Method and device for operating risk assessment of a wind turbine

Abstract

Provided are a method and device for evaluating the operation risk of a wind turbine generator. The method for evaluating the operation risk includes: obtaining operation data of the wind turbine generator; determining a wind condition index and a vibration level index of the wind turbine generator according to the operation data; and determining an operation risk type of the wind turbine generator based on the determined wind condition index and vibration level index.

Description

Technical Field

[0001] This disclosure generally relates to the field of wind power technology, and more specifically, to a method and apparatus for assessing the operational risks of wind turbine generator sets. Background Technology

[0002] To obtain better wind resources, many wind turbine generators (also known as turbines) are installed in mountainous and hilly areas. Typically, during the initial wind resource survey phase, wind speed data from one or two wind measurement points are used to estimate wind conditions at each turbine location within the wind farm using software. However, due to the complex terrain of mountainous and hilly areas, it is impossible to accurately estimate the specific wind conditions at each turbine location or in a particular sector, leading to significant differences between the actual operating performance of the turbines after installation and the design simulations.

[0003] Currently, wind speed measurement devices for wind turbine generators are typically cup anemometers or ultrasonic anemometers mounted on the top of the nacelle, both of which are single-point measurement devices. The swept area of ​​mainstream wind turbine rotors is over 10,000 square meters. Due to factors such as obstruction by mountains, trees, cliffs, or the wake of other turbines in some sectors, the actual wind speed and turbulence can vary significantly between different points on the rotor plane. A single-point wind condition indicator cannot represent the entire rotor plane. Therefore, risk assessment based solely on wind condition indicators measured by the actual turbine has certain drawbacks. Summary of the Invention

[0004] An exemplary embodiment of this disclosure provides a method and apparatus for assessing the operational risks of wind turbine generator sets, which can more accurately assess the operational risks of wind turbine generator sets.

[0005] According to a first aspect of the present disclosure, a method for assessing the operational risk of a wind turbine generator set is provided, comprising: acquiring operational data of the wind turbine generator set; determining wind condition indicators and vibration level indicators of the wind turbine generator set based on the operational data; and determining the operational risk type of the wind turbine generator set based on the determined wind condition indicators and vibration level indicators.

[0006] Optionally, the operational data includes multiple data sets, each data set including wind speed values, nacelle acceleration, and yaw position at various sampling time points within a preset duration; wherein, the step of determining the wind condition indicators and vibration level indicators of the wind turbine generator based on the operational data includes: determining the average wind speed and corresponding sector angle of each data set based on the wind speed value and yaw position within each data set; dividing the multiple data sets into wind speed compartments based on the average wind speed value of each data set to obtain data sets within each wind speed segment; dividing the data sets within each wind speed segment into sector compartments based on the sector angle corresponding to each data set to obtain data sets under each sector within each wind speed segment; and determining the wind condition indicators and vibration level indicators of the wind turbine generator based on the data sets under each sector within each wind speed segment.

[0007] Optionally, the step of determining the wind condition index and vibration level index of the wind turbine generator set based on the data sets of each sector within each wind speed segment includes: determining the wind condition index of each sector within each wind speed segment based on the data sets of each sector within each wind speed segment, and determining the wind condition index of the wind turbine generator set based on the wind condition index of all sectors within all wind speed segments; determining the vibration level index of each sector within each wind speed segment based on the data sets of each sector within each wind speed segment, and determining the vibration level index of the wind turbine generator set based on the vibration level index of all sectors within all wind speed segments.

[0008] Optionally, the step of determining the wind condition index of each sector within each wind speed segment based on each data set in each sector within each wind speed segment includes: determining the wind condition index of the corresponding sector within the wind speed segment as the product of the total number of all data sets in each sector within each wind speed segment, the turbulence mean of all data sets, and the representative wind speed value of the wind speed segment, wherein the turbulence value of each data set is the standard deviation of the wind speed values ​​within the data set.

[0009] Optionally, the step of determining the wind condition index of the wind turbine generator set based on the wind condition index of all sectors within all wind speed ranges includes: determining the wind condition index of the wind turbine generator set as the ratio of the sum of the wind condition indices of all sectors within all wind speed ranges to the cumulative value of the turbulence at the reference wind speed.

[0010] Optionally, the step of determining the vibration level index of each sector within each wind speed segment based on each data set within each sector of each wind speed segment, and determining the vibration level index of the wind turbine generator set based on the vibration level index of all sectors within all wind speed segments, includes: determining the cumulative vibration amplitude index and / or vibration exceedance frequency index of each sector within each wind speed segment based on the nacelle acceleration at each sampling time point in each data set within each sector of each wind speed segment; determining the cumulative vibration amplitude index of the wind turbine generator set based on the cumulative vibration amplitude index of all sectors within all wind speed segments, and / or determining the vibration exceedance frequency index of the wind turbine generator set based on the vibration exceedance frequency index of all sectors within all wind speed segments.

[0011] Optionally, the step of determining the cumulative vibration amplitude index of each sector within each wind speed segment based on the nacelle acceleration at each sampling time point in each data group within each sector of each wind speed segment includes: dividing the sampling time points within each sector of each wind speed segment into acceleration compartments according to the nacelle acceleration at each sampling time point, obtaining each sampling time point under each acceleration interval within each sector of each wind speed segment; multiplying the total number of all sampling time points under each acceleration interval within each sector of each wind speed segment by the representative acceleration value of that acceleration interval, and determining the cumulative acceleration value of that acceleration interval under the corresponding sector within the wind speed segment; and determining the sum of the cumulative acceleration values ​​of all acceleration intervals under each sector within each wind speed segment as the cumulative vibration amplitude index of the corresponding sector within the wind speed segment.

[0012] Optionally, the step of determining the cumulative vibration amplitude index of the wind turbine generator set based on the cumulative vibration amplitude index of all sectors in all wind speed ranges includes: determining the cumulative vibration amplitude index of the wind turbine generator set as the ratio of the sum of the cumulative vibration amplitude indices of all sectors in all wind speed ranges to the cumulative reference acceleration value.

