A hybrid model of an artificial neural network with thermodynamic model for system diagnosis of electrical power plant gas turbine. (February 2018)

• Record Type: Journal Article
• Authors: Talaat, M.; Gobran, M.H.; Wasfi, M.
• Is Part Of: Engineering applications of artificial intelligence. Volume 68(2017:Aug.)
• Page Start: 222
• Page End: 235
• Publication Date: 2018-02
• Subjects: Gas turbine -- Power plant -- Artificial neural network -- Thermodynamic model -- Fault diagnosis; Engineering -- Data processing -- Periodicals; Artificial intelligence -- Periodicals; Expert systems (Computer science) -- Periodicals; Ingénierie -- Informatique -- Périodiques; Intelligence artificielle -- Périodiques; Systèmes experts (Informatique) -- Périodiques; Artificial intelligence; Engineering -- Data processing; Expert systems (Computer science); Periodicals; 620.00285
• DOI: 10.1016/j.engappai.2017.10.014
• Languages: English
• ISSNs: 0952-1976

Abstract

Abstract: In this paper, the diagnosis system of power plant gas turbine has been developed to detect the deterioration of engine performance. This system can be analyzed the gas path measurement to predict the deterioration of engine main component by using artificial neural network. The deterioration performance data of gas turbine was generated by using the thermodynamic model. So, the artificial neural network model was built to predict the deteriorated characteristics of gas turbine. Thermodynamic model was used to simulate gas turbine performance as well as the deterioration of engine components (compressor, combustion chamber and turbine) which were represented by changing component characteristic parameters (efficiency and flow capacity). On one hand, the probability of these deteriorated components was simulated to generate deteriorated data (measurement parameters and deterioration degree of each component). On the other hand, the neural network was trained with deterioration data and the best structure of neural network (number of hidden layers, number of neurons in hidden layer and transfer function) was selected based on the minimum value of the mean square error. The different deterioration data (testing data) was generated in thermodynamic model to test the effectiveness of the neural network. The comparison between the mean square error value of single and multi-neural network output parameters at training and testing data were achieved. In final, the testing with the real engine data were achieved. Highlights: The deterioration performance data of gas turbine was generated by using the thermodynamic model. The artificial neural network model was built to predict the deteriorated characteristics of gas turbine. Thermodynamic model was used to simulate gas turbine performance as well as the deterioration of engine components. The probability of these deteriorated components was simulated to generate deteriorated data. The neural network was trained with deterioration data and the best structure of neural network.