Method and device for acquiring wind parameters of wind turbine generator
By identifying the operating conditions of the wind turbine and correcting the wind speed and turbulence intensity data, the distortion and deviation problems of CFD fluid simulation software when evaluating wind resources under special terrain are solved, and the accuracy of wind resource evaluation is improved.
Patent Information
- Application Number
- CN202311099777.3
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2023-08-29
- Publication Date
- 2025-02-28
AI Technical Summary
When using CFD fluid simulation software under special terrain, the prior art is prone to distortion or deviation, resulting in a large difference between the wind resource evaluation results and the actual wind parameter results on site.
By obtaining real-time wind measurement data and operating data of the wind turbine, identifying the operating condition type, and based on these data and operating condition types, determining the correction model of wind speed and turbulence intensity, correcting the wind speed and turbulence intensity data in the real-time wind measurement data, and then obtaining more accurate wind parameters.
The accuracy of the wind resource evaluation results at the wind turbine unit is improved, and the difference between the wind resource evaluation and actual results is reduced.
Smart Images
Figure CN119537871A_ABST
Abstract
Description
Technical Field
[0001] The present disclosure generally relates to the technical field of wind power generation, and more specifically, to a method for acquiring wind parameters of a wind turbine generator set. Background Art
[0002] The wind resource assessment method for a wind farm is usually to use a topographic file to simulate the flow field at the generator site using fluid simulation calculations, and then perform single-point flow field correction and extrapolation based on wind measurement data to obtain the wind parameters at the generator site. Figure 1 As shown in the figure, the operation process of obtaining the wind parameters at the machine site based on the wind tower data, topographic files, and CFD fluid simulation software is as follows: input the topographic data into the simulation software to simulate and obtain the reference wind flow information, then correct the reference wind flow information through the data of the wind tower to obtain the wind map information, and then propose the required wind parameters from the wind map information. However, CFD fluid simulation software will be distorted or have large deviations under special terrain, which will lead to large differences between the wind resource assessment results and the actual wind parameter results on site. Summary of the invention
[0003] An exemplary embodiment of the present disclosure provides a method and device for obtaining wind parameters of a wind turbine, which can improve the accuracy of wind parameter evaluation results of wind resources at an in-service wind turbine (ie, an operating wind turbine).
[0004] According to a first aspect of an embodiment of the present disclosure, a method for obtaining wind parameters of a wind turbine is provided, comprising: obtaining wind turbine data of a preset time length, wherein the wind turbine data comprises: real-time wind measurement data of the wind turbine and real-time operation data of the wind turbine; identifying an operating condition type corresponding to the wind turbine data; and obtaining wind parameters for characterizing wind resources at the wind turbine based on the wind turbine data and the operating condition type.
[0005] Optionally, the wind parameters include at least one of the following items: a wind direction deviation value used to characterize the deviation between the wind direction measurement value and the true wind direction value, a true north direction calibration value, an average wind speed, a wind speed distribution characteristic parameter, an average air density, a wind shear index, an absolute mean of the inflow angle, a maximum inflow angle, a minimum inflow angle, turbulence intensity, extreme wind condition parameters, extreme turbulence parameters, normal turbulence parameters, and a turbulence structure coefficient.
[0006] Optionally, the step of identifying the operating condition type corresponding to the wind turbine data includes: obtaining the free flow sector of the wind turbine, and marking the wind turbine data when the wind direction belongs to the free flow sector as wind turbine data not affected by the wake of other units; and / or, marking the wind turbine data in an abnormal operating state as wind turbine data during abnormal operation; and / or, marking the wind turbine data when the operating parameters do not meet the preset optimal parameter correspondence as wind turbine data during inefficient operation; and / or, marking the wind turbine data when the operating parameters meet any preset operating condition as wind turbine data under the preset operating condition.
[0007] Optionally, the optimal parameter correspondence includes at least one of the following items: an optimal correspondence between rotational speed and torque, an optimal correspondence between wind speed and power, an optimal correspondence between wind speed and pitch angle, and an optimal correspondence between pitch angle and power.
[0008] Optionally, the preset operating condition includes at least one of the following items: a shutdown and stationary operating condition, a shutdown and idling operating condition, an operating condition below the rated speed, and an operating condition at a rated power.
[0009] Optionally, based on the wind turbine data and the operating condition type, the step of obtaining wind parameters used to characterize the wind resources at the wind turbine includes: determining a wind speed correction model based on the wind turbine data and the operating condition type; using the wind speed correction model to correct the wind speed data in the real-time wind measurement data; and determining the average wind speed and / or wind speed distribution characteristic parameters at the wind turbine based on the corrected wind speed data.
[0010] Optionally, based on the wind turbine data and the operating condition type, the step of obtaining wind parameters used to characterize the wind resources at the wind turbine includes: determining a turbulence intensity correction model based on the wind turbine data and the operating condition type; using the turbulence intensity correction model to correct the turbulence intensity data in the real-time wind measurement data; and determining the representative turbulence intensity value of each wind speed bin under each sector based on the corrected turbulence intensity data.
[0011] Optionally, based on the corrected turbulence intensity data, the step of determining the representative turbulence intensity value of each wind speed bin under each sector includes: dividing the corrected turbulence intensity values into each wind speed bin under each sector according to the sectors and wind speed segments corresponding to the corrected turbulence intensity values; taking each wind speed bin under each sector as the target wind speed bin under the target sector, and determining the number of corrected turbulence intensity values of the target wind speed bin divided into the target sector; when the number reaches a preset threshold, determining the preset quantile of the corrected turbulence intensity value under the target wind speed bin under the target sector as the representative turbulence intensity value of the target wind speed bin under the target sector; when the number does not reach the preset threshold, determining the representative turbulence intensity value of the target wind speed bin under the target sector based on the representative turbulence intensity value of the effective wind speed bin under the target sector; wherein the effective wind speed bin is the wind speed bin whose corresponding number of corrected turbulence intensity values reaches the preset threshold.