[0013] Optionally, the step of determining the vibration over-limit frequency index of each sector in each wind speed segment based on the nacelle acceleration at each sampling time point in each data group under each sector in each wind speed segment includes: determining the total number of sampling time points in each sector under each wind speed segment where the nacelle acceleration exceeds a preset threshold as the vibration over-limit frequency index of the corresponding sector in that wind speed segment.

[0014] Optionally, the step of determining the vibration over-limit frequency index of the wind turbine generator set based on the vibration over-limit frequency index of all sectors in all wind speed ranges includes: determining the ratio of the sum of the vibration over-limit frequency indices of all sectors in all wind speed ranges to the reference acceleration over-limit frequency as the vibration over-limit frequency index of the wind turbine generator set.

[0015] Optionally, the step of determining the operational risk type of the wind turbine generator set based on the determined wind condition index and vibration level index includes: performing a weighted summation of the determined wind condition index and vibration level index to obtain the operational risk value of the wind turbine generator set; and determining the operational risk type of the wind turbine generator set based on the operational risk value.

[0016] Optionally, if the operating risk type of the wind turbine generator set is high-risk or medium-risk, the method further includes: when the wind turbine generator set is in a first specific wind speed range and a first specific sector, performing load reduction control and / or risk monitoring on the wind turbine generator set, wherein the wind condition index of the first specific sector within the first specific wind speed range meets a first preset condition and / or the vibration level index meets a second preset condition; and / or, when the wind turbine generator set is in a second specific wind speed range, performing load reduction control and / or risk monitoring on the wind turbine generator set, wherein the sum of the wind condition indexes of all sectors within the second specific wind speed range meets a third preset condition and / or the sum of the vibration level indexes meets a fourth preset condition; and / or, when the wind turbine generator set is in a second specific sector, performing load reduction control and / or risk monitoring on the wind turbine generator set, wherein the sum of the wind condition indexes of the second specific sector within all wind speed ranges meets a fifth preset condition and / or the sum of the vibration level indexes meets a sixth preset condition.

[0017] According to a second aspect of the present disclosure, a wind turbine generator set operation risk assessment device is provided, comprising: an operation data acquisition unit configured to acquire operation data of the wind turbine generator set; an index determination unit configured to determine wind condition index and vibration level index of the wind turbine generator set based on the operation data; and a risk type determination unit configured to determine the operation risk type of the wind turbine generator set based on the determined wind condition index and vibration level index.

[0018] Optionally, the operational data includes multiple data sets, each data set including wind speed values, nacelle acceleration, and yaw position at various sampling time points within a preset duration; wherein, the index determination unit is configured to: determine the average wind speed and corresponding sector angle of each data set based on the wind speed value and yaw position within each data set; divide the multiple data sets into wind speed compartments according to the average wind speed value of each data set to obtain data sets within each wind speed segment; divide the data sets within each wind speed segment into sector compartments according to the sector angle corresponding to each data set to obtain data sets under each sector within each wind speed segment; and determine the wind condition index and vibration level index of the wind turbine generator based on the data sets under each sector within each wind speed segment.

[0019] Optionally, the index determination unit is configured to: determine the wind condition index of each sector within each wind speed segment based on each data set under each sector within each wind speed segment, and determine the wind condition index of the wind turbine generator set based on the wind condition index of all sectors within all wind speed segments; determine the vibration level index of each sector within each wind speed segment based on each data set under each sector within each wind speed segment, and determine the vibration level index of the wind turbine generator set based on the vibration level index of all sectors within all wind speed segments.

[0020] Optionally, the index determination unit is configured to: determine the wind condition index of the corresponding sector within each wind speed segment by multiplying the total number of all data groups in each sector within each wind speed segment, the turbulence mean of all data groups, and the representative wind speed value of the wind speed segment, wherein the turbulence value of each data group is the standard deviation of the wind speed values ​​within the data group.

[0021] Optionally, the index determination unit is configured to determine the wind condition index of the wind turbine as the ratio of the sum of the wind condition indices of all sectors within all wind speed ranges to the cumulative value of the baseline wind speed turbulence.

[0022] Optionally, the index determination unit is configured to: determine the cumulative vibration amplitude index and / or vibration over-limit frequency index for each sector within each wind speed segment based on the nacelle acceleration at each sampling time point in each data group under each sector within each wind speed segment; determine the cumulative vibration amplitude index of the wind turbine generator set based on the cumulative vibration amplitude index of all sectors within all wind speed segments; and / or determine the vibration over-limit frequency index of the wind turbine generator set based on the vibration over-limit frequency index of all sectors within all wind speed segments.

[0023] Optionally, the index determination unit is configured to: divide the sampling time points of each sector within each wind speed segment into acceleration compartments based on the nacelle acceleration at each sampling time point, thereby obtaining each sampling time point under each acceleration interval within each sector of each wind speed segment; multiply the total number of all sampling time points under each acceleration interval within each sector of each wind speed segment by the representative acceleration value of that acceleration interval, and determine the cumulative acceleration value of that acceleration interval under the corresponding sector within the wind speed segment; and determine the sum of the cumulative acceleration values ​​of all acceleration intervals under each sector within each wind speed segment as the cumulative vibration amplitude index of the corresponding sector within the wind speed segment.

[0024] Optionally, the index determination unit is configured to determine the cumulative vibration amplitude index of the wind turbine as the ratio of the sum of the cumulative vibration amplitude indices of all sectors within all wind speed ranges to the cumulative reference acceleration value.

[0025] Optionally, the index determination unit is configured to determine the total number of sampling time points in each sector within each wind speed segment where the nacelle acceleration exceeds a preset threshold as the vibration over-limit frequency index for the corresponding sector within that wind speed segment.

[0026] Optionally, the index determination unit is configured to determine the ratio of the sum of the vibration over-limit frequency indices of all sectors within all wind speed ranges to the reference acceleration over-limit frequency as the vibration over-limit frequency index of the wind turbine generator set.

[0027] Optionally, the risk type determination unit is configured to: perform a weighted summation of the determined wind condition index and vibration level index to obtain the operating risk value of the wind turbine generator set; and determine the operating risk type of the wind turbine generator set based on the operating risk value.

