New Approach in Hydropower Plant Control Based on Neural Networks

A new approach to efficient, faster, and intelligent hydropower plant (HPP) control, where constituent equipment is described with highly non-linear mathematical models based on the recommendation from working group of IEEE prime movers, represented in this paper. HPP stability high efficiency are important factors dependent dynamic changes energy system demands starting time because obtained very flexible those system. This paper shown analysed implementation artificial neural network-based controller PID as an auxiliary which helped achieve better behaviour, faster stabilization, operation. The benefits technologies possibilities led improvements control achieved by using MATLAB® – Deep Learning Toolbox whereas simulations prepared Simulink. Artificial Neural Networks (ANN) a technique used systems have advantages getting stable response but complexity structure behind networks (NN), meaning algorithms, number hidden layers, training function, activation function can complicate destabilize process. In paper, focus put mechanical power responses improvement implementing contrary problems that occur them such destabilization minor changes, fitting parameters, learning, processes, layers/neurons, epochs, etc.