CN108087210A - Wind generating set blade abnormity identification method and device - Google Patents
Wind generating set blade abnormity identification method and device Download PDFInfo
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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
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Abstract
The invention relates to the field of wind power, in particular to a wind generating set blade abnormity identification method, which is used for monitoring the running state of a wind generating set, firstly obtaining running parameter data of the wind generating set, wherein the running parameter data at least comprises a Y-direction acceleration value, then determining abnormity detection data based on the running parameter data, then determining an abnormity detection data section in which the abnormity detection data continuously appear, then counting the total times of the abnormity detection data section, and if the total times is greater than or equal to a preset abnormity time threshold value, judging that the wind generating set blade is abnormal. Otherwise, the blade is not considered abnormal. When the performance of the blade is reduced due to cracking and other problems, the acceleration value in the Y direction in the running process of the fan is abnormal, the abnormal data continuously appear for many times in a certain time, the abnormal data indicate that the blade is abnormal, the abnormal data can be timely monitored when the blade is abnormal, and the timeliness and the accuracy of abnormal blade identification are improved.
Description
Technical field
The present invention relates to technical field of wind power, and in particular to a kind of wind generator set blade abnormality recognition method and dress
It puts.
Background technology
The kinetic energy that wind-power electricity generation refers to keep watch switchs to electric energy, since wind energy is a kind of clean regenerative resource, more
To be more taken seriously.The required device of wind-power electricity generation is referred to as wind power generating set.Wind power generating set includes wind wheel, power generation
Machine is arranged in cabin containing compositions, generators such as blade, wheel hub, reinforcing members in wind wheel, and blade is by wind-force rotary electrification.As it can be seen that
Blade is the core component of the process of wind-power electricity generation.
Blade is during rotary electrification, due to being chronically exposed in natural environment, is likely to occur after longtime running
Situations such as cracking, fracture, this will seriously affect the normal operation of wind power generating set.The blade of wind power generating set is being split
Line is a process progressively deteriorated with the accumulation of time to cracking, the process of fracture.When tiny crackle occurs in blade
When, influence to wind turbine overall operation is simultaneously little, but with the increase of cracking degree or even when there are crack conditions, it will
Seriously affect the performance of blade.At present, could only be arrived when big crackle or fracture occurs in blade by eye-observation, but
The operation of unit has been seriously affected at this time.When becoming 2 or 2.5 after leaf destruction, generated output can substantially drop
Low, the variation by monitoring generated output at this time can also learn the situation of partial blade failure.In addition, when blade occurs significantly
When cracking or fracture, the stationarity of fan operation is affected, and can cause the failure rate of fan operation to increase, frequent when occurring
During failure, also it can be found that the problem of blade failure.But aforesaid way is all seriously even to cause event in blade cracking degree
It can be just found during barrier, there are certain hysteresis qualitys, this does not only exist larger risk, but also the economic benefit of power generation is subject to
It influences.
The content of the invention
In view of this, an embodiment of the present invention provides a kind of wind generator set blade abnormality recognition method and device, with
It solves the problems, such as to find that blade compares hysteresis, can not find the problem of Abnormal Leaves in time in the prior art.
According in a first aspect, an embodiment of the present invention provides a kind of wind generator set blade abnormality recognition methods, it is used for
Wind power generating set operating status is monitored, obtains the operational parameter data of the wind power generating set, the operating parameter first
Data include at least Y-direction acceleration value, as the amplitude of second cabin acceleration signal changes；It is determined based on the operational parameter data
Abnormality detection data, abnormality detection data refer to the data relatively changed greatly with other data under the operating mode.Then, it is determined that
Continuously there is the abnormality detection data segment of the abnormality detection data, can be characterized by abnormality detection data segment and be successively happened at
There is the adjacent data of relevance in a period of time；The total degree of the abnormality detection data segment is counted afterwards, then can be obtained
The frequency that abnormality detection data occur, finally judges whether the total degree is greater than or equal to default frequency of abnormity threshold value, if institute
It states total degree and then judges that the wind generator set blade is abnormal more than or equal to default frequency of abnormity threshold value.Since blade is going out
When existing problems of crack causes hydraulic performance decline, the Y-direction acceleration value during fan operation is present with exception, works as abnormal data
It is lasting within a certain period of time occur multiple, then illustrate that blade occurs abnormal, it is necessary to take measures in advance, to avoid occurring therefore
Barrier.
In an embodiment with reference to first aspect, abnormality detection data are determined based on the operational parameter data, are wrapped
It includes：The operational parameter data during power generation of wind power generating set normal operation is obtained from the operational parameter data；From it is described just
Target detection supplemental characteristic is extracted in operational parameter data during often operation power generation；It is determined based on the target detection supplemental characteristic
Abnormality detection data.Target detection supplemental characteristic is extracted from the operational parameter data under generating set normal power generation state, this
Sample can due under generator start and stop state or malfunction caused abnormal data filter out, selection be all power generation
Parameter under unit normal power generation state is more advantageous to the problem of identification blade cracking etc. causes.
