The use of renewable energy, especially wind power, is the most practical way to mitigate the environmental effects that various countries around the world are suffering from. To meet the growing need for electricity, wind energy is, nevertheless, being used more and more. Researchers have come to understand that a near-perfect output power estimate must be sacrificed. Variations in the weather influence wind energy, including wind speed, surface temperature, and pressure. In this study, the wind turbine output power was estimated using three approaches of artificial neural networks (ANNs). The multilayer feed-forward neural network (MLFFNN), cascaded forward neural network (CFNN), and recurrent neural network (RNN) were employed for estimating the entire output power of wind turbine farms in Egypt. Therefore, each built NN made use of wind speed, surface temperature, and pressure as inputs, while the wind turbine’s output power served as its output. The data of 62 days were gathered from wind turbine farm for the training and efficiency examination techniques of every implemented ANN. The first 50 days’ worth of data were utilized to train the three created NNs, and the last 12 days’ worth of data were employed to assess the efficiency and generalization capacity of the trained NNs. The outcomes showed that the trained NNs were operating successfully and effectively estimated power. When analyzed alongside the other NNs, the RNN produced the best main square error (MSE) of 0.00012638, while the CFNN had the worst MSE of 0.00050805. A comparison between the other relevant research studies and our suggested approach was created. This comparison led us to the conclusion that the recommended method was simpler and had a lower MSE than the others. Additionally, the generalization ability was assessed and validated using the approved methodology.

Recently, wind energy is one of energy sources most have been devoted much awareness as result of shortage of energy. This study shows loads which are formed in the rotating shaft of wind turbine (WT). These loads cause disturbances on the blades, which leads to damage the blades. these loads can be exceeded, and the amount of productive energy can be controlled by using a good control unit that adjusts the angle of the WT blade. For the previous purpose, independent electric motor is used to control blade pitch, this process called pitch control (PC) system. This paper presents a mathematical model of PC system that is controlled by using two different controllers in MATLAB/Simulink; proportional -integral-derivative controller (PID) and proportional -integral-derivative-acceleration (PIDA). In this system three different function are used as reference pitch angle; unit step function, sinewave function and step change signal which are used on control system without any controller and it is found that output system isn’t reached to a reference value for three functions. The steady state error equal to 76% when step function is used, so the controller is needed for a system. When PID and PIDA controllers are used, the time domain specification of output is improved. The steady state error in PID and PIDA controllers is 9% and 0.014% respectively. It is found that the better time domain specification is given by PIDA. PID and PIDA controllers are tested with sinewave and step change, PIDA is also produced the best response.