[0028] Optionally, the device further includes a control unit configured to: when the wind turbine generator set's operating risk type is high-risk or medium-risk, and when the wind turbine generator set is in a first specific wind speed range and a first specific sector, perform load reduction control and / or risk monitoring on the wind turbine generator set, wherein the wind condition index of the first specific sector within the first specific wind speed range meets a first preset condition and / or the vibration level index meets a second preset condition; and / or when the wind turbine generator set is in a second specific wind speed range, perform load reduction control and / or risk monitoring on the wind turbine generator set, wherein the sum of the wind condition indexes of all sectors within the second specific wind speed range meets a third preset condition and / or the sum of the vibration level indexes meets a fourth preset condition; and / or when the wind turbine generator set is in a second specific sector, perform load reduction control and / or risk monitoring on the wind turbine generator set, wherein the sum of the wind condition indexes of the second specific sector within all wind speed ranges meets a fifth preset condition and / or the sum of the vibration level indexes meets a sixth preset condition.

[0029] According to a third aspect of the present disclosure, a computer-readable storage medium storing a computer program is provided, which, when executed by a processor, causes the processor to perform the wind turbine generator operation risk assessment method as described above.

[0030] According to a fourth aspect of the present disclosure, an electronic device is provided, comprising: a processor; and a memory storing a computer program that, when executed by the processor, causes the processor to perform the wind turbine generator operation risk assessment method as described above.

[0031] The wind turbine generator operation risk assessment method and apparatus according to the exemplary embodiments of this disclosure assess the operation risk based on the wind condition index and vibration level index of the wind turbine generator, which can improve the accuracy of the unit operation risk assessment, so as to facilitate effective early warning and take effective protection measures, thereby reducing the power generation loss and protection cost of the wind turbine generator.

[0032] Further aspects and / or advantages of the general concept of this disclosure will be set forth in part in the description which follows, and in part will be clear from the description or may be learned by practice of the general concept of this disclosure. Attached Figure Description

[0033] The above and other objects and features of exemplary embodiments of this disclosure will become clearer from the following description taken in conjunction with the accompanying drawings, which exemplarily illustrate the embodiments, wherein:

[0034] Figure 1 A flowchart illustrating an operational risk assessment method for a wind turbine generator set according to an exemplary embodiment of the present disclosure is provided.

[0035] Figure 2 A flowchart illustrating a method for determining wind condition indicators and vibration level indicators of a wind turbine generator set according to exemplary embodiments of the present disclosure;

[0036] Figure 3 A flowchart illustrating a method for determining wind condition indicators and vibration level indicators based on data sets under each sector within each wind speed segment, according to an exemplary embodiment of the present disclosure;

[0037] Figure 4 A flowchart illustrating a method for determining a cumulative index of vibration amplitude of a wind turbine generator set according to an exemplary embodiment of the present disclosure;

[0038] Figure 5 A flowchart illustrating a method for determining the vibration over-limit frequency index of a wind turbine generator set according to an exemplary embodiment of the present disclosure;

[0039] Figure 6 A structural block diagram of an operation risk assessment apparatus for a wind turbine generator set according to an exemplary embodiment of the present disclosure is shown. Detailed Implementation

[0040] The embodiments of this disclosure will now be described in detail with reference to the accompanying drawings, examples of which are illustrated in the drawings, wherein the same reference numerals always refer to the same parts. The embodiments will now be described with reference to the accompanying drawings in order to explain this disclosure.

[0041] Figure 1 A flowchart illustrating an operational risk assessment method for a wind turbine generator set according to an exemplary embodiment of the present disclosure is provided.

[0042] As an example, the wind turbine generator operation risk assessment method according to the exemplary embodiments of this disclosure can be executed by the controller of the wind turbine generator or the field-level controller of the wind farm. Alternatively, it can also be executed by electronic devices such as cloud computing platforms or centralized control servers; this disclosure does not impose any limitations on this.

[0043] Reference Figure 1 In step S101, the operating data of the wind turbine generator set is obtained.

[0044] As an example, the operating data of a wind turbine generator may include multiple data sets, each of which includes wind speed, nacelle acceleration, and yaw position at various sampling time points within a preset duration.

[0045] As an example, the cabin acceleration at each sampling time point can be at least one of the following: the longitudinal acceleration accx obtained by the accelerometer on the top of the cabin, the lateral acceleration accy obtained by the accelerometer on the top of the cabin, and the composite acceleration amplitude calculated based on the obtained accx and accy.

[0046] As an example, the preset duration can be 10 minutes. For instance, the operating data of the wind turbine generator set for the most recent year can be obtained, and the time series of wind speed values, nacelle acceleration, and yaw position for every 10 minutes can be divided into one data group.

[0047] In step S102, the wind condition index and vibration level index of the wind turbine generator set are determined based on the acquired operating data.

[0048] Wind condition indices for wind turbine generators are used to characterize the wind conditions affecting the generator. For example, wind condition indices can be derived based on wind speed and turbulence values.

[0049] Vibration level indicators for wind turbine generators are used to characterize the vibration level of wind turbine generators. As an example, vibration level indicators may include: cumulative vibration amplitude indicators and / or vibration exceedance frequency indicators.

[0050] The following will combine Figure 2 An exemplary embodiment of step S102 will be described below.

[0051] In step S103, the operational risk type of the wind turbine generator is determined based on the determined wind condition index and vibration level index.

[0052] This disclosure takes into account that the actual operating wind speed, turbulence, and vibration magnitude of the unit are all positively correlated with the unit's fatigue load, and that the higher the fatigue load, the higher the actual operating risk. The unit's wind condition indicators can reflect, to some extent, the level of fatigue load resulting from the unit's actual output, while the unit's vibration level indicators are a specific manifestation of the actual wind conditions across the entire impeller plane. Therefore, this disclosure proposes using the unit's wind condition indicators and vibration level indicators for operational risk assessment to improve the accuracy of the assessment.

