THE ARTIFICIAL INTELLIGENCE TECHNIQUE FOR THE ENERGY GENERATION AND ADMINISTRATION OF THE HYBRID SOLAR/WIND/DIESEL POWER SYSTEM
The use of renewable energy sources plays an irreplaceable role in remote areas where the power grid is not available. Photovoltaic power conversion (PV) and wind power conversion are the main types of renewable energy sources used. Hybrid systems are considered the most efficient solution for remote areas that are not connected to the centralized power grid. Renewable energy is attracting the attention of researchers around the world. The main challenge is to combine the various existing sources into a single model in order to benefit from each of them, while complementing each other's disadvantages. The possibilities of managing combined hybrid systems based on renewable energy sources are currently not thoroughly studied. To increase the generation of electrical energy and reduce losses during the operation of these systems, it is necessary to conduct research aimed at improving the interactions of individual nodes of the proposed generation systems and improving the calculation methods for hybrid power plants. The integrated use of solar and wind generation systems can significantly improve energy performance and increase the generation of electrical energy. This paper proposes a method for integrating a solar photovoltaic system, a wind turbine, and a diesel generator connected to a load. An additional load is also connected to the system to absorb excess power. The hybrid system model was developed in MATLAB / Simulink. A controller based on an adaptive neuro-fuzzy inference system was developed and the system analyzed in terms of energy production and consumption. The results obtained show the degree of increase in the reliability and stability of the system.