Optimization of the Initial Steam Pressure for Supercritical Unit Based on ANN and PSO Algorithms

The initial steam pressure is one of the most important parameters affecting the heat rate of supercritical steam turbine. In this paper an approach for the optimization of the initial steam pressure is proposed. Firstly, the real-time data sets acquiring from process control system are processed as training data sets. Then, the characteristic functions for the relationship of unit load, initial steam pressure and heat rate can be developed by an artificial neural network algorithm. Based on these trained functions, an improved particle swarm optimization algorithm is introduced to calculate the optimal initial steam pressure. The proposed approach is applied in a 600MW unit to optimize the initial steam pressures at different unit loads. The results reveal the good performance of proposed approach and algorithms.