CN113446167A - Wind power coupling slip fault early warning method based on big data analysis - Google Patents
Wind power coupling slip fault early warning method based on big data analysis 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
- F03D—WIND MOTORS
- F03D17/00—Monitoring or testing of wind motors, e.g. diagnostics
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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
- F03D—WIND MOTORS
- F03D15/00—Transmission of mechanical power
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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
- F03D—WIND MOTORS
- F03D80/00—Details, components or accessories not provided for in groups F03D1/00 - F03D17/00
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
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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
- Y02E10/70—Wind energy
- Y02E10/72—Wind turbines with rotation axis in wind direction
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Abstract
The invention provides a wind power coupler slip fault early warning method based on big data analysis, which comprises the following steps of: extracting real-time wind wheel rotating speed and real-time generator rotating speed from real-time operation data of the wind generating set; calculating real-time slip of a real-time measured value and a real-time predicted value of the rotating speed of the wind wheel according to the real-time rotating speed of the wind wheel and the real-time rotating speed of the generator and by combining the slope and intercept of the linear regression model; when the duration time that the real-time slip exceeds the early warning threshold value reaches the time threshold value, whether the rotating speed of the wind wheel and the rotating speed of the generator are normal or not and no signal fluctuation is judged according to the real-time operation data of the wind generating set: if yes, judging that the clutch slips; if not, the sensor or the line is judged to be abnormal. The invention can utilize the existing sensors and operation data of the wind generating set to carry out the early warning of the slipping fault of the coupling.
Description
Technical Field
The invention relates to the technical field of wind generating sets, in particular to a wind power coupling slip fault early warning method based on big data analysis.
Background
As an important part of a wind generating set, the coupler is a connecting device between a main gearbox and a generator of the wind generating set and plays important roles of transmitting the torque and the rotating speed of a wind wheel, relieving the influence of vibration on the gearbox and the generator, carrying out overload protection on a transmission chain and the like. In the process of wind power generation, if the coupler frequently slips, the stable operation of the wind generating set is influenced, and when the coupler slips to the point that the wind generating set reports a fault, the wind generating set is stopped, so that the loss of generated energy is brought.
When the coupler slips, a field operation and maintenance worker generally reports a fault on a machine set or inspects on a machine to check whether the marking line of the coupler is dislocated or not, so that the coupler can be found to slip, and the sensing technology of potential faults at the early stage of the coupler slip is lacked. Patent CN110174264A provides a wind generating set coupling slipping early warning device, a wind driven generator and an early warning method; wherein, the early warning device that skids includes: the fan control system comprises a first rotating speed fluted disc, a second rotating speed fluted disc, a first rotating speed measuring sensor, a second rotating speed measuring sensor, a double-channel counter and a fan controller; the fan controller calculates and obtains the difference value of the accumulated pulse numbers of the first rotating speed fluted disc and the second rotating speed fluted disc within the preset time range of the operation of the self-starting machine, and converts the difference value of the accumulated pulse numbers into an accumulated pulse angle difference; finally, the running/abrasion state of the coupler can be evaluated through the quantized accumulated pulse angle difference; judging whether the fan slips or not, providing real-time early warning and maintenance prompts for a user when the fan is abnormal, calculating the change rate of the slip angle according to the accumulated pulse angle difference, and judging whether the fan actually slips or not by calculating the change rate of the slip angle in a standard time period so as to prevent false alarm caused by signal interference and pulse loss.
However, in the above technical scheme, hardware devices such as a first rotating speed fluted disc, a second rotating speed fluted disc, a first rotating speed measuring sensor, a second rotating speed measuring sensor, a dual-channel counter and a wind turbine controller are additionally arranged on the basis of original hardware of the wind turbine generator system, and if effective early warning on the slippage of a coupler is to be realized, the additionally arranged hardware devices need to be installed on each wind turbine generator system, so that the equipment investment cost is greatly increased; meanwhile, after a series of hardware devices are newly added to the wind generating set, the probability of failure is increased to a certain extent.
Therefore, a method for early warning of the potential early failure of the slipping of the coupling without adding additional hardware equipment is needed.
Disclosure of Invention
Aiming at the defects in the prior art, the invention provides a wind power coupler slip fault early warning method based on big data analysis, which can be used for carrying out slip fault early warning on a coupler by utilizing the existing sensors and operation data of a wind generating set.
