Regular Papers
International Journal of Control, Automation and Systems 2022; 20(8): 2759-2767
Published online July 12, 2022
https://doi.org/10.1007/s12555-021-0061-9
© The International Journal of Control, Automation, and Systems
Fault Detection Based on Graph Model for Dead Zone of Steam Turbine Control Valve
The abnormal control valve dead zone in the steam turbine causes system oscillation severely, affecting the stable power generation of the power plant. In this study, a graph model is developed, and it can not only detect multiple faults at a time but also reduce the dependence on statistics. The graph topology of the steam turbine is built, including the process nodes and fault node indicating process variables and valve dead zone. Moreover, the graph convolution operation is conducted to classify the faulty and normal conditions. As a result, the simulation and experimental examples demonstrate that the accuracy of dead zone detection reaches 90% and 89% respectively, surpassing Multilayer Perceptron and Principal Component Analysis. The high-precision dead zone detection rate ensures that the method can be effectively applied to industry and improve economic benefits and operation safety.
Keywords Control valve dead zone, graph convolution, graph model, hidden fault node, steam turbine.
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Regular Papers
International Journal of Control, Automation and Systems 2022; 20(8): 2759-2767
Published online August 1, 2022 https://doi.org/10.1007/s12555-021-0061-9
Copyright © The International Journal of Control, Automation, and Systems.
Fault Detection Based on Graph Model for Dead Zone of Steam Turbine Control Valve
Yi-Jing Zhang, Ye-Yuan, and Li-Sheng Hu*
Shanghai Jiao Tong University
Abstract
The abnormal control valve dead zone in the steam turbine causes system oscillation severely, affecting the stable power generation of the power plant. In this study, a graph model is developed, and it can not only detect multiple faults at a time but also reduce the dependence on statistics. The graph topology of the steam turbine is built, including the process nodes and fault node indicating process variables and valve dead zone. Moreover, the graph convolution operation is conducted to classify the faulty and normal conditions. As a result, the simulation and experimental examples demonstrate that the accuracy of dead zone detection reaches 90% and 89% respectively, surpassing Multilayer Perceptron and Principal Component Analysis. The high-precision dead zone detection rate ensures that the method can be effectively applied to industry and improve economic benefits and operation safety.
Keywords: Control valve dead zone, graph convolution, graph model, hidden fault node, steam turbine.
Vol. 23, No. 3, pp. 683~972
