CN105891546A - Wind vane fault diagnosis method in wind turbine yaw system based on big data - Google Patents
Wind vane fault diagnosis method in wind turbine yaw system based on big data Download PDFInfo
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- CN105891546A CN105891546A CN201610053599.4A CN201610053599A CN105891546A CN 105891546 A CN105891546 A CN 105891546A CN 201610053599 A CN201610053599 A CN 201610053599A CN 105891546 A CN105891546 A CN 105891546A
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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
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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
- G01P13/00—Indicating or recording presence, absence, or direction, of movement
- G01P13/02—Indicating direction only, e.g. by weather vane
- G01P13/025—Indicating direction only, e.g. by weather vane indicating air data, i.e. flight variables of an aircraft, e.g. angle of attack, side slip, shear, yaw
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Abstract
The invention provides a wind vane fault diagnosis method in a wind turbine yaw system based on big data. The method carries out diagnosis on wind vane fault by analyzing characteristic signal data of a wind turbine in the operation process. The fault diagnosis method is characterized by, utilizing a wind turbine yaw relevant data sequence collected by a data acquisition unit, and after calculation processing of an operation processing unit, comparing relevant operation parameters of a fan under a current operation state with corresponding threshold values thereof to judge wind vane operation state. The method solves the problems in the prior art very well.
Description
Technical field: the invention belongs to wind power generation field, relates to all wind in region residing for wind energy turbine set or wind energy turbine set
The method of wind vane fault diagnosis in power generator yaw system, particularly relates to based on big data diagnosis wind-driven generator wind direction
Mark fault.
Background technology: yaw system is one of horizontal shaft type wind-driven generator group requisite composition system.Driftage system
The control system that system is mainly with wind power generating set cooperates, and makes the wind wheel of wind power generating set be in shape windward all the time
State, makes full use of wind energy, improves the generating efficiency of wind power generating set.
The yaw system of wind power generating set is generally divided into active yawing system and passive yawing system.For grid type wind
For power generator group, the most all use the gear drive form of active yawing.Active yawing refers to use electric power or hydraulic pressure
Drag the yawing mode to pneumatic work.
Yaw system work process is substantially: wind vector signal is converted to the signal of telecommunication and is delivered to driftage and controls processor,
Processor sends, to yaw motor, instruction of going off course clockwise or counterclockwise after relatively, drives cabin to move to direction of the wind comes from,
Reach the purpose accurately to wind.During whole driftage controls, wind direction signals is that Wind turbines starts and the weight of yaw adjustment
Wanting signal, the order of accuarcy that wind direction is measured directly affects the work efficiency of yaw system.
In view of each side factor such as cost, service life, wind vane is currently mainly used to measure wind direction.Due to big portion
Dividing wind field all to build the area that weather conditions are severe in, the wind vane using machinery rotation mode to measure wind direction is easy to by dust storm
The reasons such as blocking cause abrasion so that wind direction certainty of measurement reduces；Or occur freezing in the case of temperature is relatively low so that wind direction
The response time measured is long.
Summary of the invention:
Goal of the invention: invention provides the side of wind vane fault diagnosis in a kind of Wind turbines yaw systems based on big data
Method, its objective is to solve the most existing problem.
Technical scheme: invention is achieved through the following technical solutions:
The method of wind vane fault diagnosis in Wind turbines yaw systems based on big data, it is characterised in that: the method
(Supervisory Control And Data Acquisition, data acquisition and supervision control system to utilize wind energy turbine set SCADA
System) Back ground Information judge wind power generating set wind vane fault and running status；It is applicable to judge grid-connected separate unit wind-force
The wind vane running status of generating set, utilizes separate unit blower fan wind direction and the difference with group blower fan wind direction average with wind deflection threshold
Value compares, and difference enters wind vane breakdown judge flow process more than wind deflection threshold value.
Blower fan wind direction data acquisition includes in the SCADA system of Wind turbines, off-line or online data, Back ground Information: single
The wind direction of Fans and yaw position.
