Application of general regression neural network in fault detection of exhaust temperature sensors on gas turbines

作者：Chen, Jiao^*; Wang, Yong Hong; Weng, Shi Lie

来源：Proceedings of the CSEE, 2009, 29(32): 92-97.

• Engineering applications
• Exhaust temperature
• General regression neural network
• Measured values
• Optimizing design
• Thermal parameters
• Threshold
• Turbine exhaust temperatures

Turbine exhaust temperature is an important thermal parameter in gas turbine power plants and its measured value is affected by sensors state. General regression neural network (GRNN) was used to construct an auto-detection network for turbine exhaust temperature sensors. Optimizing design of network, error controlling and effect testing were studied, and also a method of threshold for sensor detection was advanced. The network is verified by practical data from a power plant and is proved to be with good value of engineering application for sensor state detection of unit.

• 出版日期2009
• 单位上海交通大学