Artificial Neural Network based Hydro Electric Generation Modelling

Hydropower generation is a function of discharge of the generating units and the difference between the fore bay and tailrace levels of the reservoir, and is subject to penstock head losses and to the generation unit efficiency factor, which in turn is a function of the reservoir level and reservoir capacity. For this actual plant data from Sewa Hydroelectric Project StageII which is a runofthe river project having installed capacity of 120 MW situated in Jammu region has been used.In this paper two hydro electric models such as reservoir level versus capacity model and head loss versus water discharge rate have been studied which are used for calculating net head and further can be used for online implementation of Availability Based Tariff in which day ahead scheduling is done. In order to accomplish this task, data from plant is employed for training, validating and testing the artificial neural network (ANN) model which provide accurate results. Thereafter with these models, a comparison is made with multiple regression models in terms of their prediction accuracy, mean square error and regression R value.