CN115616248A - Wind turbine generator anemometer data anomaly identification method and system - Google Patents

Wind turbine generator anemometer data anomaly identification method and system Download PDF

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Publication number
CN115616248A
CN115616248A CN202211096426.2A CN202211096426A CN115616248A CN 115616248 A CN115616248 A CN 115616248A CN 202211096426 A CN202211096426 A CN 202211096426A CN 115616248 A CN115616248 A CN 115616248A
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data
anemometer
abnormal
wind turbine
wind speed
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许瑾
邓巍
赵勇
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Xian Thermal Power Research Institute Co Ltd
Huaneng Lancang River Hydropower Co Ltd
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Xian Thermal Power Research Institute Co Ltd
Huaneng Lancang River Hydropower Co Ltd
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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01P—MEASURING LINEAR OR ANGULAR SPEED, ACCELERATION, DECELERATION, OR SHOCK; INDICATING PRESENCE, ABSENCE, OR DIRECTION, OF MOVEMENT
    • G01P21/00—Testing or calibrating of apparatus or devices covered by the preceding groups
    • G01P21/02—Testing or calibrating of apparatus or devices covered by the preceding groups of speedometers
    • G01P21/025—Testing or calibrating of apparatus or devices covered by the preceding groups of speedometers for measuring speed of fluids; for measuring speed of bodies relative to fluids
    • 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
    • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F05—INDEXING SCHEMES RELATING TO ENGINES OR PUMPS IN VARIOUS SUBCLASSES OF CLASSES F01-F04
    • F05B—INDEXING SCHEME RELATING TO WIND, SPRING, WEIGHT, INERTIA OR LIKE MOTORS, TO MACHINES OR ENGINES FOR LIQUIDS COVERED BY SUBCLASSES F03B, F03D AND F03G
    • F05B2260/00—Function
    • F05B2260/80—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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  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (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)
  • Wind Motors (AREA)

Abstract

The invention provides a method and a system for identifying data abnormity of an anemometer of a wind turbine generator, which comprises the following steps: step 1, acquiring operation data of a wind turbine generator to be analyzed within a set time period; step 2, cleaning the obtained operation data to obtain cleaned operation data; step 3, performing power sub-warehouse on the obtained cleaned operation data to obtain a plurality of power sub-warehouses; step 4, calculating the average value of the wind speed of each power bin to obtain an abnormal direction early warning value of wind speed counting data; step 5, identifying wind speed counting data abnormity of the wind turbine generator according to the obtained anemometer data abnormity direction early warning value; the invention can effectively reduce the false alarm and the false missing alarm of the diagnosis and early warning method of the wind turbine generator related to the wind speed, and has wider universality.

