LU505189B1 - Method, Device and Equipment for Monitoring the Connection State of Blade Flanges of Wind Turbine Generator System - Google Patents

Method, Device and Equipment for Monitoring the Connection State of Blade Flanges of Wind Turbine Generator System Download PDF

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Publication number
LU505189B1
LU505189B1 LU505189A LU505189A LU505189B1 LU 505189 B1 LU505189 B1 LU 505189B1 LU 505189 A LU505189 A LU 505189A LU 505189 A LU505189 A LU 505189A LU 505189 B1 LU505189 B1 LU 505189B1
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LU
Luxembourg
Prior art keywords
blade
data
real
flange
time
Prior art date
Application number
LU505189A
Other languages
French (fr)
Inventor
Xin Xiao
Weiqiang He
Wenkang Zhang
Zikuan Zhang
Hairui Shi
Zhongke Huang
Gui Shi
Songsheng Yang
Rudong Li
Biao Feng
Xi Yang
Ruilin Ma
Qibin Fang
Xin Chen
Pengfei Dong
Xiaoxuan Zhang
Feng Yang
Original Assignee
Huaneng Dali Wind Power Generation Co Ltd Xiangyun Branch
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
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Publication date
Application filed by Huaneng Dali Wind Power Generation Co Ltd Xiangyun Branch filed Critical Huaneng Dali Wind Power Generation Co Ltd Xiangyun Branch
Priority to LU505189A priority Critical patent/LU505189B1/en
Application granted granted Critical
Publication of LU505189B1 publication Critical patent/LU505189B1/en

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    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B23/00—Testing or monitoring of control systems or parts thereof
    • G05B23/02—Electric testing or monitoring
    • G05B23/0205—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults
    • G05B23/0259—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults characterized by the response to fault detection
    • G05B23/0275—Fault isolation and identification, e.g. classify fault; estimate cause or root of failure
    • G05B23/0281—Quantitative, e.g. mathematical distance; Clustering; Neural networks; Statistical analysis
    • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F03—MACHINES OR ENGINES FOR LIQUIDS; WIND, SPRING, OR WEIGHT MOTORS; PRODUCING MECHANICAL POWER OR A REACTIVE PROPULSIVE THRUST, NOT OTHERWISE PROVIDED FOR
    • F03D—WIND MOTORS
    • F03D1/00—Wind motors with rotation axis substantially parallel to the air flow entering the rotor 
    • F03D1/06—Rotors
    • F03D1/065—Rotors characterised by their construction elements
    • F03D1/0658—Arrangements for fixing wind-engaging parts to a hub
    • F03D1/066—Connection means therefor, e.g. bushings or adapters
    • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F03—MACHINES OR ENGINES FOR LIQUIDS; WIND, SPRING, OR WEIGHT MOTORS; PRODUCING MECHANICAL POWER OR A REACTIVE PROPULSIVE THRUST, NOT OTHERWISE PROVIDED FOR
    • F03D—WIND MOTORS
    • F03D17/00—Monitoring or testing of wind motors, e.g. diagnostics
    • F03D17/027—Monitoring or testing of wind motors, e.g. diagnostics characterised by the component being monitored or tested
    • F03D17/028—Blades
    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B23/00—Testing or monitoring of control systems or parts thereof
    • G05B23/02—Electric testing or monitoring
    • G05B23/0205—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults
    • G05B23/0218—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults characterised by the fault detection method dealing with either existing or incipient faults
    • G05B23/0224—Process history based detection method, e.g. whereby history implies the availability of large amounts of data
    • G05B23/024—Quantitative history assessment, e.g. mathematical relationships between available data; Functions therefor; Principal component analysis [PCA]; Partial least square [PLS]; Statistical classifiers, e.g. Bayesian networks, linear regression or correlation analysis; Neural networks

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  • Engineering & Computer Science (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Sustainable Development (AREA)
  • Sustainable Energy (AREA)
  • Chemical & Material Sciences (AREA)
  • Combustion & Propulsion (AREA)
  • Mechanical Engineering (AREA)
  • General Engineering & Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Artificial Intelligence (AREA)
  • Evolutionary Computation (AREA)
  • Algebra (AREA)
  • Mathematical Analysis (AREA)
  • Mathematical Optimization (AREA)
  • Mathematical Physics (AREA)
  • Probability & Statistics with Applications (AREA)
  • Pure & Applied Mathematics (AREA)
  • Automation & Control Theory (AREA)
  • Wind Motors (AREA)

Abstract

The invention provides a method, a device and equipment for monitoring the connection state of blade flanges of wind turbine generator system, and the method comprises the following steps: collecting real-time connection gap data and real-time waveform data of each blade flange of wind turbine generator system; comparing the real-time connection gap data and real-time waveform data of the blade flange of each installation position with the standard connection gap data and standard waveform data; the blade flange is judged to be faulty if the comparison result is that the difference between the real-time gap data and the standard gap data is greater than the gap data threshold. By collecting the data of blade flange to conduct fault judgement, the sensor data are small and the cost is low.

