CN112380699A - Wind turbine generator yaw error early warning analysis method based on multidimensional analysis - Google Patents
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Abstract
The invention discloses a wind turbine generator yaw error early warning analysis method based on multidimensional analysis, which comprises the following steps: selecting running data of wind speed, active power, pitch angle and an included angle between an engine room and wind direction by taking a single unit as a unit, and screening the data; importing the data into a yaw early warning algorithm, slicing from the dimension of an included angle between a cabin and a wind direction, and dividing M wind angle intervals; slicing each wind angle interval according to the wind speed dimension, and dividing N wind speed intervals; and calculating the weighted values of different wind speeds in the interval through Rayleigh distribution, calculating the annual energy production of each wind angle interval, and judging that the unit is abnormal in yaw if the maximum annual energy production interval is greater than an alarm threshold. The method also comprises the step of realizing alarm strategy analysis through the alarm degree and the unilateral effect. According to the method, the wind speed in each wind angle pair interval is subjected to weight distribution in three dimensions of wind angle-wind speed-power, the annual generated energy of the interval is calculated, double judgment is carried out by using a unilateral effect and an alarm degree, the abnormal yaw unit is accurately found, and the safety of the unit is protected.
Description
Technical Field
The invention relates to the field of early warning of a wind turbine control system, in particular to a wind turbine yaw error early warning analysis method based on multi-dimensional analysis.
Background
In recent years, science and technology are continuously developed, the demand for energy is also continuously promoted, and in order to balance the relationship between science and technology development and environmental protection, wind power generation becomes one of new energy sources with the most development potential by virtue of the advantages of abundant reserves, mature mining technology and the like. With the popularization of wind power intellectualization, in order to improve the operation and maintenance efficiency of a wind turbine generator and mine massive information contained in data, attention is paid to early warning analysis technology. The yaw system is used as an important control system for the wind turbine generator set to capture wind energy, and the accuracy of the yaw system directly influences the generated energy and the economy of wind power generation. Meanwhile, when yaw deviation occurs, the unit is subjected to acting force which deviates from the axial direction of the main shaft on one side, the acting force enables the main shaft to deviate from the central shaft slightly, and large wind mechanical vibration is formed. The problems of main shaft abrasion, gear box tooth surface abrasion, unit centering displacement and the like can be caused by long-term operation, and the service life of the unit is seriously influenced.
Therefore, yaw versus wind error detection and correction for wind turbines is essential. The wind generating set yaw error early warning analysis method based on the multidimensional analysis is created on the basis, so that the wind generating set yaw error early warning analysis is carried out on a wind generating set yaw system through operation data, the yaw abnormal problem of the wind generating set is timely and accurately found, the yaw wind problem is corrected, and the generated energy is prevented from being lost.
Disclosure of Invention
The invention aims to provide a wind turbine yaw error early warning analysis method based on multidimensional analysis, which can be used for early warning and analyzing a fan yaw system through operation data, timely and accurately finding out the yaw abnormity problem of the wind turbine generator, so that the yaw wind problem can be corrected, and the generated energy is prevented from being lost.
In order to solve the technical problem, the invention provides a wind turbine yaw error early warning analysis method based on multidimensional analysis, which comprises the following steps:
(1) selecting running data of a wind turbine generator unit in a period of time, including wind speed, generator active power, pitch angle and an included angle between a cabin and wind direction, and carrying out screening pretreatment on abnormal data;
(2) importing the preprocessed wind turbine generator operation data into a yaw early warning algorithm, wherein the yaw early warning algorithm comprises the following steps: firstly, slicing the preprocessed wind turbine generator operation data from the dimension of an included angle between a cabin and a wind direction, and dividing the wind turbine generator operation data into M wind angle intervals with the same angle step length; secondly, slicing each wind angle interval according to the wind speed dimension, and dividing the wind angle intervals into N wind speed intervals with the same speed step length; and calculating the weighted values of different wind speeds in the wind angle range through a Rayleigh distribution formula, wherein the Rayleigh distribution formula is as follows:
wherein V is the actual wind speed, VavgIs the average wind speed;
then, the annual energy production AEP in each wind angle interval is calculated, and the calculation formula of the annual energy production AEP is as follows:
wherein N ishThe annual generation hours, N is the number of wind speed intervals, Vi、Vi-1Actual wind speeds, P, for the wind speed intervals i and i-1, respectivelyi、Pi+1The generated power is respectively in the wind speed interval of i and i + 1.