Description

Wind turbine generator anemometer data anomaly identification method and system
Technical Field
The invention belongs to the technical field of wind power generation, and particularly relates to a method and a system for identifying data abnormality of an anemometer of a wind turbine generator.
Background
The wind speed counting data abnormity of the wind turbine generator is a typical fault of an anemorumbometer, and is usually represented by sudden increase or decrease of the wind speed measured by the anemometer; when the wind speed counting data is abnormal, all algorithms or models of the wind turbine generator related to the wind speed measured by the anemometer are abnormal; for example, multiple algorithms and models of wind turbine generator power curve fitting, power generation performance evaluation, icing early warning, power abnormity early warning, yaw error abnormity early warning and the like are developed based on wind speed measured by an anemometer; if the wind speed is abnormal, the fitted power curve and the estimated power generation performance are directly deviated from the actual situation, and false alarm or missing report is caused when the wind turbine generator freezes, has abnormal power and drifts to give early warning to wind abnormity.
In conclusion, the wind speed counting data are accurate enough and are the basis for ensuring the accuracy of various models and algorithms of the wind turbine generator, so that the accurate identification of the abnormal wind speed counting data plays an important role in improving the accuracy of other various algorithms. However, an early warning model for wind speed counting data abnormity in the industry is not mature enough, and identification of anemometer data abnormity is not accurate enough.
Disclosure of Invention
The invention aims to provide a method and a system for identifying data abnormity of an anemometer of a wind turbine generator, so that the identification precision of the anemometer data abnormity is effectively improved, and the false alarm and the missing alarm of an early warning system of the wind turbine generator are reduced.
In order to achieve the purpose, the invention adopts the technical scheme that:
the invention provides a wind turbine generator anemometer data anomaly identification method which comprises the following steps:
step 1, acquiring operation data of a wind turbine generator to be analyzed within a set time period;
step 2, cleaning the obtained operation data to obtain cleaned operation data;
step 3, performing power sub-warehouse on the obtained cleaned operation data to obtain a plurality of power sub-warehouses;
step 4, calculating the average wind speed value of each power sub-bin to obtain an abnormal direction early warning value of wind speed counting data;
and 5, identifying the data abnormity of the wind turbine generator anemometer according to the obtained anemometer data abnormity direction early warning value.
Preferably, in step 1, the operation data of the wind turbine to be analyzed within a set time period is obtained, and the specific method is as follows:
acquiring operation data of the unit to be analyzed from the first 60 days to the first 30 days to form data Q1;
and acquiring the operation data of the unit to be analyzed within 30 days to form data Q2.
Preferably, in step 3, the obtained cleaned operation data is subjected to power binning to obtain a plurality of power binning, and the specific method is as follows:
performing power binning on the two groups of cleaned operation data respectively by taking every n2 × P/n3 as a power interval, wherein each group of operation data obtains a plurality of corresponding power binning, and n2 is a set value of the maximum value of the power binning; n3 is a set value of the number of the power sub-bins; and P is the rated power of the unit to be analyzed.
Preferably, in step 4, the average wind speed value of each power bin is calculated to obtain the early warning value of the abnormal direction of the wind speed data, and the specific method is as follows:
calculating the wind speed average value of each power sub-bin corresponding to each group of operation data, and forming two corresponding matrixes;
calculating the difference value of the two matrixes, and forming a matrix;
and calculating the average value of the wind speed corresponding to the matrix to obtain the early warning value of the abnormal direction of the wind speed counting data.
Preferably, in step 5, identifying the wind turbine generator anemometer data abnormality according to the obtained anemometer data abnormality direction early warning value, and the specific method is as follows:
setting a plurality of abnormal grades of abnormal wind speed counting data, and setting a threshold value corresponding to each abnormal grade, wherein the plurality of abnormal grades are a primary abnormal grade, a secondary abnormal grade and a tertiary abnormal grade respectively;
when the early warning value of the abnormal direction of the anemometer data is greater than or equal to the threshold value corresponding to the first-level abnormal grade, judging that the anemometer data of the wind turbine generator to be analyzed is severely abnormal;
