Fault triggering adjustment method and device for wind turbine generator set

Abstract

The present disclosure provides a method and apparatus for adjusting fault triggers for a wind turbine generator set. The method includes partitioning the operating data of the wind turbine generator set to generate multiple intervals of operating data; performing statistical analysis on the operating data of each interval to generate a statistical value for the operating data of each interval; determining a fault trigger interval among the multiple intervals based on the statistical value of the operating data of each interval; and adjusting the fault trigger conditions corresponding to the fault trigger intervals. The method can optimize fault monitoring for wind turbine generator sets.

Description

Technical Field

[0001] The present disclosure relates to the field of wind power generation, and in particular to a fault triggering adjustment method and device for a wind generator set. Background Art

[0002] Wind turbines can achieve energy conversion from wind energy to mechanical energy, and from mechanical energy to electrical energy. For example, the impeller system can achieve energy conversion from wind energy to mechanical energy, and the generator and control system can achieve energy conversion from mechanical energy to electrical energy. When considering the control objectives of the wind turbine control system, the control system can achieve normal operation control, parameter monitoring and supervision, and safety protection and processing functions.

[0003] The design and implementation of wind turbine control systems aims to meet the requirements for unmanned, automated operation, state control, and monitoring. However, fault monitoring of wind turbines can easily lead to excessive turbine shutdowns, or even erroneous shutdowns due to incorrect fault identification, resulting in unstable and unsafe wind turbine operation. Summary of the Invention

[0004] The purpose of the embodiments of the present disclosure is to provide a fault triggering adjustment method and device for a wind turbine generator set to overcome the deficiencies in the prior art, at least to optimize the fault monitoring of the wind turbine generator set, adjust the fault triggering conditions of the wind turbine generator set in intervals, and ensure the safe operation of the wind turbine generator set.

[0005] According to an embodiment of the present disclosure, a fault trigger adjustment method for a wind turbine generator set is provided, the fault trigger adjustment method comprising: partitioning the operating data of the wind turbine generator set to generate operating data of multiple intervals; performing statistical analysis on the operating data of each interval to generate a statistical value of the operating data of each interval; determining a fault trigger interval among the multiple intervals based on the statistical value of the operating data of each interval; and adjusting the fault trigger condition corresponding to the fault trigger interval.

[0006] According to 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 fault trigger adjustment method described above is implemented.

[0007] According to an embodiment of the present disclosure, a computing device is provided, comprising: a processor; and a memory storing a computer program. When the computer program is executed by the processor, the fault triggering adjustment method described above is implemented.

[0008] According to an embodiment of the present disclosure, a fault trigger adjustment device for a wind turbine generator set is provided, wherein the fault trigger adjustment device includes: a data partitioning unit, configured to partition the operating data of the wind turbine generator set to generate operating data of multiple intervals; a statistical analysis unit, configured to perform statistical analysis on the operating data of each interval to generate a statistical value of the operating data of each interval; a fault trigger interval determination unit, configured to determine a fault trigger interval among the multiple intervals based on the statistical value of the operating data of each interval; and a fault trigger condition adjustment unit, configured to adjust the fault trigger condition corresponding to the fault trigger interval.

[0009] According to 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 fault trigger adjustment method described above is implemented.

[0010] According to an embodiment of the present disclosure, a computing device is provided, comprising: a processor; and a memory storing a computer program. When the computer program is executed by the processor, the fault triggering adjustment method described above is implemented.