The invention adopts the technical scheme that a wind power coupling slip fault early warning method based on big data analysis comprises the following steps in a first implementation mode:
extracting real-time wind wheel rotating speed and real-time generator rotating speed from real-time operation data of the wind generating set;
calculating real-time slip of a real-time measured value and a real-time predicted value of the rotating speed of the wind wheel according to the real-time rotating speed of the wind wheel and the real-time rotating speed of the generator and by combining the slope and intercept of the linear regression model;
when the duration time that the real-time slip exceeds the early warning threshold value reaches the time threshold value, whether the rotating speed of the wind wheel and the rotating speed of the generator are normal or not and no signal fluctuation is judged according to the real-time operation data of the wind generating set:
if yes, judging that the clutch slips;
if not, the sensor or the line is judged to be abnormal.
With reference to the first implementation manner, in a second implementation manner, the slope and intercept of the linear regression model are obtained as follows:
s1, extracting characteristic variable data required by the algorithm from historical operating data of the wind generating set, and dividing the characteristic variable data required by the algorithm into coupler slip fault data and non-coupler slip fault data;
s2, extracting the wind wheel rotating speed and the generator rotating speed in the non-coupler slip fault data, and deleting abnormal data in the wind wheel rotating speed and the generator rotating speed to obtain a second wind wheel rotating speed and a second generator rotating speed corresponding to the non-coupler slip fault data;
and S3, establishing a linear regression model according to the rotating speed of the second wind wheel and the rotating speed of the second generator, and solving the linear regression model to obtain the slope and the intercept.
In a third implementation manner, in combination with the second implementation manner, in step S3, the linear regression model is solved using a least square method.
With reference to the second implementable manner, in a fourth implementable manner, the early warning threshold and the time threshold are obtained by the following method:
t1, extracting the wind wheel rotating speed and the generator rotating speed in the coupler slip fault data, and deleting abnormal data in the wind wheel rotating speed and the generator rotating speed to obtain a first wind wheel rotating speed and a first generator rotating speed corresponding to the coupler slip fault data;
t2, calculating the historical slip of the historical record value and the historical predicted value of the first wind wheel rotating speed according to the slope and the intercept and by combining the first wind wheel rotating speed and the first generator rotating speed;
and T3, determining an early warning threshold value and a time threshold value according to the historical slip.
With reference to the fourth implementable manner, in a fifth implementable manner, the calculating a historical slip of the historical recorded value and the historical predicted value of the first wind turbine rotation speed in step T2 includes:
t21, converting the first generator rotating speed into a first wind wheel rotating speed according to the slope and the intercept to obtain a historical predicted value of the first wind wheel rotating speed;
and T22, taking the difference between the historical record value of the first wind wheel rotating speed and the historical predicted value of the first wind wheel rotating speed as historical slip.
With reference to the fourth implementation manner, in a sixth implementation manner, the determining, in step T3, an early warning threshold and a time threshold according to the historical slip includes:
t31, calculating the mean value and the standard deviation of the historical slip;
t32, taking the sum of the mean value and the standard deviation as an early warning threshold value;
and T33, taking the time threshold as the duration that the historical slip exceeds the early warning threshold during the clutch slip fault.
With reference to the sixth implementable manner, in a seventh implementable manner, the time threshold is 3 seconds.
With reference to the second implementable manner, in an eighth implementable manner, when the abnormal data in the wind wheel rotation speed and the generator rotation speed are deleted, the wind wheel rotation speed retention value range is [0, 20], and the generator rotation speed retention value range is [0, 2000 ].
In combination with the first implementation manner, in a ninth implementation manner, a mechanism analysis method is adopted to judge whether the rotation speed of the wind wheel and the rotation speed of the generator are normal or not without signal fluctuation.
According to the technical scheme, the beneficial technical effects of the invention are as follows:
1. the system can utilize the existing sensors and operation data of the wind generating set to carry out slip fault early warning on the coupling, can accurately judge the slip potential fault of the coupling of the wind generating set without adding a sensor and a monitoring device, and reduces the equipment investment cost.
