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 PID 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 neuro-controller, 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® - 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.