CN115059586A - Data preprocessing method, power curve generating method and storage medium - Google Patents
Data preprocessing method, power curve generating method and storage medium Download PDFInfo
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- CN115059586A CN115059586A CN202210727563.5A CN202210727563A CN115059586A CN 115059586 A CN115059586 A CN 115059586A CN 202210727563 A CN202210727563 A CN 202210727563A CN 115059586 A CN115059586 A CN 115059586A
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- 238000000034 method Methods 0.000 title claims abstract description 41
- 238000007781 pre-processing Methods 0.000 title claims abstract description 19
- 238000012216 screening Methods 0.000 claims abstract description 63
- 238000004590 computer program Methods 0.000 claims description 5
- 238000002203 pretreatment Methods 0.000 abstract 2
- 230000002159 abnormal effect Effects 0.000 description 35
- 238000010521 absorption reaction Methods 0.000 description 6
- 239000013256 coordination polymer Substances 0.000 description 6
- 238000010248 power generation Methods 0.000 description 5
- 238000012544 monitoring process Methods 0.000 description 3
- 238000013480 data collection Methods 0.000 description 2
- 238000005259 measurement Methods 0.000 description 2
- 230000007547 defect Effects 0.000 description 1
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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
- F03D7/00—Controlling wind motors
- F03D7/02—Controlling wind motors the wind motors having rotation axis substantially parallel to the air flow entering the rotor
- F03D7/022—Adjusting aerodynamic properties of the blades
- F03D7/0236—Adjusting aerodynamic properties of the blades by changing the active surface of the wind engaging parts, e.g. reefing or furling
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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
Abstract
The invention discloses a data preprocessing method, a power curve generating method and a storage medium, wherein the generating method comprises the following steps: firstly, obtaining screened operation data by adopting a pretreatment method, then carrying out bin interval division on the operation data, and calculating a wind speed average value and a power average value of each bin interval; finally, an operating power curve is generated from the wind speed average and the power average for all bin intervals. The pretreatment method comprises the following steps: firstly, collecting operation data and operation parameters of a wind generating set; then, primarily screening the operation data according to the operation parameters; then, carrying out interval division on the preliminarily screened operation data according to a preset bin interval, and calculating the average value and the standard deviation of the average power of each bin interval; and finally, determining the threshold range of each bin interval according to the corresponding average value and standard deviation, and performing secondary screening on the preliminarily screened operation data through the threshold range to obtain the screened operation data.
Description
Technical Field
The invention relates to the technical field of monitoring or testing of wind turbines, in particular to a data preprocessing method, a power curve generating method and a storage medium.
Background
The power curve is an important index for reflecting the power generation performance of the wind generating set, the height of the power curve is directly related to the income of the wind power plant, and the power curve is a key index for the owner of the wind power plant to check the running performance of the whole machine. The power curve is generated by screening out power-limited operation data in a power-limited state, dividing bins at wind speed intervals of 0.5m/s, and calculating the average power of bin intervals to construct a power curve.
However, in the current power curve generation method, due to the fact that operation data screening is rough, the generated power curve is poor in conformity with the actual operation condition. Therefore, an effective power curve generation method is urgently needed to quickly know the actual power generation performance of the unit and provide data support for subsequent performance assessment of the whole machine and energy scheduling of the wind power plant.
Disclosure of Invention
Aiming at the defects in the prior art, the invention provides a data preprocessing method, a power curve generating method and a storage medium. Abnormal operation data in the operation data can be effectively screened out, and a power curve conforming to the actual operation condition is generated. The specific technical scheme is as follows:
in a first aspect, a data preprocessing method is provided, including:
collecting operation data and operation parameters of a wind generating set;
performing primary screening on the operation data according to the operation parameters;
carrying out interval division on the preliminarily screened operation data according to a preset bin interval, and calculating the average value and the standard deviation of the average power of each bin interval;
and determining the threshold range of each bin interval according to the corresponding average value and standard deviation, and performing secondary screening on the preliminarily screened operation data through the threshold range to obtain the screened operation data.
With reference to the first aspect, in a first implementable manner of the first aspect, the preliminary screening of the operation data according to the operation parameter includes:
and screening out data points in which the average power in the operation data is smaller than the rated power in the operation parameters and the pitch angle is larger than the maximum pre-pitch angle.
With reference to the first aspect, in a second implementable manner of the first aspect, the preliminary screening of the operation data according to the operation parameter includes:
and screening out data points in the operation data, wherein the rotating speed of the generator is less than the rated rotating speed in the operation parameters, and the pitch angle is greater than the maximum pre-pitch angle.
