Neural network based optimum model for cascaded hydro power generating system

The objective of this research is to model a cascaded hydro power generating reservoir system in order to get the maximum usage of the stored hydro potential to generate electricity. In this study, two models have been developed. First model to schedule the generator loads and the second model to, predict the water levels of the ponds. Then, both models have been integrated to dynamically simulate the variation of pond levels and to explore the feasibility of maximizing generation electricity under the given circumstances. In this research a range of historical data available, have been used to investigate and to evaluate the correlation between inputs and outputs. As this is a multi dimensional, non-linear multi input/output (MIMO) system, application of Artificial Neural Network (ANN) technology to model this system is explored by discovering a working mechanism of the system from the examples of past behavior. Then, by coupling the above two neural network models, developed for generator load scheduling and pond water level monitoring, system was dynamically simulated to explore the feasibility of maximum electrical power generation, while keeping the pond water levels stable, within the feasible operating constraints.