Optimization of Permanent Magnet Synchronous Generator Output Power in Wind Power Plants with ANN Back Propagation

The focus of this research is optimizing a wind power plant using Permanent Magnet Synchronous Generator (PMSG). backpropagation method the artificial neural network system was chosen to optimize output generator. Based on simulation results, algorithm obtains based input variable in form changing speed. results show that best value learning rate = 0.5, error 0.0001, max. epoch= 100000, neuron hidden layer 5. Mean Square Error (MSE) obtained 0.1026 reaching goal at epoch 14845. reverse training regretion reaches 0.99917. optimization are close specified error, which while what 0.0145. generated by speed 10.7 m/s before being optimized back propagation worth 321 watts, 409 watts. difference average target 88 watts compared Artificial Neural Network (ANN).