[0053] As an example, the determined wind condition index and vibration level index can be weighted and summed to obtain the operating risk value of the wind turbine generator set; then, based on the operating risk value, the operating risk type of the wind turbine generator set can be determined.

[0054] As an example, if the operating risk value is greater than a first threshold, the operating risk type of the wind turbine generator can be determined as high risk; if the operating risk value is less than a second threshold, the operating risk type of the wind turbine generator can be determined as low risk; and if the operating risk value is less than or equal to the first threshold and greater than or equal to the second threshold, the operating risk type of the wind turbine generator can be determined as medium risk.

[0055] As an example, the weights of different indicators can be the same or different. As an example, the weights of each indicator can be determined based on simulation, experience, actual conditions, specific needs, etc.

[0056] As an example, the operational risk value of a wind turbine generator set can be: T*k+A*m+U*n, where T represents the wind condition index of the wind turbine generator set; A represents the cumulative vibration amplitude index of the wind turbine generator set; U represents the vibration over-limit frequency index of the wind turbine generator set; the reference value range for weight k is 0.3-0.5; the reference value range for weight m is 0.3-0.5; and the reference value range for weight n is 0.05-0.1.

[0057] As an example, the wind turbine generator operation risk assessment method according to an exemplary embodiment of this disclosure may further include: applying a corresponding control scheme to the wind turbine generator based on the type of operation risk. Furthermore, maintenance personnel may also adopt corresponding solutions based on the type of operation risk of the wind turbine generator.

[0058] As an example, load reduction control and / or risk monitoring can be implemented for wind turbine generators when the operating risk type is high-risk. As an example, load reduction control and / or risk monitoring can be implemented for wind turbine generators when the operating risk type is high-risk or medium-risk.

[0059] As an example, when the operational risk type of a wind turbine generator is classified as high-risk or medium-risk, corresponding protective measures can be taken for specific high-risk sectors and / or high-risk wind speed sections. Further examples will be provided below.

[0060] Figure 2 A flowchart illustrating a method for determining wind condition indicators and vibration level indicators of a wind turbine generator set according to exemplary embodiments of the present disclosure is shown.

[0061] Reference Figure 2 In step S201, based on the wind speed value and yaw position within each data group, the average wind speed and the corresponding sector angle of each data group are determined.

[0062] Specifically, the mean wind speed for each data group is the average of the wind speed values ​​at all sampling time points within that data group.

[0063] As an example, the sector angle corresponding to each data set can be obtained as follows: Based on the yaw position at each sampling time point of the data set, determine the sector angle corresponding to that sampling time point; then, determine the average of the sector angles corresponding to all sampling time points within the data set as the sector angle corresponding to the data set. For example, the remainder obtained by dividing the yaw position at each sampling time point by 360 degrees can be used as the sector angle corresponding to that sampling time point. It should be understood that the sector angle is between 0 and 360 degrees. For example, when the yaw position is -540 degrees, -540 degrees needs to be converted to a positive value first, that is, -540 degrees + 720 degrees and then divided by 360 degrees and taking the remainder.

[0064] Furthermore, the turbulence value corresponding to each data group can be determined based on the wind speed values ​​within each data group. Specifically, the turbulence value corresponding to each data group is the standard deviation of the wind speed values ​​at all sampling time points within that data group.

[0065] In step S202, the multiple data groups are divided into wind speed compartments based on the average wind speed of each data group to obtain each data group within each wind speed segment.

[0066] In step S203, based on the sector angle corresponding to each data group, the data groups in each wind speed segment are divided into sectors to obtain the data groups under each sector in each wind speed segment.

[0067] As an example, the 360 ​​degrees can be divided into sectors by a certain degree. For example, if divided by 30 degrees, that is, each sector spans 30 degrees, it can be divided into 12 sectors.

[0068] Specifically, each data group in the j-th sector within the i-th wind speed segment includes: all data groups whose average wind speed is in the i-th wind speed segment and whose corresponding sector angle belongs to the j-th sector.

[0069] In step S204, based on the data sets of each sector within each wind speed segment, the wind condition index and vibration level index of the wind turbine generator are determined.

[0070] The following will combine Figure 3 An exemplary embodiment of step S204 will be described below.

[0071] Figure 3 A flowchart illustrating a method for determining wind condition indicators and vibration level indicators based on data sets under each sector within each wind speed segment, according to an exemplary embodiment of the present disclosure.

[0072] Reference Figure 3 In step S301, based on the data sets of each sector within each wind speed segment, the wind condition index of each sector within each wind speed segment is determined, and based on the wind condition index of all sectors within all wind speed segments, the wind condition index of the wind turbine generator set is determined.

[0073] As an example, the wind condition index (i.e., the cumulative wind speed turbulence value in Table 2) for the corresponding sector within each wind speed segment can be determined by multiplying the total number of data sets in each sector within each wind speed segment (i.e., the data frequency in Table 1), the average turbulence value of all data sets, and the representative wind speed value of that wind speed segment. Specifically, the wind condition index for the j-th sector within the i-th wind speed segment is the product of the total number of data sets in the j-th sector within the i-th wind speed segment, the average turbulence value corresponding to all data sets, and the representative wind speed value of the i-th wind speed segment.

[0074] Table 1 Wind speed and turbulence matrix

[0075]

[0076] Table 2 Wind speed turbulence cumulative matrix

[0077]

[0078] As an example, the representative wind speed value for a wind speed segment can be the median value of that segment.

[0079] As an example, the wind condition index of a wind turbine generator can be determined by the ratio of the sum of the wind condition indices of all sectors within all wind speed ranges to the cumulative value of turbulence at the baseline wind speed.

[0080] As an example, the baseline cumulative wind speed turbulence value can be obtained based on the statistical values ​​obtained during the aircraft design simulation using the method described above (i.e., the sum of wind condition indices for all sectors within all wind speed ranges), or it can be obtained based on statistical values ​​obtained from a large number of similar aircraft models using the method described above, through a big data platform. For instance, the statistical values ​​obtained from a large number of similar aircraft models using the method described above can be sorted, and the 50th percentile can be taken as the baseline cumulative wind speed turbulence value.