[0011] By adopting the fault triggering adjustment method and device for wind turbine generator sets according to the embodiments of the present disclosure, at least one of the following technical effects can be achieved: based on big data analysis, the operating distribution of the wind turbine generator set and its various subsystems can be given, and possible cause analysis can be given in combination with the data, so as to provide a data basis for understanding the operating conditions, operating characteristics, load distribution, etc. of the wind turbine generator set and its various subsystems (for example, the pitch system of the wind turbine generator set); the operating data and environmental data of the wind turbine generator set can be statistically analyzed in intervals, and different fault triggering parameters (also called protection parameters) can be executed in different intervals to adjust (for example, appropriately reduce) the number of fault triggering of the wind turbine generator set; among them, for intervals with a large number of fault triggering times, the operating data related to the fault triggering parameters involved can be statistically analyzed to determine reasonable fault triggering parameters; the same model of wind turbine generator sets can be used for The unit performs fault triggering adjustment to avoid the wind turbine generator set from frequently starting and shutting down in a short period of time after a fault is falsely triggered; by partitioning the operating data of the wind turbine generator set, the operating data can be distributed and statistically analyzed to obtain fault triggering intervals with a large number of faults. Based on the analysis results, the number of fault triggers in each fault triggering interval can be counted, and the fault triggering parameters for the intervals with a large number of fault triggering times can be appropriately optimized; the operating data associated with multiple fault triggering conditions can be statistically analyzed at the same time, that is, if there is a fault triggering interval with a large number of fault triggering times, and the corresponding statistical analysis results of multiple fault triggering conditions are close, the fault triggering parameters of each fault triggering condition can be optimized based on these statistical analysis results, thereby appropriately reducing the number of fault triggering times and / or the number of false fault triggering times of the wind turbine generator without having to update the program in batches and reducing maintenance time. BRIEF DESCRIPTION OF THE DRAWINGS

[0012] The above and other objects and features of the present disclosure will become more apparent from the following description in conjunction with the accompanying drawings.

[0013] Figure 1 is a control block diagram of a pitch control system of a wind turbine generator set according to an embodiment of the present disclosure;

[0014] Figure 2 is a flow chart of a fault-triggered adjustment method for a wind turbine generator set according to an embodiment of the present disclosure;

[0015] Figure 3 is another flow chart of a fault-triggered adjustment method for a wind turbine generator set according to an embodiment of the present disclosure;

[0016] Figure 4 is an example table of operating data statistics of multiple intervals of a wind turbine generator set according to an embodiment of the present disclosure;

[0017] Figure 5 is a graph showing the average value of the pitch angle of a wind turbine generator set relative to the ambient wind speed range according to an embodiment of the present disclosure;

[0018] Figure 6 is a graph showing the average value of the rotor speed of a wind turbine generator set relative to the ambient wind speed range according to an embodiment of the present disclosure;

[0019] Figure 7 is another flow chart of a fault-triggered adjustment method for a wind turbine generator set according to an embodiment of the present disclosure;

[0020] Figure 8 is a schematic diagram of a fault-triggered adjustment device for a wind turbine generator set according to an embodiment of the present disclosure;

[0021] Figure 9 is a schematic diagram of a computing device according to an embodiment of the present disclosure. DETAILED DESCRIPTION

[0022] Normal wind turbine operation control includes unit startup and shutdown control, grid connection control, variable speed control, and constant power operation control. Parameter monitoring and supervision include grid parameters (such as grid voltage and frequency, generator output current, power, and power factor), environmental parameters (such as wind speed, wind direction, and ambient temperature), component temperatures (including generator winding temperature, gear bearing temperature, control cabinet temperature, and nacelle temperature), converter parameters (such as converter torque and converter vent temperature), nacelle vibration, cable entanglement, and pressure. In the event of an internal or external fault in the wind turbine, an emergency situation caused by monitored parameters exceeding their limits, control system failure, or the wind turbine being unable to operate within its normal operating range, the safety protection system should activate to maintain the wind turbine in a safe state. The wind turbine can shut down for faults not caused by the turbine itself. Once the fault is resolved, the wind turbine will automatically reset itself and enter standby mode.

[0023] The basic methods and steps for optimizing wind turbine parameters usually include: 1) counting the frequency of on-site faults and sorting out information on multiple triggers and multi-unit triggers; 2) checking unit fault files, or manually collecting data to confirm the cause of the fault, or modifying the test program to capture fault data; 3) after finding the cause based on the data, modifying and improving the test program; 4) sending the new test program to the project site for batch updating of the test program.

