CN112528504B - Wind turbine generator fatigue load calculation method based on turbulent flow distribution - Google Patents
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Abstract
The invention provides a wind turbine generator fatigue load calculation method based on turbulent flow distribution, which comprises the following steps: dividing wind speed intervals by combining wind speeds corresponding to the wind turbine generator, and calculating the occurrence probability of each wind speed interval according to the design annual average wind speed of the wind turbine generator; selecting a certain number of wind speed standard difference points in each wind speed interval by using an equal probability interval and local point supplementing method, and calculating the turbulence intensity of each wind speed standard difference point; calculating the occurrence probability of a turbulent flow interval; calculating to obtain a load time sequence of each coordinate position of the wind turbine generator by combining a super-turbulent shutdown method according to the wind speed and the turbulent intensity of the wind speed standard difference point; and calculating the fatigue load of each coordinate position of the wind turbine generator according to the occurrence probability and the load time sequence of each wind speed interval and each turbulence interval. The method can solve the technical problems that after the fatigue load simulation result of the wind turbine generator is over-evaluated, higher requirements can be put forward on the structural strength of the wind turbine generator during design, and the manufacturing cost is increased.
Description
Technical Field
The invention relates to the technical field of wind power generation, in particular to a wind turbine generator fatigue load calculation method based on turbulent flow distribution.
Background
When the wind turbine generator is designed, the fatigue load is an important design reference index. The fatigue load of the wind turbine generator can be understood as the load generated by normal power generation in the service life of the wind turbine generator, according to the IEC61400-1 wind turbine generator design standard, DLC1.2 working conditions are the leading working conditions for reflecting the fatigue load of the wind turbine generator, the working conditions comprise normal operation of the wind turbine generator within the range from cut-in wind speed to cut-out wind speed, then the fatigue load of each coordinate position of the wind turbine generator is cumulatively calculated by combining with the wind speed probability distribution of a wind field, and then the fatigue load of each coordinate position is used for designing and checking the corresponding part. The main influencing factors of the DLC1.2 working condition are wind speed and turbulence. According to the conventional method, fatigue loads are subjected to simulation calculation during design of a wind turbine generator, wind speed is considered through wind frequency distribution, and turbulence intensity values of 90% probability quantiles are adopted for conservative evaluation in each wind speed section.
However, the simplified estimation process of wind speed and turbulence can result in over-estimation of fatigue loads. After the fatigue load of the wind turbine generator is over-evaluated, the simulation value of the fatigue load is larger than the actually generated load; therefore, under the same design life requirement of the wind turbine generator, higher fatigue load can put forward higher requirements on the structural strength of the wind turbine generator, and the manufacturing cost is increased.
Disclosure of Invention
Aiming at the defects in the prior art, the invention provides a wind turbine fatigue load calculation method based on turbulent flow distribution, which can solve the technical problems that after the fatigue load simulation result of the wind turbine is over-evaluated, higher requirements can be put forward on the structural strength of the wind turbine during design, and the manufacturing cost is increased.
The technical scheme adopted by the invention is as follows:
in a first aspect, a wind turbine fatigue load calculation method based on turbulent distribution is provided, and includes the following steps:
dividing wind speed intervals by combining wind speeds corresponding to the wind turbine generator, and calculating the occurrence probability of each wind speed interval according to the design annual average wind speed of the wind turbine generator;
selecting a certain number of wind speed standard difference points in each wind speed interval by using an equal probability interval and local point supplementing method, and calculating the turbulence intensity of each wind speed standard difference point;
calculating the occurrence probability of a turbulent flow interval;
according to the wind speed and the turbulence intensity of the wind speed standard deviation point, simulation software is used for carrying out simulation calculation by combining a super-turbulence stopping method to obtain a load time sequence of each coordinate position of the wind turbine generator;
and calculating the fatigue load of each coordinate position of the wind turbine generator by using simulation software according to the occurrence probability of each wind speed interval and turbulence interval and the load time sequence of each coordinate position of the wind turbine generator.
Further, the number of wind speed standard deviation points is 29.
The technical effect obtained by the technical scheme is as follows: and in each wind speed interval, 29 wind speed standard deviation points are selected in total, the calculation cost is acceptable, and the overestimation degree is very small and can be ignored in the simulation calculation of the fatigue load by matching with a super-turbulent shutdown method.