[0081] In step S302, based on the data sets of each sector within each wind speed segment, the vibration level index of each sector within each wind speed segment is determined, and based on the vibration level index of all sectors within all wind speed segments, the vibration level index of the wind turbine generator set is determined.

[0082] As an example, step S302 may include: determining the cumulative vibration amplitude index of each sector within each wind speed segment based on the nacelle acceleration at each sampling time point in each data set under each sector within each wind speed segment, and determining the cumulative vibration amplitude index of the wind turbine generator set based on the cumulative vibration amplitude index of all sectors within all wind speed segments (which will be combined below). Figure 4 (Description of an exemplary embodiment of this step);

[0083] And / or, based on the nacelle acceleration at each sampling time point in each data set within each sector of each wind speed segment, determine the vibration over-limit frequency index for each sector within each wind speed segment, and based on the vibration over-limit frequency index for all sectors across all wind speed segments, determine the vibration over-limit frequency index for the wind turbine generator set (which will be combined below). Figure 5 An exemplary embodiment of this step is described.

[0084] It should be understood that this disclosure does not limit the execution order of steps S301 and S302; they can be executed in parallel or sequentially.

[0085] Figure 4 A flowchart illustrating a method for determining a cumulative index of vibration amplitude of a wind turbine generator set according to an exemplary embodiment of the present disclosure is shown.

[0086] Reference Figure 4 In step S401, based on the nacelle acceleration at each sampling time point, the acceleration is divided into compartments for each sampling time point in each sector within each wind speed segment, thus obtaining each sampling time point in each acceleration interval within each sector of each wind speed segment.

[0087] Specifically, the sampling time points in the z-th acceleration interval under the j-th sector within the i-th wind speed segment include: all sampling time points in all data groups where the mean wind speed is in the i-th wind speed segment and the corresponding sector angle belongs to the j-th sector, and the nacelle acceleration belongs to the z-th acceleration interval.

[0088] As an example, the criteria for determining the acceleration chamber are: if the acceleration is between 0 and 0.01, it falls into the 0.01 acceleration chamber; if the acceleration is between 0.01 and 0.02, it falls into the 0.02 acceleration chamber.

[0089] In step S402, the product of the total number of all sampling time points in each acceleration interval under each sector within each wind speed segment (i.e., the data frequency in Table 3) and the representative acceleration value of the acceleration interval is determined as the cumulative acceleration value of the acceleration interval under the corresponding sector within the wind speed segment.

[0090] As an example, the representative acceleration value of an acceleration range can be the right boundary value or the middle value of that acceleration range.

[0091] In step S403, the sum of the cumulative acceleration values ​​of all acceleration intervals under each sector within each wind speed segment is determined as the cumulative vibration amplitude index of the corresponding sector within that wind speed segment (i.e., the cumulative acceleration value in Table 4).

[0092] In step S404, the ratio of the sum of the cumulative vibration amplitude indices of all sectors within all wind speed ranges to the cumulative baseline acceleration value is determined as the cumulative vibration amplitude index of the wind turbine generator set.

[0093] As an example, the baseline cumulative acceleration value can be obtained based on the statistical values ​​obtained during the turbine design simulation using the method described above (i.e., the sum of the cumulative vibration amplitude indices of all sectors within all wind speed ranges), or it can be obtained based on statistical values ​​obtained from a large number of similar turbine units using the method described above, using a big data platform. For example, the statistical values ​​obtained from a large number of similar turbine units using the method described above can be sorted, and the 50th percentile can be taken as the baseline cumulative acceleration value.

[0094] Table 3 Acceleration Matrix

[0095]

[0096] Table 4 Acceleration Cumulative Matrix

[0097]

[0098] Figure 5 A flowchart illustrating a method for determining the vibration over-limit frequency index of a wind turbine generator set according to an exemplary embodiment of the present disclosure is shown.

[0099] Reference Figure 5 In step S501, the total number of sampling time points in each sector within each wind speed segment where the nacelle acceleration exceeds a preset threshold is determined as the vibration over-limit frequency index of the corresponding sector within that wind speed segment (i.e., the acceleration over-limit frequency in Table 5).

[0100] Specifically, the vibration over-limit frequency index for the j-th sector within the i-th wind speed segment is: the total number of sampling time points in the j-th sector within the i-th wind speed segment where the nacelle acceleration exceeds a preset threshold.

[0101] In step S502, the ratio of the sum of the vibration over-limit frequency indices of all sectors within all wind speed ranges to the reference acceleration over-limit frequency is determined as the vibration over-limit frequency index of the wind turbine generator set.

[0102] As an example, the preset threshold can be determined based on the maximum acceleration value obtained from the simulation design of power generation conditions. As another example, the baseline acceleration over-limit frequency can be obtained based on the statistical values ​​obtained during turbine design simulation using the above method (i.e., the sum of vibration over-limit frequency indices for all sectors within all wind speed ranges), or it can be obtained based on statistical values ​​obtained from a large number of similar turbine models using the above method, through a big data platform. For example, the statistical values ​​obtained from a large number of similar turbine models using the above method can be sorted, and the 50th percentile can be taken as the baseline acceleration over-limit frequency.

[0103] This disclosure takes into account that the higher the frequency of exceeding the preset threshold, the more complex the actual wind conditions, the higher the fatigue load on the unit, the higher the probability of approaching the limit load, and the higher the risk of the unit. Therefore, this disclosure proposes to use the vibration over-limit frequency index to assess the operating risk of wind turbine generators.

[0104] Table 5 Acceleration Exceedance Frequency Matrix

[0105]

[0106] According to exemplary embodiments of this disclosure, in order to ensure accurate assessment of risk status, the risk assessment is specifically located at the sector and wind speed range, and it also facilitates the adoption of protection schemes for specific high-risk sectors and / or high-risk wind speed ranges, thereby reducing power generation loss and protection costs.

