Enhancement of microgrid dynamic responses under fault conditions using artificial neural network for fast changes of photovoltaic radiation and FLC for wind turbine

Abstract

Microgrid is a low voltage electrical network with distributed generations, energy storage devices and controllable loads. This paper utilizes artificial neural network (ANN) to predict the optimum voltages in order to extract the maximum power and increment the efficiency of photovoltaic system. In this regard, the optimum voltages are achieved by the genetic algorithm (GA). Then these optimum values are used in ANN method. The results of ANN-GA is compared with the other methods that verified the proposed method with high accuracy which can track the maximum power point (MPP) under different insolation and temperature circumstances and also, meet the load demand with less fluctuation around the MPP.; also it can increase the convergence speed to achieve the MPP. As well as, the evaluation of fuzzy logic controller (FLC) in comparison with the PI controller in pitch angle of wind turbine (WT) is carried out. In order to control the output power of wind turbine, by implementing the wind speed and active power as inputs of FLC, it has faster responses, smoother power curves, less oscillation than aforementioned methods which lead to improve the dynamic responses of WT. The models are developed and applied in the Matlab/Simulink program.