Further, a certain number of wind speed standard deviation points are selected by using an equal probability spacing and local point supplementing method, which specifically comprises the following steps:
when the cumulative probability is within the interval of 5% to 95%, 1 wind speed standard deviation point is selected every 5% of the interval;
when the cumulative probability is 97%, 98%, 99%, 99.9% and 99.99%, respectively selecting 1 wind speed standard deviation point;
when the cumulative probability is within the interval of 99.99% to 100%, 5 wind speed standard deviation points are selected by equidistant interpolation.
Further, when the load time sequence of each coordinate position of the wind turbine generator is calculated in a simulation mode, the controller judges the turbulence intensity and executes the super-turbulence shutdown.
Further, the super-turbulent shutdown is implemented as follows:
a turbulence threshold is set during simulation calculation, and shutdown is performed when the limit turbulence is monitored to exceed the turbulence threshold.
The technical effect obtained by the technical scheme is as follows: the truncation probability evaluation can be better carried out.
Further, the turbulence threshold satisfies the following equation:
in the above formula, σ1Representing the turbulence threshold, c being a characteristic parameter, IrefDenotes the reference turbulence intensity, VaveIndicating annual mean wind speed, VhubIndicating the wind speed, V, at the hub heightave、IrefAnd representing the design index of the wind turbine generator type.
In a second aspect, an electronic device is provided, comprising:
one or more processors;
storage means for storing one or more programs;
when the one or more programs are executed by the one or more processors, the one or more processors implement the wind turbine fatigue load calculation method based on turbulent distribution provided in the first aspect.
In a third aspect, a computer readable storage medium is provided, in which a computer program is stored, and the computer program is executed by a processor to implement the wind turbine fatigue load calculation method based on turbulent distribution provided in the first aspect.
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(a) is a schematic diagram of a three-dimensional probability distribution of actual wind speed-turbulence in example 1 of the present invention;
FIG. 1(b) is a simplified schematic diagram of the wind speed probability distribution in embodiment 1 of the present invention;
FIG. 2 is a schematic diagram showing the probability distribution of class A turbulence intensity at a wind speed of 4m/s in example 1 of the present invention;
FIG. 3 is a graph showing the increase of the load with the turbulence intensity at the bearing position in example 1 of the present invention;
FIG. 4 is a flowchart of the method of embodiment 1 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.
Example 1
When fatigue load of the wind turbine generator is simulated, the design standard of the prior art is mainly used for deducing a conservative algorithm of 90% turbulence quantiles, or the turbulence subdivision is calculated by adopting a two-dimensional probability distribution method of wind speed and turbulence. As shown in fig. 1, fig. 1(a) is a three-dimensional probability distribution of actual wind speed-turbulence, and fig. 1(b) is a simplified schematic diagram of the probability distribution of wind speed; after simplifying the wind speed and the turbulence, calculating the fatigue load by using the two-dimensional probability distribution of the wind speed and the turbulence leads to overestimation of the fatigue load, which is specifically as follows:
(1) data discretization
In the simulation in the engineering, the calculation cost needs to be considered, and in order to avoid the problem that the design of a set of wind turbine generator consumes great calculation resource investment or needs unacceptable calculation time in the actual simulation, the calculation quantity needs to be controlled, so that the fine calculation of the probability resolution is limited. In addition, the basic principle of wind turbine design is that simplification must be towards a conservative direction, and the basic principle brings a coarser probability resolution; the coarser probability resolution leads to higher simulated calculations of fatigue loading, even higher than the conservative algorithm. Therefore, a reasonable data discretization method can be adopted for processing.
(2) Probability of truncation
When the transcendental probability tends to be infinitesimal, the turbulence intensity value will tend to be infinitesimal. The load of the wind turbine generator is in a monotone increasing relation with the turbulence intensity, and because the fatigue damage is linearly accumulated in the m-th direction, when the turbulence intensity tends to be infinite, the higher order of the increment of the fatigue damage and the reduction of the probability needs to be judged one by one, so that the fatigue load is judged to be convergent or tend to be infinite. However, it is proved that convergence of each load variable in each wind speed interval is a work which consumes manpower and resources, and the prior art has no manual implementation.
