CN110212585A - Running of wind generating set Reliability Prediction Method, device and Wind turbines based on statistical analysis - Google Patents
Running of wind generating set Reliability Prediction Method, device and Wind turbines based on statistical analysis Download PDFInfo
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- H02J3/386—
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
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- Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
- Y02E10/00—Energy generation through renewable energy sources
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Abstract
The present invention provides a kind of running of wind generating set Reliability Prediction Method, device and Wind turbines based on statistical analysis.The described method includes: the SCADA history keyword data in acquisition unit operation a period of time；By the statistical analysis to the SCADA history keyword data, unit operational reliability prediction model is established；With the unit operational reliability prediction model, online prediction in real time is carried out to the main component operational reliability of Wind turbines.Running of wind generating set Reliability Prediction Method, device and Wind turbines provided by the invention based on statistical analysis can main component operational reliability to Wind turbines carry out accurately online prediction in real time.
Description
Technical field
The present invention relates to technical field of wind power generation, can more particularly to a kind of running of wind generating set based on statistical analysis
By property prediction technique, device and Wind turbines.
Background technique
With the transition and adjustment of energy resource structure, wind-power electricity generation is rapidly developed, become at present main generation mode it
One.Wind turbines are generally located at remote districts, and service condition is more severe, in addition the factor of other influences, and faults frequent occurs,
Up to 10% or more operation and maintenance cost become the maximum bottleneck of Wind Power Generation Industry development.Therefore, how to guarantee wind turbine
The safe and reliable operation of group reduces hot issue of the O&M cost as wind-powered electricity generation industry concern.
In the prior art, existing patent document provides the method for some determining running of wind generating set reliabilities.As specially
Benefit number determines that method and apparatus, reliability determine to provide a kind of Wind turbines reliability in the patent of CN108876073A
Method includes: to calculate the first reliability score corresponding with the availability PBA based on generated energy；It calculates and mean repair interval
Corresponding second reliability score of time MTBI；Third reliability corresponding with average unit maintenance total time-consuming MTOTI is calculated to obtain
Point；Wind turbines reliability is determined based on the first reliability score, the second reliability score and third reliability score of calculating.
Provided in the patent of Patent No. CN106097146A it is a kind of meter and operating status Wind turbines short term reliability prediction side
Method.The key step of this method includes: to obtain Wind turbines state parameter by status monitoring and data collection system, for setting
Standby temperature parameter, establishes the state parameter prediction model based on reverse transmittance nerve network, based on prediction residual distribution character
Calculate protection act probability；For remaining parameter, protection act probability is calculated according to meter and threshold crossing time；Last comprehensive assessment wind-powered electricity generation
The risk that unit is stopped transport in short term.The patent of Patent No. CN108241917A provide a kind of part reliability appraisal procedure and
Device.The reliability estimation method and device that the patent provides are the following steps are included: (A) is based on proportional hazard model to all portions
Part carries out the parameter Estimation of environmental factor, obtains the first environment set of factors for influencing part reliability；(B) by supplier to component
Classify, and based on the component of proportional hazard model and the first environment set of factors to different suppliers carry out environment because
The parameter Estimation of element, filters out the multiple second environment set of factors for the component for influencing different suppliers；(C) according to the multiple
Two environmental factor collection respectively model the service life of the component of different suppliers, and are assessed according to multiple life models of foundation
The part reliability of different suppliers.
The reliability assessment or prediction technique that above-mentioned existing patented technology provides provide one for the operation of Wind turbines health
Fixed priori effect, but bring certain subjective determination into actual assessment and prediction and it is assumed that make to assess practical operation
Getting up has larger difficulty.Therefore, how to find a kind of new method and carry out assessment or pre- come the operational reliability to Wind turbines
It surveys, so that it is overcome deficiency in the prior art, become those skilled in the art's urgent problem to be solved.