With reference to the first aspect, in a third implementation manner of the first aspect, the preliminary screening the operation data according to the operation parameter includes:
and screening out data points in the operation data, wherein the rotating speed of the generator is less than the grid-connected rotating speed in the operation parameters.
With reference to the first aspect, in a fourth implementable manner of the first aspect, the performing preliminary screening on the operation data according to the operation parameter includes:
and screening out data points in the operating data where the torque is lower than the theoretical speed-torque curve in the operating parameters.
With reference to the first aspect, in a fifth implementable manner of the first aspect, the performing preliminary screening on the operation data according to the operation parameter includes:
and screening out data points in the operation data, wherein the 30-second average wind direction is larger than the yaw starting wind direction deviation angle in the operation parameters.
With reference to the first aspect, in a sixth implementable manner of the first aspect, the bin interval is 0.1 m/s.
With reference to the first aspect, in a seventh implementable manner of the first aspect, the threshold range is (a-3 σ, a +3 σ), where a is the average value and σ is the standard deviation.
In a second aspect, a power curve generation method is provided, including:
obtaining screened operation data by adopting any one data preprocessing method in the first aspect and the first to the seventh realizable modes of the first aspect;
bin interval division is carried out on the screened operation data, and the wind speed average value and the power average value of each bin interval are calculated;
and generating an operation power curve of the wind generating set through the wind speed average value and the power average value of all bin intervals.
In a third aspect, a storage medium is provided, which stores a computer program that executes the power curve generation method as provided in the second aspect.
Has the advantages that: by adopting the data preprocessing method, the power curve generating method and the storage medium, the operation data are screened based on the operation parameters, abnormal operation data in the operation data can be eliminated, the operation data after screening out abnormal operation data are subjected to interval division, secondary screening is carried out on the operation data after primarily screening out the abnormal operation data based on the standard deviation and the average value of the average power of each interval, the abnormal operation data in the operation data are further screened out, so that the operation data which are more in line with the actual operation condition of the wind generating set are obtained, the power curve generated by the operation data which are screened for many times is more in line with the actual operation condition of the wind generating set, the actual power generation performance of the set is rapidly known, and data support is provided for the subsequent performance check of the whole machine and energy scheduling of the wind power plant.
Drawings
In order to more clearly illustrate the embodiments of the present invention, the drawings, which are required to be used in the embodiments, will be briefly described below. In all the drawings, the elements or parts are not necessarily drawn to actual scale.
Fig. 1 is a flowchart of an operation data preprocessing method for a wind turbine generator system according to an embodiment of the present invention;
fig. 2 is a flowchart of a power curve generation method of a wind turbine generator system 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.
A flow chart of a method of data pre-processing as shown in fig. 1, the method comprising:
step 1, collecting operation data and operation parameters of a wind generating set;
step 2, performing primary screening on the operation data according to the operation parameters;
step 3, carrying out interval division on the preliminarily screened operation data according to a preset bin interval, and calculating the average value and the standard deviation of the average power of each bin interval;
and 4, determining the threshold range of each bin interval according to the corresponding average value and standard deviation, and performing secondary screening on the preliminarily screened operation data through the threshold range to obtain the screened operation data.
Specifically, firstly, the operation data can be primarily screened according to the operation parameters, abnormal operation data in the operation data are removed, then the operation data after the abnormal operation data are screened out are divided into sections, the operation data after the abnormal operation data are primarily screened out are secondarily screened out based on the standard deviation and the average value of the average power of each section, the abnormal operation data in the operation data are further screened out, and therefore the operation data which are more in line with the actual operation condition of the wind generating set are obtained. The power curve generated by the operation data screened for many times is more consistent with the actual operation condition of the wind generating set, so that the actual generating performance of the set can be known quickly, and data support is provided for subsequent performance check of the whole machine and energy dispatching of a wind power plant.
The details of step 2 will be further described below.
In this embodiment, the operation data of the wind turbine generator system may be collected by an SCADA system (i.e. a data collection and monitoring system) of the wind turbine generator system, and the collected operation data is composed of a plurality of data points, each of which includes: wind speed, 10min average power, generator speed, generator torque, pitch angle, and 30s average wind direction.
The collected operating parameters include: the system comprises grid-connected rotating speed, rated power, rated rotating speed, a rotating speed-torque control curve, a maximum pre-variable pitch angle and yaw starting wind direction deviation. These operating parameters may all be provided directly by the manufacturer.
The preliminary screening of the operation data according to the operation parameters includes:
and screening out data points of which the average power in the operation data is less than the rated power in the operation parameters and the pitch angle is greater than the maximum pre-pitch angle.
