Heat recovery steam generators (HRSGs) are at present largely utilized in energy production power plants, especially in combined cycles that are characterized by high efficiency and flexible behaviour in the deregulated electricity market of today. Due to their critical importance, HRSGs operation is continuously controlled by the installation of several sensors in strategic positions, both in the water/steam and in the exhaust gas streams, to measure temperatures, pressures, flow rates and other quantities. The sensors working must be strictly monitored to provide to the operators a correct description of the plant status in each moment. This work describes the development and the testing of a diagnostic system able to individuate and isolate single faults (i.e. faults affecting only one sensor at a time) of different entity occurring to plant sensors. The applied failure detection methodology is based on the analytical redundancy technique, with the use of two black box model types: Artificial Neural Network and Fuzzy Logic. In order to obtain a reliable fault identification of the monitored sensors, two failure detection lines have been put in parallel, one for each of the two above mentioned model types. The failure event is declared as both of the detection lines perceive the malfunction. The training procedure, necessary for the correct operation of the black box models, has been carried out by the data coming from a dynamic simulator of the heat recovery steam generator. As described in the present paper, this simulator has been implemented in the Matlab/Simulink environment and permits to estimate, both in steady-state and in transient conditions, the most important working parameters of both exhaust gas and water in the HRSG. It is important to remark that the diagnostic system, implemented in the Matlab/Simulink environment, has proved robust capacity to detect the sensor failure also in the most difficult transient conditions.

A Fault Detection Method for the Sensors Installed on a One Pressure Level Heat Recovery Steam Generator

BRACCO, STEFANO;CROSA, GIAMPAOLO;DAMIANI, LORENZO
2011

Abstract

Heat recovery steam generators (HRSGs) are at present largely utilized in energy production power plants, especially in combined cycles that are characterized by high efficiency and flexible behaviour in the deregulated electricity market of today. Due to their critical importance, HRSGs operation is continuously controlled by the installation of several sensors in strategic positions, both in the water/steam and in the exhaust gas streams, to measure temperatures, pressures, flow rates and other quantities. The sensors working must be strictly monitored to provide to the operators a correct description of the plant status in each moment. This work describes the development and the testing of a diagnostic system able to individuate and isolate single faults (i.e. faults affecting only one sensor at a time) of different entity occurring to plant sensors. The applied failure detection methodology is based on the analytical redundancy technique, with the use of two black box model types: Artificial Neural Network and Fuzzy Logic. In order to obtain a reliable fault identification of the monitored sensors, two failure detection lines have been put in parallel, one for each of the two above mentioned model types. The failure event is declared as both of the detection lines perceive the malfunction. The training procedure, necessary for the correct operation of the black box models, has been carried out by the data coming from a dynamic simulator of the heat recovery steam generator. As described in the present paper, this simulator has been implemented in the Matlab/Simulink environment and permits to estimate, both in steady-state and in transient conditions, the most important working parameters of both exhaust gas and water in the HRSG. It is important to remark that the diagnostic system, implemented in the Matlab/Simulink environment, has proved robust capacity to detect the sensor failure also in the most difficult transient conditions.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11567/631976
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