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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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 invention relates to the technical field of wind power generation, in particular to a wind turbine generator operation reliability prediction method and device based on statistical analysis and a wind turbine generator.
Background
With the transformation and adjustment of energy structure, wind power generation is rapidly developed and becomes one of the main power generation modes at present. Wind turbines are generally located in remote areas, the operation conditions are severe, in addition, other factors influencing the operation conditions and faults frequently occur, and the operation and maintenance cost of more than 10 percent becomes the biggest bottleneck of the development of the wind power industry. Therefore, how to ensure the safe and reliable operation of the wind turbine generator and reduce the operation and maintenance cost becomes a hot point problem concerned by the wind power industry.
In the prior art, there are patent documents that provide some methods for determining the operational reliability of wind turbines. For example, patent No. CN108876073A provides a method and an apparatus for determining reliability of a wind turbine, where the method for determining reliability includes: calculating a first reliability score corresponding to a power generation amount-based availability factor PBA; calculating a second reliability score corresponding to the mean time between overhauls MTBI; calculating a third reliability score corresponding to the MTOTI (mean time to complete) of the average unit overhaul; determining the reliability of the wind turbine based on the calculated first, second and third reliability scores. A method for predicting the short-term reliability of a wind turbine generator considering the operation state is provided in patent No. CN 106097146A. The method mainly comprises the following steps: acquiring state parameters of the wind turbine generator through a state monitoring and data acquisition system, establishing a state parameter prediction model based on a back propagation neural network aiming at the temperature parameters of equipment, and calculating protection action probability based on prediction residual distribution characteristics; calculating the probability of the protection action according to the calculation of the out-of-limit time aiming at other parameters; and finally, comprehensively evaluating the risk of short-term shutdown of the wind turbine generator. Patent No. CN108241917A provides a method and apparatus for evaluating reliability of components. The reliability evaluation method and device provided by the patent comprise the following steps: (A) performing parameter estimation on environmental factors of all components based on a proportional risk model to obtain a first environmental factor set influencing the reliability of the components; (B) classifying the components according to suppliers, performing parameter estimation of environmental factors on the components of different suppliers based on a proportional risk model and the first environmental factor set, and screening out a plurality of second environmental factor sets influencing the components of different suppliers; (C) and respectively modeling the service lives of the components of different suppliers according to the plurality of second environment factor sets, and evaluating the reliability of the components of different suppliers according to the plurality of established service life models.
The reliability assessment or prediction method provided by the prior patent technology provides a certain prior effect for the healthy operation of the wind turbine generator, but certain subjective judgment and hypothesis are brought into the actual assessment and prediction, so that the actual operation assessment is difficult. Therefore, how to find a new method for evaluating or predicting the operational reliability of the wind turbine generator so as to overcome the defects in the prior art becomes a problem to be solved by technical personnel in the field.
Disclosure of Invention
The invention aims to provide a method and a device for predicting the running reliability of a wind turbine generator based on statistical analysis and the wind turbine generator, so that the running reliability of large parts of the wind turbine generator can be accurately predicted on line in real time.
In order to solve the technical problem, the invention provides a wind turbine generator operation reliability prediction method based on statistical analysis, which comprises the following steps: collecting SCADA historical key data of a unit in a period of time; establishing a unit operation reliability prediction model by applying a six-sigma quality control technology and through statistical analysis on the SCADA historical key data, wherein the unit operation reliability prediction model belongs to a Gaussian model; and performing online real-time prediction on the operational reliability of the main components of the wind turbine generator by using the unit operational reliability prediction model.
In some embodiments, the SCADA historical critical data is:
Z＝(Ta，P，Tcomp)
wherein Z is SCADA historical data matrix, TaAmbient temperature for operation of the main components, P for operation of the unitPower, TcompOperating temperature for the main components; establishing a unit operation reliability prediction model by applying a six-sigma quality control technology and through statistical analysis of the SCADA historical key data, wherein the model comprises the following steps: the method comprises the following steps of performing one-time correction on the operating temperature parameter of the main part to eliminate the influence trend of the environment temperature on the operating temperature of the main part; performing secondary correction on the operating temperature parameters of the main components to eliminate the influence trend of power on the operating temperature of the main components; and establishing an operation reliability prediction model of the main component based on the corrected operation temperature parameter of the main component.
In some embodiments, a primary correction of the primary component operating temperature parameter is performed, comprising: reordering the parameters of the SCADA historical data matrix according to the sequence of power from small to large to obtain a new SCADA historical data matrix; and dividing the power into intervals, and then performing linear regression correction on the temperature of the main component on different power intervals.
