities of water have changed a little during the last 10 years, and the use of energies renewable energy, such as photovoltaic (PV) energy, has been decisive in curbing the increase in conventional and thus reduce the production of greenhouse gases. Optimum matching of loads to PV generator is most desirable for more accurate sizing. Because of the high PV generator's cost, the system designer is mainly interested in its full utilization by optimum matching of the system components during all operating period. Application of PV power to electromechanical loads requires an understanding of the dynamics of such systems. Our system has a hybrid and complex character and requires a good modeling of the energetic point of view before passing the phase of design of control laws. So, I use the graphical approach of modeling called dedicated bond graph for this type of system, and this tool showed very interesting results in the level of the modeling, dimensioning, monitoring, and diagnostics. The application of bond graph technique for the modeling of PV systems is not yet widespread. On the other hand, in order to ensure optimal operation of the system, to guarantee maximum output energy, and to reduce the adverse effects due to load disturbances, it is necessary to have a robust control of the induction machine against its composition and the existence of the coupling between the magnetic flux and the driving torque on the one hand and on the other hand the use of an estimator for the reconstruction of the flux, which is an almost unmeasurable quantity (requires an expensive and fragile). The purpose of this work is to study the pumping station coupled to a PV generator by a graphical modeling called bond graph, after which I design the laws of robust commutations by fuzzy logic. The design of robust control laws using fuzzy logic in the vector control aims to determine the maximum power point by the optimized P&O algorithm, to control of the mechanical speed of the pump, and to determinate of the reference speed that I get a maximum efficiency of the structure. The results of simulations show the robustness and performance of these fuzzy controllers.
An agricultural greenhouse is a complex and Multi-Input Multi-Output MIMO system in which the internal parameters create a favorable microclimate for agricultural production. Temperature and internal humidity are two parameters that have a major impact on greenhouse yield. The objective of this study was to propose a simulated dynamic model in a MATLAB/Simulink environment for experimental validation. Moreover, a fuzzy controller was designed to manage a greenhouse indoor climate by means of an asynchronous motor for ventilation, heating, humidification, etc. An intelligent system to control these actuators for an optimal inside climate was implemented in the model. The dynamic model was validated by comparing the simulation results to experimental measurements. These results showed the effectiveness of the control strategy in regulating the greenhouse indoor climate. Finally, a photovoltaic generator was modeled, with the aim of reducing the costs of agricultural production. It feeds the asynchronous motor with a vector control optimized by fuzzy logic that drives a variable speed fan.
Climate dependence requires robust control of the photovoltaic system. The current paper is divided in two main sections: the first part is dedicated to compare and evaluate the behaviors of three different maximum power point tracking (MPPT) techniques applied to photovoltaic energy systems, which are: incremental and conductance (IC), perturb and observe (P&O) and fuzzy logic controller (FLC) based on incremental and conductance. A model of a photovoltaic generator and DC/DC buck converter with different MPPT techniques is simulated and compared using Matlab/Simulink software. The comparison results show that the fuzzy controller is more effective in terms of response time, power loss and disturbances around the operating point. IC and P&O methods are effective but sensitive to high-frequency noise, less stable and present more oscillations around the PPM. In the second section, the FPGA platform is used to implement the proposed control. The FLC architecture is implemented on an FPGA Spartan 3E using the ISE Design Suite software. Simulation results showed the effectiveness of the proposed fuzzy logic controller.
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