Summary of the invention
The technical problem to be solved in the present invention is to provide a kind of running of wind generating set reliability prediction based on statistical analysis
Method, apparatus and Wind turbines, to carry out accurately online prediction in real time to the big component operational reliability of Wind turbines.
In order to solve the above technical problems, the present invention provides a kind of, the running of wind generating set reliability based on statistical analysis is pre-
Survey method, which comprises the SCADA history keyword data in acquisition unit operation a period of time；With Six Sigma matter
It measures control technology and unit operational reliability prediction model is established by the statistical analysis to the SCADA history keyword data,
Also, the unit operational reliability prediction model belongs to Gauss model；It is right with the unit operational reliability prediction model
The main component operational reliability of Wind turbines carries out online prediction in real time.
In some embodiments, the SCADA history keyword data are as follows:
Z=(Ta, P, Tcomp)
Wherein, Z is SCADA historical data matrix, TaFor the environment temperature of main component operation, P is that unit runs power,
TcompFor main component running temperature；With Six Sigma Quality Control Technology, by the SCADA history keyword data
Statistical analysis, establishes unit operational reliability prediction model, comprising: the primary amendment of main component running temperature parameter is carried out,
To eliminate environment temperature to the effect tendency of main component running temperature；Carry out secondary the repairing of main component running temperature parameter
Just, to eliminate power to the effect tendency of main component running temperature；Based on revised main component running temperature parameter, build
The operational reliability prediction model of vertical main component.
In some embodiments, the primary amendment of main component running temperature parameter is carried out, comprising: by SCADA history
Sequence rearrangement parameter of the data matrix according to power from small to large, obtains new SCADA historical data matrix；By power into
Then row interval division carries out once linear to the temperature of main component on different power intervals respectively and returns amendment.
In some embodiments, an innovation representation of the main component running temperature in different capacity section are as follows:
Tr1,i=Tcomp,i-ki,tTa,i
Wherein, Tr1,iFor a correction value of main component running temperature in i-th section of power interval, Tcomp,iFor i-th section of function
Main component running temperature in rate section, Ta,iFor the environment temperature of main component in i-th section of power interval, kI, tIt is i-th section
The primary amendment regression coefficient of power interval.
In some embodiments, the second-order correction of main component running temperature parameter is carried out, comprising: pass through linear regression
Method eliminates temperature parameter of the power to the effect tendency of main component running temperature, after obtaining the second-order correction of main component.
In some embodiments, the temperature parameter after second-order correction are as follows:
Tr2,i=Tr1,i-ki,pP
Wherein, Tr2,iFor the second-order correction value of main component running temperature in i-th section of power interval, Tr1,iFor i-th section of power
A correction value of main component running temperature in section, P are that unit runs power, ki,pIt is repaired for i-th section of the secondary of power interval
Positive regression coefficient.
In some embodiments, it is based on revised main component running temperature parameter, is established based on temperature parameter
The operational reliability prediction model of main component, comprising: the expectation of the temperature parameter after calculating main component second-order correction and
Variance, the desired calculating formula are as follows:
The calculating formula of the variance are as follows:
Wherein,For the expectation,For the variance, Tr2,jFor main component operation temperature in jth section power interval
The second-order correction value of degree, n are the sum of power interval；According to statistical theory, the temperature after main component second-order correction is joined
Several distribution shifts are standardized normal distribution；Reliability prediction model R is established according to Six Sigma Quality Control Technologyr0。
In some embodiments, with the unit operational reliability prediction model, to the main component of Wind turbines
Operational reliability carries out online prediction in real time, comprising: after obtaining the temperature parameter of main component, calculates dependability parameter Rr；
Check parameter RrWhether function range Rr0It is interior, if in the range R of reliability modelr0Interior, then Wind turbines are working properly,
Otherwise it is abnormal, and the range R of functionr0It is provided by following formula:
Wherein,For the expectation,For the variance.