[0012] Optionally, based on the representative turbulence intensity value of the effective wind speed bin under the target sector, the step of determining the representative turbulence intensity value of the target wind speed bin under the target sector includes: fitting the representative standard deviation and the representative wind speed value of each effective wind speed bin under the target sector to obtain the corresponding relationship between the representative standard deviation of the wind speed bin and the representative wind speed value; based on the corresponding relationship and the representative wind speed value of the target wind speed bin, obtaining the representative standard deviation of the target wind speed bin under the target sector; taking the ratio of the representative standard deviation of the target wind speed bin under the target sector to the representative wind speed value of the target wind speed bin as the representative turbulence intensity value of the target wind speed bin under the target sector; wherein, the representative standard deviation of the effective wind speed bin is the product of the representative turbulence intensity value of the effective wind speed bin and the representative wind speed value of the effective wind speed bin.
[0013] Optionally, based on the wind turbine data and the operating condition type, the step of obtaining wind parameters used to characterize the wind resources at the wind turbine also includes: determining at least one of the extreme turbulence parameters, normal turbulence parameters, and turbulence structure coefficients at the wind turbine based on the representative turbulence intensity values of each wind speed bin under each sector.
[0014] According to a second aspect of an embodiment of the present disclosure, a wind parameter acquisition device for a wind turbine is provided, comprising: a data acquisition unit, configured to acquire wind turbine data of a preset duration, wherein the wind turbine data comprises: real-time wind measurement data of the wind turbine and real-time operation data of the wind turbine; a data classification unit, configured to identify an operating condition type corresponding to the wind turbine data; and a wind parameter acquisition unit, configured to acquire wind parameters for characterizing wind resources at the wind turbine based on the wind turbine data and the operating condition type.
[0015] Optionally, the wind parameters include at least one of the following items: a wind direction deviation value used to characterize the deviation between the wind direction measurement value and the true wind direction value, a true north direction calibration value, an average wind speed, a wind speed distribution characteristic parameter, an average air density, a wind shear index, an absolute mean of the inflow angle, a maximum inflow angle, a minimum inflow angle, turbulence intensity, extreme wind condition parameters, extreme turbulence parameters, normal turbulence parameters, and a turbulence structure coefficient.
[0016] Optionally, the data classification unit is configured to: obtain the free flow sector of the wind turbine set, and mark the wind turbine set data when the wind direction belongs to the free flow sector as the wind turbine set data not affected by the wake of other sets; and / or, mark the wind turbine set data in an abnormal operating state as the wind turbine set data during abnormal operation; and / or, mark the wind turbine set data when the operating parameters do not meet the preset optimal parameter correspondence as the wind turbine set data during inefficient operation; and / or, mark the wind turbine set data when the operating parameters meet any preset operating condition as the wind turbine set data under the preset operating condition.
[0017] Optionally, the optimal parameter correspondence includes at least one of the following items: an optimal correspondence between rotational speed and torque, an optimal correspondence between wind speed and power, an optimal correspondence between wind speed and pitch angle, and an optimal correspondence between pitch angle and power.
[0018] Optionally, the preset operating condition includes at least one of the following items: a shutdown and stationary operating condition, a shutdown and idling operating condition, an operating condition below the rated speed, and an operating condition at a rated power.
[0019] Optionally, the wind parameter acquisition unit is configured to: determine a wind speed correction model based on the wind turbine data and the operating condition type; use the wind speed correction model to correct the wind speed data in the real-time wind measurement data; and determine the average wind speed and / or wind speed distribution characteristic parameters at the wind turbine based on the corrected wind speed data.
[0020] Optionally, the wind parameter acquisition unit is configured to: determine a turbulence intensity correction model based on the wind turbine data and the operating condition type; use the turbulence intensity correction model to correct the turbulence intensity data in the real-time wind measurement data; and determine the representative turbulence intensity value of each wind speed bin under each sector based on the corrected turbulence intensity data.
[0021] Optionally, the wind parameter acquisition unit is configured to: divide the corrected turbulence intensity values into wind speed bins under each sector according to the sectors and wind speed segments corresponding to the corrected turbulence intensity values; respectively take each wind speed bin under each sector as the target wind speed bin under the target sector, and determine the number of corrected turbulence intensity values divided into the target wind speed bin under the target sector; when the number reaches a preset threshold, determine the preset quantile of the corrected turbulence intensity value under the target wind speed bin under the target sector as the representative turbulence intensity value of the target wind speed bin under the target sector; when the number does not reach the preset threshold, determine the representative turbulence intensity value of the target wind speed bin under the target sector based on the representative turbulence intensity value of the effective wind speed bin under the target sector; wherein the effective wind speed bin is the wind speed bin whose corresponding number of corrected turbulence intensity values reaches the preset threshold.
[0022] Optionally, the wind parameter acquisition unit is configured to: fit the representative standard deviation and representative wind speed value of each effective wind speed bin under the target sector to obtain the corresponding relationship between the representative standard deviation of the wind speed bin and the representative wind speed value; obtain the representative standard deviation of the target wind speed bin under the target sector based on the corresponding relationship and the representative wind speed value of the target wind speed bin; take the ratio of the representative standard deviation of the target wind speed bin under the target sector to the representative wind speed value of the target wind speed bin as the representative turbulence intensity value of the target wind speed bin under the target sector; wherein, the representative standard deviation of the effective wind speed bin is the product of the representative turbulence intensity value of the effective wind speed bin and the representative wind speed value of the effective wind speed bin.