[0107] In one embodiment, when the wind turbine's operational risk type is high-risk or medium-risk, and the wind turbine is located in a first specific wind speed range and a first specific sector, load reduction control and / or risk monitoring are implemented. The wind condition indicators of the first specific sector within the first specific wind speed range meet a first preset condition and / or the vibration level indicators meet a second preset condition. In other words, when the wind turbine's operational risk type is high-risk or medium-risk, and it is located in a high-risk sector within a high-risk wind speed range that meets the first and / or second preset conditions, corresponding protective measures are taken to reduce the harm to the wind turbine from the high-risk sector within the high-risk wind speed range.

[0108] As an example, when the ratio of the wind condition index of a sector within a wind speed range to the cumulative value of turbulence at the baseline wind speed exceeds a certain threshold, it can be determined that the sector within that wind speed range meets the first preset condition.

[0109] As an example, when the ratio of the cumulative vibration amplitude index to the cumulative reference acceleration value of a sector within a wind speed range exceeds a certain threshold, and / or the ratio of the vibration over-limit frequency index to the reference acceleration over-limit frequency exceeds a certain threshold, it can be determined that the sector within that wind speed range meets the second preset condition.

[0110] In another embodiment, when the wind turbine's operational risk type is high-risk or medium-risk, load reduction control and / or risk monitoring are implemented when the wind turbine is in a second specific wind speed range. The sum of wind condition indicators for all sectors within the second specific wind speed range meets a third preset condition and / or the sum of vibration level indicators meets a fourth preset condition. In other words, when the wind turbine's operational risk type is high-risk or medium-risk, and it is in a high-risk wind speed range that meets the third and / or fourth preset conditions, corresponding protective measures are taken to reduce the harm to the wind turbine caused by the high-risk wind speed range.

[0111] In another embodiment, when the wind turbine's operational risk type is high-risk or medium-risk, load reduction control and / or risk monitoring are implemented when the wind turbine is located in a second specific sector. The sum of wind condition indicators across all wind speed ranges within the second specific sector meets a fifth preset condition and / or the sum of vibration level indicators meets a sixth preset condition. In other words, when the wind turbine's operational risk type is high-risk or medium-risk, and it is located in a high-risk sector that meets the fifth and / or sixth preset conditions, corresponding protective measures are taken to reduce the harm to the wind turbine from the high-risk sector.

[0112] According to exemplary embodiments of this disclosure, it is possible to assess the high operational risks of generator sets caused by complex terrain or wake-induced complex wind conditions. Complex wind conditions result in large generator set vibrations, high fatigue loads, and thus high operational risks. Furthermore, exemplary embodiments of this disclosure allow for risk assessment of generator sets in all terrains (including onshore plains and offshore areas).

[0113] According to exemplary embodiments of this disclosure, automatic early warning of operational risks can be realized, so as to automatically implement different control schemes for the unit based on the early warning results; health diagnosis can also be performed based on historical operating data of the unit, and after the risk is diagnosed, operation and maintenance personnel can intervene to take solutions.

[0114] According to exemplary embodiments of this disclosure, by accurately assessing the risk level of the generating unit from multiple dimensions, the safety of major components of the unit can be ensured, the unit's lifespan can be guaranteed, power generation damage can be reduced, and revenue can be guaranteed. Exemplary embodiments of this disclosure can be used for both offline post-assessment and real-time unit diagnostics, automatically issuing warnings or directly switching to different control modes based on the diagnostic results.

[0115] Figure 6 A structural block diagram of an operation risk assessment apparatus for a wind turbine generator set according to an exemplary embodiment of the present disclosure is shown.

[0116] like Figure 6 As shown, the wind turbine generator set operation risk assessment device according to an exemplary embodiment of the present disclosure includes: an operation data acquisition unit 101, an index determination unit 102, and a risk type determination unit 103.

[0117] Specifically, the operation data acquisition unit 101 is configured to acquire the operation data of the wind turbine generator set.

[0118] The index determination unit 102 is configured to determine the wind condition index and vibration level index of the wind turbine generator set based on the operating data.

[0119] The risk type determination unit 103 is configured to determine the operational risk type of the wind turbine generator set based on determined wind condition indicators and vibration level indicators.

[0120] As an example, the operational data may include multiple data sets, each data set including wind speed, cabin acceleration, and yaw position at various sampling time points within a preset duration.

[0121] As an example, the index determination unit 102 can be configured to: determine the average wind speed and corresponding sector angle of each data group based on the wind speed value and yaw position within each data group; divide the multiple data groups into wind speed compartments according to the average wind speed of each data group to obtain each data group within each wind speed segment; divide each data group within each wind speed segment into sector compartments according to the sector angle corresponding to each data group to obtain each data group under each sector within each wind speed segment; and determine the wind condition index and vibration level index of the wind turbine generator based on each data group under each sector within each wind speed segment.

[0122] As an example, the index determination unit 102 can be configured to: determine the wind condition index of each sector in each wind speed segment based on each data set under each sector in each wind speed segment, and determine the wind condition index of the wind turbine generator set based on the wind condition index of all sectors in all wind speed segments; determine the vibration level index of each sector in each wind speed segment based on each data set under each sector in each wind speed segment, and determine the vibration level index of the wind turbine generator set based on the vibration level index of all sectors in all wind speed segments.

[0123] As an example, the index determination unit 102 can be configured to determine the wind condition index of the corresponding sector within each wind speed segment by multiplying the total number of data groups in each sector within each wind speed segment, the turbulence mean of all data groups, and the representative wind speed value of the wind speed segment. The turbulence value of each data group is the standard deviation of the wind speed values ​​within the data group.

[0124] As an example, the index determination unit 102 can be configured to determine the wind condition index of the wind turbine as the ratio of the sum of the wind condition indices of all sectors within all wind speed ranges to the cumulative value of the baseline wind speed turbulence.

[0125] As an example, the index determination unit 102 can be configured to: determine the cumulative vibration amplitude index and / or vibration over-limit frequency index for each sector within each wind speed segment based on the nacelle acceleration at each sampling time point in each data group under each sector within each wind speed segment; determine the cumulative vibration amplitude index of the wind turbine generator set based on the cumulative vibration amplitude index of all sectors within all wind speed segments; and / or determine the vibration over-limit frequency index of the wind turbine generator set based on the vibration over-limit frequency index of all sectors within all wind speed segments.