In order to solve the technical problem, in this embodiment, the normal power generation condition of the wind turbine generator is simulated according to the data of two dimensions of wind speed and turbulence, the data discretization problem is solved by adopting a method of equal probability interval plus local compensation points, and the problem of difficult estimation of the truncation probability is solved by adopting a method of 'super-turbulence shutdown'.
As can be seen from fig. 2 and 3, the equivalent load increases with turbulence in a quasi-linear trend, while the probability is locally concentrated with the turbulence intensity. If the method of equal turbulence step length is adopted, a plurality of turbulence intervals occupy a large number of probabilities to play a leading role, and due to the conservative algorithm of taking the interval upper limit, the simulation calculation result of the fatigue load is inevitably increased, and the increase of the resolution of turbulence calculation causes a large amount of waste. The problem can be better solved by adopting the equal probability spacing method. The specific arrangement of the equal probability intervals is not limited, and in the embodiment, it is preferable to perform discrete calculation according to the turbulence intensity points corresponding to the cumulative probabilities of 5%, 10%, 15%, … …, and 95%, as an example. When the cumulative probability reaches more than 95%, the interval of 95% -100% belongs to a limit state, especially tends to 100% probability, the turbulence intensity becomes very sensitive, if the interval is distinguished by equal probability, the load difference is too large, the load is conservative, and the simulation calculation result of the fatigue load is increased. In this embodiment, it is preferable to take the turbulence corresponding to the 5 probability points of the intermediate transition region 97%, 98%, 99%, 99.9%, 99.99%, and interpolate 5 points at equal intervals from 99.99% to 100% in the cumulative probability. In the implementation, according to the method, 29 wind speed standard deviation points are selected in each wind speed interval in total, the calculation cost is acceptable, and the overestimation degree is very small and can be ignored in the simulation calculation of the fatigue load by matching with the method of 'super-turbulent shutdown'.
By "super turbulent shutdown", a turbulence threshold is set at the time of calculation, and shutdown is performed when the limit turbulence is monitored to exceed the turbulence threshold. For monitoring the extreme turbulence, the technical scheme disclosed in the patent with the publication number of CN107656091B and the name of a wind speed measuring method and system based on a fan control sensor is adopted in the implementation, the current wind speed v can be obtained in real time through calculating the average free flow wind speed, and then whether the Extreme Turbulence (ETM) exceeds the turbulence threshold value is monitored through calculating the wind speed standard deviation of 10min (minutes). The wind speed standard deviation sigma satisfies the following formula:
in the above formula (1), σ represents a wind speed standard deviation, viRepresenting the instantaneous value of the wind speed, n representing the number of sample points, in this embodiment n is taken as 28;
is the wind speed at the hub height.
Turbulence threshold σ1The following formula is used for calculation:
in the above formula (2), σ1Represents a turbulence threshold; c is a characteristic parameter, and 2 is taken in the embodiment; i isrefRepresenting a reference turbulence intensity; vaveIndicating annual mean wind speed, VhubRepresenting the wind speed at the hub height. Vave、IrefAnd determining according to the design index of the wind turbine generator type.
In the simulation process, when the sigma is identified to be more than or equal to sigma1Stopping the machine; the method can solve the problem that the truncation probability is difficult to evaluate, and can better evaluate the truncation probability. .
As shown in fig. 4, the simulation calculation process for the fatigue load of the wind turbine generator is specifically as follows:
1. and dividing wind speed intervals by combining the corresponding wind speeds of the wind generation sets, and calculating the occurrence probability of each wind speed interval according to the design annual average wind speed of the wind generation sets.
The corresponding wind speed is usually 3-25m/s when the wind turbine generator operates normally, and in the embodiment, the wind speed is 3-25 m/s. The wind speed range of 3-25m/s is divided into a plurality of wind speed intervals, and in the embodiment, each wind speed interval is divided according to the interval of 2 m/s. And then calculating the occurrence probability of each wind speed interval in the whole wind speed range according to the design annual average wind speeds of different types of the wind turbine generator.
2. And selecting a certain number of wind speed standard difference points in each wind speed interval by using an equal probability interval and local point supplementing method, and calculating the turbulence intensity of each wind speed standard difference point.