[0023] Optionally, the wind parameter acquisition unit is configured to determine at least one of an extreme turbulence parameter, a normal turbulence parameter, and a turbulence structure coefficient at the wind turbine generator based on a representative turbulence intensity value of each wind speed bin under each sector.
[0024] According to a third aspect of an embodiment of the present disclosure, a computer-readable storage medium storing a computer program is provided. When the computer program is executed by a processor, the processor is prompted to execute the wind parameter acquisition method of the wind turbine group as described above.
[0025] According to a fourth aspect of an embodiment of the present disclosure, an electronic device is provided, comprising: a processor; and a memory storing a computer program, wherein when the computer program is executed by the processor, the processor is prompted to execute the wind parameter acquisition method for a wind turbine as described above.
[0026] According to the wind parameter acquisition method and device of the wind turbine group of the exemplary embodiment of the present disclosure, the wind parameters at the wind turbine group are acquired based on the wind turbine group data and its corresponding operating condition type, which can improve the accuracy of the wind parameter evaluation results of the wind resources at the in-service wind turbine group.
[0027] In the following description, some aspects and / or advantages of the general inventive concept will be set forth, and some other aspects and / or advantages will be known through the following description or the implementation of the general inventive concept. BRIEF DESCRIPTION OF THE DRAWINGS
[0028] These and / or other aspects and advantages of the present application will become clearer and easier to understand from the following detailed description of the embodiments of the present application in conjunction with the accompanying drawings, in which:
[0029] Figure 1 An example of obtaining wind parameters at a wind turbine generator system in the prior art is shown;
[0030] Figure 2 A flow chart showing a method for obtaining wind parameters of a wind turbine according to an exemplary embodiment of the present disclosure;
[0031] Figure 3 A flow chart showing a method for acquiring wind parameters for characterizing wind resources at a wind turbine generator system based on wind turbine generator system data and corresponding operating condition types according to an exemplary embodiment of the present disclosure;
[0032] Figure 4 A flow chart showing a method for acquiring wind parameters for characterizing wind resources at a wind turbine generator system based on wind turbine generator system data and corresponding operating condition types according to another exemplary embodiment of the present disclosure;
[0033] Figure 5 A flow chart showing a method for determining a representative turbulence intensity value of each wind speed bin under each sector based on the corrected turbulence intensity data according to an exemplary embodiment of the present disclosure;
[0034] Figure 6 An example of a method for acquiring wind parameters of a wind turbine according to an exemplary embodiment of the present disclosure is shown;
[0035] Figure 7 A structural block diagram of a wind parameter acquisition device for a wind turbine generator system according to an exemplary embodiment of the present disclosure is shown. DETAILED DESCRIPTION
[0036] Reference will now be made in detail to the embodiments of the present disclosure, examples of which are illustrated in the accompanying drawings, wherein like reference numerals refer to like parts throughout. The embodiments will be described below with reference to the drawings in order to explain the present disclosure.
[0037] It should be noted that the terms "first", "second", etc. in the specification and claims of the present disclosure and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments of the present disclosure described herein can be implemented in an order other than those illustrated or described herein. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present disclosure. Instead, they are merely examples of devices and methods consistent with some aspects of the present disclosure as detailed in the appended claims.
[0038] It should be noted that the phrase "at least one of the items" in the present disclosure includes three types of parallel situations: "any one of the items", "a combination of any number of the items", and "all of the items". For example, "including at least one of A and B" includes the following three parallel situations: (1) including A; (2) including B; (3) including A and B. Another example is "executing at least one of step 1 and step 2" which means the following three parallel situations: (1) executing step 1; (2) executing step 2; (3) executing step 1 and step 2.
[0039] Figure 2 A flow chart showing a method for acquiring wind parameters of a wind turbine according to an exemplary embodiment of the present disclosure.
[0040] As an example, the wind parameter acquisition method of the wind turbine according to the exemplary embodiment of the present disclosure may be executed by a wind turbine controller or a field-level controller of a wind farm. In addition, the wind parameter acquisition method of the wind turbine according to the exemplary embodiment of the present disclosure may also be executed by an electronic device with data processing capabilities, for example, the electronic device may be a terminal (such as a personal notebook, a desktop computer, etc.), or a server (such as an independent server, a server cluster, a cloud platform, etc.). The embodiments of the present disclosure are not limited to this.
[0041] Reference Figure 2 In step S101, wind turbine data of a preset time period is obtained.
[0042] Wind turbine data includes: real-time wind measurement data of wind turbines and real-time operation data of wind turbines.
[0043] The real-time wind measurement data of the wind turbine may include wind measurement data measured in real time by a wind measurement device of the wind turbine. For example, the wind measurement device may include but is not limited to at least one of the following items: an anemometer, a wind vane, and a laser radar. For example, the anemometer may be a cabin anemometer installed on the top of the cabin behind the impeller.
[0044] As an example, the real-time wind measurement data of the wind turbine generator system may include, but is not limited to, at least one of the following items: wind speed, wind direction, turbulence intensity, and wind shear index.
[0045] As an example, the real-time operating data of the wind turbine generator system may include, but is not limited to, at least one of the following items: power, pitch angle, actual generator speed, torque, and impeller speed.
[0046] As an example, wind turbine data may be obtained from SCADA and / or the wind turbine's own PLC.