[0126] As an example, the index determination unit 102 can be configured to: divide the sampling time points of each sector within each wind speed segment into acceleration compartments based on the nacelle acceleration at each sampling time point, thereby obtaining each sampling time point under each acceleration interval within each sector of each wind speed segment; multiply the total number of all sampling time points under each acceleration interval within each sector of each wind speed segment by the representative acceleration value of that acceleration interval, and determine the cumulative acceleration value of that acceleration interval under the corresponding sector within the wind speed segment; and determine the sum of the cumulative acceleration values ​​of all acceleration intervals under each sector within each wind speed segment as the cumulative vibration amplitude index of the corresponding sector within the wind speed segment.

[0127] As an example, the index determination unit 102 can be configured to determine the cumulative vibration amplitude index of the wind turbine as the ratio of the sum of the cumulative vibration amplitude indices of all sectors within all wind speed ranges to the cumulative reference acceleration value.

[0128] As an example, the index determination unit 102 can be configured to determine the total number of sampling time points in each sector within each wind speed segment where the nacelle acceleration exceeds a preset threshold as the vibration over-limit frequency index for the corresponding sector within that wind speed segment.

[0129] As an example, the index determination unit 102 can be configured to determine the ratio of the sum of the vibration over-limit frequency indices of all sectors within all wind speed ranges to the reference acceleration over-limit frequency as the vibration over-limit frequency index of the wind turbine generator set.

[0130] As an example, the risk type determination unit 103 can be configured to: perform a weighted summation of the determined wind condition index and vibration level index to obtain the operating risk value of the wind turbine generator set; and determine the operating risk type of the wind turbine generator set based on the operating risk value.

[0131] As an example, the device may further include: a control unit (not shown), configured to: when the wind turbine generator set's operating risk type is high-risk or medium-risk, and when the wind turbine generator set is in a first specific wind speed range and a first specific sector, perform load reduction control and / or risk monitoring on the wind turbine generator set, wherein the wind condition index of the first specific sector within the first specific wind speed range meets a first preset condition and / or the vibration level index meets a second preset condition; and / or, when the wind turbine generator set is in a second specific wind speed range, perform load reduction control and / or risk monitoring on the wind turbine generator set, wherein the sum of the wind condition indexes of all sectors within the second specific wind speed range meets a third preset condition and / or the sum of the vibration level indexes meets a fourth preset condition; and / or, when the wind turbine generator set is in the second specific sector, perform load reduction control and / or risk monitoring on the wind turbine generator set, wherein the sum of the wind condition indexes of the second specific sector within all wind speed ranges meets a fifth preset condition and / or the sum of the vibration level indexes meets a sixth preset condition.

[0132] As an example, the wind turbine generator operation risk assessment device according to an exemplary embodiment of the present disclosure can be installed in the controller of the wind turbine generator or the field-level controller of the wind farm. Furthermore, as an example, the wind turbine generator operation risk assessment device according to an exemplary embodiment of the present disclosure can also be installed in a cloud computing platform or a centralized control server; the present disclosure does not impose any limitations on this.

[0133] It should be understood that the specific processing performed by the wind turbine generator operation risk assessment apparatus according to the exemplary embodiments of this disclosure has been referenced. Figures 1 to 5 A detailed description has been provided, and the relevant details will not be repeated here.

[0134] It should be understood that the various units in the wind turbine generator operation risk assessment apparatus according to exemplary embodiments of this disclosure may be implemented as hardware components and / or software components. Those skilled in the art, based on the processes performed by the defined various units, may implement the various units, for example, using field-programmable gate arrays (FPGAs) or application-specific integrated circuits (ASICs).

[0135] An exemplary embodiment of this disclosure provides a computer-readable storage medium storing a computer program that, when executed by a processor, causes the processor to perform an operational risk assessment method for a wind turbine generator as described in the exemplary embodiment above. The computer-readable storage medium is any data storage device capable of storing data readable 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 waves (such as data transmission via the Internet through wired or wireless transmission paths).

[0136] 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 that, when executed by the processor, causes the processor to perform the wind turbine generator operation risk assessment method as described in the exemplary embodiment above.

[0137] As an example, the electronic device may be a controller for a wind turbine generator or a field-level controller for a wind farm. Furthermore, as an example, the electronic device may also be a cloud computing platform or a centralized control server; this disclosure does not impose any limitations on this.

[0138] While some exemplary embodiments of this disclosure have been shown and described, those skilled in the art will understand that modifications may be made to these embodiments without departing from the principles and spirit of this disclosure, which are defined by the claims and their equivalents.

Claims

1. A method for assessing the operational risk of a wind turbine generator set, characterized in that, include: Obtain operating data of wind turbine generators; Based on the operational data, the wind condition index and vibration level index of the wind turbine generator set are determined; Based on the determined wind condition and vibration level indicators, the operational risk type of the wind turbine generator set is determined. The step of determining the operational risk type of the wind turbine generator set based on the determined wind condition indicators and vibration level indicators includes: The operating risk value of the wind turbine generator is obtained by weighted summation of the determined wind condition index and vibration level index. Based on the operational risk value, the operational risk type of the wind turbine generator set is determined. The vibration level index is determined based on the vibration level index of each sector within each wind speed segment, and includes the cumulative vibration amplitude index and / or the vibration over-limit frequency index.

2. The method according to claim 1, characterized in that, The operational data includes multiple data sets, each of which includes wind speed, nacelle acceleration, and yaw position at various sampling time points within a preset duration. The steps for determining the wind condition indicators and vibration level indicators of the wind turbine generator set based on the operational data include: Based on the wind speed value and yaw position within each data group, determine the mean wind speed and the corresponding sector angle for each data group; Based on the average wind speed of each data group, the multiple data groups are divided into wind speed compartments to obtain the data groups within each wind speed segment. Based on the sector angle corresponding to each data group, the data groups in each wind speed segment are divided into sectors to obtain the data groups under each sector in each wind speed segment. Based on the data sets of each sector within each wind speed range, the wind condition index and vibration level index of the wind turbine generator set are determined.