[0024] Among the above steps, steps 2) and 4) are particularly time-consuming, often totaling about 1 to 2 weeks. For step 2), on the one hand, some data may not be captured immediately (e.g., the abnormal situation lasts for a short time), and sometimes manual collection takes a long time to capture the data, that is, capturing data and analyzing the causes will take time. In the process, workload will be generated due to analyzing, checking, and inspecting the data. On the other hand, the test program is modified for testing, and the test period is also uncertain, generally at least 3 to 5 days. For step 4), issuing program flows and batch updating programs within the wind farm will also consume a long time and manpower workload, and during this period, the wind turbine generator set may trigger similar faults and shut down.

[0025] In order to overcome the shortcomings of the existing technology (for example, optimizing the fault detection of wind turbines), the present disclosure proposes a fault triggering adjustment method and device for wind turbines, which can provide the operating distribution of the wind turbine and its various subsystems based on big data analysis, and provide possible cause analysis in combination with the data, providing a data basis for understanding the operating conditions, operating characteristics, load distribution, etc. of the wind turbine and its various subsystems (for example, the variable pitch system of the wind turbine).

[0026] According to the embodiments of the present disclosure, the fault trigger adjustment method and device can perform interval statistical analysis on the operating data and environmental data of the wind turbine generator set, and execute different fault trigger parameters (also called protection parameters) in different intervals to adjust (for example, appropriately reduce) the number of fault triggers of the wind turbine generator set; among them, for intervals with a large number of fault triggers, the operating data related to the fault trigger parameters involved are statistically analyzed to determine reasonable fault trigger parameters.

[0027] The fault trigger adjustment method and device disclosed herein can perform fault trigger adjustment on wind turbines of the same model, thereby preventing the wind turbines from frequently starting and shutting down in a short period of time after a fault is falsely triggered.

[0028] Figure 1 4 is a control block diagram of a pitch control system of a wind turbine generator set according to an embodiment of the present disclosure. Figure 1The control logic of the control block diagram of the pitch system shown is as follows: in normal pitch adjustment mode, the main control system 101 sends the required pitch speed command to the pitch controller 103 through the communication unit 102 (for example, a slip ring communication unit), and the pitch controller 103 sends the required speed command received from the main control system 101 to the pitch driver 104, and at the same time sends an enable signal, thereby driving the pitch motor 105 to operate and realize the blade pitch adjustment function. Among them, the operating mechanism related to the pitch system is: when the wind speed is greater than the rated speed (or when the wind speed increases under the power-limited operation state), the pitch system of the wind turbine generator set will perform pitch adjustment. When the pitch motor 105 performs pitch adjustment, the motor temperature will increase and the backup power supply voltage of the pitch system will decrease. At the same time, when the wind speed increases, the impeller speed of the wind turbine generator set will increase, and the pitch adjustment speed will also increase.

[0029] The fault trigger adjustment method and device disclosed in the present invention can perform big data analysis on the operating data of the wind turbine generator set based on the operating mechanism of the variable pitch system to obtain the operating status of the wind turbine generator set and its subsystems (for example, the variable pitch system), and based on the analysis results, appropriately optimize the parameters involved (for example, fault trigger parameters).

[0030] The fault-triggered adjustment method and device for a wind turbine generator set according to the present disclosure will be described below with reference to the accompanying drawings, but the present disclosure is not limited to the described embodiments.

[0031] Figure 2 It is a flow chart of a fault trigger adjustment method for a wind turbine generator set according to an embodiment of the present disclosure. In an embodiment of the present disclosure, operating data can be obtained for the wind turbine generator set, for example, the operating data of the wind turbine generator set can be obtained from the data acquisition and monitoring control system (SCADA system) of the wind farm. In addition, environmental data of the wind turbine generator set can also be obtained. The operating data may include but is not limited to the rotor speed, pitch angle, generator speed, and backup power supply voltage of the pitch system; the environmental data may include but is not limited to the ambient wind speed, ambient temperature, and ambient humidity. According to an embodiment of the present disclosure, fault trigger adjustment can be performed for wind turbine generator sets of the same model (for example, MW-class wind turbine generator sets), and therefore, operating data can be obtained for wind turbine generator sets of the same model.

