Thermal power plant analysis using artificial neural network

Coal-based thermal power stations are the leaders in electricity generation in India and are highly complex nonlinear systems. The thermal performance data obtained from MAHAGENCO KORADI UNIT 5 thermal power plant shows that heat rate and boiler efficiency is changing constantly and the plant is probably losing some Megawatts of electric power, and more fuel usage thus resulting in much higher carbon footprints. It is very difficult to analyse the raw data recorded weekly during the full power operation of the plant because a thermal power plant is a very complex system with thousands of parameters. Thus there is a need for nonlinear modeling for the power plant performance analysis in order to meet the growing demands of economic and operational requirements. The intention of this paper is to give an overview of using artificial neural network (ANN) techniques in power systems. Here Back Propagation Neural Network (BPNN) and Radial Basis Neural Network (RBNN) are used for comparative purposes to model the thermodynamic process of a coal-fired power plant, based on actual plant data and this works as the internal model for prediction of the Heat Rate and Boiler Efficiency. This ANN model of the thermodynamics of a power plant is used to determine the influence of changes in different variables upon the heat rate and boiler efficiency through the use of sensitivity coefficients, which indicate the directions of change in the variable that will improve heat rate and boiler efficiency, and thus indicates the relative importance of these different variables. This information can be used to provide guidance to the plant operators and engineers as to where they should expend their efforts to improve the heat rate and boiler efficiency. Further variation in these key parameters predicted by sensitivity analysis helps in improvisation of Heat Rate and Boiler Efficiency.