It is described based on the target detection parameter number in the present embodiment in another embodiment with reference to first aspect
According to definite abnormality detection data, including calculating the Y-direction acceleration average in all target detection supplemental characteristics first；Then sentence
Whether the difference between Y-direction acceleration value and the Y-direction acceleration average in disconnected each target detection supplemental characteristic surpasses
Go out preset difference value threshold value；When the difference is greater than or equal to preset difference value threshold value, judge that the target detection supplemental characteristic is
Abnormality detection data；When the difference is less than preset difference value threshold value, judge the target detection supplemental characteristic for non-abnormal inspection
Measured data.By the way that Y-direction acceleration value compared with Y-direction acceleration average, beyond normal range (NR), is just recognized in the program
It is set to data exception, therefore this method can more accurately identify abnormal data, have higher accuracy.By non-different
Often detection data, it is out of service can preferably to judge that the interval between abnormality detection data segment is up or handles
State improves accuracy of detection.
In another embodiment with reference to first aspect, the preset difference value threshold value is according to the target detection parameter number
According to standard deviation determine.The second cabin acceleration signal numerical value being less than away from average within a standard deviation accounts for whole numerical value
68%, the numerical value accounting within two standard deviations is 95%, and the numerical value accounting within three standard deviations is 99%.Y is set herein
K times of direction cabin acceleration signal standard deviation is worth the target difference threshold value y_amplitude as acceleration amplitude anomaly, i.e.,
Most of non-abnormal second cabin acceleration signal numerical value of amplitude is within this threshold range, more than can defining for threshold value
It is abnormal for second cabin acceleration signal amplitude.
In another embodiment with reference to first aspect, wind power generating set is being obtained just from the operational parameter data
Operational parameter data during often operation power generation, including being based on state flag bit, active power, propeller pitch angle and/or generator speed
Screen the operational parameter data during power generation of wind power generating set normal operation.
In another embodiment with reference to first aspect, determine the abnormality detection of the abnormality detection data continuously occur
Data segment, including：Determine the interval between the adjacent abnormality detection data；Judge the interval between the abnormality detection data
Whether predetermined interval threshold value is exceeded；When the interval is without departing from predetermined interval threshold value, an abnormality detection data segment is recorded.Such as
Interval between fruit abnormality detection data segment is very long, then illustrates that this relevance twice between exception is smaller, therefore be not suitable for number
According to merging, it is necessary to exclude, so as to improve the precision of judgement.
In another embodiment with reference to first aspect, determine the abnormality detection of the abnormality detection data continuously occur
Data segment, including：Determine the number of the continuous non-abnormality detection data between the adjacent abnormality detection data；Judge the company
Whether the number for continuing non-abnormality detection data exceeds predetermined number threshold value；Company between the adjacent abnormality detection data
When continuing the number of non-abnormality detection data without departing from the predetermined number threshold value, an abnormality detection data segment is recorded.By non-
The number of abnormality detection data can preferably represent the state of the generating set normal operation, and wind turbine is in and is shut down
State or the data of long interval of time are got rid of, and improve the accuracy of this method.
In another embodiment with reference to first aspect, the default frequency of abnormity threshold value and/or the preset difference value
Threshold value is determined according to the type of target detection supplemental characteristic and the operating condition of the wind power generating set by training.The wind
The operating condition of power generator group includes at least one of wind speed, rotating speed, power.In view of the difference under normal power generation state
Wind speed, rotating speed and power, it is therefore desirable to pass through repeatedly trained side according to different operating modes and the different detection parameters of selection
Formula obtains above-mentioned threshold value so that these threshold values have better specific aim, improve recognition efficiency.
According to second aspect, the embodiment of the present invention provides a kind of wind generator set blade anomalous identification device, including ginseng
Number acquiring unit, for obtaining the operational parameter data of the wind power generating set, the operational parameter data includes at least Y side
To acceleration value；First processing units, for determining abnormality detection data based on the operational parameter data；Second processing list
Member, for determining the abnormality detection data segment of the abnormality detection data continuously occur；3rd processing unit, it is described for counting
The total degree of abnormality detection data segment；Abnormal deciding means, for judging it is default abnormal whether the total degree is greater than or equal to
Frequency threshold value judges that the wind generator set blade is different if the total degree is greater than or equal to default frequency of abnormity threshold value
Often.
With reference to an embodiment of second aspect, the first processing units include：Data select subelement, for from
The operational parameter data during power generation of wind power generating set normal operation is obtained in the operational parameter data；Data extraction is single
Member extracts target detection supplemental characteristic in operational parameter data during for generating electricity from the normal operation；Abnormality detection data
Determination subelement, for determining abnormality detection data based on the target detection supplemental characteristic..
With reference to the another embodiment of second aspect, the abnormality detection data determination subelement includes：First calculates
Subelement, for calculating the Y-direction acceleration average in all target detection supplemental characteristics；Comparing subunit, it is every for judging
Whether the difference between Y-direction acceleration value and the Y-direction acceleration average in a target detection supplemental characteristic is beyond pre-
If difference threshold；Abnormality detection data generate subelement, for when the difference is greater than or equal to preset difference value threshold value, judging
The target detection supplemental characteristic is abnormality detection data；Non- abnormality detection data generate subelement, small for working as the difference
When preset difference value threshold value, it is non-abnormality detection data to judge the target detection supplemental characteristic..