(3) And analyzing the annual energy production AEP value in each pair wind angle interval obtained by calculation, finding the interval where the maximum annual energy production AEP is located, and judging that the wind turbine yaw system is abnormal if the interval where the maximum annual energy production AEP is located is larger than an alarm threshold value.
Further improved, the step (3) further includes a step of implementing alarm policy analysis by an alarm degree WD, where:
and when the interval of the maximum annual generated energy AEP is greater than an alarm threshold value and the alarm degree WD is greater than an alarm set value, judging that the wind turbine generator has the problem of yaw abnormity.
The improvement is further that the alarm threshold value in the step (3) is +/-5-10 degrees, and the alarm set value is 5-6 percent.
The method is further improved, the step (3) further comprises a step of realizing alarm strategy analysis through a power curve unilateral effect, 0 degrees are used as wind alignment centers, when obvious deviation exists in annual energy production of intervals of mirror image positions, the fact that wind zero points are deviated to one side is shown, the unilateral effect is achieved, and when the interval where the maximum annual energy production AEP is located is larger than an alarm threshold value, the wind turbine generator is judged to have the yaw abnormity problem.
Further improvement, the alarm threshold value in the step (3) is +/-5-10 degrees.
And (2) further improving, selecting 2-3 months of operation data of a single wind turbine generator set in the step (1) to participate in yaw error early warning analysis of the wind turbine generator set.
In a further improvement, the method for screening data in the step (1) comprises the following steps: screening data with power data P <1KW, and eliminating data of operation maintenance and fault shutdown; screening data between cut-in wind speed and rated wind speed, and rejecting full-transmission data; and thirdly, screening data with the pitch angle Pg of less than 1 degree, and rejecting power-limiting operation data.
Further improved, the specific method for dividing the wind angle interval in the step (2) is as follows: slicing the dimension of an included angle between an engine room and a wind direction of preprocessed wind turbine generator system operation data from-10.5 degrees to 10.5 degrees, and dividing the dimension into 21 wind angle intervals by taking 1 degree as a step length;
further improved, the specific method for dividing the wind speed interval in the step (2) is as follows: and slicing each wind angle range from 3m/s to 10m/s according to the wind speed dimension, and dividing the wind angle range into 15 wind speed ranges by taking 0.5m/s as a step length.
After adopting such design, the invention has at least the following advantages:
the wind turbine yaw error early warning analysis method based on the multidimensional analysis analyzes three dimensions of wind angle, wind speed and power, and weight distribution is carried out on the wind speed in each wind angle range by utilizing Rayleigh distribution, so that a power curve and Annual Energy Production (AEP) of each wind angle range are calculated. Meanwhile, on the basis, an alarm strategy is formulated according to a unilateral effect and an interval AEP, an alarm degree concept is introduced, and double judgment is carried out on the wind turbine with abnormal yaw so as to accurately and timely find the wind turbine with yaw wind alignment errors in the wind power plant, provide alarm prompts for field personnel, improve the power generation efficiency and protect the safety of the wind turbine.
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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 yaw error early warning analysis method based on multi-dimensional analysis.
FIG. 2 is a wind interval AEP trend graph in the wind turbine yaw error early warning analysis method based on multi-dimensional analysis.
Detailed Description
The wind turbine yaw error early warning analysis method based on the multidimensional analysis comprises the following specific steps:
(1) data selection and preprocessing
In this embodiment, a single wind turbine is taken as a unit, and minute-level operation data such as wind speed, generator active power, pitch angle, and included angle between a cabin and wind direction of the wind turbine is selected for early warning analysis.
In order to ensure the validity of interval data and the accuracy of data analysis results, the operation data of 2-3 months of wind turbines in the wind power plant are selected for early warning analysis.
In order to avoid the influence of abnormal conditions on the yaw early warning analysis result, the operation data needs to be preprocessed, and the method for screening the effective data comprises the following steps:
a. screening a value of power data P <1KW, and rejecting data such as operation maintenance, fault shutdown and the like;
b. in order to avoid the influence of the change of the pitch angle on the power, only scattered points in a power climbing stage are selected, operation data above a cut-in wind speed and below a rated wind speed are screened and reserved, and full-transmission data are removed;
c. screening the value of the pitch angle data Pg <1 degree, and rejecting the electricity-limiting operation data.
