Development of optimal reduced‐order model for gas turbine power plants using particle swarm optimization technique

Abstract

Summary Analysis of higher‐order gas turbine plant in real time would be tedious and expensive. In order to overcome this complexity, reduced‐order model for 5001M heavy‐duty gas turbine rated 18.2 MW has been obtained by Routh approximation, clustering technique, modified pole clustering, eigen permutation, Mihailov criterion, and Padé approximation algorithms. The step responses are obtained using MATLAB/Simulink and compared based on time domain specifications and performance index criteria. It indicates that the mixed method, namely, Routh approximation–Padé approximation algorithm–based reduced‐order model, retains the original characteristics. Further, particle swarm optimization (PSO) algorithm has also been applied to develop an optimal reduced‐order model. Based on the dynamic response against the load disturbance and set point variations, PSO‐based reduced‐order model has been identified as an optimal reduced‐order model for heavy‐duty gas turbine. The reduced‐order model proposed in this paper will be suitable for analyzing the dynamic behavior of heavy‐duty gas turbine plants in real‐time environment.