Utilize data digging method to analyze each blower fan terrain data in wind energy turbine set, same wind energy turbine set inner blower is grouped.
In wind energy turbine set, each blower fan terrain data is the longitude and latitude data in geographical position residing for blower fan.
In wind vane breakdown judge flow process, relate to each operational factor and all use 3 σ criterions to carry out parameter optimization.
Utilize the wind driven generator yaw correlated sequences of data that data acquisition unit collects, through operation processing unit
Calculating process after, relevant operational factor and its respective threshold and then judge that wind vane runs under contrast blower fan current operating conditions
State；Above-mentioned operational factor includes: yaw position and wind angle, and data acquisition is from wind energy turbine set SCADA (Supervisory
Control And Data Acquisition, data acquisition and supervisor control) Back ground Information；
Operational factor threshold value includes: wind deflection threshold value, wake effect scope, blower fan are from determining the wind direction autocorrelation coefficient threshold
Value, blower fan are from determining the wind direction and actual wind direction cross-correlation coefficient threshold value.
Above-mentioned wind deflection, blower fan certainly determine the wind direction and actual wind direction is that wind-driven generator operational factor is through calculation process list
Unit's calculating obtains after processing；Additionally, the Back ground Information of above-mentioned wind energy turbine set SCADA comes from wind energy turbine set all blower fans identical time
In the range of online or off-line data；The determination of above-mentioned wake effect scope need to determine according to wind energy turbine set actual assembling position.
First the method will gather data compilation and become effective three-dimensional wind-powered electricity generation sequence, and wherein wind-powered electricity generation data include from each
The survey wind data of blower fan sensor, yaw position data and the time series relevant to the electricity that follows the wind；And then, according to each blower fan wind
Calculate to angle and yaw position from determining the wind direction；It addition, use clustering method in big data technique, according to each blower fan of the whole audience
Blower fan classification is divided into different group by the rugged index in residing geographical position, takes with group blower fan from determining the wind direction average as reality
Wind direction；Finally, what wind deflection was each blower fan determine the wind direction certainly with this blower fan belonging to the group actual wind direction difference that calculated
Absolute value.
Utilize above-mentioned draw each blower fan wind deflection, certainly determine the wind direction and actual wind direction, carry out various calculation process, first
First, scope is rejected according to each blower fan sector, it is judged that in the range of whether actual wind direction is in wind direction wake effect；And calculate each
Individual blower fan is from the auto-correlation determined the wind direction with actual wind direction and cross-correlation coefficient；
Whether the most above-mentioned actual wind direction is in wind direction wake effect method of determining range: hinder according to around place
Hindering the situation of thing, be estimated the flow distortion situation in place, the sector region that eliminating significant obstacle thing wake flow causes i.e. picks
Except sector, if direction of the wind comes from is in the rejecting Sector Range of monitoring blower fan, it is whether actual wind direction is in wind direction tail
In stream coverage, the rejecting sector of Wind turbines is calculated as follows:
(1) if barrier is tall and big object
Utilize formula (1-1) that the size of barrier is equivalent to rotor diameter,
In formula, De equivalence rotor diameter；The height of Lh barrier；The width (taking Breadth Maximum) of Lw barrier；
Recycling formula (1-2) can try to achieve the interference sector affecting measuring wind speed,
α=1.3arctan (2.5De/Le+0.15)+10 (1-2)
In formula, α interference sector；De equivalence rotor diameter；The Le barrier distance away from blower fan；
(2) if barrier is the wind power generating set closed on
If barrier is to close on wind power generating set, directly interference sector just can be tried to achieve with formula (1-3),
α=1.3arctan (2.5Dn/Ln+0.15)+10 (1-3)
In formula, α interference sector；Dn closes on the rotor diameter of wind power generating set；Ln closes on wind power generating set
Distance away from tested blower fan；
Flow chart according to wind vane method for diagnosing faults and the result of calculation of above-mentioned operation processing unit 13 are to each wind
The wind vane running status of machine diagnoses, and judges wind vane failure condition；
In breakdown judge unit 14, cross-correlation coefficient computing formula is as follows:
In formula, xi: monitor the wind direction average that blower fan i-th adds up 1 minute in every 4 minutes；Monitoring blower fan is in 4 minutes
Wind direction average；
yi: carry out wind direction i-th in every 4 minutes and add up 1 minute average；Machine carrys out wind direction average and is averaging in 4 minutes.
