CN112228290B - Intelligent early warning method for faults of variable pitch system of wind turbine - Google Patents

Intelligent early warning method for faults of variable pitch system of wind turbine Download PDF

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CN112228290B
CN112228290B CN202011140230.XA CN202011140230A CN112228290B CN 112228290 B CN112228290 B CN 112228290B CN 202011140230 A CN202011140230 A CN 202011140230A CN 112228290 B CN112228290 B CN 112228290B
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blade
pitch
early warning
following
pitch angle
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CN112228290A (en
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韩斌
甘勇
孙世辉
王忠杰
李颖峰
赵勇
郑新建
程方
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Hunan Clean Energy Branch Of Huaneng International Power Co ltd
Xian Thermal Power Research Institute Co Ltd
Huaneng Power International Inc
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Hunan Clean Energy Branch Of Huaneng International Power Co ltd
Xian Thermal Power Research Institute Co Ltd
Huaneng Power International Inc
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    • 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
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
    • Y02E10/00—Energy generation through renewable energy sources
    • Y02E10/70—Wind energy
    • Y02E10/72—Wind turbines with rotation axis in wind direction

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  • 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)
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Abstract

The invention discloses an intelligent early warning method for faults of a variable pitch system of a wind turbine, which comprises the steps of obtaining given variable pitch speed and feedback variable pitch speed of each blade, and calculating variable pitch following errors and variable pitch following error coefficients of each blade; when the following error coefficient does not exceed a specified value, considering that the pitch system has no fault, and when the following error coefficient exceeds the specified value, reading pitch angles before and after pitch of each blade, and calculating the pitch angle mutual difference of each blade; if the pitch angle mutual difference of each blade is not larger than a specified value, giving early warning information: the number of the blade with the largest pitch angle mutual difference is followed, if the number is larger than the number of the blade with the largest pitch angle mutual difference, the number of the blade with the largest pitch angle error is determined according to the maximum value and the minimum value of the pitch angle mutual difference of each blade; meanwhile, vibration monitoring data of the main bearing at the same time are called, whether vibration exceeds the limit is judged, and if the vibration does not exceed the limit, early warning information is given: following the blade number with the largest pitch angle error, if the blade number exceeds the limit, giving alarm information: blade number with maximum pitch angle error; the invention provides three early warning results, and the early warning is more accurate.