In some embodiments, the primary modified expression for the operating temperature of the primary component in the different power intervals is:
Tr1,i＝Tcomp,i-ki,tTa,i
wherein, Tr1,iIs a primary correction value of the operating temperature of the main component in the ith power interval, Tcomp,iFor the operating temperature, T, of the main part in the ith power intervala,iIs the ambient temperature, k, of the main component in the ith power intervali，tAnd correcting the regression coefficient for the first time of the ith section of power interval.
In some embodiments, making a second correction of the primary component operating temperature parameter comprises: and eliminating the influence trend of the power on the operation temperature of the main component by a linear regression method, and obtaining the temperature parameter of the main component after secondary correction.
In some embodiments, the temperature parameter after the second correction is:
Tr2,i＝Tr1,i-ki,pP
wherein, Tr2,iIs a secondary correction value of the operating temperature of the main component in the ith power interval, Tr1,iIs a primary corrected value of the operating temperature of the main component in the ith section of power interval, P is the operating power of the unit, ki,pAnd the quadratic correction regression coefficient of the ith section of power interval.
In some embodiments, establishing a prediction model of operational reliability of the primary component based on the temperature parameter based on the modified primary component operating temperature parameter comprises: calculating the expectation and the variance of the temperature parameters after the secondary correction of the main component, wherein the expectation calculation formula is as follows:
the variance is calculated as:
wherein,in order to be able to do so in the said desire,is the variance, Tr2,jThe secondary correction value of the operating temperature of the main component in the jth section of power interval is obtained, and n is the total number of the power intervals; according to a statistical theory, converting the distribution of the temperature parameters of the main part after secondary correction into standard normal distribution; establishing a reliability prediction model R according to a six-sigma quality control techniquer0。
In some embodiments, the unit operation reliability prediction model is applied to the windThe online real-time prediction of the operational reliability of the main components of the motor set comprises the following steps: after acquiring the temperature parameters of the main components, calculating the reliability parameters Rr(ii) a View parameter RrWhether or not in the range R of the functionr0If in the range R of the reliability modelr0If the wind turbine generator works normally, otherwise, the wind turbine generator works abnormally, and the range R of the functionr0Given by the following equation:
wherein,in order to be able to do so in the said desire,is the variance.
In addition, the invention also provides a wind turbine generator operation reliability prediction device based on statistical analysis, and the device comprises: one or more processors; a storage device, configured to store one or more programs, which when executed by the one or more processors, cause the one or more processors to implement the wind turbine generator operation reliability prediction method based on statistical analysis as described above.
In addition, the invention also provides a wind turbine generator, which comprises the wind turbine generator operation reliability prediction device based on statistical analysis.
After adopting such design, the invention has at least the following advantages:
according to the wind turbine generator operation reliability prediction method and device based on statistical analysis and the wind turbine generator, the wind turbine generator establishes the generator operation reliability prediction model through statistical analysis of the SCADA historical key data, and therefore the large component operation reliability of the wind turbine generator is accurately predicted on line in real time.
Drawings
The foregoing is only an overview of the technical solutions of the present invention, and in order to make the technical solutions of the present invention more clearly understood, the present invention is further described in detail below with reference to the accompanying drawings and the detailed description.
FIG. 1 is a flow chart of a wind turbine generator operation reliability prediction method based on statistical analysis according to the present invention;
fig. 2 is a structural diagram of the wind turbine generator operation reliability prediction device based on statistical analysis according to the present invention.
Detailed Description
The preferred embodiments of the present invention will be described in conjunction with the accompanying drawings, and it will be understood that they are described herein for the purpose of illustration and explanation and not limitation.
FIG. 1 shows a flow chart of a wind turbine generator operation reliability prediction method based on statistical analysis provided by the invention. Referring to fig. 1, the wind turbine generator operation reliability prediction method based on statistical analysis includes:
s1: and (6) collecting data.
The collection unit operates for a period of time tnInternal SCADA historical data matrix X, tnMore than or equal to 0.5 year.
S2: and (5) cleaning data.
Preprocessing an acquired SCADA historical data matrix X: and eliminating abnormal data such as unit shutdown, start-stop process and the like to obtain cleaned data evidence Y.
S3: and selecting key parameters.
Selecting the operating ambient temperature T of the unit from the cleaned SCADA data matrixaPower P and temperature parameters T of main components (such as a generator, a gearbox, a converter, a pitch system and the like)compA new matrix Z is composed. The expression of Z is as follows:
Z＝(Ta，P，Tcomp)
wherein, TaRepresenting ambient temperature of unit operation, P representing power, TcompA column vector representing a certain temperature of a certain main component.
S4: and establishing a unit operation reliability prediction model based on statistical analysis. Preferably, the reliability modeling method mainly includes the steps of:
s41: and (4) primary correction of the operating temperature parameters of the main components. The one-time correction of the operating temperature of the main part is mainly to eliminate the environmental temperature TaFor main component operating temperature TcompThe influence trend of (c). The specific correction method comprises the following steps:
s411: and reordering the parameters of the matrix Z according to the sequence of the power P from small to large to obtain a new matrix W.