2. Through early warning of the slipping fault of the coupling, the wind field can be helped to realize predictive maintenance, the troubleshooting difficulty is reduced, and therefore the power generation loss of the wind generating set is reduced.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below. Throughout the drawings, like elements or portions are generally identified by like reference numerals. In the drawings, elements or portions are not necessarily drawn to scale.
Fig. 1 is a flowchart of a fault early warning method according to an embodiment of the present invention.
Detailed Description
Embodiments of the present invention will be described in detail below with reference to the accompanying drawings. The following examples are only for illustrating the technical solutions of the present invention more clearly, and therefore are only examples, and the protection scope of the present invention is not limited thereby.
It is to be noted that, unless otherwise specified, technical or scientific terms used herein shall have the ordinary meaning as understood by those skilled in the art to which the invention pertains.
Examples
The embodiment provides a wind power coupling slip fault early warning method based on big data analysis, as shown in fig. 1, firstly, an early warning threshold value and a time threshold value of a wind power coupling slip fault are determined based on big data analysis, and the method specifically comprises the following steps:
step 1, extracting characteristic variable data required by an algorithm from historical operating data of the wind generating set, and dividing the characteristic variable data required by the algorithm into coupler slip fault data and non-coupler slip fault data.
The historical operation data of the wind generating set comprises various records of faults, and the fault record of the slipping of the coupler is extracted as fault data. In a specific embodiment, the historical operating data of the wind turbine generator system is extracted on the order of seconds.
According to the recording condition of the fault, the data of the characteristic variables required by the algorithm can be divided into two categories: coupler slip failure and non-coupler slip failure. The coupler slipping fault is the condition that the coupler slipping is confirmed to be caused by the overhaul of operation and maintenance personnel.
And 2, respectively extracting the wind wheel rotating speed and the generator rotating speed in the coupler slip fault data and the non-coupler slip fault data, and deleting abnormal data in the wind wheel rotating speed and the generator rotating speed to obtain a first wind wheel rotating speed and a first generator rotating speed corresponding to the coupler slip fault data, and a second wind wheel rotating speed and a second generator rotating speed corresponding to the non-coupler slip fault data.
And recording the data of the rotating speed of the wind wheel and the rotating speed of the generator at corresponding time points in the coupler slip fault data and the non-coupler slip fault data, and extracting the rotating speed of the wind wheel and the rotating speed of the generator.
According to a data preprocessing method, deleting abnormal data in the extracted wind wheel rotating speed and the extracted generator rotating speed to obtain a first wind wheel rotating speed and a first generator rotating speed corresponding to coupling slip fault data, and a second wind wheel rotating speed and a second generator rotating speed corresponding to non-coupling slip fault data. In a specific embodiment, during preprocessing, data of normal operation conditions of the wind turbine are reserved, the numerical range of the wind wheel rotation speed is reserved as [0, 20], the numerical range of the generator rotation speed is reserved as [0, 2000], and meanwhile, abnormal data of the wind wheel rotation speed and the generator rotation speed, including a null value, a repetition value and a fluctuation value, are deleted.
And 3, establishing a linear regression model according to the rotating speed of the second wind wheel and the rotating speed of the second generator, and solving the linear regression model to obtain the slope and the intercept.
Step 31, establishing a linear regression model according to the rotating speed of the second wind wheel and the rotating speed of the second generator
In a specific embodiment, it is preferable to extract valid data for 1 week or more for the second rotor speed and the second generator speed, and if there are n data for both the second rotor speed and the second generator speed, the data set of the second rotor speed is used as the dependent variable Y, and Y is [ Y ═ Y [1,y2,…,yn]Taking the data set of the second generator speed as an independent variable X, X ═ X1,x2,…,xn]. According to mechanism analysis and data correlation analysis of two variables, the rotating speed of a wind wheel and the rotating speed of a generator are in a linear relation under the condition that a transmission chain of a wind driven generator is not abnormal, so that a linear regression model is established as follows:
Y＝kX+b
step 32, solving the linear regression model by using a least square method to obtain a slope and an intercept
Solving the above formula by using a least square method to obtain a slope k and an intercept b, wherein the calculation formula is as follows:
in this embodiment, the calculated slope k is the gear ratio of the gearbox.