With reference to the another embodiment of second aspect, the second processing unit, including：Determination subelement is spaced, is used
Interval between the adjacent abnormality detection data are determined；Judgment sub-unit is spaced, for judging the abnormality detection data
Between interval whether exceed predetermined interval threshold value；First abnormality detection data segment record subelement, for working as the interval not
During beyond predetermined interval threshold value, an abnormality detection data segment is recorded.
With reference to the another embodiment of second aspect, the second processing unit, including：Number determination subelement is used
The number of continuous non-abnormality detection data between the adjacent abnormality detection data are determined；Number judgment sub-unit, is used for
Judge the number of the continuous non-abnormality detection data whether beyond predetermined number threshold value；Second abnormality detection data segment record
Unit, it is pre- without departing from described for working as the number of the continuous non-abnormality detection data between the adjacent abnormality detection data
If during number threshold value, record an abnormality detection data segment.
According to the third aspect, an embodiment of the present invention provides a kind of wind power generating set control devices, which is characterized in that bag
Memory and processor are included, connection is communicated between the memory and the processor, meter is stored in the memory
Calculation machine instructs, and the processor is by performing the computer instruction, so as to the wind described in first aspect and its optional mode
Power generator group Abnormal Leaves recognition methods.
It is described computer-readable an embodiment of the present invention provides a kind of computer readable storage medium according to fourth aspect
Storage medium is stored with computer instruction, and the computer instruction is used to that the computer to be made to perform first aspect and its optional side
Wind generator set blade abnormality recognition method described in formula.
Description of the drawings
The features and advantages of the present invention can be more clearly understood by reference to attached drawing, attached drawing is schematically without that should manage
It solves to carry out any restrictions to the present invention, in the accompanying drawings：
Fig. 1 shows the flow chart of wind generator set blade abnormality recognition method according to embodiments of the present invention；
Fig. 2 shows the flow chart of wind generator set blade abnormality recognition method according to another embodiment of the present invention；
Fig. 3 shows the flow chart of the method for acquisition abnormality detection data according to another embodiment of the present invention；
Fig. 4 shows the design sketch of the wind generator set blade abnormality recognition method of another embodiment of the present invention；
Fig. 5 shows a kind of signal of wind generator set blade anomalous identification device according to another embodiment of the present invention
Figure；
Fig. 6 shows the schematic diagram of wind power generating set control device according to embodiments of the present invention.
Specific embodiment
To make the purpose, technical scheme and advantage of the embodiment of the present invention clearer, below in conjunction with the embodiment of the present invention
In attached drawing, the technical solution in the embodiment of the present invention is clearly and completely described, it is clear that described embodiment is
Part of the embodiment of the present invention, instead of all the embodiments.Based on the embodiments of the present invention, those skilled in the art are not having
All other embodiments obtained under the premise of creative work are made, belong to the scope of protection of the invention.
Embodiment 1
A kind of wind generator set blade abnormality recognition method in the present embodiment is provided, shape is run for wind power generating set
State security monitoring, this method may operate in the controller of wind power generating set, for finding in time in wind power generating set
Blade whether crack and influence blade aeroperformance or impeller operation balance, so as to find in time it is abnormal
Blade.
Wind generator set blade abnormality recognition method in the present embodiment, flow chart is as shown in Figure 1, including following step
Suddenly：
S11, the operational parameter data for obtaining the wind power generating set, the operational parameter data include at least Y-direction
Acceleration value.
The operating parameter selection historical data of wind power generating set described in the present embodiment, can be record wind-driven generator
The historical data of operating condition in a period of time is organized, Y-direction acceleration value is included at least in the data, is selected in the present embodiment
It is the acceleration signal of blade Y-direction.As the embodiment that other can be replaced, which is also an option that
Impeller, cabin or the corresponding amplitude signal of pylon, the Y-direction acceleration value of displacement signal.
S12, abnormality detection data are determined based on the operational parameter data.
In the step, it is in all operational parameter datas, finds and abnormal detection data occur.Blade can be obtained
The average value of Y-direction acceleration value under normal power generation state, when the Y-direction acceleration value of blade and the difference of average value surpass
When going out certain amplitude scope, it is believed that the data are abnormality detection data, and amplitude range herein can be according to wind power generating set
Operating condition determine.
S13, the abnormality detection data segment for the abnormality detection data continuously occur is determined.
Under normal circumstances, abnormality detection data may also once in a while occur because of emergency situations, with other abnormality detection numbers
It is long according to being spaced, it is not considered as between these data that there are relevances in this case.It can be according to adjacent abnormal testing number evidence
Interval come determine these abnormal datas with the presence or absence of association.Concrete mode is as follows：
First, the interval between the adjacent abnormality detection data is determined.
Secondly, judge the interval between the abnormality detection data whether beyond predetermined interval threshold value.If duration threshold
When value selected as 0.5 is small, count to obtain by algorithm model training result, certainly in other embodiments, time threshold
It can also rationally set as needed, can generally select the time span in 15 minutes -1 hours.If beyond pre-
If interval threshold, then illustrating this, there is no relevances between abnormal data twice, it is impossible to while embody in a period
Abnormal Leaves, then it is relevant abnormality detection data to be not considered as these data.When the interval is without departing from predetermined interval threshold value,
Then think that this has association between abnormality detection data twice, this abnormality detection data merging twice is recorded as once abnormal inspection
Measured data section.
The total degree of S14, the statistics abnormality detection data segment.
