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Fault detection method and fault diagnosis method for the non-stationary process of large coal-fired generator set

A technology for generating sets and fault detection, which is applied in the testing of machines/structural components, measuring devices, instruments, etc., and can solve problems that have not yet been reported in research reports.

Active Publication Date: 2017-05-17
ZHEJIANG UNIV
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  • Fault detection method and fault diagnosis method for the non-stationary process of large coal-fired generator set
  • Fault detection method and fault diagnosis method for the non-stationary process of large coal-fired generator set
  • Fault detection method and fault diagnosis method for the non-stationary process of large coal-fired generator set

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Embodiment Construction

[0062] The present invention will be described in further detail below in conjunction with the accompanying drawings and specific examples.

[0063] Large-scale coal-fired power generation is a typical non-stationary process, and some of the variables have obvious non-stationary characteristics, such as the circulating water pressure of the condenser, the inlet steam pressure of the high-pressure heater, and the condensed water volume at the inlet of the deaerator. The present invention takes Unit 8 of Jiahua Power Plant as an example. The power of this unit is 10,000MW, which is a large-scale coal-fired power generation process, including 159 process variables, which involve pressure, temperature, water level, flow rate, etc.

[0064] like figure 1 As shown, the present invention is a fault detection and online diagnosis method for the non-stationary process of large-scale coal-fired generating units combined with cointegration analysis and sparse variable selection, includin...

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Abstract

The invention discloses a fault detection method and fault diagnosis method for the non-stationary process of a large coal-fired generator set. The fault detection method and fault diagnosis method for the non-stationary process of a large coal-fired generator set integrate a co-integration analysis method with a sparse variable selection method for fault detection and on-line diagnosis, aiming at the classical non-stationary process of the large coal-fired generator set. The fault detection method and fault diagnosis method for the non-stationary process of a large coal-fired generator set can directly automatically isolate fault variables on line in real time, and do not need any historical fault information at the same time. The fault detection method and fault diagnosis method for the non-stationary process of a large coal-fired generator set effectively solve the problem of difficulty in fault detection and on-line diagnosis during the non-stationary process, thus greatly improving the performance of fault detection and on-line diagnosis during the non-stationary process, being conductive to accurate and quick repair of faults for a field engineer so as to guarantee safety of the large coal-fired generator set and improve the production benefit.

Description

technical field [0001] The invention belongs to the field of statistical monitoring of non-stationary processes, in particular to a fault detection method and a diagnostic method for non-stationary processes of large-scale coal-fired generating sets. Background technique [0002] With the advancement of science and technology and the needs of life, the process of coal-fired power generation has become increasingly complex and large-scale. The environment of the large-scale coal-fired power generation process is complex, and the site has the characteristics of high temperature, high pressure, high noise, etc., and has obvious non-stationary characteristics. In order to ensure the safety of large-scale coal-fired power generation process and improve production efficiency, effective fault detection and fault diagnosis methods must be adopted. Fault detection is to monitor the operation of the process and issue an alarm in time when an abnormality occurs; fault diagnosis acts o...

Claims

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Application Information

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IPC IPC(8): G01M99/00
CPCG01M99/005
Inventor 赵春晖孙鹤
Owner ZHEJIANG UNIV
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