[0047] As an example, wind turbine data may also include other types of real-time data for obtaining wind parameters that characterize wind resources at the wind turbine, which is not limited in the present disclosure. For example, it may also include meteorological data such as ambient temperature, ambient air pressure, and ambient humidity.
[0048] As an example, the length of the preset duration may be set according to actual conditions and specific needs. For example, the length of the preset duration may be 1 month / 3 months / 6 months / 12 months.
[0049] In step S102, the operating condition type corresponding to the acquired wind turbine generator set data is identified.
[0050] As an example, the operating condition type may include but is not limited to at least one of the following items: the wind turbine generator set is not affected by the wake of other turbine generator sets, the wind turbine generator set is operating abnormally, the wind turbine generator set is operating inefficiently, and a preset condition.
[0051] As an example, the preset operating condition may include, but is not limited to, at least one of the following: a shutdown condition, a shutdown idling condition, an operating condition below the rated speed, and an operating condition at a rated power.
[0052] As an example, step S102 may include: obtaining a free flow sector of a wind turbine, and marking the wind turbine data when the wind direction belongs to the free flow sector as the wind turbine data when it is not affected by the wake of other wind turbines. The free flow sector of a wind turbine is the wind direction range in which the wind turbine is not affected by the wake of other wind turbines.
[0053] The present disclosure takes into account that in the process of wind parameter analysis, it is necessary to separate and process the wind turbine data when the wind direction belongs to the free flow sector and the wind turbine data when it does not belong to the free flow sector (i.e., the wind turbine data when affected by the wake of other turbines). The wind turbine data when affected by the wake of other turbines cannot be used directly to avoid affecting the wind parameter analysis results. Therefore, the present disclosure proposes to mark whether the wind turbine data is the wind turbine data when the wind direction belongs to the free flow sector.
[0054] As an example, step S102 may include: marking the wind turbine set data in an abnormal operation state as wind turbine set data in abnormal operation.
[0055] As an example, the abnormal operating state may include, but is not limited to, at least one of the following items: power restriction, shutdown.
[0056] As an example, step S102 may include: marking the wind turbine group data when the operating parameters do not meet the preset optimal parameter correspondence relationship as wind turbine group data when the operation is inefficient. Here, the optimal parameter correspondence relationship can be understood as a preset optimal control curve, and the operating parameters not meeting the preset optimal parameter correspondence relationship can be understood as the operating parameters deviating from the optimal control curve.
[0057] As an example, the optimal parameter correspondence may include but is not limited to at least one of the following items: the optimal correspondence between speed and torque, the optimal correspondence between wind speed and power, the optimal correspondence between wind speed and pitch angle, and the optimal correspondence between pitch angle and power. For example, the optimal correspondence between speed and torque can be understood as the speed-torque optimal control curve; the optimal correspondence between wind speed and power can be understood as the wind speed-power optimal control curve; the optimal correspondence between wind speed and pitch angle can be understood as the wind speed-pitch angle optimal control curve; the optimal correspondence between pitch angle and power can be understood as the pitch angle-power optimal control curve.
[0058] As an example, an optimal control curve may be drawn according to the control strategy of the unit, and scattered points deviating from the optimal control curve may be identified and marked.
[0059] As an example, step S102 may include: marking the wind turbine data when the operating parameters meet any preset operating condition as the wind turbine data under the preset operating condition. As an example, the preset operating condition may include but is not limited to at least one of the following items: shutdown static condition, shutdown idling condition, operating condition below rated speed, and rated power operating condition. The operating parameters meeting any preset operating condition can be understood as the operating parameters meeting the requirements of any preset operating condition for the operating parameters. Here, the operating parameters used to divide the preset operating conditions may include but are not limited to at least one of the following items: rotation speed, pitch angle.
[0060] In step S103, based on the wind turbine generator set data and its corresponding operating condition type, wind parameters for characterizing wind resources at the wind turbine generator set are obtained.
[0061] As an example, wind parameters may include, but are not limited to, at least one of the following items: a wind direction deviation value used to characterize the deviation between the wind direction measurement value and the true wind direction value, a true north direction calibration value, an average wind speed, a wind speed distribution characteristic parameter, an average air density, a wind shear index, an absolute mean of the inflow angle, a maximum inflow angle, a minimum inflow angle, turbulence intensity, an extreme wind condition parameter, an extreme turbulence parameter ETM, a normal turbulence parameter NTM, and a turbulence structure coefficient.
[0062] As an example, the wind speed distribution characteristic parameter may be a Weibull parameter.
[0063] As an example, extreme wind condition parameters may include but are not limited to: maximum 10-minute average wind speed V50 once in 50 years, maximum 3-second average wind speed Ve50 once in 50 years, maximum 10-minute average wind speed V1 once in 1 year, and maximum 3-second average wind speed Ve1 once in 1 year.
[0064] Table 1 Wind parameters for wind resource assessment
[0065]
[0066] When technical transformation of in-service units is carried out (for example, to increase power generation, reduce failure rate, etc.), wind resource assessment is currently carried out based on data from the early stage of wind farm construction, and the wind parameter results of the assessment are quite different from the actual wind parameter results on site. The present disclosure proposes to use actual wind turbine unit data to assess the wind parameters of wind resources at the unit site, and the wind parameters obtained by the assessment are relatively close to the actual wind parameter results, which can be used for unit analysis and assessment, for example, power generation analysis and assessment, load analysis and assessment, unit adaptability assessment, etc.
[0067] The following will be combined Figure 3 and Figure 4 An exemplary embodiment of step S103 is described below.