Due to being directed to adjacent abnormality detection data in step S13 by judging the adjacent abnormality detection for the condition that meets
Data record is an abnormality detection data segment, the so number by counting these abnormality detection data segments, it is possible to be accumulated
Go out the number of abnormality detection data.
S15, judge whether the total degree is greater than or equal to default frequency of abnormity threshold value, if the total degree is more than or waits
Then judge that the wind generator set blade is abnormal in default frequency of abnormity threshold value, be otherwise not considered as the wind power generating set leaf
Piece is abnormal.
When total degree exceeds default frequency of abnormity threshold value, then illustrate within a certain period of time, unit operation appearance is repeatedly held
Continuous abnormal data, these abnormal datas run the problem of there are larger to associate, and blade appearance is larger with blade, sentence at this time
It is set to blade and there is exception, it is necessary to which timely processing, avoids the generation of failure.
Wind generator set blade abnormality recognition method provided in this embodiment, passes through the operating parameter of wind power generating set
Whether the Y-direction acceleration value in data is abnormal, continuous abnormal situation occur, to judge that wind generator set blade is
It is no to be abnormal.When causing hydraulic performance decline cracking the problems such as due to blade, the Y side in wind power generating set operational process
Be present with exception to acceleration value, when abnormal data continuously occur within a certain period of time it is multiple, then illustrate Abnormal Leaves, it is necessary to and
When pay close attention to, to avoid the blade occur failure caused by occur, so as to reduce wind present in wind power generating set operational process
Danger.
Embodiment 2
A kind of wind generator set blade abnormality recognition method is provided in the present embodiment, usage scenario is same as Example 1,
Flow chart is as shown in Fig. 2, this method comprises the following steps：
S21, the operational parameter data for obtaining the wind power generating set, the operational parameter data include at least Y-direction
Acceleration value.
The operational parameter data of the wind power generating set inputted herein is live transient operation data, can be real-time number
According to or pre-stored historical data.For example, data sampling frequency is 1/7Hz, separately claim 7 seconds data or 7s data,
The instantaneous value conduct of different variable signals in unit running process can be recorded as requested every 7 seconds central monitoring systems
In real time or history data store is got off.Basic variable signal includes contained by Site Detection data：Live code name [1], unit code name
[1], time [ymd_hms], wind speed [m/s], rotating speed [r/min], active power [kW], x directions second cabin acceleration [g], Y-direction
Second cabin acceleration [g], 1-3 blade pitch angles [°], 1-3 vane propeller-changings rate [°/s], data availability status flag bit
[1], g represents acceleration of gravity here, and unit is m/s2.For example, wherein Y-direction second cabin acceleration work is selected in the present embodiment
For target detection supplemental characteristic, foundation that the variations of other variable signals can directly or indirectly as each judgement in algorithm,
Such as rotating speed, time and for determining the signals such as the wind speed of different operating modes, power, propeller pitch angle.In other implementations,
(or combination) unit x direction second cabin accelerations and the situation of second cabin acceleration virtual value amplitude anomaly variation, inspection can also be detected
Method of determining and calculating is identical, but specific threshold value has difference compared with detection Y-direction second cabin acceleration amplitude anomaly variation.The present embodiment
In, by the 7s data of the detected unit of detection beginning and ending time extraction.The length of time can rationally be set herein, time-domain analysis one
As it is more with the data of second grade, the useful information that longer data preserve is fewer, and service efficiency is not high.Operation in the present embodiment
Supplemental characteristic can also be compatible with the number at other different sampling stages intervals in addition to general common unit 7s transient operation data
According to, such as the data such as 20ms, 1s, 10s, 1min and 10min, since the sample frequency difference of different data is, it is necessary to according to difference
Each threshold value in sampling time interval re -training model algorithm is to arrive optimum detection effect.It is special that consideration is conducive to identification failure
The data volume of sign and the duration of data processing, the detected unit of extraction carve the 7s data in first 30 days when detecting.Data pick-up
Process realizes that data to be tested can also be accessed from big data cloud platform by fan operation platform in running background, such as gold
Wind KMX big datas cloud platform, Amazon AWS cloud platforms etc. can also take line under type to import data, it might even be possible to again under line
Operating scheme model is detected.
S22, abnormality detection data are determined based on the operational parameter data.
The first step obtains the operating parameter number during power generation of wind power generating set normal operation from the operational parameter data
According to.For under start and stop and malfunction, the parameter of fan operation can have greatly changed, start and stop and failure occur
When second cabin acceleration variable quantity it is larger.In order to ensure the validity of data, target detection supplemental characteristic is normal from generating set
It is extracted in operating parameter under generating state, without considering the data under improper generating state.
For example, if if the rotating speed of generator exceeds rated speed, fan operation stationarity is present with larger impact,
It is that generator speed exceeds caused by rated speed or caused by blade failure to be difficult to identification at this time, it is therefore desirable in generator
Group is under normal power generation state, that is, selection rotating speed is less than or equal to the data obtained during rated speed and is identified.