when the early warning value of the abnormal direction of the anemometer data is smaller than the threshold value corresponding to the first-level abnormal grade and is larger than or equal to the threshold value corresponding to the second-level abnormal grade, judging that the anemometer data of the wind generation set to be analyzed is in heavy degree abnormal;
when the early warning value of the abnormal direction of the anemometer data is smaller than the threshold value corresponding to the second-level abnormal grade and is greater than or equal to the threshold value corresponding to the third-level abnormal grade, judging that the anemometer data of the wind turbine generator to be analyzed is slightly abnormal;
and when the early warning value of the abnormal direction of the anemometer data is smaller than the threshold value corresponding to the three-level abnormal grade, judging that the anemometer data of the wind turbine generator to be analyzed is normal.
Preferably, after step 5, a method for determining a cause of anemometer data abnormality is further included, specifically:
obtaining an abnormal direction early warning value of wind speed counting data according to the average value of the wind speed absolute values of each power sub-bin;
and judging the reason of the anemometer data abnormity according to the obtained anemometer data abnormity direction early warning value.
Preferably, when the anemometer data abnormal direction early warning value is larger than 0, judging that the reason of the anemometer data abnormality of the wind turbine generator to be analyzed is that the anemometer wind speed is small; otherwise, judging that the reason for the abnormal data of the wind turbine anemometer of the wind turbine to be analyzed is that the wind speed of the anemometer is larger.
A wind turbine generator system wind speed count data anomaly identification system comprises:
the data acquisition unit is used for acquiring the operation data of the wind turbine generator to be analyzed within a set time period and equally dividing the acquired operation data to obtain two groups of data;
the data cleaning unit is used for respectively cleaning the two groups of obtained data to obtain two groups of cleaned operation data;
the data binning unit is used for performing power binning on each group of cleaned operation data, and each group of cleaned operation data obtains a plurality of corresponding power binning;
the calculating unit is used for calculating the wind speed average value of each power sub-bin corresponding to each group of cleaned running data to obtain an abnormal direction early warning value of the wind speed counting data;
and the identification unit is used for identifying the wind turbine generator anemometer data abnormity according to the obtained anemometer data abnormity direction early warning value.
An apparatus for identifying data anomalies of an anemometer of a wind turbine comprises a processor and a computer program capable of running on the processor, wherein the processor implements the steps of the method when executing the computer program.
Compared with the prior art, the invention has the beneficial effects that:
the invention provides a data anomaly identification method for a wind turbine generator anemometer, which extracts historical operating data within a set time period of the wind turbine generator at fixed time intervals, equally divides the historical operating data into two groups of data and respectively cleans the data, and realizes the identification of wind turbine generator anemometer data anomaly by analyzing, comparing and cleaning the corresponding wind speed and difference value of the same power section of the two groups of operating data.
Drawings
FIG. 1 is a flow chart of the method of the present invention;
FIG. 2 is a wind speed power data case of the method under normal wind speed count data;
FIG. 3 is a wind speed power data case of the method under an abnormal condition of anemometer data.
Detailed Description
The invention is further described with reference to the accompanying drawings.
The invention provides a wind turbine generator anemometer data anomaly identification method which comprises the following steps:
step 1, acquiring operation data of a wind turbine generator to be analyzed within a set time period, and equally dividing the acquired operation data to obtain two groups of data;
step 2, respectively cleaning the two groups of obtained data to obtain two groups of cleaned operation data;
step 3, performing power binning on each group of cleaned operation data, wherein each group of cleaned operation data obtains a plurality of corresponding power binning;
step 4, calculating the wind speed average value of each power sub-bin corresponding to each group of cleaned running data to obtain an abnormal direction early warning value of wind speed counting data;
and 5, identifying the anemometer data abnormity of the wind turbine generator according to the obtained anemometer data abnormity direction early warning value.
The invention relates to a wind turbine generator system wind speed counting data abnormity identification system, which comprises:
the system comprises a data acquisition unit, a data analysis unit and a data analysis unit, wherein the data acquisition unit is used for acquiring operation data of a wind turbine generator to be analyzed within a set time period and equally dividing the acquired operation data to obtain two groups of data;