[0068] Figure 3 A flow chart showing a method for acquiring wind parameters for characterizing wind resources at a wind turbine based on wind turbine data and corresponding operating condition types according to an exemplary embodiment of the present disclosure.
[0069] Reference Figure 3 In step S201, a wind speed correction model is determined based on wind turbine data and its corresponding operating condition type.
[0070] The present disclosure takes into account that the real-time wind measurement data of the unit (for example, wind speed data measured by an anemometer) is easily affected by factors such as impeller disturbance, cabin shape, control status, and anemometer factory deviation. Therefore, the wind speed data needs to be corrected.
[0071] As an example, the wind speed correction model may be determined based on wind turbine data that is not affected by the wake of other turbines and is not abnormal or inefficient (ie, data when the turbine is in normal operation). For example, the wind speed correction model may be a wind speed correction coefficient.
[0072] In step S202, the wind speed correction model is used to correct the wind speed data in the real-time wind measurement data.
[0073] In step S203, the average wind speed and / or wind speed distribution characteristic parameters at the wind turbine are determined based on the corrected wind speed data.
[0074] According to the exemplary embodiments of the present disclosure, the correction effect of the wind speed correction model can be improved, so that the corrected wind speed data is more consistent with the actual wind speed conditions, thereby improving the evaluation accuracy of wind speed-related wind parameters.
[0075] Figure 4 A flow chart showing a method for acquiring wind parameters for characterizing wind resources at a wind turbine based on wind turbine data and corresponding operating condition types according to another exemplary embodiment of the present disclosure.
[0076] Reference Figure 4 In step S301, a turbulence intensity correction model is determined based on wind turbine data and its corresponding operating condition type.
[0077] The present disclosure takes into account that the real-time wind measurement data of the unit (for example, the turbulence intensity data measured by the anemometer) is easily affected by factors such as impeller disturbance, nacelle shape, control status, and anemometer factory deviation, and is quite different from the actual free stream turbulence. Therefore, the turbulence intensity data needs to be corrected.
[0078] As an example, a turbulence intensity correction model for each preset working condition may be determined based on the wind turbine data under the preset working condition. In other words, different preset working conditions correspond to their own turbulence intensity correction models. For example, the turbulence intensity correction model may be a turbulence intensity correction coefficient.
[0079] In step S302, the turbulence intensity data in the real-time wind measurement data is corrected using the turbulence intensity correction model.
[0080] In step S303, based on the corrected turbulence intensity data, the representative turbulence intensity value of each wind speed bin in each sector is determined.
[0081] As an example, according to another exemplary embodiment of the present disclosure, obtaining wind parameters for characterizing wind resources at a wind turbine based on wind turbine data and its corresponding operating condition types may also include: determining at least one of extreme turbulence parameters, normal turbulence parameters, and turbulence structure coefficients at the wind turbine based on representative turbulence intensity values of each wind speed bin under each sector.
[0082] The following will be combined Figure 5 An exemplary embodiment of step S303 is described below.
[0083] According to an exemplary embodiment of the present disclosure, the correction effect of the turbulence intensity correction model can be improved, so that the corrected turbulence intensity data is more consistent with the actual turbulence situation.
[0084] Figure 5 A flow chart showing a method for determining a representative turbulence intensity value for each wind speed bin in each sector based on corrected turbulence intensity data according to an exemplary embodiment of the present disclosure.
[0085] Reference Figure 5 In step S401, according to the sectors and wind speed segments corresponding to the corrected turbulence intensity values, the corrected turbulence intensity values are divided into wind speed bins under each sector.
[0086] In step S402, each wind speed bin under each sector is used as a target wind speed bin under the target sector, and the number of corrected turbulence intensity values of the target wind speed bins divided into the target sector is determined.
[0087] In step S403, when the number reaches the preset threshold, the preset quantile of the corrected turbulence intensity value under the target wind speed bin under the target sector is determined as the representative turbulence intensity value of the target wind speed bin under the target sector. For example, the preset quantile may be 90%.
[0088] In step S404, when the number does not reach the preset threshold, the representative turbulence intensity value of the target wind speed bin under the target sector is determined based on the representative turbulence intensity value of the effective wind speed bin under the target sector.
[0089] The effective wind speed bin is the wind speed bin whose corresponding corrected turbulence intensity value number reaches a preset threshold.
[0090] As an example, the specific value of the preset threshold can be set according to actual conditions and specific needs, for example, it can be set to 10.
[0091] The present disclosure takes into account that due to insufficient number of sample points in the bin data of some sectors, the calculated turbulence quantiles are obviously abnormal. Therefore, the abnormal turbulence quantiles need to be eliminated. After elimination, the turbulence quantiles of the bin points are missing and need to be supplemented.
[0092] As an example, step S404 may include: fitting the representative standard deviation and representative wind speed value of each valid wind speed bin under the target sector to obtain the corresponding relationship between the representative standard deviation of the wind speed bin and the representative wind speed value; based on the corresponding relationship and the representative wind speed value of the target wind speed bin, obtaining the representative standard deviation of the target wind speed bin under the target sector; and taking the ratio of the representative standard deviation of the target wind speed bin under the target sector to the representative wind speed value of the target wind speed bin as the representative turbulence intensity value of the target wind speed bin under the target sector.
[0093] The representative standard deviation of an effective wind speed bin is the product of the representative turbulence intensity value of the effective wind speed bin and the representative wind speed value of the effective wind speed bin. For example, the representative wind speed value of a wind speed bin may be the median wind speed of the wind speed bin.