Filter out the 7s data under unit normal power generation state using state flag bit in the present embodiment, and by its according to when
Between sequencing merge line by line, screening conditions are that data availability status flag bit is equal to 1 here, every in the 7s data after merging
A line all records data of each variable at the moment.The present embodiment carries out data sieve using data using state flag bit
Choosing, the data filtered out under unit normal power generation state are detected.In other embodiments, wattful power can also be used
Operational parameter data when rate, propeller pitch angle and/or generator speed screening wind power generating set normal operation power generation, that is, it is logical
It crosses to choose and meets the data of pitch angular region and power bracket to screen operational parameter data during normal operation power generation, such as machine
Group becomes the data that propeller angle is less than 30 ° and active power is more than or equal to 20kW, carrys out the data under approximate unit normal power generation state.
Second step extracts target detection supplemental characteristic in operational parameter data when generating electricity from the normal operation.
The amplitude change that Y-direction second cabin acceleration signal is chosen in the present embodiment is turned to detection object, mainly considers
(such as blade cracking, fracture) can influence blade aerodynamic characteristic after turbines vane failure, cause the imbalance of impeller dynamic characteristics,
The vibration inside and outside impeller face is generated under the action of centrifugal force, which can more or less fold according to the size of blade failure degree
It is added in second cabin acceleration signal, Y-direction second cabin acceleration is found in the statistics to live blade failure data unit operation
The amplitude variation of signal becomes apparent compared with X-direction, and failure characteristics are more easy to identify and extract.
3rd step determines abnormality detection data based on the target detection supplemental characteristic, and flow chart is as shown in Figure 3.
First, the Y-direction acceleration average y_mean in all Y-direction second cabin acceleration signals is calculated.
Then, the Y-direction acceleration value in each target detection supplemental characteristic and the Y-direction acceleration average y_ are judged
Whether the difference between mean exceeds preset difference value threshold value.
Preset difference value threshold value herein is determined according to the standard deviation of Y-direction acceleration average.One kind as the present embodiment
Specific embodiment calculates the standard deviation of Y-direction second cabin acceleration signal, according to the definition of normal distribution, away from average y_mean
The 68% of whole numerical value are accounted for less than the second cabin acceleration signal numerical value within a standard deviation, and the numerical value within two standard deviations accounts for
Than for 95%, the numerical value accounting within three standard deviations is 99%.The k of Y-direction second cabin acceleration signal standards difference is set herein
Target difference threshold value y_amplitude of the value as acceleration amplitude anomaly again, i.e., most of non-abnormal cabin of amplitude accelerate
Degree signal numerical value is within this threshold range, and second cabin acceleration signal amplitude exception can be defined as more than threshold value.
Here k values are directed to a large amount of field data training and statistics per money type according to algorithm model and obtain.K herein is equal to 5,
K more mini systems are sensitiveer, easier wrong report, and k values are less susceptible to more greatly wrong report, it is necessary to obtain a desired value by statistics to carry
Height alarm accuracy rate.
When the difference is greater than or equal to preset difference value threshold value, it is abnormality detection to judge the target detection supplemental characteristic
Data.When the difference is less than preset difference value threshold value, it is non-abnormality detection data to judge the target detection supplemental characteristic.Difference
Value it is bigger, illustrate that the possibility that Y-direction acceleration is abnormal is bigger, when more than to a certain degree when then think the data occur it is different
Often, it is non-abnormality detection data otherwise then to judge the Y-direction acceleration.
S23, the abnormality detection data segment for the abnormality detection data continuously occur is determined.
If the whole time span of operational parameter data is larger, as time span be one month or multiple months when
Between, if the time interval between abnormality detection data is longer, illustrate that the appearance relevance of this abnormal data twice is smaller, this
Abnormal data twice should treat respectively rather than merging treatment, and abnormality detection data segment herein can be in the following way
It determines, specifically includes：
The first step determines the number of the continuous non-abnormality detection data between the adjacent abnormality detection data.
It is longer in view of the interval time between abnormality detection data, it may be possible to due to unit is not opened for a long time
It is caused, and then there are abnormality detection data in unit after opening, and in order to exclude such case, passes through continuous non-abnormality detection number
According to number determine that unit is in the interval of abnormality detection data under operating status.
Whether second step judges the number of the continuous non-abnormality detection data beyond predetermined number threshold value.
Non- abnormality detection data between the adjacent abnormality detection data are more than certain data, then it is assumed that this is different twice
The relevance often occurred is poor, without merging treatment.Number threshold value herein is rationally set according to operating mode.
In the embodiment, for the detection data that time span is larger, choose non-abnormality detection data and counted, it will
The smaller data removal of relevance, has better accuracy of identification.
3rd step, when the continuous non-abnormality detection data between the adjacent abnormality detection data number without departing from
During the predetermined number threshold value, an abnormality detection data segment is recorded.
When non-abnormal data amount check is less between adjacent abnormality detection data, during without departing from default number threshold value,
Illustrate that this occurs having relevance twice extremely, then it is assumed that this belongs to the data segment of abnormality detection, can reflect by merging
The overall operation state of blade.
The total degree of S24, the statistics abnormality detection data segment.It is identical with the S13 in embodiment 1, it repeats no more.
S25, judge whether the total degree is greater than or equal to default frequency of abnormity threshold value, if the total degree is more than or waits
Then judge that the wind generator set blade is abnormal in default frequency of abnormity threshold value, be otherwise not considered as Abnormal Leaves.With embodiment 1
In S15 it is identical, repeat no more.