[0032] like Figure 2 As shown, the fault trigger adjustment method may include: partitioning the operating data of the wind turbine generator set to generate operating data of multiple intervals (step S11); performing statistical analysis on the operating data of each interval to generate operating data statistics of each interval (step S12); determining the fault trigger interval among the multiple intervals based on the operating data statistics of each interval (step S13); and adjusting the fault trigger condition corresponding to the fault trigger interval (step S14).

[0033] In step S11 , the operation data of the wind turbine generator set may be partitioned according to time intervals to generate operation data of multiple time intervals.

[0034] The fault-triggered adjustment method may further include partitioning the environmental data of the wind turbine generator set to generate multiple intervals of environmental data (e.g., ambient wind speed, ambient temperature, etc.). Optionally, in step S11, the operating data may be partitioned based on the multiple intervals of environmental data to generate multiple intervals of operating data corresponding to the multiple intervals of environmental data.

[0035] According to an embodiment of the present disclosure, the operating data of a wind turbine generator set may include: first operating data and second operating data. Optionally, in step S11, the first operating data may be partitioned to generate first operating data in multiple intervals; and the second operating data may be partitioned based on the first operating data in the multiple intervals to generate second operating data in multiple intervals corresponding to the first operating data in the multiple intervals.

[0036] As described above, by dividing the operating data into intervals, statistical analysis can be performed on the operating data in each interval. By analyzing massive amounts of data, the statistical results can be made closer to the real data, ignoring accidental sample values. For example, when calculating the maximum value of the three-blade angle difference, the encoder may experience a large jump, such as a jump from 10 degrees to 30 degrees. However, in the case of massive data statistical analysis, accidental jump values will be averaged out, so the final statistical results are closer to accurate and reliable results. For example, the data jump amount is 20 degrees, but in 100,000 data files, the average effect of the jump is only 20 / 100,000 = 0.0002, which is basically negligible and has almost no impact on the adjustment of fault trigger conditions or the operational safety of the wind turbine.

[0037] According to embodiments of the present disclosure, the ambient wind speed of a wind turbine generator system can be partitioned to generate multiple intervals of ambient wind speed. Operational data can then be partitioned based on the multiple intervals of ambient wind speed to generate multiple intervals of operational data corresponding to the multiple intervals of ambient wind speed. For example, the operational data can be partitioned into corresponding ambient wind speed intervals based on the corresponding temporal intervals of the operational data.

[0038] In step S12, statistical analysis is performed on the operating data of each interval to generate the operating data statistics of each interval. In the embodiment of the present disclosure, the statistical analysis method includes but is not limited to finding one of the average value, the median value, the maximum value, the minimum value, the variance, and the standard deviation. Therefore, the operating data of each interval can be subjected to one of the average value, the median value, the maximum value, the minimum value, the variance, and the standard deviation to generate the operating data statistics of each interval. In this way, the operating data statistics of each interval of the massive operating data can be used to approximate the true value of the operating data of each interval, and the operating status of each interval can be analyzed from multiple data dimensions. For example, the maximum value of the pitch angle of each interval, the minimum value of the ambient temperature of each interval, and the minimum value of the backup power supply voltage of the pitch system of each interval can be obtained.

[0039] Optionally, before performing statistical analysis, zeroed operational data from each interval can be removed to generate zeroed operational data for each interval. Then, one of the following operations is performed on the zeroed operational data for each interval to generate operational data statistics for each interval. In embodiments of the present disclosure, zeroed operational data may indicate that the operational data is meaningless. Therefore, zeroed operational data can be removed. Furthermore, missing operational data can be ignored.

[0040] According to an embodiment of the present disclosure, the operating data of each interval may be read in sequence to obtain the operating data statistics of each interval. Figure 3 is another flow chart of a fault-triggered adjustment method for a wind turbine generator set according to an embodiment of the present disclosure.