[0094] For example, based on the corresponding relationship and the representative wind speed value of the target wind speed bin, the step of obtaining the representative standard deviation of the target wind speed bin under the target sector may include: substituting the representative wind speed value of the target wind speed bin into the corresponding relationship (for example, a fitting function) to obtain the representative standard deviation of the target wind speed bin under the target sector.
[0095] According to an exemplary embodiment of the present disclosure, by eliminating abnormal turbulence quantiles and completing missing turbulence quantiles, the assessment accuracy of turbulence intensity and its related wind parameters can be improved.
[0096] Figure 6 An example of a method for acquiring wind parameters of a wind turbine according to an exemplary embodiment of the present disclosure is shown.
[0097] Reference Figure 6 ,1. Data collection: wind resource assessment data, unit configuration data, and wind farm operation data.
[0098] Wind resource assessment data include wind farm micro-site selection review reports, wind farm topography and geomorphology files, wind farm early wind tower data, and wind farm regional meteorological reanalysis data or mesoscale data.
[0099] The unit configuration information includes fan type, hub height, impeller diameter, anemometer type, etc.
[0100] Wind farm operation data includes transient data (second-level data) of wind turbine operation, machine site location coordinates, wind power prediction tower data or other wind measurement tower data during the wind farm operation phase, and lidar data used for post-evaluation measurement.
[0101] 2. Data preprocessing: includes checking and judging the validity of data, classifying and identifying data, counting the data completeness rate, and interpolating basic data.
[0102] The identification of data classification includes the evaluation of free flow sectors and the identification of data when not affected by the wake of other units; the identification of data in abnormal operating states such as power restriction data and shutdown data; the identification of data when the unit is operating inefficiently; and the identification of data under preset operating conditions.
[0103] 3. Wind parameter assessment:
[0104] 3.1. Wind direction correction, to obtain the wind direction deviation value and the true north direction calibration value used to characterize the deviation between the wind direction measurement value and the true wind direction value. As an example, the relative wind direction of the cabin can be calculated based on the yaw position of the cabin and the wind angle measured by the cabin wind vane, and then the absolute wind direction can be calculated by the cabin initial position correction value (that is, the true north direction calibration value).
[0105] 3.2. Wind speed correction: determine the wind speed correction model based on the wind turbine data and its corresponding operating condition type; use the wind speed correction model to correct the wind speed data in the real-time wind measurement data to determine the average wind speed and / or wind speed distribution characteristic parameters at the wind turbine based on the corrected wind speed data.
[0106] 3.3. Turbulence correction: Based on the wind turbine data and its corresponding operating condition type, determine the turbulence intensity correction model; use the turbulence intensity correction model to correct the turbulence intensity data in the real-time wind measurement data.
[0107] 3.4. Turbulence interpolation. When calculating wind parameters, it is necessary to extract the turbulence quantiles of each wind speed bin in each sector. If there are insufficient samples in a bin, the turbulence quantiles in that bin will be unavailable, and it is necessary to use the turbulence quantiles in other bins for interpolation. The data of some bins with large wind speed segments need to be extrapolated. As an example, the standard deviation can be calculated using the turbulence quantiles in the effective bin, a scatter plot of wind speed and standard deviation can be drawn, and the least squares method can be used for fitting. The standard deviation corresponding to the wind speed bin that needs to be interpolated and extrapolated is calculated according to the fitting function, and then the interpolated and extrapolated turbulence is calculated.
[0108] In addition, the calculation of various wind parameters is also included, which will not be repeated here.
[0109] The present disclosure proposes to use actual wind turbine data to evaluate the wind parameters of wind resources at the turbine site. The evaluated wind parameters are relatively close to the actual wind parameter results and can be used for turbine analysis and evaluation, such as power generation analysis and evaluation, load analysis and evaluation, and turbine adaptability evaluation.
[0110] Figure 7 A structural block diagram of a wind parameter acquisition device for a wind turbine generator system according to an exemplary embodiment of the present disclosure is shown.
[0111] Reference Figure 7 According to an exemplary embodiment of the present disclosure, a wind parameter acquisition device for a wind turbine generator system includes: a data acquisition unit 101 , a data classification unit 102 , and a wind parameter acquisition unit 103 .
[0112] Specifically, the data acquisition unit 101 is configured to acquire wind turbine data of a preset duration, wherein the wind turbine data includes: real-time wind measurement data of the wind turbine and real-time operation data of the wind turbine.
[0113] The data classification unit 102 is configured to identify the operating condition type corresponding to the wind turbine generator set data.
[0114] The wind parameter acquisition unit 103 is configured to acquire wind parameters for characterizing wind resources at the wind turbine generator set based on the wind turbine generator set data and the operating condition type.
[0115] As an example, the wind parameters may include at least one of the following items: a wind direction deviation value used to characterize the deviation between the wind direction measurement value and the true wind direction value, a true north direction calibration value, an average wind speed, a wind speed distribution characteristic parameter, an average air density, a wind shear index, an absolute mean of the inflow angle, a maximum inflow angle, a minimum inflow angle, turbulence intensity, extreme wind condition parameters, extreme turbulence parameters, normal turbulence parameters, and a turbulence structure coefficient.
[0116] As an example, the data classification unit 102 can be configured to: obtain the free flow sector of the wind turbine set, and mark the wind turbine set data when the wind direction belongs to the free flow sector as the wind turbine set data not affected by the wake of other units; and / or, mark the wind turbine set data in an abnormal operating state as the wind turbine set data during abnormal operation; and / or, mark the wind turbine set data when the operating parameters do not meet the preset optimal parameter correspondence as the wind turbine set data during inefficient operation; and / or, mark the wind turbine set data when the operating parameters meet any preset operating condition as the wind turbine set data under the preset operating condition.