Default frequency of abnormity threshold value and/or the preset difference value threshold value and/or predetermined number precognition root in the present embodiment
It is determined according to the type of target detection supplemental characteristic and the operating condition of the wind power generating set by training.Consider normal power generation
Different wind speed, rotating speed and power section under state are needed exist for according to the above-mentioned each threshold value of different operating mode re -training models.Pin
A feasible program to megawatt-stage direct-drive wind power generating set is exactly that small wind speed section (5-8m/s) is selected to press generating unit speed
Mono- point of storehouse of 0.5r/min (or power is by mono- point of storehouse of 50kW) come comparative analysis each under point storehouse second cabin acceleration amplitude it is different
Often variation.
Specific embodiment includes as follows：
1) small wind speed section (5-8m/s) data of unit are selected, divide storehouse per 0.5m/s by wind speed, machine is selected by data training
Cabin acceleration signal amplitude anomaly significantly divides storehouse, is detected with this programme for the position in storehouse data, target component reference value
It is also required to determine in data training process with the target difference threshold value.
2) high-power section of unit is selected (to be more than or equal to (rated power -200kW)) data, divides storehouse per 50kW by power, leads to
It crosses data training and selects the abnormal significantly point storehouse of second cabin acceleration signal amplitude, examined with this programme for the position in storehouse data
It surveys, target component reference value and the target difference threshold value are also required to determine in data training process in algorithm.
3) the big rotating speed of unit is selected (to be more than or equal to (rated speed -2r/min)) data, divides storehouse per 0.5r/min by rotating speed,
The abnormal significantly point storehouse of second cabin acceleration signal amplitude is selected by data training, the position in storehouse data are directed to this programme algorithm
It detects, target component reference value and the target difference threshold value are also required to determine in data training process in algorithm.
Wind generator set blade abnormality recognition method in the present embodiment, goes to obtain in the case of generator normal operation
Detection parameters, and the situation for having discharged due to unit off-duty to cause time interval longer by non-abnormal data are taken, when different
Often detection data exceed normal range (NR), regard as data exception, therefore this method can more accurately identify abnormal data,
With higher accuracy.
Fieldtesting results are as shown in figure 4, blade cracking feelings occur in Gansu wind power plant 15# units on April 3rd, 2015
Condition.Before failure in the 7s real time datas an on April 2 it can be seen that, Y-direction second cabin acceleration signal is from the 18 of the same day:03:
53 to 20:38:02 exception for the long period occur significantly is vibrated, and totally 170 amplitudes are more than the threshold value y_ of acceleration amplitude anomaly
amplitude.What flow, data access and result output in the present embodiment can be realized under R language environments, it can also
The function of this programme is realized under the translation and compiling environments such as Python, Matlab, Scala, and different Modular Modeling Softwares can be implanted into
Operation, such as golden wind MD4X Modular Modeling Softwares.
Embodiment 3
A kind of wind generator set blade anomalous identification device, structure diagram as shown in figure 5, including：
Parameter acquiring unit 31, for obtaining the operational parameter data of the wind power generating set, the operating parameter number
According to including at least Y-direction acceleration value；Specific implementation is identical with the S11 in embodiment 1 or the S21 in embodiment 2, herein
It repeats no more.
First processing units 32, for determining abnormality detection data based on the operational parameter data；Specific implementation
Identical with the S12 in embodiment 1 or the S22 in embodiment 2, details are not described herein.
Second processing unit 33, for determining the abnormality detection data segment of the abnormality detection data continuously occur；Specifically
Realization method is identical with the S13 in embodiment 1 or the S23 in embodiment 2, and details are not described herein.
3rd processing unit 34, for counting the total degree of the abnormality detection data segment；Specific implementation is with implementing
The S24 in S14 or embodiment 2 in example 1 is identical, and details are not described herein.
Abnormal deciding means 35, for judging whether the total degree is greater than or equal to default frequency of abnormity threshold value, if institute
It states total degree and then judges that the wind generator set blade is abnormal more than or equal to default frequency of abnormity threshold value.Specific implementation
Identical with the S15 in embodiment 1 or the S25 in embodiment 2, details are not described herein.
Wherein, the first processing units 32 include：
Data select subelement, when generating electricity for obtaining wind power generating set normal operation from the operational parameter data
Operational parameter data；
Data extract subelement, and target detection is extracted in operational parameter data during for generate electricity from the normal operation and is joined
Number data；
Abnormality detection data determination subelement, for determining abnormality detection data based on the target detection supplemental characteristic.
Wherein, the abnormality detection data determination subelement includes：
First computation subunit, for calculating the Y-direction acceleration average in all target detection supplemental characteristics；
Comparing subunit, for judging that the Y-direction acceleration value in each target detection supplemental characteristic and the Y-direction add
Whether the difference between speed average exceeds preset difference value threshold value；
Abnormality detection data generate subelement, for when the difference is greater than or equal to preset difference value threshold value, judging institute
Target detection supplemental characteristic is stated as abnormality detection data；
Non- abnormality detection data generate subelement, for when the difference is less than preset difference value threshold value, judging the mesh
It is non-abnormality detection data to mark detection parameters data.
As a kind of concrete implementation mode, the second processing unit 33 includes：
Determination subelement is spaced, for determining the interval between the adjacent abnormality detection data；
Judgment sub-unit is spaced, for judging the interval between the abnormality detection data whether beyond predetermined interval threshold
Value；
First abnormality detection data segment record subelement, for when the interval is without departing from predetermined interval threshold value, recording
Abnormality detection data segment.