Description

METHOD, DEVICE AND EQUIPMENT FOR MONITORING THE CONNECTION 0505188
STATE OF BLADE FLANGES OF WIND TURBINE GENERATOR SYSTEM
TECHNICAL FIELD
The invention relates to the technical field of wind power safety, in particular to a method, a device and equipment for monitoring the connection state of blade flanges of wind turbine generator system.
BACKGROUND
Blades are the key parts of wind turbine generator system. During the rotation of the blades, when the blade tips rotate from top to bottom, the force changes alternately and the wind condition 1s unstable so that the wind turbine generator system vibrates and the connecting parts loosen to cause the fracture failure of connecting bolts and the fatigue failure of blade flange connection, thus greatly increasing the probability of blade fault.
At present, the monitoring technologies for flange connection state include ultrasonic wave, magnetic rotation angle, etc, and a corresponding monitoring sensor needs to be arranged for each bolt to achieve the monitoring purpose, so that more sensors need to be arranged to lead to higher cost.
SUMMARY
The invention provides a method, a device and equipment for monitoring the connection state of blade flanges of wind turbine generator system, so as to solve the problem that the fault detection cost of blade flange of wind turbine generator system is high in the prior art.
The invention provides a method for monitoring the connection state of blade flanges of wind turbine generator system, and the method comprises the following steps: collecting real-time connection gap data and real-time waveform data of each blade flange of wind turbine generator system, wherein the real-time waveform data is formed by the connection gap data within a preset time period; determining the installation position of each blade flange respectively; comparing the real-time connection gap data and real-time waveform data of the blade flange of each installation position with the standard connection gap data and standard waveform data; determining that the blade flange is faulty if the comparison result is that the difference between the real-time gap data and the standard gap data is greater than the gap data threshold 02189 and the difference between the real-time waveform data and the standard waveform data is greater than the waveform data threshold.
The invention provides a device for monitoring the connection state of blade flanges of wind turbine generator system, and the device comprises: an collection module, which is used for collecting real-time connection gap data and real-time waveform data of each blade flange of wind turbine generator system, wherein the real-time waveform data is formed by the connection gap data within a preset time period; a first determination module, which is used for determining the installation position of each blade flange respectively; a comparison module, which is used for comparing the real-time connection gap data and real-time waveform data of the blade flange of each installation position with the standard connection gap data and standard waveform data; a second determination module, which is used for determining that the blade flange is faulty if the comparison result is that the difference between the real-time gap data and the standard gap data is greater than the gap data threshold, and the difference between the real-time waveform data and the standard waveform data is greater than the waveform data threshold.
The invention also provides an electronic equipment, wherein the electronic equipment comprises a memory, a processor and a computer program stored in the memory and can be run on the processor, and when the processor executes the program, any one of the above methods for monitoring the connection state of blade flanges of wind turbine generator system can be realized.
The invention provides a method, a device and equipment for monitoring the connection state of blade flanges of wind turbine generator system, and the method comprises the following steps: collecting real-time connection gap data and real-time waveform data of each blade flange of wind turbine generator system, wherein the real-time waveform data is formed by the connection gap data within a preset time period; determining the installation position of each blade flange respectively; comparing the real-time connection gap data and real-time waveform data of the blade flange of each installation position with the standard connection gap data and standard waveform data; determining that the blade flange is faulty if the comparison result is that the difference between the real-time gap data and the standard gap data is greater than the 905189 gap data threshold, and the difference between the real-time waveform data and the standard waveform data is greater than the waveform data threshold. According to the technical scheme of the invention, by collecting the data of blade flange to judge the fault, compared with by collecting the data of each bolt, the sensor data of the former are small and the cost is low.
BRIEF DESCRIPTION OF THE DRAWINGS
In order to explain the technical scheme of the invention or the prior art more clearly, the drawings needed in the description of the examples or the prior art will be briefly introduced below. Obviously, the drawings in the following description are some examples of the invention, and other drawings can be obtained according to these drawings without creative labor for the skilled in the art.
FIG.1 is a flowchart of the method for monitoring the connection state of blade flanges of wind turbine generator system in an example of the invention;
FIG.2 is a schematic structural diagram of the device for monitoring the connection state of blade flanges of wind turbine generator system in an example of the invention;