[0117] As an example, the optimal parameter correspondence may include at least one of the following items: an optimal correspondence between rotational speed and torque, an optimal correspondence between wind speed and power, an optimal correspondence between wind speed and pitch angle, and an optimal correspondence between pitch angle and power.
[0118] As an example, the preset operating condition may include at least one of the following items: a shutdown static operating condition, a shutdown idling operating condition, an operating condition below the rated speed, and an operating condition at a rated power.
[0119] As an example, the wind parameter acquisition unit 103 can be configured to: determine a wind speed correction model based on the wind turbine data and the operating condition type; use the wind speed correction model to correct the wind speed data in the real-time wind measurement data; and determine the average wind speed and / or wind speed distribution characteristic parameters at the wind turbine based on the corrected wind speed data.
[0120] As an example, the wind parameter acquisition unit 103 can be configured to: determine a turbulence intensity correction model based on the wind turbine data and the operating condition type; use the turbulence intensity correction model to correct the turbulence intensity data in the real-time wind measurement data; and determine the representative turbulence intensity value of each wind speed bin under each sector based on the corrected turbulence intensity data.
[0121] As an example, the wind parameter acquisition unit 103 can be configured to: divide the corrected turbulence intensity values into wind speed bins under each sector according to the sectors and wind speed segments corresponding to the corrected turbulence intensity values; respectively take each wind speed bin under each sector as the target wind speed bin under the target sector, and determine the number of corrected turbulence intensity values divided into the target wind speed bin under the target sector; when the number reaches a preset threshold, determine the preset quantile of the corrected turbulence intensity value under the target wind speed bin under the target sector as the representative turbulence intensity value of the target wind speed bin under the target sector; when the number does not reach the preset threshold, determine the representative turbulence intensity value of the target wind speed bin under the target sector based on the representative turbulence intensity value of the effective wind speed bin under the target sector; wherein the effective wind speed bin is the wind speed bin whose corresponding number of corrected turbulence intensity values reaches the preset threshold.
[0122] As an example, the wind parameter acquisition unit 103 can be configured to: fit the representative standard deviation and representative wind speed value of each effective wind speed bin under the target sector to obtain the corresponding relationship between the representative standard deviation of the wind speed bin and the representative wind speed value; based on the corresponding relationship and the representative wind speed value of the target wind speed bin, obtain the representative standard deviation of the target wind speed bin under the target sector; use the ratio of the representative standard deviation of the target wind speed bin under the target sector to the representative wind speed value of the target wind speed bin as the representative turbulence intensity value of the target wind speed bin under the target sector; wherein, the representative standard deviation of the effective wind speed bin is the product of the representative turbulence intensity value of the effective wind speed bin and the representative wind speed value of the effective wind speed bin.
[0123] As an example, the wind parameter acquisition unit 103 may be configured to determine at least one of the extreme turbulence parameters, normal turbulence parameters, and turbulence structure coefficients at the wind turbine based on the representative turbulence intensity values of each wind speed bin in each sector.
[0124] It should be understood that the specific processing performed by the wind parameter acquisition device of the wind turbine according to the exemplary embodiment of the present disclosure has been referred to. Figures 2 to 6 The details have been described in detail and will not be repeated here.
[0125] It should be understood that each unit in the wind parameter acquisition device of the wind turbine according to the exemplary embodiment of the present disclosure can be implemented as a hardware component and / or a software component. Those skilled in the art can implement each unit, for example, using a field programmable gate array (FPGA) or an application specific integrated circuit (ASIC) according to the processing performed by each defined unit.
[0126] An exemplary embodiment of the present disclosure provides a computer-readable storage medium storing a computer program, which, when executed by a processor, causes the processor to execute the wind parameter acquisition method of the wind turbine as described in the above exemplary embodiment. The computer-readable storage medium is any data storage device that can store data read by a computer system. Examples of computer-readable storage media include: read-only memory, random access memory, read-only optical disk, magnetic tape, floppy disk, optical data storage device, and carrier wave (such as data transmission through the Internet via a wired or wireless transmission path).
[0127] An electronic device according to an exemplary embodiment of the present disclosure includes: a processor (not shown) and a memory (not shown), wherein the memory stores a computer program, and when the computer program is executed by the processor, it prompts the processor to execute the wind parameter acquisition method of the wind turbine group as described in the above exemplary embodiment.
[0128] As an example, the electronic device may be a unit controller or a field-level controller of a wind farm. In addition, it may also be other electronic devices with data processing capabilities, for example, the electronic device may be a terminal (such as a personal notebook, a desktop, etc.), or a server (such as an independent server, a server cluster, a cloud platform, etc.). The embodiments of the present disclosure do not limit this.
[0129] Although some exemplary embodiments of the present disclosure have been shown and described, it will be appreciated by those skilled in the art that modifications may be made to these embodiments without departing from the scope and spirit of the present disclosure as defined by the claims and their equivalents.
Claims
1. A method for obtaining wind parameters of a wind turbine generator set, characterized in that: include: Acquire wind turbine data of a preset time period, wherein the wind turbine data includes: real-time wind measurement data of the wind turbine and real-time operation data of the wind turbine; Identifying the operating condition type corresponding to the wind turbine generator set data; Based on the wind turbine generator set data and the operating condition type, a wind parameter for characterizing wind resources at the wind turbine generator set is acquired.