Autocorrelation coefficient computing formula is as follows:
In formula, xi,The same β of n, i=1,2....n.
Use Pauta criterion that auto-correlation and cross-correlation coefficient are carried out parameter optimization；Concrete methods of realizing is as follows: utilize
Standard deviation formulaCalculate the standard deviation of autocorrelation coefficient, wherein xiFor autocorrelation coefficient；Contrast is from phase
Relation number scale record result is more than the value of 3 σ, and is rejected；Reject more than remaining autocorrelation coefficient after 3 σ, according to counting
Mean Value Formulas calculates the average of residue autocorrelation coefficient, and as final autocorrelation coefficient threshold value, cross-correlation coefficient
Optimization process is identical with autocorrelation coefficient process.
Advantageous effect:
The present invention proposes a kind of method of wind vane fault diagnosis in Wind turbines yaw system based on big data, a kind of
Based on analyzing the characteristic signal data that gather in running of wind power generating set to wind vane diagnosing malfunction.The present invention
In the method for diagnosing faults related to, utilize the wind driven generator yaw correlated sequences of data that data acquisition unit collects, warp
Cross after the calculating of operation processing unit processes, under contrast blower fan current operating conditions relevant operational factor and its respective threshold and then
Judge wind vane running status.It well solves the most existing problem.
Accompanying drawing illustrates:
Fig. 1. the system construction drawing of the present invention；
Fig. 2. the data acquisition of the present invention and process flowchart；
Fig. 3. the rugged Index for Calculation principle schematic of the present invention；
Fig. 4. wind vane method for diagnosing faults flow process in the wind driven generator yaw system of the present invention.
Detailed description of the invention:
Below in conjunction with the accompanying drawings the present invention is described in further detail.
As shown in Figure 1, during the present invention provides a kind of Wind turbines yaw system based on big data, wind vane fault is examined
Disconnected method, the specific implementation method of the wind vane fault diagnosis that present invention will be described in detail with reference to the accompanying relates to.
As it is shown in figure 1, wind driven generator yaw system wind vane fault diagnosis system includes data acquisition unit 11, number
According to processing unit 12, operation processing unit 13, failure diagnosis unit 14.Fig. 3 shows that the wind vane fault that the present invention relates to is examined
The flow chart of disconnected method.
Wherein, data acquisition unit 11 is for the data acquisition of power system, and it is by resolving wind energy turbine set and electrical network
The communication protocol of SCADA Yu EMS (Energy Management System, competence management system), it is possible to obtain in wind energy turbine set
The Back ground Information of each blower fan.
Wind-powered electricity generation data from above-mentioned data acquisition unit 11 are carried out comprehensively (including that computing is located in advance by data processing unit 12
Reason and arrangement), process of data preprocessing is as shown in Figure 2.First will gather data compilation and become effective three-dimensional wind-powered electricity generation sequence.Wherein
Wind-powered electricity generation data include the survey wind data from each blower fan sensor, yaw position data and the time sequence relevant to the electricity that follows the wind
Row.And then, calculate from determining the wind direction according to each blower fan wind angle and yaw position；Cluster it addition, use in big data technique
Analysis method, is divided into different group according to the rugged index in geographical position residing for each blower fan of the whole audience by blower fan classification, takes same group
Blower fan is from determining the wind direction average as actual wind direction.Finally, wind deflection be each blower fan certainly determine the wind direction with this blower fan belonging to group
The absolute value of other calculated actual wind direction difference.