In addition, the present invention also provides a kind of running of wind generating set reliability prediction device based on statistical analysis, described
Device includes: one or more processors；Storage device, for storing one or more programs, when one or more of journeys
Sequence is executed by one or more of processors, so that one or more of processors are realized according to previously described based on system
Count the running of wind generating set Reliability Prediction Method of analysis.
In addition, the Wind turbines include previously described based on statistical the present invention also provides a kind of Wind turbines
The running of wind generating set reliability prediction device of analysis.
By adopting such a design, the present invention has at least the following advantages:
Running of wind generating set Reliability Prediction Method based on statistical analysis, device and Wind turbines proposed by the present invention are logical
The statistical analysis to the SCADA history keyword data is crossed, unit operational reliability prediction model is established, thus to Wind turbines
Big component operational reliability carry out accurately online prediction in real time.
Detailed description of the invention
The above is merely an overview of the technical solutions of the present invention, in order to better understand the technical means of the present invention, below
In conjunction with attached drawing, the present invention is described in further detail with specific embodiment.
Fig. 1 is the flow chart of the running of wind generating set Reliability Prediction Method provided by the invention based on statistical analysis；
Fig. 2 is the structure chart of the running of wind generating set reliability prediction device provided by the invention based on statistical analysis.
Specific embodiment
Hereinafter, preferred embodiments of the present invention will be described with reference to the accompanying drawings, it should be understood that preferred reality described herein
Apply example only for the purpose of illustrating and explaining the present invention and is not intended to limit the present invention.
Fig. 1 shows the process of the running of wind generating set Reliability Prediction Method provided by the invention based on statistical analysis
Figure.Referring to Fig. 1, the running of wind generating set Reliability Prediction Method based on statistical analysis includes:
S1: the acquisition of data.
It acquires unit and runs a period of time tnInterior SCADA historical data matrix X, tn>=0.5 year.
S2: the cleaning of data.
The SCADA historical data matrix X of acquisition is pre-processed: compressor emergency shutdown, shutdown process etc. being run abnormal
Data reject, data after clean proof Y.
S3: the selection of key parameter.
The environment temperature T of unit operation is selected from the SCADA data matrix after cleaninga, power P and each main component
The temperature parameter T of (such as generator, gear-box, current transformer, pitch-controlled system)compForm new matrix Z.The expression formula of Z is as follows:
Z=(Ta, P, Tcomp)
Wherein, TaThe environment temperature of unit operation is represented, P represents power, TcompRepresent some temperature of certain main component
Column vector.
S4: the unit operational reliability prediction model based on statistical analysis is established.Preferably, Reliability Modeling is main
Include the following steps:
S41: the primary amendment of main component running temperature parameter.The primary amendment of main component running temperature mainly disappears
Except environment temperature TaTo main component running temperature TcompEffect tendency.Specific modification method includes the following steps:
S411: the sequence rearrangement parameter by matrix Z according to power P from small to large obtains new matrix W.
S412: power is subjected to interval division, it is preferable that the intervals of power in every section of section is 20kW~50kW.Then
Once linear is carried out to the temperature of main component on different power intervals respectively and returns amendment, obtains the primary of main component
Revised temperature parameter Tr1.Specific expression formula is as follows:
Tcomp,i=ki,tTa,i+Tr1,i
In formula, ki--- the regression coefficient on i-th of power interval；Ci--- the constant term on i-th of power interval.
The innovation representation that the main component running temperature in different capacity section is obtained according to above formula is as follows:
Tr1,i=Tcomp,i-ki,tTa,i
S42: the second-order correction of main component running temperature.Power is eliminated to main component by once linear homing method
The effect tendency of running temperature, the temperature parameter T after obtaining the second-order correction of main componentr2.Specific expression formula is as follows:
Tr1,i=ki,pP+Tr2,i
It can be obtained in different capacity section by formula above, the temperature of the main component after second-order correction, specifically
Expression formula it is as follows:
Tr2,i=Tr1,i-ki,pP
S43: the main component operational reliability prediction model based on temperature parameter is established.