(2) Yaw early warning algorithm analysis
Importing the preprocessed wind turbine generator operation data into a yaw early warning algorithm, wherein the yaw early warning algorithm has the following principle: when the wind turbine generator system is accurately aligned to wind, the included angle between the engine room and the wind direction is 0 degree, and when the yaw is abnormal, the wind alignment interval deviates to a positive or negative direction. And slicing the preprocessed wind turbine generator operation data from the dimension of the included angle between the engine room and the wind direction, and dividing the wind turbine generator operation data into M wind angle intervals with the same angle step length. And slicing each wind angle interval according to the wind speed dimension, and dividing the wind angle intervals into N wind speed intervals by the same speed step length.
According to the energy conversion principle, in order to maximize the generated energy of the wind turbine generator, the wind wheel is required to capture the maximum wind energy. When the wind turbine generator is accurate in wind alignment, namely the included angle between the engine room and the wind direction is 0 degree, under the condition of normal operation, main wind energy is concentrated near a 0-degree interval, and therefore the generated energy in the 0-degree interval is theoretically the highest value. When the highest interval of the generated energy is in an abnormal angle (outside +/-5-10 degrees), the yaw is abnormal.
Referring to fig. 1, a specific yaw early warning algorithm flow in this embodiment is as follows:
step1, importing the preprocessed data of the wind turbine generator after preprocessing into an algorithm;
step2, slicing the preprocessed operation data according to the dimension of an included angle between the cabin and the wind direction, dividing the operation data into 21 wind angle intervals from-10.5 degrees to 10.5 degrees by taking 1 degree as a Step length;
step3, slicing each wind angle range according to the wind speed dimension, slicing the data in each wind angle range from 3m/s to 10m/s, and dividing the data into 15 wind speed ranges by taking 1m/s as a Step length; this divides the total data into 315 sub-intervals.
Step4, calculating the weight of different wind speeds in the wind angle range through Rayleigh distribution, wherein the Rayleigh distribution calculation formula (1) is as follows:
wherein V is the actual wind speed, VavgIs the average wind speed.
And Step5, calculating the annual energy production AEP in each wind angle interval according to the formula (2).
Wherein N ishThe annual generation hours are about 8760, N is the number of wind speed intervals, Vi、Vi-1Actual wind speeds, P, for the wind speed intervals i and i-1, respectivelyi、Pi+1The generated power is respectively in the wind speed interval of i and i + 1.
And Step6, analyzing the annual energy production AEP value in each wind angle interval obtained by calculation, finding the interval where the maximum annual energy production AEP is located, and judging that the unit has an abnormal yaw when the maximum annual energy production interval is larger than an alarm threshold, such as more than +/-5 degrees.
Considering some special cases, such as the case of a large instantaneous wind speed, a stuck wind vane, etc., there may be a case where the difference between the annual energy production of the interval is not obvious, resulting in an abnormal yaw analysis result, which is specifically represented as the case where the pole of the annual energy production of the interval is present in an abnormal interval, but the difference between the pole and the normal interval of yaw is small. In order to solve the problem, an alarm strategy analysis auxiliary judgment step is added, and the accuracy of yaw early warning is guaranteed. The yaw alarm strategy analysis of the present embodiment includes two methods, as follows.
(1) Alarm strategy analysis realized through alarm degree
The alarm degree WD calculation formula is as follows (3):
on the basis that the interval where the maximum annual energy production AEP is calculated is larger than the alarm threshold, when WD is larger than the alarm set value, the problem that the current wind turbine generator is abnormal in yaw can be accurately determined, and an alarm is given.
In this embodiment, the alarm setting value is 5.5%, and the setting value can be set according to different units, for example, the setting value is 5-6%.
(2) Alarm strategy analysis realized by power curve unilateral effect
The method takes 0 degrees as a wind center, when the annual energy production of the interval of the mirror image position has obvious deviation, the deviation of the zero point of the wind to one side is shown, and a certain unilateral effect is achieved, as shown in the attached drawing 2, on the basis, when the interval where the maximum annual energy production AEP is located is larger than an alarm threshold value, the condition that the current wind turbine generator has yaw abnormity can be accurately determined, and an alarm is given to the wind turbine generator.
According to the invention, three-dimensional analysis is carried out on the included angle between the engine room and the wind direction, wind speed and power slice, the power curve and Annual Energy Production (AEP) of each wind-facing interval are calculated, and the accuracy of the analysis result is ensured by introducing Rayleigh distribution in consideration of the wind frequency difference. Meanwhile, on the basis, an alarm strategy is formulated according to a unilateral effect and an interval AEP difference value, an alarm degree concept is introduced, secondary double judgment is carried out on the wind turbine generator with abnormal yawing, an accurate and reliable alarm prompt is finally given, the loss of generated energy is avoided, the generating efficiency is improved, and the service life of the wind turbine generator is prolonged.