About the composition of three-dimensional wind-powered electricity generation sequence, with reference to table 1.In table, constituted three-dimensional with time, wind angle and yaw position
Sequence, every string is a dimension of data.As the table shows, the first dimension be time, the second dimension be wind angle, last
Dimension is yaw position.
Table 1
Above-mentioned rugged index refers to that every radius all may be with landform etc. in certain some polar coordinate system with R as radius
High line intersects, if intersection point is then divided into main section radius.The line segment summation of the crucial gradient is exceeded, divided by all with terrain slope
Line segment summation (radius R) just obtains the value of rugged index.Computing Principle schematic diagram is as shown in Figure 3.
Operation processing unit 13 utilizes each blower fan wind deflection that above-mentioned data processing unit 12 draws, certainly determines the wind direction and real
Border wind direction, carries out various calculation process.First, operation processing unit 13 rejects scope according to each blower fan sector, it is judged that actual
In the range of whether wind direction is in wind direction wake effect；And calculate each blower fan from determining the wind direction and the auto-correlation of actual wind direction and mutual
Correlation coefficient.
Whether the most above-mentioned actual wind direction is in wind direction wake effect method of determining range: hinder according to around place
Hindering the situation of thing, be estimated the flow distortion situation in place, the sector region that eliminating significant obstacle thing wake flow causes i.e. picks
Except sector.In direction is in the rejecting Sector Range of monitoring blower fan if the wind comes from, it is whether actual wind direction is in wind direction tail
In stream coverage.The rejecting sector of Wind turbines is calculated as follows:
(1) if barrier is tall and big object
Utilize formula (1-1) that the size of barrier is equivalent to rotor diameter,
In formula, De equivalence rotor diameter；The height of Lh barrier；The width (taking Breadth Maximum) of Lw barrier.
Recycling formula (1-2) can try to achieve the interference sector affecting measuring wind speed,
α=1.3arctan (2.5De/Le+0.15)+10 (1-2)
In formula, α interference sector；De equivalence rotor diameter；The Le barrier distance away from blower fan.
(2) if barrier is the wind power generating set closed on
If barrier is to close on wind power generating set, directly interference sector just can be tried to achieve with formula (1-3),
α=1.3arctan (2.5Dn/Ln+0.15)+10 (1-3)
In formula, α interference sector；Dn closes on the rotor diameter of wind power generating set；Ln closes on wind power generating set
Distance away from tested blower fan.
Breakdown judge unit 14 is according to the flow chart of wind vane method for diagnosing faults and the meter of above-mentioned operation processing unit 13
Calculate result the wind vane running status of each blower fan is diagnosed, and judge wind vane failure condition.
In breakdown judge unit 14, cross-correlation coefficient computing formula is as follows:
In formula, xi: monitor the wind direction average that blower fan i-th adds up 1 minute in every 4 minutes；Monitoring 4 minutes endogenous wind of blower fan
To average.
yi: carry out wind direction i-th in every 4 minutes and add up 1 minute average；Machine carrys out wind direction average and is averaging in 4 minutes.
Autocorrelation coefficient computing formula is as follows:
In formula, xi,The same β of n, i=1,2....n.
In breakdown judge unit 14, use Pauta criterion that auto-correlation and cross-correlation coefficient are carried out parameter optimization.Tool
Body implementation method is as follows: utilize standard deviation formulaThe standard deviation calculating autocorrelation coefficient (is wherein
Autocorrelation coefficient)；Contrast autocorrelation coefficient record result is more than the value of 3 σ, and is rejected.Reject remaining more than after 3 σ
Autocorrelation coefficient, calculates the average of residue autocorrelation coefficient according to arithmetic average formula, and as final auto-correlation system
Number threshold value, the optimization process of cross-correlation coefficient is identical with autocorrelation coefficient process.