Temperature parameter after second-order correction meets normal distribution, using statistical theory to temperature parameter after second-order correction
The modeling of unit operational reliability is carried out, specific steps include:
S431: the expectation of the temperature parameter after calculating main component second-order correctionAnd varianceThe expression of calculating
Formula is as follows:
S432: according to statistical theory, just for standard by the distribution shifts of the temperature parameter after main component second-order correction
State distribution, the expression formula of conversion are as follows:
To obtain standardized normal distribution:Mean valueVariance is
S433: reliability prediction model R is established according to 6 Sigma's Quality Control Technologiesr0, expression formula is as follows:
Above formula can be reduced to following expression:
3≤Rr0≤3
S5: online prediction in real time is carried out to the big component operational reliability of low wind speed Intelligent wind power unit.Specific step
It is as follows:
S51: after obtaining the temperature parameter of main component, calculating since step S411, calculates arrive S433 step always,
Obtain dependability parameter Rr。
S52: R is checkedrWhether parameter is in Rr0In the range of function, if in reliability model Rr0In the range of, then wind-powered electricity generation
Unit is working properly, otherwise abnormal.
The prediction technique for the running of wind generating set reliability based on statistical analysis that the present invention provides a kind of, this method use
Statistical method and Six Sigma theory model Wind turbines main component reliability of operation, final to realize to wind-powered electricity generation
Unit reliability of operation carries out online prediction in real time.
Fig. 2 is the structure chart of the running of wind generating set reliability prediction device the present invention is based on statistical analysis.Referring to fig. 2,
Running of wind generating set reliability prediction device based on statistical analysis includes: central processing unit (CPU) 201, can basis
The program that is stored in read-only memory (ROM) is loaded into random access storage device (RAM) 203 from storage section 208
Program and execute various movements appropriate and processing.In RAM203, be also stored with various programs needed for system operatio and
Data.CPU201, ROM202 and RAM203 are connected with each other by bus 204.Input/output (I/O) interface 205 is also connected to
Bus 204.
I/O interface 205 is connected to lower component: the importation 206 including keyboard, mouse etc.；It is penetrated including such as cathode
The output par, c 207 of spool (CRT), liquid crystal display (LCD) etc. and loudspeaker etc.；Storage section 208 including hard disk etc.；
And the communications portion 209 of the network interface card including LAN card, modem etc..Communications portion 209 via such as because
The network of spy's net executes communication process.Driver 210 is also connected to I/O interface 205 as needed.Detachable media 211, such as
Disk, CD, magneto-optic disk, semiconductor memory etc. are mounted on as needed on driver 210, in order to read from thereon
Computer program be mounted into storage section 208 as needed.
Particularly, according to embodiments of the present invention, it is soft to may be implemented as computer for the process above with reference to flow chart description
Part program.For example, the embodiment of the present invention includes a kind of computer program product comprising carrying is on a computer-readable medium
Computer program, which includes the program code for method shown in execution flow chart.In such implementation
In example, which can be downloaded and installed from network by communications portion 209, and/or from detachable media 211
It is mounted.The computer program by central processing unit (CPU) 201 execute when, execute limited in method of the invention it is upper
State function.It should be noted that computer-readable medium of the invention can be computer-readable signal media or computer
Readable storage medium storing program for executing either the two any combination.Computer readable storage medium for example can be --- but it is unlimited
In system, device or the device of --- electricity, magnetic, optical, electromagnetic, infrared ray or semiconductor, or any above combination.It calculates
The more specific example of machine readable storage medium storing program for executing can include but is not limited to: have the electrical connection, portable of one or more conducting wires
Formula computer disk, hard disk, random access storage device (RAM), read-only memory (ROM), erasable programmable read only memory
(EPROM or flash memory), optical fiber, portable compact disc read-only memory (CD-ROM), light storage device, magnetic memory device or
The above-mentioned any appropriate combination of person.In the present invention, computer readable storage medium can be it is any include or storage program
Tangible medium, which can be commanded execution system, device or device use or be used in combination.And in the present invention
In, computer-readable signal media may include in a base band or as carrier wave a part propagate data-signal, wherein
It carries and calculates readable program code.The data-signal of this propagation can take various forms, including but not limited to electromagnetism
Signal, optical signal or above-mentioned any appropriate combination.Computer-readable signal media can also be computer-readable storage
Any computer-readable medium other than medium, the computer-readable medium can send, propagate or transmit for by instructing
Execution system, device or device use or program in connection.The program generation for including on computer-readable medium
Code can use any appropriate medium transmission, including but not limited to: wirelessly, electric wire, optical cable, RF etc. or above-mentioned any
Suitable combination.