Specifically, the 10min average power and the variable pitch angle of all data points are respectively compared with the rated power and the maximum pre-variable pitch angle of the wind generating set, and the data points of which the 10min average power is smaller than the rated power and the variable pitch angle is larger than the maximum pre-variable pitch angle in the operation data are screened out. In this way, data points of non-maximum wind energy absorption in the operational data can be screened out, thereby screening out abnormal operational data in the operational data.
In this embodiment, when the operation data is preliminarily screened, the operation data may be screened again according to the rated rotation speed and the maximum pre-pitch angle in the operation parameters.
Specifically, the rotation speed and the pitch angle of the generator set, which are screened out all data points after absorption of non-maximum wind energy, can be compared with the rated rotation speed and the maximum pre-pitch angle respectively, the data points, which are smaller than the rated rotation speed and larger than the maximum pre-pitch angle, of the generator set are screened out, data points which are not tracked along with the optimal CP (namely, the wind energy utilization coefficient) are screened out, and abnormal operation data in the operation data are further screened out.
In this embodiment, optionally, after the operation data is screened according to the rotation speed of the generator and the pitch angle, the operation data after the data point not tracked along with the optimal CP is screened may be screened according to the grid-connected rotation speed. The method specifically comprises the following steps: and respectively comparing the generator rotating speed of each data point of the operation data after the data point which is not tracked along with the optimal CP is screened out with the grid-connected rotating speed, screening out the data point of which the generator rotating speed is less than the grid-connected rotating speed, namely screening out the data point of the wind generating set which is not operated in a grid-connected mode, and further screening out abnormal operation data in the operation data.
In this embodiment, optionally, when the operation data is preliminarily screened, the operation data after the data points of the wind turbine generator system which are not in grid-connected operation are screened may be screened again according to the theoretical rotation speed-torque curve.
Specifically, after the data points which are not in grid-connected operation are screened out, the generator torques of all the remaining data points are respectively compared with the theoretical rotating speed-torque curve, and the data points which are lower than the theoretical rotating speed-torque curve are screened out, so that the abnormal operation data in the operation data can be further screened out for the data points which are not in operation along with the control curve.
In this embodiment, optionally, the operation data after the data points that do not operate with the control curve are screened may be further screened according to the yaw starting wind direction deviation angle.
Specifically, the 30-second average wind direction of each data point in the operation data after the data point which does not operate along with the control curve is screened out may be compared with the yaw starting wind direction deviation angle, respectively, to screen out the data point of which the 30-second average wind direction is greater than the yaw starting wind direction deviation angle in the operation parameters, thereby screening out the data point which has too large wind deviation and does not keep the maximum wind energy absorption, and further screening out the abnormal operation data in the operation data.
Details of step 3 will be described in detail below.
In the present embodiment, in step 3, the 10min average wind speed of the operation data may be divided into a plurality of bin intervals based on the interval of 0.1 m/s. And finally, calculating the average value and the standard deviation of the average power of the corresponding bin interval according to the 10min average power of all the data points in the bin interval.
In step 4, first, a threshold range required for performing secondary screening on each bin interval, that is, a threshold range corresponding to each bin interval, may be determined according to the average value and the standard deviation calculated in step 3. In this embodiment, the threshold range may be defined as (a-3 σ, a +3 σ), where a is the average value and σ is the standard deviation. Then, the 10min average power of all data points in each bin interval is compared with the corresponding threshold range, and data points with the 10min average power not within the threshold range are screened out, so that abnormal points caused by factors such as measurement and the like are further screened out of abnormal operation data in the operation data.
The method comprises the steps of screening operation data according to operation parameters, eliminating abnormal operation data in the operation data, carrying out interval division on the operation data after screening the abnormal operation data, carrying out secondary screening on the operation data after primarily screening the abnormal operation data based on the standard deviation and the average value of the average power of each interval, and further screening the abnormal operation data in the operation data, so that the operation data which are more in line with the actual operation condition of the wind generating set are obtained, a power curve generated by the operation data which are screened for many times is more in line with the actual operation condition of the wind generating set, the actual power generation performance of the set is rapidly known, and data support is provided for subsequent complete machine performance check and wind power plant energy scheduling.