As another concrete implementation mode, the second processing unit 33 includes：
Number determination subelement, for determining the continuous non-abnormality detection data between the adjacent abnormality detection data
Number；
Number judgment sub-unit, for judging the number of the continuous non-abnormality detection data whether beyond predetermined number threshold
Value；
Second abnormality detection data segment record subelement, it is continuous between the adjacent abnormality detection data for working as
When the number of non-abnormality detection data is without departing from the predetermined number threshold value, an abnormality detection data segment is recorded.
Wind generator set blade anomalous identification device in the present embodiment, can be in the cracking influence blade gas of blade
The problem of initial stage of dynamic performance just identifies blade failure, pinpoints the problems in time, predicts potential risk, has to fan operation
Effect monitoring, has preferable Abnormal Leaves accuracy of identification.
Embodiment 4
A kind of wind power generating set control device is provided in the present embodiment, as shown in fig. 6, including memory 42 and processor
41, connection is communicated between the memory 42 and the processor 41, can be connected by bus or other modes, figure
In 6 exemplified by being connected by bus.
Processor 41 can be central processing unit (Central Processing Unit, CPU).Processor 41 can be with
For other general processors, digital signal processor (Digital Signal Processor, DSP), application-specific integrated circuit
(Application Specific Integrated Circuit, ASIC), field programmable gate array (Field-
Programmable Gate Array, FPGA) either other programmable logic device, discrete gate or transistor logic,
The combination of the chips such as discrete hardware components or above-mentioned all kinds of chips.
Memory 42 is used as a kind of non-transient computer readable storage medium storing program for executing, available for storing non-transient software program, non-
Transient computer executable program and module, such as the wind generator set blade abnormality recognition method pair in the embodiment of the present invention
Answer program instruction/module (for example, parameter acquiring unit 31, first processing units 32, second processing unit 33 shown in Fig. 5,
3rd processing unit 34 and abnormal deciding means 35).Processor 41 is stored in the non-transient software in memory 42 by operation
Program, instruction and module so as to perform the various function application of processor and data processing, that is, realize that the above method is implemented
Wind generator set blade abnormality recognition method method in example 1 or 2.
Memory 42 can include storing program area and storage data field, wherein, storing program area can storage program area,
At least one required application program of function；Storage data field can store data that processor 41 is created etc..In addition, storage
Device 42 can include high-speed random access memory, can also include non-transient memory, for example, at least a magnetic disk storage
Part, flush memory device or other non-transient solid-state memories.In some embodiments, memory 42 is optional including compared with place
The remotely located memory of device 41 is managed, these remote memories can pass through network connection to processor 41.The reality of above-mentioned network
Example includes but not limited to internet, intranet, LAN, mobile radio communication and combinations thereof.
One or more of modules are stored in the memory 42, when being performed by the processor 41, are performed
Wind generator set blade abnormality recognition method in embodiment as shown in Figs. 1-2.
Above-mentioned wind generator set blade abnormality recognition method detail can be corresponded to refering to Fig. 1 to reality shown in Fig. 2
It applies corresponding associated description and effect in example and is understood that details are not described herein again.
It is that can lead to it will be understood by those skilled in the art that realizing all or part of flow in above-described embodiment method
Computer program is crossed relevant hardware to be instructed to complete, the program can be stored in a computer read/write memory medium
In, the program is upon execution, it may include such as the flow of the embodiment of above-mentioned each method.Wherein, the storage medium can be magnetic disc,
CD, read-only memory (Read-Only Memory, ROM), random access memory (Random Access
Memory, RAM), flash memory (Flash Memory), hard disk (Hard Disk Drive, abbreviation：) or solid state disk HDD
(Solid-State Drive, SSD) etc.；The storage medium can also include the combination of the memory of mentioned kind.
Although being described in conjunction with the accompanying the embodiment of the present invention, those skilled in the art can not depart from the present invention
Spirit and scope in the case of various modification can be adapted and modification, such modifications and variations are each fallen within by appended claims institute
Within the scope of restriction.
Claims (16)
1. a kind of wind generator set blade abnormality recognition method, which is characterized in that including：
The operational parameter data of the wind power generating set is obtained, the operational parameter data includes at least Y-direction acceleration value；
Abnormality detection data are determined based on the operational parameter data；
Determine the abnormality detection data segment of the abnormality detection data continuously occur；
Count the total degree of the abnormality detection data segment；
Judge whether the total degree is greater than or equal to default frequency of abnormity threshold value, if the total degree is different more than or equal to default
Normal frequency threshold value then judges that the wind generator set blade is abnormal.
2. according to the method described in claim 1, it is characterized in that, described determine abnormality detection based on the operational parameter data
Data, including：
The operational parameter data during power generation of wind power generating set normal operation is obtained from the operational parameter data；
Target detection supplemental characteristic is extracted in operational parameter data when generating electricity from the normal operation；
Abnormality detection data are determined based on the target detection supplemental characteristic.