Flow chart and block diagram in attached drawing are illustrated according to the system of various embodiments of the invention, method and computer journey
The architecture, function and operation in the cards of sequence product.In this regard, each box in flowchart or block diagram can generation
A part of one module, program segment or code of table, a part of the module, program segment or code include one or more use
The executable instruction of the logic function as defined in realizing.It should also be noted that in some implementations as replacements, being marked in box
The function of note can also occur in a different order than that indicated in the drawings.For example, the box of two a sequence of expressions is actually
Execution that can be substantially parallel, they can also be executed in the opposite order sometimes, and this depends on the function involved.Also it to infuse
Meaning, the combination of each box in block diagram and or flow chart and the box in block diagram and/or flow chart can be with holding
The dedicated hardware based system of functions or operations as defined in row is realized, or can use specialized hardware and computer instruction
Combination realize.
Typically, the previously described running of wind generating set reliability prediction device based on statistical analysis can be set in wind
In motor group.Also, it is provided with the previously described running of wind generating set reliability prediction device based on statistical analysis
Wind turbines can be low wind speed Wind turbines.
The above described is only a preferred embodiment of the present invention, be not intended to limit the present invention in any form, this
Field technical staff makes a little simple modification, equivalent variations or modification using the technology contents of the disclosure above, all falls within this hair
In bright protection scope.
Claims (10)
1. a kind of running of wind generating set Reliability Prediction Method based on statistical analysis characterized by comprising
Acquire the SCADA history keyword data in unit operation a period of time；
Unit fortune is established by the statistical analysis to the SCADA history keyword data with Six Sigma Quality Control Technology
Row reliability prediction model；
With the unit operational reliability prediction model, the main component operational reliability of Wind turbines is carried out online real-time
Prediction.
2. the running of wind generating set Reliability Prediction Method according to claim 1 based on statistical analysis, which is characterized in that
The SCADA history keyword data are as follows:
Z=(Ta,P,Tcomp)
Wherein, Z is SCADA historical data matrix, TaFor the environment temperature of main component operation, P is that unit runs power, Tcomp
For main component running temperature；
Unit fortune is established by the statistical analysis to the SCADA history keyword data with Six Sigma Quality Control Technology
Row reliability prediction model, comprising:
The primary amendment of main component running temperature parameter is carried out, to eliminate influence of the environment temperature to main component running temperature
Trend；
The second-order correction for carrying out main component running temperature parameter, is become with eliminating influence of the power to main component running temperature
Gesture；
Based on revised main component running temperature parameter, the operational reliability prediction model of main component is established.
3. the running of wind generating set Reliability Prediction Method according to claim 2 based on statistical analysis, which is characterized in that
Carry out the primary amendment of main component running temperature parameter, comprising:
Sequence rearrangement parameter by SCADA historical data matrix according to power from small to large, obtains new SCADA history number
According to matrix；
Power is subjected to interval division, once linear then is carried out to the temperature of main component on different power intervals respectively
Return amendment.
