Neural network based inflow forecasting for optimum pond operation of a run-of-river type hydro plant

Abstract

The current practise of pond operation of Upper Kotmale Hydropower Station is studied, where management of the pond is by subjective judgements of the operator. Accurate and reliable inflow forecast makes up an important basis for optimum pond operation connected with effective spillway gate operation. This research proposes a novel technique to forecast inflow to the pond and utilise these forecasts to optimise the operation of the pond. In the first phase of the research, an artificial neural network based Nonlinear Autoregressive eXogenous model, which is a dynamic neural network meant for time series forecasting, is used to develop the real time inflow forecasting system. Cross correlation analysis is used as feature selection for effective selection of the inputs to the Nonlinear Autoregressive eXogenous network. In the second phase, real time inflow forecast for next six hours is used to optimise the pond operation focusing on goals of shorter-term nature, such as maximising power generation, maximising pond storage and minimising spillway discharge. Multiobjective global optimisation using MATLAB “fmincon” algorithm and weighted approach of solving multi-objective problem are utilised to solve the optimisation problem. Trading-off conflicting objectives by this approach proves very effective. This optimisation approach enhances the flexibility of the operator in the decision making process resulting in achievement of efficiency in pond operation. The results show that the Nonlinear Autoregressive eXogenous modelling is an efficient tool for inflow forecasting and MATLAB “fmincon” algorithm can be used effectively to carry out the multi-objective optimisation of run-of-river pond. Simulation studies for the past years show that there exists an opportunity for optimising run-of river ponds for generation using inflow forecast and with the use of the proposed methodology, it enhances the hydropower generation with gains of over 5% which is significant in a plant of this type.