FIG.3 is a schematic structural diagram of the electronic equipment in an example of the invention.
DETAILED DESCRIPTION
In order to make the purpose, technical schemes and advantages of the examples of the invention more clear, the technical schemes in the examples of the invention will be described clearly and completely with the attached drawings. Obviously, the described examples are only some examples of the invention, but not the whole examples. Based on the examples in the invention, all other examples obtained by those skilled in the art without creative efforts belong to the protection scope of the invention.
FIG.1 is a flowchart of the method for monitoring the connection state of blade flanges of wind turbine generator system in an example of the invention;
As shown in FIG.1, a method for monitoring the connection state of blade flanges of wind turbine generator system provided by an example of the invention can be executed by a server, and it mainly includes the following steps: 101. collecting real-time connection gap data and real-time waveform data of each blade flange of wind turbine generator system, wherein the real-time waveform data is formed by the 905769 connection gap data within a preset time period;
In a specific implementation process, the real-time connection gap data and real-time waveform data of each blade flange are collected by sensors and other collection equipment, wherein the connection gap data refers to the distance between the two sides of the flange and the connector, and the real-time waveform data refers to the waveform formed by the connection gap data within a preset time.
According to the structural characteristics of fan blade flange installation, four sensors and other components can be arranged at an average interval of 90° along the flange surface to better realize data collection. Fan blades are rotating parts, so anti-dropping measures should be considered in sensor installation, and lightning protection modules should be installed in sensor design in order to prevent lightning strike.
The vibration signal generated during blade operation is sensitive to the real-time operation state of the equipment, so the signal characteristics have a strong corresponding relationship with the fault state of the parts in the equipment. Therefore, it is necessary to determine the nonlinearity, operating conditions and background noise of wind turbine generator system during operation; a mapping relationship between signal space and feature space is built based on the nonlinearity, operating conditions and background noise, and the vibration data of each blade flange of wind turbine generator system is collected by a magnetic induction sensor; the vibration data are converted into real-time connection gap data and real-time waveform data based on the mapping relationship between the signal space and the feature space. As a result, the finally collected real-time connection gap data and real-time waveform data are ensured to be more accurate and reliable. 102. determining the installation position of each blade flange respectively.
According to the different positions of the blade flanges, the connection gap data and waveform data of flange are different, so it is necessary to accurately determine the installation position of each blade flange, specifically, label the blade flanges at different positions, and label the corresponding sensor at the installation position at the same time, the flange data at which installation position can be directly determined according to the feedback data of the sensor, in this way, it is more convenient for monitoring the state of different blade flanges and can also ensure that the final analysis results of flange faults at different installation positions are more 905189 accurate. 103. comparing the real-time connection gap data and real-time waveform data of the blade flange of each installation position with the standard connection gap data and standard waveform 5 data.
The standard connection gap data and standard waveform data are obtained by analyzing and processing the data collected in advance during the normal operation of the fan. Therefore, comparing the real-time connection gap data and real-time waveform data with the standard connection gap data and standard waveform data can better determine whether the current data are abnormal according to the difference between the comparison results. 104. determining that the blade flange is faulty if the comparison result is that the difference between the real-time gap data and the standard gap data is greater than the gap data threshold, and the difference between the real-time waveform data and the standard waveform data is greater than the waveform data threshold.
If the comparison result is that the difference between the real-time gap data and the standard gap data is greater than the gap data threshold, and the difference between the real-time waveform data and the standard waveform data is greater than the waveform data threshold, then the flange connection data at this time are judged to be faulty. Only when both the real-time connection gap data and the real-time waveform data are abnormal can the blade flange be judged to be faulty.
Further, in the example, after “determining that the blade flange is faulty”, the following shall also be carried out: determining a blade flange fault detection model corresponding to the installation position; inputting real-time connection gap data and real-time waveform data of the blade flange corresponding to the installation position to the blade flange fault detection model, and outputting the fault type of the blade flange, wherein the blade flange fault detection model is pre-trained and constructed based on the connection gap data sample, the waveform data sample and the blade flange fault type sample.