A flow chart of a power curve generation method as shown in fig. 2, the generation method comprising:
s1, collecting the operation data and the operation parameters of the wind generating set;
s2, performing primary screening on the operation data according to the operation parameters;
s3, carrying out interval division on the preliminarily screened operation data according to preset bin interval intervals, and calculating the average value and the standard deviation of the average power of each bin interval;
s4, determining the threshold range of each bin interval according to the corresponding average value and standard deviation, and performing secondary screening on the primarily screened operation data through the threshold range to obtain screened operation data;
s5, carrying out bin interval division on the screened operation data, and calculating a wind speed average value and a power average value of each bin interval;
and 6, generating an operating power curve of the wind generating set through the wind speed average value and the power average value of all bin intervals.
Specifically, firstly, the operation data can be primarily screened according to the operation parameters, abnormal operation data in the operation data are removed, then the operation data after the abnormal operation data are screened out are divided into sections, the operation data after the abnormal operation data are primarily screened out are secondarily screened out based on the standard deviation and the average value of the average power of each section, the abnormal operation data in the operation data are further screened out, and therefore the operation data which are more in line with the actual operation condition of the wind generating set are obtained. The power curve generated by the operation data after multiple screening is more consistent with the actual operation condition of the wind generating set, so that the actual generating performance of the set can be rapidly known, and data support is provided for subsequent complete machine performance check and wind power plant energy scheduling.
Details of S2 will be further described below.
In this embodiment, the operation data of the wind turbine generator system may be collected by an SCADA system (i.e. a data collection and monitoring system) of the wind turbine generator system, and the collected operation data is composed of a plurality of data points, each of which includes: wind speed, 10min average power, generator speed, generator torque, pitch angle, and 30s average wind direction.
The collected operating parameters include: grid-connected rotating speed, rated power, rated rotating speed, a rotating speed-torque control curve, a maximum pre-variable pitch angle and yaw starting wind direction deviation. These operating parameters may all be provided directly by the manufacturer.
The preliminary screening of the operation data according to the operation parameters includes:
and screening out data points in which the average power in the operation data is smaller than the rated power in the operation parameters and the pitch angle is larger than the maximum pre-pitch angle.
Specifically, the 10min average power and the variable pitch angle of all data points are respectively compared with the rated power and the maximum pre-variable pitch angle of the wind generating set, and the data points of which the 10min average power is smaller than the rated power and the variable pitch angle is larger than the maximum pre-variable pitch angle in the operation data are screened out. In this way, data points of non-maximum wind energy absorption in the operational data can be screened out, thereby screening out abnormal operational data in the operational data.
In this embodiment, when the operation data is preliminarily screened, the operation data may be screened again according to the rated rotation speed and the maximum pre-pitch angle in the operation parameters.
Specifically, the rotation speed and the pitch angle of the generator set, which are screened out all data points after non-maximum wind energy absorption, can be compared with the rated rotation speed and the maximum pre-pitch angle respectively, the data points, of which the rotation speed of the generator set is less than the rated rotation speed and the pitch angle is greater than the maximum pre-pitch angle, are screened out, data points which are not tracked along with the optimal CP (namely, the wind energy utilization coefficient) are screened out, and abnormal operation data in the operation data are further screened out.
In this embodiment, optionally, after the operation data is screened according to the rotation speed of the generator and the pitch angle, the operation data after the data point not tracked along with the optimal CP is screened may be screened according to the grid-connected rotation speed. The method specifically comprises the following steps: and respectively comparing the generator rotating speed of each data point of the operation data after the data point which is not tracked along with the optimal CP is screened out with the grid-connected rotating speed, screening out the data point of which the generator rotating speed is less than the grid-connected rotating speed, namely screening out the data point of the wind generating set which is not in grid-connected operation, and further screening out abnormal operation data in the operation data.
In this embodiment, optionally, when the operation data is preliminarily screened, the operation data after screening the data points of the wind turbine generator set which are not in grid-connected operation may be screened again according to the theoretical rotation speed-torque curve.
Specifically, after the data points which are not in grid-connected operation are screened out, the generator torques of all the remaining data points are respectively compared with the theoretical rotating speed-torque curve, and the data points which are lower than the theoretical rotating speed-torque curve are screened out, so that the abnormal operation data in the operation data can be further screened out for the data points which are not in operation along with the control curve.
In this embodiment, optionally, the operation data after the data points that do not operate with the control curve are screened may be further screened according to the yaw starting wind direction deviation angle.
Specifically, the 30-second average wind direction of each data point in the operation data after the data point which does not operate with the control curve is screened out can be respectively compared with the yaw starting wind direction deviation angle, and the data point of which the 30-second average wind direction is larger than the yaw starting wind direction deviation angle in the operation parameters is screened out, so that the data point which has overlarge wind deviation and does not keep the maximum wind energy absorption is screened out, and abnormal operation data in the operation data is further screened out.