S412: the power is divided into intervals, and the power interval of each interval is preferably 20kW to 50 kW. Then, the temperature of the main component is subjected to linear regression correction on different power intervals respectively to obtain a corrected temperature parameter T of the main componentr1. The specific expression is as follows:
Tcomp,i＝ki,tTa,i+Tr1,i
in the formula, ki-regression coefficients over the ith power interval; ci-constant term over the ith power interval.
The primary correction expression of the operating temperature of the main components in different power intervals is obtained according to the formula as follows:
Tr1,i＝Tcomp,i-ki,tTa,i
s42: and (5) performing secondary correction on the operating temperature of the main component. Eliminating the influence trend of power on the operation temperature of the main component by a primary linear regression method to obtain a temperature parameter T of the main component after secondary correctionr2. The specific expression is as follows:
Tr1,i＝ki,pP+Tr2,i
the temperature of the main component after the secondary correction in different power intervals can be obtained by the above formula, and the specific expression is as follows:
Tr2,i＝Tr1,i-ki,pP
s43: and establishing a main component operation reliability prediction model based on the temperature parameters.
The temperature parameters after the secondary correction meet normal distribution, and the unit operation reliability modeling is carried out on the temperature parameters after the secondary correction by utilizing a statistical theory, and the method specifically comprises the following steps:
s431: calculating the expectation of the temperature parameter of the main component after secondary correctionSum varianceThe calculated expression is as follows:
s432: according to a statistical theory, converting the distribution of the temperature parameters of the main part after secondary correction into standard normal distribution, wherein the converted expression is as follows:
thus, a standard normal distribution is obtained:mean valueVariance of
S433: establishing a reliability prediction model R according to 6 sigma quality control technologyr0The expression is as follows:
the above equation can be simplified as the following expression:
3≤Rr0≤3
s5: and carrying out online real-time prediction on the operation reliability of the large components of the low-wind-speed intelligent wind turbine generator. The method comprises the following specific steps:
s51: after acquiring the temperature parameter of the main part, the calculation is started from step S411 until step S433, and the reliability parameter R is acquiredr。
S52: view RrWhether the parameter is at Rr0Within the scope of the function, if in the reliability model Rr0Within the range of (1), the wind turbine works normallyOften, the other is abnormal.
The invention provides a wind turbine generator operation reliability prediction method based on statistical analysis.
Fig. 2 is a structural diagram of the wind turbine generator operation reliability prediction device based on statistical analysis according to the present invention. Referring to fig. 2, the wind turbine generator operation reliability prediction apparatus based on statistical analysis includes: a Central Processing Unit (CPU)201, which can perform various appropriate actions and processes according to a program stored in a Read Only Memory (ROM) or a program loaded from a storage section 208 into a Random Access Memory (RAM) 203. In the RAM203, various programs and data necessary for system operation are also stored. The CPU201, ROM202, and RAM203 are connected to each other via a bus 204. An input/output (I/O) interface 205 is also connected to bus 204.
The following components are connected to the I/O interface 205: an input portion 206 including a keyboard, a mouse, and the like; an output section 207 including a display such as a Cathode Ray Tube (CRT), a Liquid Crystal Display (LCD), and the like, and a speaker; a storage section 208 including a hard disk and the like; and a communication section 209 including a network interface card such as a LAN card, a modem, or the like. The communication section 209 performs communication processing via a network such as the internet. A drive 210 is also connected to the I/O interface 205 as needed. A removable medium 211 such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, or the like is mounted on the drive 210 as necessary, so that a computer program read out therefrom is mounted into the storage section 208 as necessary.
In particular, according to an embodiment of the present invention, the processes described above with reference to the flowcharts may be implemented as computer software programs. For example, embodiments of the invention include a computer program product comprising a computer program embodied on a computer-readable medium, the computer program comprising program code for performing the method illustrated in the flow chart. In such an embodiment, the computer program may be downloaded and installed from a network through the communication section 209 and/or installed from the removable medium 211. The above-described functions defined in the method of the present invention are performed when the computer program is executed by the Central Processing Unit (CPU) 201. Note that the computer-readable medium of the present invention can be a computer-readable signal medium or a computer-readable storage medium or any combination of the two. A computer readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples of the computer readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the present invention, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. In the present invention, however, a computer readable signal medium may include a propagated data signal with computer readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated data signal may take many forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to: wireless, wire, fiber optic cable, RF, etc., or any suitable combination of the foregoing.
The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present invention. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
Typically, the aforementioned wind turbine generator operation reliability prediction device based on statistical analysis may be arranged in the wind turbine generator. Moreover, the wind turbine generator provided with the wind turbine generator operation reliability prediction device based on statistical analysis can be a low wind speed wind turbine generator.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the present invention in any way, and it will be apparent to those skilled in the art that the above description of the present invention can be applied to various modifications, equivalent variations or modifications without departing from the spirit and scope of the present invention.