According to the method of "adding local point compensation at equal probability intervals" in this embodiment, 29 wind speed standard deviation points are selected for each wind speed interval, and the turbulence intensity of each wind speed standard deviation point is calculated, where the turbulence intensity is the ratio of the wind speed standard deviation to the average wind speed.
3. And calculating the occurrence probability of the turbulence interval.
The method for calculating the occurrence probability of the turbulent flow interval is not limited in this embodiment, and can be implemented by any method in the prior art, and the following examples illustrate that the occurrence probability distribution of the turbulent flow interval satisfies the following formula:
in the above formula (3), k is 0.27Vhub(s/m)+1.4，C＝Iref(0.75Vhub+3.3m/s)；
VhubIndicating the wind speed at the hub height, IrefIndicating a reference turbulence intensity.
4. And according to the wind speed and the turbulence intensity of the wind speed standard difference point, carrying out simulation calculation by using simulation software to obtain the load time sequence of each coordinate position of the wind turbine generator.
In the simulation link, the controller judges the turbulence intensity and executes 'super-turbulence halt'. The choice of simulation software is not limited, but in this embodiment, Bladed is used for example.
5. And calculating the fatigue load of each coordinate position of the wind turbine generator by using simulation software according to the occurrence probability of each wind speed interval and turbulence interval and the load time sequence of each coordinate position of the wind turbine generator.
The choice of simulation software is not limited, but in this embodiment, Bladed is used for example.
The technical solution of the present embodiment is verified by using the actual simulation result. The following table 1 lists the comparison of simulation calculation results of fatigue loads of a wind turbine generator set by using the prior art and the technical scheme of the embodiment respectively, specifically as follows:
TABLE 1 wind turbine fatigue load simulation calculation result comparison table
As can be seen from the above table, by adopting the technical scheme in the embodiment, a more accurate fatigue load simulation calculation result of the wind turbine generator can be obtained; from the overall trend, the fatigue load obtained by simulation calculation of the technical scheme in the embodiment is lower than that obtained by the prior art, and is about a few to a dozen percent. This avoids overestimating the fatigue load of the wind turbine.
Example 2
Provided is an electronic device including:
one or more processors;
storage means for storing one or more programs;
when executed by one or more processors, the one or more programs cause the one or more processors to implement the wind turbine fatigue load calculation method based on turbulence distribution provided in embodiment 1.
Example 3
There is provided a computer-readable storage medium storing a computer program which, when executed by a processor, implements the wind turbine fatigue load calculation method based on turbulent distribution provided in embodiment 1.
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 (5)
1. A wind turbine generator fatigue load calculation method based on turbulent flow distribution is characterized by comprising the following steps:
dividing wind speed intervals by combining wind speeds corresponding to the wind turbine generator, and calculating the occurrence probability of each wind speed interval according to the design annual average wind speed of the wind turbine generator;
selecting a certain number of wind speed standard difference points in each wind speed interval by using an equal probability interval and local point supplementing method, and calculating the turbulence intensity of each wind speed standard difference point;
calculating the occurrence probability of a turbulent flow interval;
according to the wind speed and the turbulence intensity of the wind speed standard difference point, simulation software is used for carrying out simulation calculation by combining a super-turbulence stop method, and the load time sequence of each coordinate position of the wind turbine generator is obtained, wherein the method comprises the following steps: the controller judges the turbulence intensity and executes the super-turbulence halt, and the specific implementation mode of the super-turbulence halt is as follows: setting a turbulence threshold value during simulation calculation, and executing shutdown when the limit turbulence exceeds the turbulence threshold value; the turbulence threshold satisfies the following equation:
in the above formula, σ1Denotes the turbulence threshold, c is a characteristic parameter, IrefDenotes the reference turbulence intensity, VaveIndicating annual mean wind speed, VhubIndicating the wind speed at the hub height, Vave、IrefDetermining according to the design index of the wind turbine generator type;
and calculating the fatigue load of each coordinate position of the wind turbine generator by using simulation software according to the occurrence probability of each wind speed interval and turbulence interval and the load time sequence of each coordinate position of the wind turbine generator.
2. The wind turbine generator fatigue load calculation method based on turbulent distribution as claimed in claim 1, wherein the number of wind speed standard deviation points is 29.