3. The method according to claim 2, characterized in that, The steps for determining the wind condition indicators and vibration level indicators of the wind turbine generator set based on the data sets of each sector within each wind speed range include: Based on the data sets of each sector within each wind speed segment, the wind condition index of each sector within each wind speed segment is determined, and based on the wind condition index of all sectors within all wind speed segments, the wind condition index of the wind turbine generator set is determined. Based on the data sets of each sector within each wind speed segment, the vibration level index of each sector within each wind speed segment is determined, and based on the vibration level index of all sectors within all wind speed segments, the vibration level index of the wind turbine generator set is determined.

4. The method according to claim 3, characterized in that, The steps for determining the wind condition indicators for each sector within each wind speed range, based on the data sets for each sector within each wind speed range, include: The product of the total number of data sets in each sector within each wind speed segment, the turbulence mean of all data sets, and the representative wind speed value of that wind speed segment is determined as the wind condition index for the corresponding sector within that wind speed segment. The turbulence value for each data set is the standard deviation of the wind speed values ​​within that data set.

5. The method according to claim 3, characterized in that, The steps for determining the wind condition index of the wind turbine generator set based on the wind condition index of all sectors within all wind speed ranges include: The wind condition index of the wind turbine generator set is determined by the ratio of the sum of the wind condition indices of all sectors within all wind speed ranges to the cumulative value of turbulence at the baseline wind speed.

6. The method according to claim 3, characterized in that, The steps for determining the vibration level index of each sector within each wind speed segment based on data sets, and for determining the vibration level index of the wind turbine generator based on the vibration level indices of all sectors across all wind speed segments, include: Based on the nacelle acceleration at each sampling time point in each data set of each sector within each wind speed segment, determine the cumulative vibration amplitude index and / or vibration over-limit frequency index for each sector within each wind speed segment. The cumulative vibration amplitude index of the wind turbine generator set is determined based on the cumulative vibration amplitude index of all sectors within all wind speed ranges, and / or the frequency index of vibration exceeding the limit of the wind turbine generator set is determined based on the frequency index of vibration exceeding the limit of all sectors within all wind speed ranges.

7. The method according to claim 6, characterized in that, The steps for determining the cumulative vibration amplitude index for each sector within each wind speed range, based on the nacelle acceleration at each sampling time point in each data set within each sector of each wind speed range, include: Based on the nacelle acceleration at each sampling time point, the acceleration is divided into compartments for each sampling time point in each sector within each wind speed segment, resulting in each sampling time point in each acceleration interval within each sector of each wind speed segment. The product of the total number of all sampling time points in each acceleration interval under each sector within each wind speed segment and the representative acceleration value of that acceleration interval is determined as the cumulative acceleration value of that acceleration interval under the corresponding sector within that wind speed segment. The sum of the cumulative acceleration values ​​of all acceleration intervals under each sector within each wind speed segment is determined as the cumulative vibration amplitude index of the corresponding sector within that wind speed segment.

8. The method according to claim 6, characterized in that, The steps for determining the cumulative vibration amplitude index of the wind turbine generator set based on the cumulative vibration amplitude index of all sectors within all wind speed ranges include: The sum of the cumulative vibration amplitude indices of all sectors within all wind speed ranges is compared with the cumulative baseline acceleration value to determine the cumulative vibration amplitude index of the wind turbine generator set.

9. The method according to claim 6, characterized in that, The steps for determining the vibration over-limit frequency index for each sector within each wind speed range, based on the nacelle acceleration at each sampling time point in each data set within each sector of each wind speed range, include: The total number of sampling time points in each sector within each wind speed range where the nacelle acceleration exceeds a preset threshold is determined as the vibration over-limit frequency index for the corresponding sector within that wind speed range.

10. The method according to claim 6, characterized in that, The steps for determining the vibration over-limit frequency index of the wind turbine generator set based on the vibration over-limit frequency index of all sectors within all wind speed ranges include: The ratio of the sum of the vibration over-limit frequency indices of all sectors within all wind speed ranges to the reference acceleration over-limit frequency is determined as the vibration over-limit frequency index of the wind turbine generator set.

11. The method according to claim 3, characterized in that, If the operational risk type of the wind turbine generator is high-risk or medium-risk, the method further includes: When the wind turbine is in a first specific wind speed range and in a first specific sector, load reduction control and / or risk monitoring are performed on the wind turbine, wherein the wind condition index of the first specific sector within the first specific wind speed range meets a first preset condition and / or the vibration level index meets a second preset condition. And / or, when the wind turbine is in the second specific wind speed range, load reduction control and / or risk monitoring are performed on the wind turbine, wherein the sum of wind condition indicators of all sectors in the second specific wind speed range meets the third preset condition and / or the sum of vibration level indicators meets the fourth preset condition. And / or, when the wind turbine is in the second specific sector, load reduction control and / or risk monitoring are performed on the wind turbine, wherein the sum of wind condition indicators of the second specific sector in all wind speed ranges meets the fifth preset condition and / or the sum of vibration level indicators meets the sixth preset condition.

12. A device for assessing the operational risk of a wind turbine generator set, characterized in that, include: The operation data acquisition unit is configured to acquire the operation data of the wind turbine generator set; The index determination unit is configured to determine the wind condition index and vibration level index of the wind turbine generator set based on the operating data. The risk type determination unit is configured to determine the operational risk type of the wind turbine generator based on defined wind condition indicators and vibration level indicators. The risk type determination unit is configured to: perform a weighted summation of determined wind condition indicators and vibration level indicators to obtain the operating risk value of the wind turbine generator set; and determine the operating risk type of the wind turbine generator set based on the operating risk value. The vibration level index is determined based on the vibration level index of each sector within each wind speed segment, and includes the cumulative vibration amplitude index and / or the vibration over-limit frequency index.

13. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it causes the processor to perform the wind turbine generator operation risk assessment method as described in any one of claims 1 to 11.

14. An electronic device, characterized in that, include: processor; A memory storing a computer program that, when executed by a processor, causes the processor to perform the wind turbine generator operation risk assessment method as described in any one of claims 1 to 11.

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