[0041] exist Figure 3 In the example shown, the statistical analysis is performed by averaging. The average value of the initial running data can be set to 0. Figure 3As shown, the operating data of the current interval can be read in sequence (step S31), for example, the next operating data of the current interval (i.e., the newly read operating data) is read. Then, it is determined whether the previous average value (i.e., the average value before reading the new operating data) is 0 (step S32). If the previous average value is 0, the current average value is set to the value of the newly read operating data (step S37); otherwise, it is determined whether the newly read operating data is missing (step S33). If it is determined that the newly read operating data is missing, step S36 is executed to determine whether all the operating data of the current interval has been read. If it is determined that the newly read operating data is not missing, it is determined whether the value of the newly read operating data is 0 (step S34). If the value of the newly read operating data is 0, step S36 is executed; otherwise, step S35 is executed to set the current average value to: current average value = (previous average value + value of the newly read operating data) ÷ 2. In step S36, if it is determined that all the operating data of the current interval have been read, the statistical analysis of the current interval is terminated, and the statistical analysis of the operating data of the next interval can be continued. If it is determined that reading of all the operation data of the current interval has not been completed, step S31 is executed.

[0042] Can be combined Figure 4 To understand the example table in Figure 3 . Figure 4 : is an example table of operating data statistics of multiple intervals of a wind turbine generator set according to an embodiment of the present disclosure. Figure 4 As shown in FIG, the average value of the zero-deleted running data in each interval can be used as the running data statistical value of each interval. The more running data for statistical analysis, the more accurate the statistical value obtained.

[0043] Refer again Figure 2 According to an embodiment of the present disclosure, in step S13, a shutdown interval among multiple intervals may be identified based on the operating data statistics of each interval; and the shutdown interval may be determined as a fault triggering interval. A fault triggering interval may be an interval in which a fault protection mechanism is triggered (e.g., shutting down the wind turbine) due to a detected fault.

[0044] The following combination Figure 5 and Figure 6 An example is shown to explain in detail how to determine the fault triggering interval.

[0045] Figure 5 This graph shows the average pitch angle of a wind turbine generator set according to an embodiment of the present disclosure versus the ambient wind speed range. The ordinate represents the pitch angle (in degrees), and the abscissa represents the ambient wind speed range (in m / s). Each node on the graph represents the average pitch angle for the corresponding ambient wind speed range.

[0046] like Figure 5As shown, the maximum pitch angle is approximately 86 degrees. When the pitch angle reaches 86 degrees, the wind turbine is shut down. When the ambient wind speed is between 4m / s and 8m / s, the average pitch angle is larger, reaching a maximum of 86 degrees. When the ambient wind speed exceeds 22m / s, the average pitch angle is approximately 86 degrees, indicating that the wind turbine is shut down.

[0047] exist Figure 5 In the illustrated embodiment, the ambient wind speed intervals of 4 m / s to 8 m / s and 22 m / s to 32 m / s are the shutdown intervals of the wind turbine generator set, and the shutdown intervals can be identified as fault triggering intervals of the wind turbine generator set.

[0048] According to an embodiment of the present disclosure, the fault trigger interval may include a fault false trigger interval. When determining the fault trigger interval among the multiple intervals, an abnormal shutdown interval may be identified from the shutdown interval and determined as the fault false trigger interval.

[0049] Under normal operating conditions, the wind turbine should be operating and not shut down within the fault triggering interval of 4m / s to 8m / s. Therefore, within this interval, the wind turbine is likely to experience a high number of fault triggering events, leading to abnormal wind turbine shutdowns and, in other words, false fault triggering. Therefore, the fault triggering interval of 4m / s to 8m / s can be identified as a false fault triggering interval.

[0050] Figure 6 This graph shows the average rotor speed of a wind turbine generator set according to an embodiment of the present disclosure versus the ambient wind speed range. The ordinate represents the rotor speed (in m / s), and the abscissa represents the ambient wind speed range (in m / s). Each node on the graph represents the average rotor speed for the corresponding ambient wind speed range.

[0051] like Figure 6 As shown in the figure, when the ambient wind speed is between 4m / s and 8m / s, the average impeller speed is small and close to 0. When the ambient wind speed exceeds 22m / s, the impeller speed decreases rapidly to approach 0. When the impeller speed reaches 0, it indicates that the wind turbine is in the shutdown state.

