Artificial Neural Network based Process History Data Model for Gas Turbine Compressor Systems

Abstract

Gas turbine-based power plants are found to play a vital role in electric power generation and act as spinning reserves for renewable electric power. A robust performance assessment tool is inevitable for a gas turbine system to maintain high operational flexibility, availability, and reliability at different operating conditions. A suitable simulation model of the gas turbine provides detailed information about the system operation under varying ambient and load conditions. This paper illustrates a systematic methodology for process history data-based modelling of a gas turbine compressor system. The ReliefF feature selection method is applied for the proper identification of the parameters influencing the compressor efficiency. Appropriate Artificial Neural Network (ANN) based models are developed for data classification and system modelling of the compressor. The model performance has been validated using actual plant operational data, and the standard deviation of the error in model output was found to be 0.38. A novel approach for suitable integration of data processing methods, machine learning tools and gas turbine domain knowledge has led to the development of a robust compressor model. The model has been utilized for the health assessment of an existing gas turbine compressor, demonstrated through an illustrative case study. The model has been found suitable for parametric analysis of compressor efficiency with operating hours, which is helpful for operational decision-making involving studies on the influence of part-load operation, compressor wash planning, maintenance planning etc.