Step 4, according to the slope and intercept obtained in the step S3, combining the first wind wheel rotating speed and the first generator rotating speed, calculating the historical slip of the historical recorded value and the historical predicted value of the first wind wheel rotating speed when the coupler slip fault occurs
Step 41, converting the first generator rotating speed into a first wind wheel rotating speed according to the slope and the intercept to obtain a historical predicted value of the first wind wheel rotating speed
In a specific embodiment, it is assumed that the first rotor speed and the first generator speed both have m data, and the data set of the first rotor speed is taken as a dependent variable Y, where Y is [ Y ═ Y1,y2,…,ym]The data set of the first generator speed is used as an independent variable X, X ═ X1,x2,…,xm]Converting the first generator rotating speed to the first wind wheel rotating speed based on the slope k and the slope b calculated in the step S3 to obtain a predicted value Y ^ of the first wind wheel rotating speed, and enabling Y ^ to be [ Y ^1^,y2^,…,ym^]Then, there are:
Y^＝kX+b
step 42, calculating the historical slip epsilon between the historical record value of the first wind wheel rotating speed and the historical predicted value of the first wind wheel rotating speed
The first wind wheel rotating speed is a historical record value corresponding to the slip fault data of the coupler, the slip difference between the first wind wheel rotating speed and the historical predicted value of the first wind wheel rotating speed is set as epsilon, and epsilon is [ epsilon ═1,ε2,…,εm]The calculation formula is as follows:
ε＝Y-Y^
step 5, determining an early warning threshold value and a time threshold value according to historical slip
Step 51, calculating the mean value mu and standard deviation sigma of the historical slip epsilon
The formula for the mean μ is:
the formula for calculating the standard deviation σ is:
step 52, taking the sum of the mean value and the standard deviation as an early warning threshold value
In a specific embodiment, the sum of the mean value and the standard deviation, that is, "μ + σ", is used as the warning threshold of the rotation speed slip, for example, when μ is-1.2 and σ is 0.5, 0.7 is used as the warning threshold.
Step 53, taking the duration that the historical slip exceeds the early warning threshold value during the slip fault period of the coupler as a time threshold value
In a specific embodiment, the time threshold t is preferably set to be t ═ 3 s.
After the early warning threshold and the time threshold are determined according to the previous steps, as shown in fig. 1, the slip fault of the coupling is early warned according to the following steps:
firstly, extracting real-time wind wheel rotating speed and real-time generator rotating speed from real-time operation data of a wind generating set.
And secondly, calculating the real-time slip between the real-time measured value and the real-time predicted value of the rotating speed of the wind wheel according to the real-time rotating speed of the wind wheel and the real-time rotating speed of the generator and by combining the slope and intercept of the linear regression model.
In a specific embodiment, the slope k and the intercept b are determined in accordance with step S32. Converting the real-time generator rotating speed into a real-time predicted value y ^ of the wind wheel rotating speed according to the method of the step S41:
y^＝kx+b
obtaining the real-time slip epsilon 1 of the real-time measured value y and the real-time predicted value y ^ of the wind wheel rotating speed,
ε1＝y-y^
in a specific embodiment, the real-time slip ε 1 may be determined as described above.
When the duration time that the real-time slip exceeds the early warning threshold value reaches a time threshold value, judging whether the wind wheel rotating speed and the generator rotating speed at the moment are normal or not and have no signal fluctuation according to the real-time operation data of the wind generating set: if yes, judging that the clutch slips; if not, the sensor or the line is judged to be abnormal.
In the step, whether the wind wheel rotating speed and the generator rotating speed at the moment are normal or not and have no signal fluctuation is judged according to the real-time operation data of the wind generating set, and in a specific implementation mode, a mechanism analysis method is adopted for judgment.
Through the technical scheme of this embodiment of adoption, can utilize existing sensor of wind generating set and operational data to carry out the trouble early warning that skids to the shaft coupling, need not newly-increased sensor and monitoring devices and can accurately judge the latent fault that the wind generating set shaft coupling skids, reduced the equipment input cost. Meanwhile, through early warning of the slipping fault of the coupling, the wind field can be helped to realize predictive maintenance, the troubleshooting difficulty is reduced, and therefore the power generation loss of the wind generating set is reduced.