the data cleaning unit is used for respectively cleaning the two groups of obtained data to obtain two groups of cleaned operation data;
the data binning unit is used for performing power binning on each group of cleaned operation data, and each group of cleaned operation data obtains a plurality of corresponding power binning;
the calculating unit is used for calculating the wind speed average value of each power sub-bin corresponding to each group of cleaned running data to obtain an abnormal direction early warning value of the wind speed counting data;
and the identification unit is used for identifying the anemometer data abnormity of the wind turbine generator according to the obtained anemometer data abnormity direction early warning value.
The wind turbine generator anemometer data abnormity identification device can be a desktop computer, a notebook, a palm computer, a cloud server and other computing devices. The wind turbine anemometer data anomaly identification device can comprise, but is not limited to, a processor and a memory. 8230and 8230.
The processor may be a Central Processing Unit (CPU), or may be other general purpose processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), an off-the-shelf programmable gate array (FPGA) or other programmable logic device, discrete gate or transistor logic, discrete hardware components, etc. \8230 \\ 8230;, or may be a Digital Signal Processor (DSP).
Examples
As shown in fig. 1, the method for identifying data abnormality of the anemometer of the wind turbine generator specifically includes the following steps:
step 1, identifying rated power P and cut-out wind speed V of a unit to be analyzed; setting an algorithm implementation minimum data volume threshold n1, a power bin maximum value-to-rated power ratio n2, a power bin number n3, a wind speed counting data abnormal primary abnormal threshold n4, a wind speed counting data abnormal secondary abnormal threshold n5 and a wind speed counting data abnormal tertiary abnormal threshold n6 related in the anemometer data abnormal automatic identification method;
step 2, obtaining operation data of 10 min-level wind speed, active power, wind wheel rotating speed, blade 1 pitch angle and the like in 30 days from the first 60 days to the first 30 days of the unit to be analyzed at fixed time intervals and defining the operation data as Q1; acquiring operating data such as wind speed, active power, wind wheel rotating speed, blade 1 pitch angle and the like of a 10min level in the previous 30 days of a unit to be analyzed, and defining the operating data as Q2; recording the date of the first 30 days of the unit to be analyzed as d;
here, the interval time of the early warning method analysis is in units of days and can be 1-10 days; the calculation amount is too large in less than 1 day, and the early warning accuracy is reduced in more than 10 days.
Step 3, respectively carrying out data cleaning on the Q1 and the Q2, and removing invalid data points recorded in the Q1 and the Q2, data points of which the running state of the wind turbine generator is not a normal power generation state, data points of which the wind speed is less than or equal to 0, data points of which the wind speed is greater than V, data points of which the power is less than or equal to 0, data points of which the limited power control is realized by changing the pitch or reducing the rotating speed in advance and data points of which the wind speed-power data display large-range outliers;
step 4, storing the wind speed and active power data of the Q1 and the Q2 after data cleaning in the step 3, and respectively defining the wind speed and active power data as Q3 and Q4; judging the relative size of the data volume of Q3 and Q4 and n1, entering a step 5 when the data volume of Q3 and Q4 is greater than or equal to n1, and pushing 'data volume is insufficient' to the wind turbine generator monitoring system when the data volume of any one or two of Q3 and Q4 is less than n1, and stopping calculation;
step 5, dividing the Q3 and Q4 data by taking every n2 × P/n3 as a power interval, calculating the wind speed mean value in each power bin in the Q3 and forming a matrix Q1, and calculating the wind speed mean value in each power bin in the Q4 and forming a matrix Q2;
here, the maximum power binning value is the product of the power interval n2 × P/n3 and the number of power bins n3, i.e. n2 × P. Under the normal condition of the wind turbine generator, the data volume of the low-wind-speed low-power section is large, and the data volume which can reach a certain proportion of rated power is relatively small, so that the ratio n2 of the maximum value of the power sub-bins to the rated power can be set according to the actual operation condition of the wind turbine generator, for example, the area n2 with poor wind conditions can be set to be 50%, the area n2 with good wind conditions can be set to be 80%, and therefore the data volume in each power bin is sufficient.
Step 6, calculating a difference value between the matrix q1 and the matrix q2, forming a matrix which is defined as q3, and averaging all the wind speed data in the q3 to obtain an abnormal direction early warning value a of the wind speed counting data; averaging absolute values of all the wind speed data in q3 to obtain an abnormal early warning value b of the wind speed counting data;
