Startup optimization of a combined cycle power plant based on cooperative fuzzy reasoning and a neural network

Abstract

A startup optimization control system for a gas and steam turbine combined cycle power plant is developed. The system can minimize startup time of the plant through cooperative fuzzy reasoning and a neural network autonomously adapting to varying process dynamics due to varying operational conditions, i.e. the ambient temperature and humidity. The operational conditions are taken into consideration by the neural network with a learning mechanism to optimize the schedule. The system is applied to a simulation for a plant with a three pressure staged reheat type 235.7 MW rated capacity, and the following points are seen. (1) The system can harmonize machines operations making good use of the operational margins, i.e. machine thermal stress and NO/sub x/ emission. (2) Startup time and energy loss are reduced by 35.6% and 26.3%, respectively, compared with the conventional off-line startup scheduling method.