The increase in energy sources using fossil fuels is the main source of pollution. Diesel power stations are adapted to remote sites of energy sources. From an economic and technical point of view the integration of a hybrid system is beneficial. In this situation, the combination of hybrid power system (wind-diesel-battery)-based renewable energy is a strategy for the use of three complementary sources that will be suitable for continuity of service, reducing energy costs, longevity of the generator and the elimination of part of the greenhouse gas, this is the reason that motivated us to develop this product that addresses the optimal management of the production of electronic system energy to weather conditions (wind speed, temperature, relief) and the technical and economic analysis to meet our energy requirements to reduce emissions of greenhouse gases.
The climate in Algeria can be considered semiarid in most areas. The water demand is expected to increase because of population growth in the coming years. Water supply is always a great concern for the populations in the landlocked regions. Pumping water using the solar photovoltaic energy is a well-suited solution where the traditional electricity grid is absent. Indeed, the majority of these regions are very sunny, and water is available in underground aquifers. Our system consists on a photovoltaic generator (PVG), a direct current (DC)-DC converters, a multilevel inverter with five levels, and an asynchronous motor coupled to a centrifugal pump. The objective of this process is to ensure the operation at maximum power of the PVG system under various conditions by the maximum power point tracking (MPPT) method based on fuzzy logic control (FLC). The modeling and simulation of this system were carried out and based on our experimental work in the site meteorological database of city Adrar in Algeria. This system avoids the use of a motor pump (diesel) and eliminates costs and operating constraints in an environment friendly manner according to the COP21 recommendation.
Photovoltaic (PVS) generators’ nonlinear electrical characteristics allow for greater performance and efficiency when they are forced to operate at their peak power (MPP). This article suggests an adaptive method for maximizing power point tracking that makes use of artificial neural network (ANN) techniques (MPPT). A step-up converter powered by a separate solar generator is under the control of an ANN controller built on a neural network training database (PVS). The results show that ANN-MPPT has good control performance and is near to the maximum power point of PVS when compared to conventional MPPT methods like perturb and observe and incremental conductance.
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