Description

Intelligent early warning method for faults of variable pitch system of wind turbine
Technical Field
The invention relates to the technical field of fault early warning of a variable pitch system of a wind turbine, in particular to an intelligent fault early warning method of the variable pitch system of the wind turbine.
Background
In recent years, intelligent operation and maintenance of a wind farm have become development hot spots in the industry, but in the intelligent operation and maintenance process of the wind farm, early warning of faults of various devices through various intelligent algorithms is particularly important. The SCADA system data and the CMS system data in the wind power plant contain a large amount of equipment operation state information, and the information needs to be fully mined. The variable pitch system is an important component of the wind turbine, and the running state of the variable pitch system is directly related to the safety of the wind turbine, so that the early warning of the variable pitch system is extremely important.
At present, the industry provides early warning for a variable pitch system of a wind turbine, and although a plurality of different methods are proposed at home and abroad, most of the early warning is performed based on a constant single threshold or a single parameter. The early warning scheme has certain defects, so that the early warning false alarm rate is high, and the early warning method cannot be effectively applied due to the fact that the false alarm rate is too high.
Disclosure of Invention
In order to overcome the defects in the prior art, the invention aims to provide an intelligent early warning method for faults of a wind turbine pitch system, three early warning results are given, and the early warning is more accurate.
In order to achieve the above purpose, the invention adopts the following technical scheme:
a wind turbine pitch system fault intelligent early warning method comprises the steps of obtaining given pitch speeds V of 1#, 2#, 3# blades from second-level data of a wind power plant data acquisition and monitoring control system 10 、V 20 、V 30 Feedback pitch speed V of 1#, 2#, 3# blades 11 、V 21 、V 31 Calculating the pitch following errors e of the blades # 1, # 2 and # 3 1 、e 2 、e 3 ：
e 1 ＝|V 10 -V 11 |
e 2 ＝|V 20 -V 21 |
e 3 ＝|V 30 -V 31 |
By comparing the blade pitch following errors e of the 1#, 2#, 3# 1 、e 2 、e 3 Determining the blade number with the largest pitch following error, and simultaneously calculating the average value E of the pitch following errors of 3 blades:
Figure GDA0004156024820000021
further calculating a pitch following error coefficient epsilon:
Figure GDA0004156024820000022
in order to eliminate the influence of discrete data on an analysis result, a plurality of groups of continuous data are calculated at the same time, then the following error coefficient epsilon is judged, and when the following error coefficient epsilon does not exceed a specified value, the pitch system is considered to have no fault; when the following error coefficient epsilon exceeds a specified value, the pitch angle A before the pitch of the 1# blade, the 2# blade and the 3# blade in the wind power plant data acquisition and monitoring control system needs to be read 10 、A 20 、A 30 Pitch angle A after pitch variation 11 、A 21 、A 31 And calculates the pitch angle mutual difference A of the 1# blade, the 2# blade and the 3# blade 12 、A 23 、A 31 Wherein:
A 12 ＝||A 10 -A 11 |-|A 20 -A 21 ||
A 23 ＝||A 20 -A 21 |-|A 30 -A 31 ||
A 31 ＝||A 30 -A 31 |-|A 10 -A 11 ||
then judging whether the mutual difference of the variable pitch angles of the blades is larger than a specified value, and if not, giving out early warning information: the serial number of the blade with the largest pitch angle mutual difference is followed; if the variation angle mutual difference of each blade is larger than a specified value, determining the blade number with the largest variation angle error according to the maximum value and the minimum value of the variation angle mutual differences of the 1# blade, the 2# blade and the 3# blade; meanwhile, vibration monitoring data of the main bearing at the same time are called, whether vibration exceeds the limit is judged, and if the vibration does not exceed the limit, early warning information is given: following the blade number with the largest pitch angle error, if the blade number exceeds the limit, giving alarm information: blade number with largest pitch angle error.
Preferably, 20 sets of consecutive data are calculated simultaneously and then the following error coefficient ε is determined.
Compared with the prior art, the invention has the following advantages:
(1) Meanwhile, the fault early warning is carried out on the variable pitch system by utilizing the variable pitch following error, the variable pitch angle error and the vibration data value, so that the problem of high early warning false alarm rate caused by adopting a single parameter is avoided.
(2) The method of the invention gives three early warning results at the same time, and the early warning is more accurate.
Drawings
FIG. 1 is a flow chart of the early warning method of the present invention.
Detailed Description
The invention will be described in further detail with reference to the drawings and the specific examples.
As shown in FIG. 1, according to the intelligent early warning method for faults of a wind turbine pitch system, given pitch speeds V of 1#, 2#, 3# blades are obtained from second-level data of a wind power plant data acquisition and monitoring control system (SCADA system) 10 、V 20 、V 30 Feedback pitch speed V of 1#, 2#, 3# blades 11 、V 21 、V 31 Calculating the pitch following errors e of the blades # 1, # 2 and # 3 1 、e 2 、e 3 ：
e 1 ＝|V 10 -V 11 |
e 2 ＝|V 20 -V 21 |
e 3 ＝|V 30 -V 31 |
By comparing the blade pitch following errors e of the 1#, 2#, 3# 1 、e 2 、e 3 Determining the blade number with the largest pitch following error, and simultaneously calculating the average value E of the pitch following errors of 3 blades:
Figure GDA0004156024820000041
further calculating a pitch following error coefficient epsilon:
Figure GDA0004156024820000042
in order to eliminate the influence of discrete data on an analysis result, 20 groups of continuous data are calculated at the same time, then the following error coefficient epsilon is judged, and when the following error coefficient epsilon does not exceed a specified value, the pitch system is considered to have no fault; when the following error coefficient epsilon exceeds a prescribed value, the wind needs to be readElectric field data acquisition and monitoring control system 1#, 2#, 3# blade pitch angle A before pitch variation 10 、A 20 、A 30 Pitch angle A after pitch variation 11 、A 21 、A 31 And calculates the pitch angle mutual difference A of the 1# blade, the 2# blade and the 3# blade 12 、A 23 、A 31 Wherein:
A 12 ＝||A 10 -A 11 |-|A 20 -A 21 ||
A 23 ＝||A 20 -A 21 |-|A 30 -A 31 ||
A 31 ＝||A 30 -A 31 |-|A 10 -A 11 ||
then judging whether the mutual difference of the variable pitch angles of the blades is larger than a specified value, and if not, giving out early warning information: the serial number of the blade with the largest pitch angle mutual difference is followed; if the variation angle mutual difference of each blade is larger than a specified value, determining the blade number with the largest variation angle error according to the maximum value and the minimum value of the variation angle mutual differences of the 1# blade, the 2# blade and the 3# blade; meanwhile, vibration monitoring data of the main bearing at the same time are called, whether vibration exceeds the limit is judged, and if the vibration does not exceed the limit, early warning information is given: following the blade number with the largest pitch angle error, if the blade number exceeds the limit, giving alarm information: blade number with largest pitch angle error.