Claims (10)
1. A wind turbine generator operation reliability prediction method based on statistical analysis is characterized by comprising the following steps:
collecting SCADA historical key data of a unit in a period of time;
establishing a unit operation reliability prediction model by applying a six-sigma quality control technology and through statistical analysis on the SCADA historical key data;
and performing online real-time prediction on the operational reliability of the main components of the wind turbine generator by using the unit operational reliability prediction model.
2. The statistical analysis-based wind turbine generator operation reliability prediction method according to claim 1, wherein the SCADA historical key data is:
Z＝(Ta,P,Tcomp)
wherein Z is SCADA historical data matrix, TaFor the ambient temperature of the main component operation, P is the unit operating power, TcompOperating temperature for the main components;
establishing a unit operation reliability prediction model by applying a six-sigma quality control technology and through statistical analysis of the SCADA historical key data, wherein the model comprises the following steps:
performing primary correction on the operating temperature parameters of the main components to eliminate the influence trend of the environmental temperature on the operating temperature of the main components;
performing secondary correction on the operating temperature parameters of the main components to eliminate the influence trend of power on the operating temperature of the main components;
and establishing an operation reliability prediction model of the main component based on the corrected operation temperature parameter of the main component.
3. The statistical analysis-based wind turbine generator operation reliability prediction method according to claim 2, wherein the primary correction of the main component operation temperature parameter comprises:
reordering the parameters of the SCADA historical data matrix according to the sequence of power from small to large to obtain a new SCADA historical data matrix;
and dividing the power into intervals, and then performing linear regression correction on the temperature of the main component on different power intervals.
4. The statistical analysis-based wind turbine generator operation reliability prediction method according to claim 3, wherein the primary correction expression of the operation temperature of the main components in different power intervals is as follows:
Tr1,i＝Tcomp,i-ki,tTa,i
wherein, Tr1,iIs a primary correction value of the operating temperature of the main component in the ith power interval, Tcomp,iFor the operating temperature, T, of the main part in the ith power intervala,iIs the ambient temperature, k, of the main component in the ith power intervali，tAnd correcting the regression coefficient for the first time of the ith section of power interval.
5. The statistical analysis-based wind turbine generator system operation reliability prediction method according to claim 2, wherein performing secondary correction of the main component operation temperature parameters comprises:
and eliminating the influence trend of the power on the operation temperature of the main component by a linear regression method, and obtaining the temperature parameter of the main component after secondary correction.
6. The wind turbine generator operation reliability prediction method based on statistical analysis according to claim 5, wherein the temperature parameter after the secondary correction is:
Tr2,i＝Tr1,i-ki,pP
wherein, Tr2,iIs a secondary correction value of the operating temperature of the main component in the ith power interval, Tr1,iIs a primary corrected value of the operating temperature of the main component in the ith section of power interval, P is the operating power of the unit, ki,pAnd the quadratic correction regression coefficient of the ith section of power interval.
7. The wind turbine generator operation reliability prediction method based on statistical analysis according to claim 2, wherein the establishing of the operation reliability prediction model of the main component based on the temperature parameter based on the corrected operation temperature parameter of the main component comprises:
calculating the expectation and the variance of the temperature parameters after the secondary correction of the main component, wherein the expectation calculation formula is as follows:
the variance is calculated as:
wherein,in order to be able to do so in the said desire,is the variance, Tr2,jThe secondary correction value of the operating temperature of the main component in the jth section of power interval is obtained, and n is the total number of the power intervals;
according to a statistical theory, converting the distribution of the temperature parameters of the main part after secondary correction into standard normal distribution;
establishing a reliability prediction model R according to a six-sigma quality control techniquer0。
8. The wind turbine generator operation reliability prediction method based on statistical analysis according to claim 7, wherein the on-line real-time prediction of the operation reliability of the main components of the wind turbine generator by using the generator operation reliability prediction model comprises:
after acquiring the temperature parameters of the main components, calculating the reliability parameters Rr；
View parameter RrWhether or not in the range R of the functionr0If in the range R of the reliability modelr0If the wind turbine generator works normally, otherwise, the wind turbine generator works abnormally, and the range R of the functionr0Given by the following equation:
wherein,in order to be able to do so in the said desire,is the variance.
9. The utility model provides a wind turbine generator system operational reliability prediction device based on statistical analysis which characterized in that includes:
one or more processors;
a storage device for storing one or more programs,
when executed by the one or more processors, cause the one or more processors to implement the statistical analysis-based wind turbine operational reliability prediction method according to any one of claims 1 to 8.
10. Wind turbine, characterized in that it comprises a statistical analysis based wind turbine operational reliability prediction device according to claim 9.
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