3. according to the method described in claim 2, it is characterized in that, described determine exception based on the target detection supplemental characteristic
Data are detected, including：
Calculate the Y-direction acceleration average in all target detection supplemental characteristics；
Judge the difference between the Y-direction acceleration value in each target detection supplemental characteristic and the Y-direction acceleration average
Whether preset difference value threshold value is exceeded；
When the difference is greater than or equal to preset difference value threshold value, judge the target detection supplemental characteristic for abnormality detection number
According to；
When the difference is less than preset difference value threshold value, it is non-abnormality detection data to judge the target detection supplemental characteristic.
4. according to the method described in claim 3, it is characterized in that, the preset difference value threshold value is according to the target detection parameter
The standard deviations of data determines.
5. according to the method any one of claim 2-4, which is characterized in that described to be obtained from the operational parameter data
The operational parameter data during power generation of wind power generating set normal operation is taken, including：Based on state flag bit, active power, pitch
Operational parameter data when angle and/or generator speed screening wind power generating set normal operation power generation.
6. according to the described method of any one of claim 1-4, which is characterized in that described to determine the abnormal inspection continuously occur
The abnormality detection data segment of measured data, including：
Determine the interval between the adjacent abnormality detection data；
Judge the interval between the abnormality detection data whether beyond predetermined interval threshold value；
When the interval is without departing from predetermined interval threshold value, an abnormality detection data segment is recorded.
7. according to the described method of any one of claim 1-4, which is characterized in that described to determine the abnormal inspection continuously occur
The abnormality detection data segment of measured data, including：
Determine the number of the continuous non-abnormality detection data between the adjacent abnormality detection data；
Judge the number of the continuous non-abnormality detection data whether beyond predetermined number threshold value；
When the number of the continuous non-abnormality detection data between the adjacent abnormality detection data is without departing from described default
During number threshold value, an abnormality detection data segment is recorded.
8. according to the method described in Claims 2 or 3 or 4, which is characterized in that the default frequency of abnormity threshold value and/or described
Preset difference value threshold value is true by training according to the type of target detection supplemental characteristic and the operating condition of the wind power generating set
It is fixed.
9. according to the method described in claim 8, it is characterized in that, the operating condition of the wind power generating set include wind speed,
At least one of rotating speed, power.
10. a kind of wind generator set blade anomalous identification device, which is characterized in that including：
Parameter acquiring unit, for obtaining the operational parameter data of the wind power generating set, the operational parameter data is at least
Including Y-direction acceleration value；
First processing units, for determining abnormality detection data based on the operational parameter data；
Second processing unit, for determining the abnormality detection data segment of the abnormality detection data continuously occur；
3rd processing unit, for counting the total degree of the abnormality detection data segment；
Abnormal deciding means, for judging whether the total degree is greater than or equal to default frequency of abnormity threshold value, if described total time
Number is greater than or equal to default frequency of abnormity threshold value and then judges that the wind generator set blade is abnormal.
11. device according to claim 10, which is characterized in that the first processing units include：
Data select subelement, for obtaining the fortune during power generation of wind power generating set normal operation from the operational parameter data
Row supplemental characteristic；
Data extract subelement, extraction target detection parameter number in operational parameter data during for generate electricity from the normal operation
According to；
Abnormality detection data determination subelement, for determining abnormality detection data based on the target detection supplemental characteristic.
12. according to the devices described in claim 11, which is characterized in that the abnormality detection data determination subelement includes：
First computation subunit, for calculating the Y-direction acceleration average in all target detection supplemental characteristics；
Comparing subunit, for judging the Y-direction acceleration value in each target detection supplemental characteristic and the Y-direction acceleration
Whether the difference between average exceeds preset difference value threshold value；
Abnormality detection data generate subelement, for when the difference is greater than or equal to preset difference value threshold value, judging the mesh
It is abnormality detection data to mark detection parameters data；
Non- abnormality detection data generate subelement, for when the difference is less than preset difference value threshold value, judging the target inspection
Survey supplemental characteristic is non-abnormality detection data.
13. according to the device described in claim 10 or 11 or 12, which is characterized in that the second processing unit, including：
Determination subelement is spaced, for determining the interval between the adjacent abnormality detection data；
Judgment sub-unit is spaced, for judging the interval between the abnormality detection data whether beyond predetermined interval threshold value；
First abnormality detection data segment record subelement, for when the interval is without departing from predetermined interval threshold value, record to be once
Abnormality detection data segment.
14. according to the device described in claim 10 or 11 or 12, which is characterized in that the second processing unit, including：
Number determination subelement, for determining of the continuous non-abnormality detection data between the adjacent abnormality detection data
Number；
Number judgment sub-unit, for judging the number of the continuous non-abnormality detection data whether beyond predetermined number threshold value；
Second abnormality detection data segment record subelement, it is continuous non-different between the adjacent abnormality detection data for working as
When often the number of detection data is without departing from the predetermined number threshold value, an abnormality detection data segment is recorded.
15. a kind of wind power generating set control device, which is characterized in that including memory and processor, the memory and institute
It states and communicates connection between processor, computer instruction is stored in the memory, the processor passes through described in execution
Computer instruction, so as to which perform claim requires 1-9 any one of them wind generator set blade abnormality recognition methods.
16. a kind of computer readable storage medium, which is characterized in that the computer-readable recording medium storage has computer to refer to
Order, the computer instruction are used to make computer perform claim requirement 1-9 any one of them wind generator set blades
Abnormality recognition method.
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