V94.2 gas turbine identification using neural network

• Published in: 2013 First RSI/ISM International Conference on Robotics and Mechatronics (ICRoM) pp. 523 - 529
• Main Authors: Yari, M., Aliyari Shoorehdeli, Mahdi, Yousefi, I.
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.02.2013
• Subjects: Computational modeling; gas turbine; Heating; linear model; MATLAB; neural network; Neural networks; nonlinear model; system identification; Turbines
• ISBN: 1467358096; 9781467358095
• DOI: 10.1109/ICRoM.2013.6510160

This paper presents the identification of V94.2 gas turbine. This turbine is built by Siemens. It has 162.1 MW nominal power and 50 Hz nominal frequency and is located at Kermanshah power plant, Kermanshah city of Iran. The stored data from turbine include fuel pressure valve angle and IGV 1 angle as inputs and compressor output pressure, compressor output temperature, fuel pressure, turbine output power and turbine output temperature as outputs. To simplify identification process, the system turns into MISO 2 systems to the number of outputs, and then correlation analysis is used to examine the dependence of the outputs to each input and other outputs. For turbine identification, dynamic linear models are estimated and then Feedforward neural network with one hidden layer is trained. The result shows dynamic linear models have poor performance in comparison with Feedforward neural network with one hidden layer. The neural network is able to identify a predictor model with fitness over 96% for outputs of V94.2 gas turbine.