Wherein, “inputting real-time connection gap data and real-time waveform data of the blade flange corresponding to the installation position to the blade flange fault detection model, and outputting the fault type of the blade flange” comprises: inputting real-time connection gap data and real-time waveform data of the blade flange corresponding to the installation position fo 905189 a primary neural network, and determining fault types of the blade flange, wherein the fault types include bolt faults and flange faults; identifying the bolt faults and the flange faults through a secondary neural network, and determining the types of the bolt faults and the flange faults, wherein the types of the bolt faults include bolt looseness and bolt fracture, and the flange faults include flange deformation and flange wear. Specific identification of fault types by neural network model can better ensure the accuracy and speed of identification.
Further, the example also includes: collecting operation data of three blades in each wind turbine generator system respectively; determining the operation stability of each blade based on a relationship among the operation data of the three blades and an operation data relationship of each blade under different conditions at the same time; calculating a deviation trajectory of fan operation by fitting algorithm based on the operation stability of each blade, and determining the deformation state of flange based on the deviation trajectory. “Determining the operation stability of each blade” comprises: determining the characteristic curve of each blade based on the principle of condition statistics; extracting data of the characteristic curve by using the frequency division vector monitoring algorithm, so as to determine the operation stability of each blade.
By analyzing and treating the blade's own attributes, the deformation state of blade flange is judged according to the specific situation, so that the judgment result is more accurate and reliable.
Further, in the example, after “determining that the blade flange is faulty”, the following shall also be carried out: determining the number of flange faults; sending out an early warning prompt if the number of flange faults is less than a first preset number; sending out a first-level alarm prompt if the number of flange faults is greater than the first preset number and less than a second preset number; sending out a second-level early warning prompt and sending an alarm prompt to the user terminal by short message, mail or telephone if the number of flange faults is greater than the second preset number.
Notifying the user of flange fault in time by alarm can better reduce economic losses and better ensure work safety.
FIG.2 is a schematic structural diagram of the device for monitoring the connection state of blade flanges of wind turbine generator system in an example of the invention. 0505188
The invention also provides a device for monitoring monitoring the connection state of blade flanges of wind turbine generator system, and the device comprises: an collection module 201, which is used for collecting real-time connection gap data and real-time waveform data of each blade flange of wind turbine generator system, wherein the real-time waveform data is formed by the connection gap data within a preset time period; a first determination module 202, which is used for determining the installation position of each blade flange respectively; a comparison module 203, which is used for comparing the real-time connection gap data and real-time waveform data of the blade flange of each installation position with the standard connection gap data and standard waveform data; a second determination module 204, which is used for determining that the blade flange is faulty if the comparison result is that the difference between the real-time gap data and the standard gap data is greater than the gap data threshold, and the difference between the real-time waveform data and the standard waveform data is greater than the waveform data threshold.
FIG.3 is a schematic structural diagram of the electronic equipment in an example of the invention.
As shown in FIG.3, the electronic equipment may include a processor 310, a communication interface 320, a memory 330 and a communication bus 340, wherein the processor 310, the communication interface 320 and the memory 330 communicate with each other through the communication bus 340. The processor 310 can call the logic instruction in the memory 330 to execute the method for monitoring the connection state of blade flanges of wind turbine generator system, and the method comprises the following steps: collecting real-time connection gap data and real-time waveform data of each blade flange of wind turbine generator system, wherein the real-time waveform data is formed by the connection gap data within a preset time period; determining the installation position of each blade flange respectively; comparing the real-time connection gap data and real-time waveform data of the blade flange of each installation position with the standard connection gap data and standard waveform data; determining that the blade flange is faulty if the comparison result is that the difference between the real-time gap data and the standard gap data is greater than the gap data threshold, and the difference between the real-time waveform data and the standard waveform data is greater than 0°] 89 the waveform data threshold.
The device example described above is only schematic, and the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place or distributed to multiple network units. Some or all of the modules can be selected according to actual needs so as to achieve the purpose of the example, so that those skilled in the art can understand and implement it without creative labor.

