Using artificial neural networks and sliding mode control, the performance of hybrid renewable energy sources connected to the grid is improved

Electrical engineering

In hybrid renewable energy source (HRES) systems, the primary goal of this research is to evaluate and analyse three different types of controllers for three-phase DC-AC inverters. To do this, two contemporary controllers based on artificial neural network and sliding mode control (SMC) methodologies are designed and compared. Among the HRESs are solar (PV), step-up transformers connecting transmission lines, battery storage systems and wind turbines to infinite bus bars. Both voltage control and current regulation are used by the developed controllers at the inverter side. To provide a voltage demand at the point of common coupling, a DC–DC boost converter is used (PCC). Next, a presentation of the HRES formulation using the created controllers follows. It is thought that the created controllers will function under a range of solar radiation, temperature, and wind speed loading scenarios. To confirm the effectiveness of the constructed controllers, MATLAB/Simulink is used to simulate the HRESs with the controllers. The acquired outcomes show that adaptive SMC additionally. When compared to traditional PI control, artificial neural network (ANN) control techniques yield superior outcomes in terms of input power, output power, current, and voltage

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