The enforcement row that present invention mentioned above relates to, can be ordered by the program that various computer element performs
Language form is made to realize.The example of program command language, including formed by compiling and language codes, also includes using explaining journey
The most executable higher-level language code such as sequence.
Claims (10)
1. the method for wind vane fault diagnosis in Wind turbines yaw systems based on big data, it is characterised in that: the method profit
By wind energy turbine set SCADA, (Supervisory Control And Data Acquisition, data acquisition and supervision control system
System) Back ground Information judge wind power generating set wind vane fault and running status；It is applicable to judge grid-connected separate unit wind-force
The wind vane running status of generating set, utilizes separate unit blower fan wind direction and the difference with group blower fan wind direction average with wind deflection threshold
Value compares, and difference enters wind vane breakdown judge flow process more than wind deflection threshold value.
The method of wind vane fault diagnosis in Wind turbines yaw system based on big data the most according to claim 1,
It is characterized in that: blower fan wind direction data acquisition includes in the SCADA system of Wind turbines, off-line or online data, Back ground Information:
The wind direction of separate unit blower fan and yaw position.
The method of wind vane fault diagnosis in Wind turbines yaw system based on big data the most according to claim 1,
It is characterized in that: utilize data digging method to analyze each blower fan terrain data in wind energy turbine set, same wind energy turbine set inner blower is grouped.
The method of wind vane fault diagnosis in Wind turbines yaw system based on big data the most according to claim 1,
It is characterized in that: in wind energy turbine set, each blower fan terrain data is the longitude and latitude data in geographical position residing for blower fan.
The method of wind vane fault diagnosis in Wind turbines yaw system based on big data the most according to claim 1,
It is characterized in that: in wind vane breakdown judge flow process, relate to each operational factor and all use 3 σ criterions to carry out parameter optimization.
The method of wind vane fault diagnosis in Wind turbines yaw system based on big data the most according to claim 1,
It is characterized in that: the wind driven generator yaw correlated sequences of data utilizing data acquisition unit to collect, through calculation process
After the calculating of unit processes, relevant operational factor and its respective threshold and then judge wind vane under contrast blower fan current operating conditions
Running status；Above-mentioned operational factor includes: yaw position and wind angle, and data acquisition is from wind energy turbine set SCADA (Supervisory
Control And Data Acquisition, data acquisition and supervisor control) Back ground Information；
Operational factor threshold value includes: wind deflection threshold value, wake effect scope, blower fan are from determining the wind direction autocorrelation coefficient threshold value, wind
Machine is from determining the wind direction and actual wind direction cross-correlation coefficient threshold value.
The method of wind vane fault diagnosis in Wind turbines yaw system based on big data the most according to claim 1,
It is characterized in that: above-mentioned wind deflection, blower fan certainly determine the wind direction and actual wind direction is that wind-driven generator operational factor is through calculation process
Unit calculating obtains after processing；Additionally, the Back ground Information of above-mentioned wind energy turbine set SCADA come from all blower fans of wind energy turbine set identical time
Online or off-line data in the range of between；The determination of above-mentioned wake effect scope need to be true according to wind energy turbine set actual assembling position
Fixed.
The method of wind vane fault diagnosis in Wind turbines yaw system based on big data the most according to claim 1,
It is characterized in that: first the method will gather data compilation and become effective three-dimensional wind-powered electricity generation sequence, wherein wind-powered electricity generation data include from
The survey wind data of each blower fan sensor, yaw position data and the time series relevant to the electricity that follows the wind；And then, according to each wind
Machine wind angle and yaw position calculate from determining the wind direction；It addition, use clustering method in big data technique, each according to the whole audience
Blower fan classification is divided into different group by the rugged index in geographical position residing for blower fan, takes with group blower fan from determining the wind direction average conduct
Actual wind direction；Finally, what wind deflection was each blower fan determine the wind direction certainly with this blower fan belonging to the actual wind direction that calculated of group poor
The absolute value of value.