Claims (10)

CLAIMS LU505189
1. A method for monitoring the connection state of blade flanges of wind turbine generator system, comprising: collecting real-time connection gap data and real-time waveform data of each blade flange of wind turbine generator system, wherein the real-time waveform data is formed by the connection gap data within a preset time period; determining the installation position of each blade flange respectively; comparing the real-time connection gap data and real-time waveform data of the blade flange of each installation position with the standard connection gap data and standard waveform data: determining that the blade flange is faulty if the comparison result is that the difference between the real-time gap data and the standard gap data is greater than the gap data threshold, and the difference between the real-time waveform data and the standard waveform data is greater than the waveform data threshold.
2. The method for monitoring the connection state of blade flanges of wind turbine generator system according to claim 1, wherein after “determining that the blade flange is faulty”, the following shall also be carried out: determining a blade flange fault detection model corresponding to the installation position; inputting real-time connection gap data and real-time waveform data of the blade flange corresponding to the installation position to the blade flange fault detection model, and outputting the fault type of the blade flange, wherein the blade flange fault detection model is pre-trained and constructed based on the connection gap data sample, the waveform data sample and the blade flange fault type sample.
3. The method for monitoring the connection state of blade flanges of wind turbine generator system according to claim 2, wherein “inputting real-time connection gap data and real-time waveform data of the blade flange corresponding to the installation position to the blade flange fault detection model, and outputting the fault type of the blade flange” comprises: inputting real-time connection gap data and real-time waveform data of the blade flange corresponding to the installation position to a primary neural network, and determining fault types of the blade flange, wherein the fault types include bolt faults and flange faults; 0505188 identifying the bolt faults and the flange faults through a secondary neural network, and determining the types of the bolt faults and the flange faults, wherein the types of the bolt faults include bolt looseness and bolt fracture, and the flange faults include flange deformation and flange wear.
4. The method for monitoring the connection state of blade flanges of wind turbine generator system according to claim 1, wherein “collecting real-time connection gap data and real-time waveform data of each blade flange of wind turbine generator system” comprises: collecting vibration data of each blade flange of the wind turbine generator system by a magnetic induction sensor; converting the vibration data into real-time connection gap data and real-time waveform data based on a mapping relationship between signal space and feature space.
5. The method for monitoring the connection state of blade flanges of wind turbine generator system according to claim 4, wherein before “collecting vibration data of each blade flange of the wind turbine generator system by a magnetic induction sensor”, the following shall be carried out: determining the nonlinearity, operating conditions and background noise of the wind turbine generator system during operation; building the mapping relationship between the signal space and the feature space based on the nonlinearity, operating conditions and background noise.
6. The method for monitoring the connection state of blade flanges of wind turbine generator system according to claim 1, also comprising: collecting operation data of three blades in each wind turbine generator system respectively; determining the operation stability of each blade based on a relationship among the operation data of the three blades and an operation data relationship of each blade under different conditions at the same time; calculating a deviation trajectory of fan operation by fitting algorithm based on the operation stability of each blade, and determining the deformation state of flange based on the deviation trajectory.
7. The method for monitoring the connection state of blade flanges of wind turbine 905189 generator system according to claim 6, wherein “determining the operation stability of each blade” comprises: determining the characteristic curve of each blade based on the principle of condition statistics; extracting data of the characteristic curve by using the frequency division vector monitoring algorithm, so as to determine the operation stability of each blade.
8. The method for monitoring the connection state of blade flanges of wind turbine generator system according to any one of claims 1-7, wherein after “determining that the blade flange is faulty”, the following shall also be carried out: determining the number of flange faults; sending out an early warning prompt if the number of flange faults is less than a first preset number; sending out a first-level alarm prompt if the number of flange faults is greater than the first preset number and less than a second preset number; sending out a second-level early warning prompt and sending an alarm prompt to the user terminal by short message, mail or telephone if the number of flange faults is greater than the second preset number.
9. A device for monitoring the connection state of blade flanges of wind turbine generator system, comprising: an collection module, which is used for collecting real-time connection gap data and real-time waveform data of each blade flange of wind turbine generator system, wherein the real-time waveform data is formed by the connection gap data within a preset time period; a first determination module, which is used for determining the installation position of each blade flange respectively; a comparison module, which is used for comparing the real-time connection gap data and real-time waveform data of the blade flange of each installation position with the standard connection gap data and standard waveform data; a second determination module, which is used for determining that the blade flange is faulty if the comparison result is that the difference between the real-time gap data and the standard gap data is greater than the gap data threshold, and the difference between the real-time 205189 waveform data and the standard waveform data is greater than the waveform data threshold.
10. An electronic equipment, comprising a memory, a processor and a computer program stored in the memory and can be run on the processor, wherein when the processor executes the program, the method for monitoring the connection state of blade flanges of wind turbine generator system according to any one of claims 1 to 8 can be realized.
LU505189A 2023-09-27 2023-09-27 Method, Device and Equipment for Monitoring the Connection State of Blade Flanges of Wind Turbine Generator System LU505189B1 (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
LU505189A LU505189B1 (en) 2023-09-27 2023-09-27 Method, Device and Equipment for Monitoring the Connection State of Blade Flanges of Wind Turbine Generator System

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
LU505189A LU505189B1 (en) 2023-09-27 2023-09-27 Method, Device and Equipment for Monitoring the Connection State of Blade Flanges of Wind Turbine Generator System

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LU505189B1 true LU505189B1 (en) 2024-03-28

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Effective date: 20240328