the conventional phenomenon of anemometer data abnormality is that the wind speed measured by the anemometer is suddenly increased or decreased compared with the actual wind speed, and when the wind speed measured by the anemometer at a certain moment is suddenly decreased, the wind speed measured by the corresponding anemometer after the moment under the same power is obviously smaller than the wind speed measured by the anemometer before the moment. Similarly, when the wind speed measured by the anemometer suddenly increases at a certain moment, the wind speed measured by the corresponding anemometer after the moment under the same power is obviously higher than the wind speed measured by the anemometer before the moment. And analyzing the relative wind speed measured by the anemometers in the same power section in different time periods to judge whether the data of the anemometers are abnormal. Here, the difference of the wind speeds measured by the anemometers in different power segments is averaged to avoid errors.
Step 7, judging the relative size of the wind speed counting data abnormity early warning value b and the early warning threshold values of abnormal wind speed counting data at all levels, when the wind speed counting data abnormity early warning value b is larger than or equal to the wind speed counting data abnormity first-level abnormity threshold value n4, judging that the wind speed counting data is seriously abnormal, and entering a step 8; when the wind speed counting data abnormity early warning value b is smaller than the wind speed counting data abnormity primary abnormity threshold value n4 and is larger than or equal to the wind speed counting data abnormity secondary abnormity threshold value n5, judging that the wind speed counting data is in serious abnormity and entering the step 9; when the wind speed counting data abnormity early warning value b is smaller than an anemometer data abnormity secondary abnormity threshold value n5 and is larger than or equal to an anemometer data abnormity tertiary abnormity threshold value n6, judging that the wind speed counting data is slightly abnormal and entering a step 10; when the wind speed counting data abnormity early warning value b is smaller than a three-level abnormity threshold value n6 of anemometer data abnormity, judging that the wind speed counting data is normal and pushing ' the wind speed data measured by the d-date anemometer ' to a wind turbine set monitoring system to be normal ';
and 8, judging that the wind speed of the anemometer is small when the anemometer data abnormal direction judgment value a is larger than 0, and pushing 'first-level early warning' to a wind turbine monitoring system: d, the data of the wind speed measured by the anemometer on the date has small gravity; when the anemometer data abnormal direction judgment value a is smaller than 0, judging that the anemometer wind speed is large, and pushing' first-level early warning to a wind turbine generator monitoring system: d, the data of wind speed measured by the anemometer on date has large gravity;
and 9, judging that the wind speed of the anemometer is small when the anemometer data abnormal direction judgment value a is larger than 0, and pushing 'secondary early warning' to a wind turbine monitoring system: d, the wind speed data measured by the date anemometer is moderate and small; when the anemometer data abnormal direction judgment value a is smaller than 0, judging that the anemometer wind speed is large, and pushing a secondary early warning to a wind turbine monitoring system: d, the wind speed data measured by the anemometer on date is moderate and large;
step 10, when the anemometer data abnormal direction judgment value a is larger than 0, judging that the anemometer wind speed is small, and pushing a 'three-stage early warning' to a wind turbine monitoring system: d, the data of the wind speed measured by the anemometer on date is slightly smaller; when the anemometer data abnormal direction judgment value a is smaller than 0, judging that the anemometer wind speed is large, and pushing a 'three-stage early warning' to a wind turbine monitoring system: and d, slightly larger wind speed data measured by the anemometer on the date.
FIG. 2 is a wind speed power data case in Q3 and Q4 when the wind speed counting data is judged to be normal by the method. As can be seen from fig. 2, when the anemometer data is normal, the wind speed power data in Q3 and Q4 obtained through calculation in steps 1 to 4 are in good consistency, and the wind speed count data abnormality early warning value b obtained through calculation in steps 5 to 7 is far smaller than the anemometer data abnormality three-level early warning threshold value n6.
FIG. 3 is a case of wind speed power data in Q3 and Q4 when the method determines that the anemometer data is severely abnormal. As can be seen from fig. 3, when the anemograph data is abnormal, the wind speed power data in Q3 and Q4 calculated in steps 1 to 4 has an obvious deviation, the wind speed count data abnormal early warning value b calculated in steps 5 to 10 is obviously greater than the wind speed count data abnormal primary early warning threshold value n4, and the wind speed count data abnormal direction determination value a is greater than 0, so that the early warning system gives a corresponding date "primary early warning: and (4) early warning that the wind speed data measured by the anemometer is low in severity.