The method of wind vane fault diagnosis in Wind turbines yaw system based on big data the most according to claim 8,
It is characterized in that: utilize above-mentioned draw each blower fan wind deflection, certainly determine the wind direction and actual wind direction, carry out various calculation process,
First, scope is rejected according to each blower fan sector, it is judged that in the range of whether actual wind direction is in wind direction wake effect；And calculate
Each blower fan is from the auto-correlation determined the wind direction with actual wind direction and cross-correlation coefficient；
Whether the most above-mentioned actual wind direction is in wind direction wake effect method of determining range: according to place peripheral obstacle
Situation, the flow distortion situation in place is estimated, gets rid of the sector region that causes of significant obstacle thing wake flow and i.e. reject fan
District, if direction of the wind comes from is in the rejecting Sector Range of monitoring blower fan, is whether actual wind direction is in wind direction wake flow shadow
In the range of sound, the rejecting sector of Wind turbines is calculated as follows:
(1) if barrier is tall and big object
Utilize formula (1-1) that the size of barrier is equivalent to rotor diameter,
In formula, De equivalence rotor diameter；The height of Lh barrier；The width (taking Breadth Maximum) of Lw barrier；Profit again
Can try to achieve with formula (1-2) and affect the interference sector of measuring wind speed,
α=1.3arctan (2.5De/Le+0.15)+10 (1-2)
In formula, α interference sector；De equivalence rotor diameter；The Le barrier distance away from blower fan；
(2) if barrier is the wind power generating set closed on
If barrier is to close on wind power generating set, directly interference sector just can be tried to achieve with formula (1-3),
α=1.3arctan (2.5Dn/Ln+0.15)+10 (1-3)
In formula, α interference sector；Dn closes on the rotor diameter of wind power generating set；Ln closes on wind power generating set away from quilt
Survey the distance of blower fan；
Flow chart according to wind vane method for diagnosing faults and the result of calculation of above-mentioned operation processing unit 13 are to each blower fan
Wind vane running status diagnoses, and judges wind vane failure condition；
In breakdown judge unit 14, cross-correlation coefficient computing formula is as follows:
In formula, xi: monitor the wind direction average that blower fan i-th adds up 1 minute in every 4 minutes；Monitoring blower fan wind direction in 4 minutes
Average；
yi: carry out wind direction i-th in every 4 minutes and add up 1 minute average；Machine carrys out wind direction average and is averaging in 4 minutes.
Autocorrelation coefficient computing formula is as follows:
In formula, xi,The same β of n, i=1,2....n.
The method of wind vane fault diagnosis in Wind turbines yaw system based on big data the most according to claim 9,
It is characterized in that: use Pauta criterion that auto-correlation and cross-correlation coefficient are carried out parameter optimization；Concrete methods of realizing is as follows: profit
Use standard deviation formulaCalculate the standard deviation of autocorrelation coefficient, wherein xiFor autocorrelation coefficient；Contrast is certainly
Correlation coefficient record result is more than the value of 3 σ, and is rejected；Reject more than remaining autocorrelation coefficient after 3 σ, according to counting
Mean Value Formulas calculates the average of residue autocorrelation coefficient, and as final autocorrelation coefficient threshold value, cross-correlation coefficient
Optimization process is identical with autocorrelation coefficient process.
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| CN107869420A (en) * | 2016-09-27 | 2018-04-03 | 远景能源（江苏）有限公司 | The wind turbine yaw control method and system of wind turbine farm |
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| CN112796940A (en) * | 2021-01-29 | 2021-05-14 | 东方电气风电有限公司 | Wind alignment method for wind direction data missing fan |
| CN113653609A (en) * | 2021-09-17 | 2021-11-16 | 中节能风力发电股份有限公司 | Wind vane fault identification method, system, equipment and storage medium for wind turbine generator |
| CN114113683A (en) * | 2021-11-02 | 2022-03-01 | 上海电气风电集团股份有限公司 | Wind direction indicator monitoring method and system for wind turbine in wind power plant and computer readable storage medium |
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