Details of S3 will be described later.
In the present embodiment, in S3, the 10min average wind speed of the operational data may be divided into a plurality of bin intervals based on the interval of 0.1 m/S. And then, according to the wind speed range corresponding to each bin interval, collecting each data point in the preliminarily screened operating data into the corresponding bin interval, and finally, calculating the average value and the standard deviation of the average power of the corresponding bin interval according to the 10min average power of all the data points in the bin interval.
In step 4, first, a threshold range required for performing secondary screening on each bin interval, that is, a threshold range corresponding to each bin interval, may be determined according to the average value and the standard deviation calculated in step 3. In this embodiment, the threshold range may be defined as (a-3 σ, a +3 σ), where a is the average value and σ is the standard deviation. Then, the 10min average power of all data points in each bin interval is compared with the corresponding threshold range, and data points with the 10min average power not within the threshold range are screened out, so that abnormal points caused by factors such as measurement and the like are further screened out of abnormal operation data in the operation data.
In step S5, the 10min average wind speed of the operating data may be divided into a plurality of bin intervals according to the interval of 0.5m/S, and each data point in the secondarily-filtered operating data may be collected into a corresponding bin interval according to the wind speed range corresponding to each bin interval. And finally, calculating the wind speed average value and the power average value of the corresponding bin interval according to the 10min average wind speed and the 10min average power of all data points in the bin interval, and generating a power curve of the wind generating set according to the wind speed average value and the power average value.
The method comprises the steps of screening operation data according to operation parameters, eliminating abnormal operation data in the operation data, carrying out interval division on the operation data after screening the abnormal operation data, carrying out secondary screening on the operation data after primarily screening the abnormal operation data based on the standard deviation and the average value of the average power of each interval, and further screening the abnormal operation data in the operation data, so that the operation data which are more in line with the actual operation condition of the wind generating set are obtained, a power curve generated by the operation data which are screened for many times is more in line with the actual operation condition of the wind generating set, the actual power generation performance of the set is rapidly known, and data support is provided for subsequent complete machine performance check and wind power plant energy scheduling.
A storage medium stores a computer program, and the computer program executes a power curve generation method.
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 (10)
1. A method of pre-processing data, comprising:
collecting operation data and operation parameters of a wind generating set;
performing primary screening on the operation data according to the operation parameters;
carrying out interval division on the preliminarily screened operation data according to a preset bin interval, and calculating the average value and the standard deviation of the average power of each bin interval;
and determining the threshold range of each bin interval according to the corresponding average value and standard deviation, and performing secondary screening on the preliminarily screened operation data through the threshold range to obtain the screened operation data.
2. The data preprocessing method of claim 1, wherein the preliminary screening of the operational data according to the operational parameters comprises:
and screening out data points of which the average power in the operation data is less than the rated power in the operation parameters and the pitch angle is greater than the maximum pre-pitch angle.
3. The data preprocessing method of claim 1, wherein the preliminary screening of the operational data according to the operational parameters comprises:
and screening out data points in the operation data, wherein the rotating speed of the generator is less than the rated rotating speed in the operation parameters, and the pitch angle is greater than the maximum pre-pitch angle.
4. The data preprocessing method of claim 1, wherein the preliminary screening of the operational data according to the operational parameters comprises:
and screening out data points in the operation data, wherein the rotating speed of the generator is less than the grid-connected rotating speed in the operation parameters.
5. The data preprocessing method of claim 1, wherein the preliminary screening of the operational data according to the operational parameters comprises:
and screening out data points in the operating data where the torque is lower than the theoretical speed-torque curve in the operating parameters.
6. The data preprocessing method of claim 1, wherein the preliminary screening of the operational data according to the operational parameters comprises:
and screening out data points in the operation data, wherein the 30-second average wind direction is larger than the yaw starting wind direction deviation angle in the operation parameters.
7. The method of data pre-processing according to claim 1, wherein the bin interval is 0.1m/s wind speed.
8. The data pre-processing method of claim 1, wherein the threshold range is (a-3 σ, a +3 σ), where a is the mean and σ is the standard deviation.
9. A method of generating a power curve, comprising:
obtaining screened operational data using a data pre-processing method as claimed in any one of claims 1 to 8;
bin interval division is carried out on the screened operation data, and the wind speed average value and the power average value of each bin interval are calculated;
and generating an operation power curve of the wind generating set through the wind speed average value and the power average value of all bin intervals.
10. A storage medium storing a computer program, characterized in that: the computer program performs the power curve generation method as claimed in claim 9.
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