Claims (8)

1. A wind turbine generator anemometer data anomaly identification method is characterized by comprising the following steps:
step 1, acquiring operation data of a wind turbine generator to be analyzed within a set time period, and equally dividing the acquired operation data to obtain two groups of data;
step 2, respectively cleaning the two groups of obtained data to obtain two groups of cleaned operation data;
step 3, performing power binning on each group of cleaned operation data, wherein each group of cleaned operation data obtains a plurality of corresponding power binning;
step 4, calculating the wind speed average value of each power sub-bin corresponding to each group of cleaned operation data to obtain an abnormal direction early warning value of wind speed counting data;
and 5, identifying the data abnormity of the wind turbine generator anemometer according to the obtained anemometer data abnormity direction early warning value.
2. The method for identifying the data abnormality of the anemometer of the wind turbine generator according to claim 2, wherein in step 3, each group of the obtained cleaned operation data is subjected to power binning to obtain a plurality of power binning, and the specific method comprises the following steps:
performing power binning on each group of obtained cleaned operation data by taking every n2 × P/n3 as a power interval, wherein each group of operation data obtains a plurality of corresponding power binning, and n2 is a set value of the maximum value of the power binning; n3 is a set value of the number of the power sub-bins; and P is the rated power of the unit to be analyzed.
3. The method for identifying the wind turbine generator anemometer data abnormality according to claim 1, wherein in step 4, the average wind speed value of each power bin is calculated to obtain an early warning value of the abnormal direction of the wind speed count data, and the specific method is as follows:
calculating the wind speed average value of each power sub-bin corresponding to each group of operation data, and forming two corresponding matrixes;
calculating the difference value of the two matrixes and forming a matrix;
and calculating the average value of the wind speed corresponding to the matrix to obtain the early warning value of the abnormal direction of the wind speed counting data.
4. The method for identifying the data abnormality of the wind turbine generator anemometer according to claim 1, wherein in step 5, the wind turbine generator anemometer data abnormality is identified according to the obtained anemometer data abnormality direction early warning value, and the specific method is as follows:
setting a plurality of abnormal grades of abnormal wind speed counting data, and setting a threshold value corresponding to each abnormal grade, wherein the plurality of abnormal grades are a primary abnormal grade, a secondary abnormal grade and a tertiary abnormal grade respectively;
when the early warning value of the abnormal direction of the anemometer data is greater than or equal to the threshold value corresponding to the first-level abnormal grade, judging that the anemometer data of the wind turbine generator to be analyzed is severely abnormal;
when the early warning value of the abnormal direction of the anemometer data is smaller than the threshold value corresponding to the first-level abnormal grade and is larger than or equal to the threshold value corresponding to the second-level abnormal grade, judging that the anemometer data of the wind generation set to be analyzed is in heavy degree abnormal;
when the early warning value of the abnormal direction of the anemometer data is smaller than the threshold value corresponding to the second-level abnormal grade and is greater than or equal to the threshold value corresponding to the third-level abnormal grade, judging that the anemometer data of the wind turbine generator to be analyzed is slightly abnormal;
and when the early warning value of the abnormal direction of the anemometer data is smaller than the threshold value corresponding to the three-level abnormal grade, judging that the anemometer data of the wind turbine generator to be analyzed is normal.
5. The wind turbine generator system anemometer data abnormality identification method according to claim 1, further comprising a method for determining a cause of anemometer data abnormality after step 5, specifically:
obtaining an abnormal direction early warning value of wind speed counting data according to the average value of the wind speed absolute values of each power sub-bin;
and judging the reason of the anemometer data abnormity according to the obtained anemometer data abnormity direction early warning value.
6. The method for identifying the data abnormality of the wind turbine generator anemometer according to claim 5, wherein when the anemometer data abnormality direction early warning value is greater than 0, the reason that the wind turbine generator anemometer data abnormality of the wind turbine generator to be analyzed is that the wind speed of the anemometer is small is judged; otherwise, judging that the reason for the abnormal data of the wind turbine anemometer of the wind turbine to be analyzed is that the wind speed of the anemometer is larger.
7. The utility model provides a wind turbine generator system wind speed count data anomaly identification system which characterized in that includes:
the system comprises a data acquisition unit, a data analysis unit and a data analysis unit, wherein the data acquisition unit is used for acquiring operation data of a wind turbine generator to be analyzed within a set time period and equally dividing the acquired operation data to obtain two groups of data;
the data cleaning unit is used for respectively cleaning the two groups of obtained data to obtain two groups of cleaned operation data;
the data binning unit is used for performing power binning on each group of cleaned operation data, and each group of cleaned operation data obtains a plurality of corresponding power binning;
the calculating unit is used for calculating the wind speed average value of each power sub-bin corresponding to each group of cleaned running data to obtain an abnormal direction early warning value of wind speed counting data;
and the identification unit is used for identifying the anemometer data abnormity of the wind turbine generator according to the obtained anemometer data abnormity direction early warning value.
8. Wind turbine anemometer data anomaly recognition device comprising a processor and a computer program executable on said processor, characterized in that the processor implements the steps of the method according to any one of claims 1 to 6 when executing said computer program.
CN202211096426.2A 2022-09-08 2022-09-08 Wind turbine generator anemometer data anomaly identification method and system Pending CN115616248A (en)

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Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN115951088A (en) * 2023-03-10 2023-04-11 南京华盾电力信息安全测评有限公司 Wind turbine generator anemograph abnormity analysis method

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN115951088A (en) * 2023-03-10 2023-04-11 南京华盾电力信息安全测评有限公司 Wind turbine generator anemograph abnormity analysis method
CN115951088B (en) * 2023-03-10 2023-08-25 南京南自华盾数字技术有限公司 Wind turbine anemometer anomaly analysis method

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