Finally, it should be noted that: the above examples are only intended to illustrate the technical solution of the present invention, but not to limit it; while the invention has been described in detail and with reference to the foregoing embodiments, it will be understood by those skilled in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some or all of the technical features may be equivalently replaced; such modifications and substitutions do not depart from the spirit and scope of the present invention, and they should be construed as being included in the following claims and description.
Claims (9)
1. A wind power coupler slip fault early warning method based on big data analysis is characterized by comprising the following steps:
extracting real-time wind wheel rotating speed and real-time generator rotating speed from real-time operation data of the wind generating set;
calculating real-time slip of a real-time measured value and a real-time predicted value of the rotating speed of the wind wheel according to the real-time rotating speed of the wind wheel and the real-time rotating speed of the generator and by combining the slope and intercept of the linear regression model;
when the duration time that the real-time slip exceeds the early warning threshold value reaches the time threshold value, whether the rotating speed of the wind wheel and the rotating speed of the generator are normal or not and no signal fluctuation is judged according to the real-time operation data of the wind generating set:
if yes, judging that the clutch slips;
if not, the sensor or the line is judged to be abnormal.
2. The wind power coupling slip fault early warning method based on big data analysis according to claim 1, characterized in that the slope and intercept of the linear regression model are obtained by the following method:
s1, extracting characteristic variable data required by the algorithm from historical operating data of the wind generating set, and dividing the characteristic variable data required by the algorithm into coupler slip fault data and non-coupler slip fault data;
s2, extracting the wind wheel rotating speed and the generator rotating speed in the non-coupler slip fault data, and deleting abnormal data in the wind wheel rotating speed and the generator rotating speed to obtain a second wind wheel rotating speed and a second generator rotating speed corresponding to the non-coupler slip fault data;
and S3, establishing a linear regression model according to the rotating speed of the second wind wheel and the rotating speed of the second generator, and solving the linear regression model to obtain the slope and the intercept.
3. The wind power coupling slip fault early warning method based on big data analysis as claimed in claim 2, wherein in step S3, the linear regression model is solved using the least square method.
4. The wind power coupling slip fault early warning method based on big data analysis as claimed in claim 2, wherein the early warning threshold and the time threshold are obtained by the following method:
t1, extracting the wind wheel rotating speed and the generator rotating speed in the coupler slip fault data, and deleting abnormal data in the wind wheel rotating speed and the generator rotating speed to obtain a first wind wheel rotating speed and a first generator rotating speed corresponding to the coupler slip fault data;
t2, calculating the historical slip of the historical record value and the historical predicted value of the first wind wheel rotating speed according to the slope and the intercept and by combining the first wind wheel rotating speed and the first generator rotating speed;
and T3, determining an early warning threshold value and a time threshold value according to the historical slip.
5. The wind turbine coupling slip fault early warning method based on big data analysis according to claim 4, wherein the step T2 of calculating the historical slip between the historical recorded value and the historical predicted value of the first wind turbine speed comprises:
t21, converting the first generator rotating speed into a first wind wheel rotating speed according to the slope and the intercept to obtain a historical predicted value of the first wind wheel rotating speed;
and T22, taking the difference between the historical record value of the first wind wheel rotating speed and the historical predicted value of the first wind wheel rotating speed as historical slip.
6. The wind power coupling slip fault early warning method based on big data analysis as claimed in claim 4, wherein the step T3 of determining the early warning threshold and the time threshold according to the historical slip includes:
t31, calculating the mean value and the standard deviation of the historical slip;
t32, taking the sum of the mean value and the standard deviation as an early warning threshold value;
and T33, taking the time threshold as the duration that the historical slip exceeds the early warning threshold during the clutch slip fault.
7. The wind power coupling slip fault early warning method based on big data analysis as claimed in claim 6, wherein the time threshold is 3 seconds.
8. The wind power coupling slip fault early warning method based on big data analysis according to claim 2, wherein when abnormal data of the wind wheel rotation speed and the generator rotation speed are deleted, the wind wheel rotation speed reserve value range is [0, 20], and the generator rotation speed reserve value range is [0, 2000 ].
9. The wind power coupling slip fault early warning method based on big data analysis according to claim 1, characterized in that a mechanism analysis method is adopted to judge whether the wind wheel rotation speed and the generator rotation speed are normal and have no signal fluctuation.
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