3. The wind turbine generator fatigue load calculation method based on turbulent distribution as recited in claim 1 or 2, wherein a certain number of wind speed standard deviation points are selected by using an equal probability spacing and local point supplementing method, specifically as follows:
when the cumulative probability is within the interval of 5% to 95%, 1 wind speed standard deviation point is selected every 5% of the interval;
when the cumulative probability is 97%, 98%, 99%, 99.9% and 99.99%, respectively selecting 1 wind speed standard deviation point;
when the cumulative probability is within the interval from 99.99% to 100%, 5 wind speed standard deviation points are selected through equidistant interpolation.
4. An electronic device, comprising:
one or more processors;
storage means for storing one or more programs;
when executed by the one or more processors, cause the one or more processors to implement the method of wind turbine fatigue load calculation based on turbulence distribution of any of claims 1-3.
5. A computer-readable storage medium storing a computer program, characterized in that: the computer program, when executed by a processor, implements the method for wind turbine fatigue load calculation based on turbulent distribution of any of claims 1-3.
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Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104462703A (en) * | 2014-12-16 | 2015-03-25 | 中船重工(重庆)海装风电设备有限公司 | Method and device for determining yaw drive design load |
| CN110094298A (en) * | 2018-01-31 | 2019-08-06 | 北京金风科创风电设备有限公司 | Adaptive adjustment method and device for switching-out strategy |
| CN110206681A (en) * | 2018-02-28 | 2019-09-06 | 北京金风科创风电设备有限公司 | Method and device for adaptively adjusting rotating speed of wind generating set |
| CN110321573A (en) * | 2018-03-29 | 2019-10-11 | 北京金风科创风电设备有限公司 | Strength calculation method and device of wind generating set |
| CN111120219A (en) * | 2018-10-31 | 2020-05-08 | 北京金风科创风电设备有限公司 | Method and device for determining fatigue load of wind generating set |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2016082838A1 (en) * | 2014-11-24 | 2016-06-02 | Vestas Wind Systems A/S | Determination of wind turbine configuration |
-
2020
- 2020-12-14 CN CN202011474074.0A patent/CN112528504B/en active Active
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104462703A (en) * | 2014-12-16 | 2015-03-25 | 中船重工(重庆)海装风电设备有限公司 | Method and device for determining yaw drive design load |
| CN110094298A (en) * | 2018-01-31 | 2019-08-06 | 北京金风科创风电设备有限公司 | Adaptive adjustment method and device for switching-out strategy |
| WO2019148770A1 (en) * | 2018-01-31 | 2019-08-08 | 北京金风科创风电设备有限公司 | Cut-out strategy adaptive adjustment method and apparatus |
| CN110206681A (en) * | 2018-02-28 | 2019-09-06 | 北京金风科创风电设备有限公司 | Method and device for adaptively adjusting rotating speed of wind generating set |
| CN110321573A (en) * | 2018-03-29 | 2019-10-11 | 北京金风科创风电设备有限公司 | Strength calculation method and device of wind generating set |
| CN111120219A (en) * | 2018-10-31 | 2020-05-08 | 北京金风科创风电设备有限公司 | Method and device for determining fatigue load of wind generating set |
Non-Patent Citations (5)
| Title |
|---|
| Adaptive decision fusion based framework for short-term wind speed and turbulence intensity forecasting: case study for North West of Turkey;Dinckal, C 等;《TURKISH JOURNAL OF ELECTRICAL ENGINEERING AND COMPUTER SCIENCES》;20171231;第25卷(第4期);第2770-2783页 * |
| THE EFFECTS OF CAVITY GEOMETRY ON AN ASPIRATED COMPRESSOR CASCADE;Guo, Shuang 等;《JOURNAL OF THEORETICAL AND APPLIED MECHANICS》;20140528;第52卷(第1期);第117-128页 * |
| 一种计及等效损伤载荷约束的风机黏性控制策略;林湘宁等;《中国电机工程学报》;20170901(第07期);第1951-1961页 * |
| 大规模海上风电场建设的技术支撑体系研究;刘勇等;《资源科学》;20091115;第31卷(第11期);第1862-1869页 * |
| 风电机组载荷建模及仿真优化研究;韩花丽;《中国博士学位论文全文数据库 工程科技Ⅱ辑》;20160115(第1期);C042-16 * |
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