2. The method for obtaining wind parameters according to claim 1, characterized in that: The wind parameter includes at least one of the following: Wind direction deviation value used to characterize the deviation between the measured wind direction value and the true wind direction value, true north direction calibration value, average wind speed, wind speed distribution characteristic parameters, average air density, wind shear index, mean absolute value of inflow angle, maximum inflow angle, minimum inflow angle, turbulence intensity, extreme wind condition parameters, extreme turbulence parameters, normal turbulence parameters, and turbulence structure coefficient.
3. The method for obtaining wind parameters according to claim 1, characterized in that: The step of identifying the operating condition type corresponding to the wind turbine generator set data comprises: Acquire the free flow sector of the wind turbine set, and mark the wind turbine set data when the wind direction belongs to the free flow sector as the wind turbine set data when not affected by the wake of other sets; and / or, marking the wind turbine set data in an abnormal operation state as wind turbine set data in abnormal operation; and / or, marking the wind turbine data when the operating parameters do not conform to the preset optimal parameter correspondence as wind turbine data when operating inefficiently; And / or, the wind turbine generator set data when the operating parameters meet any preset working condition is marked as the wind turbine generator set data under the preset working condition.
4. The method for obtaining wind parameters according to claim 3, characterized in that: The optimal parameter correspondence relationship includes at least one of the following items: an optimal correspondence relationship between rotation speed and torque, an optimal correspondence relationship between wind speed and power, an optimal correspondence relationship between wind speed and pitch angle, and an optimal correspondence relationship between pitch angle and power; Among them, the preset operating conditions include at least one of the following items: a shutdown and stationary operating condition, a shutdown and idling operating condition, an operating condition below the rated speed, and an operating condition at a rated power.
5. The method for obtaining wind parameters according to claim 1, characterized in that: Based on the wind turbine data and the operating condition type, the step of obtaining wind parameters for characterizing wind resources at the wind turbine comprises: Determining a wind speed correction model based on the wind turbine data and the operating condition type; Using the wind speed correction model, correcting the wind speed data in the real-time wind measurement data; Based on the corrected wind speed data, the average wind speed and / or wind speed distribution characteristic parameters at the wind turbine generator set are determined.
6. The method for obtaining wind parameters according to claim 1, characterized in that: Based on the wind turbine data and the operating condition type, the step of obtaining wind parameters for characterizing wind resources at the wind turbine comprises: Determining a turbulence intensity correction model based on the wind turbine data and the operating condition type; Using the turbulence intensity correction model, correcting the turbulence intensity data in the real-time wind measurement data; Based on the corrected turbulence intensity data, the representative turbulence intensity value of each wind speed bin under each sector is determined.
7. The method for obtaining wind parameters according to claim 6, characterized in that: Based on the corrected turbulence intensity data, the steps of determining the representative turbulence intensity value of each wind speed bin under each sector include: According to the sectors and wind speed sections corresponding to the corrected turbulence intensity values, the corrected turbulence intensity values are divided into wind speed bins under each sector; Respectively taking each wind speed bin under each sector as a target wind speed bin under a target sector, and determining the number of corrected turbulence intensity values of the target wind speed bins divided into the target sector; In the case where the number reaches a preset threshold, determining a preset quantile of the corrected turbulence intensity value under the target wind speed bin under the target sector as a representative turbulence intensity value of the target wind speed bin under the target sector; In a case where the number does not reach the preset threshold, determining the representative turbulence intensity value of the target wind speed bin under the target sector based on the representative turbulence intensity value of the effective wind speed bin under the target sector; The effective wind speed bin is a wind speed bin whose corresponding corrected turbulence intensity value reaches the preset threshold.
8. The method for obtaining wind parameters according to claim 7, characterized in that: Based on the representative turbulence intensity value of the effective wind speed bin under the target sector, the step of determining the representative turbulence intensity value of the target wind speed bin under the target sector comprises: Fitting the representative standard deviation and the representative wind speed value of each effective wind speed bin under the target sector to obtain a corresponding relationship between the representative standard deviation and the representative wind speed value of the wind speed bin; Based on the corresponding relationship and the representative wind speed value of the target wind speed bin, obtaining the representative standard deviation of the target wind speed bin under the target sector; taking the ratio of the representative standard deviation of the target wind speed bin under the target sector to the representative wind speed value of the target wind speed bin as the representative turbulence intensity value of the target wind speed bin under the target sector; The representative standard deviation of the effective wind speed bin is the product of the representative turbulence intensity value of the effective wind speed bin and the representative wind speed value of the effective wind speed bin.
9. The method for obtaining wind parameters according to claim 6, characterized in that: Based on the wind turbine data and the operating condition type, the step of obtaining wind parameters for characterizing wind resources at the wind turbine also includes: At least one of an extreme turbulence parameter, a normal turbulence parameter, and a turbulence structure coefficient at the wind turbine generator set is determined based on the representative turbulence intensity value of each wind speed bin under each sector.
10. A wind parameter acquisition device for a wind turbine generator set, characterized in that: include: A data acquisition unit is configured to acquire wind turbine data of a preset time period, wherein the wind turbine data includes: real-time wind measurement data of the wind turbine and real-time operation data of the wind turbine; A data classification unit, configured to identify the operating condition type corresponding to the wind turbine generator set data; The wind parameter acquisition unit is configured to acquire wind parameters for characterizing wind resources at the wind turbine generator set based on the wind turbine generator set data and the operating condition type.
11. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the processor is prompted to execute the method for obtaining wind parameters of a wind turbine set according to any one of claims 1 to 9.
12. An electronic device, characterized in that: The electronic device comprises: processor; A memory storing a computer program, which, when executed by a processor, prompts the processor to execute the method for obtaining wind parameters of a wind turbine set as claimed in any one of claims 1 to 9.