Neuro-PID Controller Application for Hydro Power Plant Control

In this paper, the design process of neural network-based controller application to hydropower plant control system is presented in order to improve the dynamic behavior when only a Proportional-Integral-Derivative controller is in use. Through simulation experiments, the proper function of the neuro-PID control technique has been successfully verified. Ensuring better results rather than using only gain scheduling PM control lies in designing a suitable neuro controller. That includes sizing the right number of neurons in each layer, a number of hidden and output layers, fitting and training the network behind the neurocontroller, and data normalization. Activation function type determination in each layer is also an important parameter that depends on the output response of the system. In this paper, MATLAB (R) - Deep Learning Toolbox is used, whereas the simulations are prepared in Simulink. The obtained results show that the optimized turbine model gives a slight improvement in the behavior of the hydropower plant.