4. the running of wind generating set Reliability Prediction Method according to claim 3 based on statistical analysis, which is characterized in that
Innovation representation of the main component running temperature in different capacity section are as follows:
Tr1,i=Tcomp,i-ki,tTa,i
Wherein, Tr1,iFor a correction value of main component running temperature in i-th section of power interval, Tcomp,iFor i-th section of power area
Interior main component running temperature, Ta,iFor the environment temperature of main component in i-th section of power interval, kI, tFor i-th section of power
The primary amendment regression coefficient in section.
5. the running of wind generating set Reliability Prediction Method according to claim 2 based on statistical analysis, which is characterized in that
Carry out the second-order correction of main component running temperature parameter, comprising:
Power is eliminated to the effect tendency of main component running temperature by linear regression method, is obtained the secondary of main component and is repaired
Temperature parameter after just.
6. the running of wind generating set Reliability Prediction Method according to claim 5 based on statistical analysis, which is characterized in that
Temperature parameter after second-order correction are as follows:
Tr2,i=Tr1,i-ki,pP
Wherein, Tr2,iFor the second-order correction value of main component running temperature in i-th section of power interval, Tr1,iFor i-th section of power interval
Correction value of interior main component running temperature, P are that unit runs power, ki,pIt is returned for the second-order correction of i-th section of power interval
Return coefficient.
7. the running of wind generating set Reliability Prediction Method according to claim 2 based on statistical analysis, which is characterized in that
Based on revised main component running temperature parameter, the operational reliability for establishing the main component based on temperature parameter predicts mould
Type, comprising:
The expectation and variance of temperature parameter after calculating main component second-order correction, the desired calculating formula are as follows:
The calculating formula of the variance are as follows:
Wherein,For the expectation,For the variance, Tr2,jFor main component running temperature in jth section power interval
Second-order correction value, n are the sum of power interval；
It is standardized normal distribution by the distribution shifts of the temperature parameter after main component second-order correction according to statistical theory；
Reliability prediction model R is established according to Six Sigma Quality Control Technologyr0。
8. the running of wind generating set Reliability Prediction Method according to claim 7 based on statistical analysis, which is characterized in that
With the unit operational reliability prediction model, the main component operational reliability of Wind turbines is carried out online pre- in real time
It surveys, comprising:
After obtaining the temperature parameter of main component, dependability parameter R is calculatedr；
Check parameter RrWhether function range Rr0It is interior, if in the range R of reliability modelr0Interior, then Wind turbines work
Normally, otherwise it is abnormal, and the range R of functionr0It is provided by following formula:
Wherein,For the expectation,For the variance.
9. a kind of running of wind generating set reliability prediction device based on statistical analysis characterized by comprising
One or more processors；
Storage device, for storing one or more programs,
When one or more of programs are executed by one or more of processors, so that one or more of processors are real
Now according to claim 1 to the running of wind generating set Reliability Prediction Method described in 8 any one based on statistical analysis.
10. a kind of Wind turbines, which is characterized in that the Wind turbines include according to claim 9 based on statistical
The running of wind generating set reliability prediction device of analysis.
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| CN111192163A (en) * | 2019-12-23 | 2020-05-22 | 明阳智慧能源集团股份公司 | Generator reliability medium-short term prediction method based on wind turbine generator operating data |
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| 张穆勇: "基于运行数据的风力发电设备可靠性分析方法和评估技术的研究", 《中国博士学位论文全文数据库（电子期刊） 工程科技Ⅱ辑》 * |
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| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110912188A (en) * | 2019-11-27 | 2020-03-24 | 天津瑞能电气有限公司 | Novel micro-grid energy management system based on AI |
| CN111192163A (en) * | 2019-12-23 | 2020-05-22 | 明阳智慧能源集团股份公司 | Generator reliability medium-short term prediction method based on wind turbine generator operating data |
| CN111192163B (en) * | 2019-12-23 | 2023-03-28 | 明阳智慧能源集团股份公司 | Generator reliability medium-short term prediction method based on wind turbine generator operating data |
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