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 (9)
1. A wind turbine yaw error early warning analysis method based on multi-dimensional analysis is characterized by comprising the following steps:
(1) selecting running data of a wind turbine generator unit in a period of time, including wind speed, generator active power, pitch angle and an included angle between a cabin and wind direction, and carrying out screening pretreatment on abnormal data;
(2) importing the preprocessed wind turbine generator operation data into a yaw early warning algorithm, wherein the yaw early warning algorithm comprises the following steps: firstly, slicing the preprocessed wind turbine generator operation data from the dimension of an included angle between a cabin and a wind direction, and dividing the wind turbine generator operation data into M wind angle intervals with the same angle step length; secondly, slicing each wind angle interval according to the wind speed dimension, and dividing the wind angle intervals into N wind speed intervals with the same speed step length; and calculating the weighted values of different wind speeds in the wind angle range through a Rayleigh distribution formula, wherein the Rayleigh distribution formula is as follows:
wherein V is the actual wind speed, VavgIs the average wind speed;
then, the annual energy production AEP in each wind angle interval is calculated, and the calculation formula of the annual energy production AEP is as follows:
wherein N ishThe annual generation hours, N is the number of wind speed intervals, Vi、Vi-1Actual wind speeds, P, for the wind speed intervals i and i-1, respectivelyi、Pi+1The generated power is respectively in the wind speed interval of i and i + 1.
(3) And analyzing the annual energy production AEP value in each pair wind angle interval obtained by calculation, finding the interval where the maximum annual energy production AEP is located, and judging that the wind turbine yaw system is abnormal if the interval where the maximum annual energy production AEP is located is larger than an alarm threshold value.
2. The wind turbine yaw error early warning analysis method based on multidimensional analysis according to claim 1, wherein the step (3) further comprises a step of implementing alarm strategy analysis by an alarm degree WD:
and when the interval of the maximum annual generated energy AEP is greater than an alarm threshold value and the alarm degree WD is greater than an alarm set value, judging that the wind turbine generator has the problem of yaw abnormity.
3. The wind turbine yaw error early warning analysis method based on multidimensional analysis as claimed in claim 2, wherein the alarm threshold in the step (3) is ± 5-10 °, and the alarm set value is 5-6%.
4. The wind turbine yaw error early warning analysis method based on multi-dimensional analysis according to claim 1, characterized in that the step (3) further comprises a step of realizing alarm strategy analysis through a power curve unilateral effect, a 0-degree wind center is used, when the annual energy production of the interval of the mirror image position has obvious deviation, the wind zero point is deviated to one side, the unilateral effect is achieved, and when the interval of the maximum annual energy production AEP is larger than an alarm threshold value, the wind turbine is judged to have the yaw abnormity problem.
5. The wind turbine yaw error early warning analysis method based on multi-dimensional analysis as claimed in claim 4, wherein the alarm threshold in the step (3) is ± 5-10 °.
6. The wind turbine generator yaw error early warning analysis method based on the multidimensional analysis according to any one of claims 1 to 5, wherein the wind turbine generator yaw error early warning analysis is performed by selecting 2-3 months of operation data of a single wind turbine generator in the step (1).
7. The wind turbine yaw error early warning analysis method based on multi-dimensional analysis as claimed in claim 6, wherein the data screening method in the step (1) comprises: screening data with power data P <1KW, and eliminating data of operation maintenance and fault shutdown; screening data between cut-in wind speed and rated wind speed, and rejecting full-transmission data; and thirdly, screening data with the pitch angle Pg of less than 1 degree, and rejecting power-limiting operation data.
8. The wind turbine yaw error early warning analysis method based on multidimensional analysis as claimed in claim 7, wherein the specific method for dividing the wind angle interval in step (2) is as follows: and slicing the operation data of the preprocessed wind turbine generator from-10.5 degrees to 10.5 degrees of the dimension of the included angle between the engine room and the wind direction, and dividing the operation data into 21 wind angle intervals by taking 1 degree as a step length.
9. The wind turbine yaw error early warning analysis method based on multidimensional analysis as claimed in claim 8, wherein the specific method for dividing the wind speed interval in step (2) is as follows: and slicing each wind angle range from 3m/s to 10m/s according to the wind speed dimension, and dividing the wind angle range into 15 wind speed ranges by taking 0.5m/s as a step length.
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