Claims (2)

1. An intelligent early warning method for faults of a variable pitch system of a wind turbine is characterized by comprising the following steps of: the given pitch speed V of the No. 1, no. 2 and No. 3 blades is obtained from second-level data of a wind power plant data acquisition and monitoring control system 10 、V 20 、V 30 Feedback pitch speed V of 1#, 2#, 3# blades 11 、V 21 、V 31 Calculating the pitch following errors e of the blades # 1, # 2 and # 3 1 、e 2 、e 3 ：
e 1 ＝|V 10 -V 11 |
e 2 ＝|V 20 -V 21 |
e 3 ＝|V 30 -V 31 |
By comparing the blade pitch following errors e of the 1#, 2#, 3# 1 、e 2 、e 3 Determining the blade number with the largest pitch following error, and simultaneously calculating the average value E of the pitch following errors of 3 blades:
Figure FDA0004156024810000011
further calculating a pitch following error coefficient epsilon:
Figure FDA0004156024810000012
in order to eliminate the influence of discrete data on an analysis result, a plurality of groups of continuous data are calculated at the same time, then the following error coefficient epsilon is judged, and when the following error coefficient epsilon does not exceed a specified value, the pitch system is considered to have no fault; when the following error coefficient epsilon exceeds a specified value, the pitch angle A before the pitch of the 1# blade, the 2# blade and the 3# blade in the wind power plant data acquisition and monitoring control system needs to be read 10 、A 20 、A 30 Pitch angle A after pitch variation 11 、A 21 、A 31 And calculates the pitch angle mutual difference A of the 1# blade, the 2# blade and the 3# blade 12 、A 23 、A 31 Wherein:
A 12 ＝||A 10 -A 11 |-|A 20 -A 21 ||
A 23 ＝||A 20 -A 21 |-|A 30 -A 31 ||
A 31 ＝||A 30 -A 31 |-|A 10 -A 11 ||
then judging whether the mutual difference of the variable pitch angles of the blades is larger than a specified value, and if not, giving out early warning information: the serial number of the blade with the largest pitch angle mutual difference is followed; if the variation angle mutual difference of each blade is larger than a specified value, determining the blade number with the largest variation angle error according to the maximum value and the minimum value of the variation angle mutual differences of the 1# blade, the 2# blade and the 3# blade; meanwhile, vibration monitoring data of the main bearing at the same time are called, whether vibration exceeds the limit is judged, and if the vibration does not exceed the limit, early warning information is given: following the blade number with the largest pitch angle error, if the blade number exceeds the limit, giving alarm information: blade number with largest pitch angle error.
2. The intelligent early warning method for faults of a variable pitch system of a wind turbine according to claim 1 is characterized by comprising the following steps: and simultaneously calculating 20 groups of continuous data, and then judging the following error coefficient epsilon.
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CN113530763A (en) * 2021-06-10 2021-10-22 北京国电思达科技有限公司 Method and system for monitoring zero position abnormity of blades of wind turbine generator
CN113898528B (en) * 2021-09-30 2023-07-28 江苏金风软件技术有限公司 Abnormality detection method, model training method and related device for fan variable pitch bearing
CN117662395A (en) * 2022-08-31 2024-03-08 北京金风慧能技术有限公司 Variable pitch fault prediction method and device for wind generating set

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