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Fault diagnosis and online monitoring method of wind turbine main control system based on data-driven

A main control system, data-driven technology, applied in wind turbine monitoring, engines, wind turbines, etc., can solve the problems of complex implementation and cumbersome methods, and achieve the goal of improving power generation efficiency, economic benefits, and maintenance and repair efficiency. Effect

Active Publication Date: 2019-04-23
NORTHEASTERN UNIV LIAONING
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Problems solved by technology

Most of the existing studies use pattern recognition or analytical model methods, which are cumbersome and complicated to implement.

Method used

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  • Fault diagnosis and online monitoring method of wind turbine main control system based on data-driven
  • Fault diagnosis and online monitoring method of wind turbine main control system based on data-driven
  • Fault diagnosis and online monitoring method of wind turbine main control system based on data-driven

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Embodiment approach

[0060] Such as figure 1 As shown, the data-driven wind turbine main control system fault diagnosis and online monitoring method includes PCA modeling of the normal operation data of the wind turbine main control system, fault diagnosis of the wind turbine main control system operation fault data and real-time There are three stages of real-time monitoring of operating data, including the following steps:

[0061] Step 1: When the main control system of the fan is running normally, periodically sample the variables of the main control system of the main control system of the fan, and perform data processing on the sampled data to calculate T 2 Control Limits and SPE Control Limits:

[0062] Step 1-1: When the main control system of the wind turbine is running normally, the data acquisition equipment periodically samples n main control system variables during the operation of the main control system of the wind turbine, and samples m times to obtain the normal sampling matrix X...

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Abstract

The data-driven fault diagnosis and online monitoring method of the fan main control system belongs to the field of fault diagnosis technology; it includes: calculating the control limits of normal data of the fan main control system; calculating the statistics of the fault data of the fan main control system; based on the statistics and control Determine the fault occurrence time T based on the limited relationship, and determine the contribution rate of each main control system variable and the fault variable in the fault data collected at time T; sequentially calculate the signal-to-noise ratio and number of principal elements corresponding to the fault variables in the real-time data of the wind turbine main control system and statistics. If the statistics are within the control limit, the operation process of the fan main control system is normal. Otherwise, the sensor corresponding to the fault variable is used as the fault sensor; the present invention avoids the use of complex mechanism modeling and signal analysis of the fan; it can Multiple faults in the same period are monitored, and the faults that can be discovered are multi-dimensional; the maximum signal-to-noise ratio is used to determine the number of principal elements, so the monitoring sensitivity of faults is improved.

Description

technical field [0001] The invention belongs to the technical field of fault diagnosis, and in particular relates to a data-driven fault diagnosis and online monitoring method for a main control system of a fan. Background technique [0002] As a new energy source, wind power has been paid more and more attention by the government. In the cost of wind power enterprises, maintenance costs and repair costs account for a large proportion. Reasonable and effective monitoring of the operation process of the fan and accurate fault diagnosis of the fan are of great significance to the maintenance and operation of the fan. [0003] In recent years, research on wind turbine monitoring and fault diagnosis has emerged in an endless stream: In "Research on the Condition Monitoring and Fault Diagnosis System of Double-fed Asynchronous Wind Turbines [D], Song Lei, 2015", the faults of wind turbines under variable working conditions have been solved Feature extraction, but a large amount...

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): F03D17/00
CPCF05B2260/80F05B2260/83
Inventor 许美蓉赵磊王良勇崔东亮徐泉
Owner NORTHEASTERN UNIV LIAONING
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