[0052] exist Figure 6 In the illustrated embodiment, the ambient wind speed intervals of 4 m / s to 8 m / s and 22 m / s to 36 m / s are the shutdown intervals of the wind turbine generator set, and the shutdown intervals can be identified as fault triggering intervals of the wind turbine generator set.

[0053] According to an embodiment of the present disclosure, the fault trigger interval may include a fault false trigger interval. When determining the fault trigger interval among the multiple intervals, an abnormal shutdown interval may be identified from the shutdown interval and determined as the fault false trigger interval.

[0054] and Figure 5 The situation shown is similar. Under normal operating conditions, the wind turbine should be operating and not shut down within the fault triggering interval of 4 m / s to 8 m / s. Therefore, within the fault triggering interval of 4 m / s to 8 m / s, it is likely that the wind turbine will experience a high number of fault triggering events, leading to abnormal wind turbine shutdowns and, in other words, erroneous fault triggering. Therefore, the fault triggering interval of 4 m / s to 8 m / s can be identified as a false fault triggering interval.

[0055] Refer again Figure 2 In step S14, a fault trigger condition corresponding to the fault trigger interval may be obtained, and the fault trigger condition may include a fault trigger parameter; a statistical analysis is performed on the operating data of the wind turbine generator associated with the fault trigger condition to generate a statistical value of the operating data of the fault trigger interval; and based on the statistical value of the operating data of the fault trigger interval, the fault trigger parameter corresponding to the fault trigger interval is adjusted.

[0056] According to an embodiment of the present disclosure, a fault trigger condition may include a fault trigger parameter and may also have multiple judgment categories, as shown in Table 1. Referring to Table 1, a statistical analysis (e.g., averaging) of the operating data (e.g., the angle difference of the three blades) associated with the fault trigger condition (e.g., the angle difference of the three blades is greater than 3 degrees) of the wind turbine generator set may be performed to generate operating data statistics (e.g., average values) for a fault trigger interval (e.g., a fault trigger interval of 4 m / s to 8 m / s or 22 m / s to 36 m / s). Then, based on the operating data statistics for the fault trigger interval, the fault trigger parameter corresponding to the fault trigger interval may be adjusted (e.g., the current fault trigger parameter is 3 degrees).

[0057]

[0058]

[0059] Table 1

[0060] Figure 7 Another flow chart of a fault trigger adjustment method for a wind turbine generator set according to an embodiment of the present disclosure is provided. According to an embodiment of the present disclosure, a fault trigger parameter corresponding to the fault trigger interval may be adjusted based on whether the operating data statistics of the fault trigger interval are within a predetermined range of the fault trigger parameter (step S21).

[0061] For example, in response to the operating data statistics of the fault trigger interval being within the predetermined range of the fault trigger parameter, the operating data statistics of the fault trigger interval may be set to a new fault trigger parameter corresponding to the fault trigger interval (step S22). Alternatively, in response to the operating data statistics of the fault trigger interval being outside the predetermined range of the fault trigger parameter, the fault trigger parameter of the fault trigger interval may be maintained unchanged (step S23).

[0062] Furthermore, the corresponding fault trigger parameters can be adjusted in real time based on the fault trigger interval in which the wind turbine is currently operating. For example, if the operating data statistics for the fault trigger interval with an ambient wind speed of 4m / s to 8m / s are determined to be within a predetermined range of the fault trigger parameters, then if it is determined that the wind turbine is currently in the fault trigger interval with an ambient wind speed of 4m / s to 8m / s, the operating data statistics for that fault trigger interval are set as new fault trigger parameters corresponding to the fault trigger interval. Thus, by adjusting the fault trigger parameters for the fault trigger interval in real time, the number of fault triggers and / or false fault triggers of the wind turbine can be appropriately reduced.

[0063] According to an embodiment of the present disclosure, taking a "pitch position comparison fault" as an example, its fault trigger condition is "the angle difference between the three blades is greater than 3 degrees." That is, the fault trigger parameter is initially set to 3 degrees. The angle difference between the three blades can be the absolute value of the angle difference between the first and second blades, the absolute value of the angle difference between the third blade and the second blade, or the average, maximum, minimum, or median of the absolute values of the angle difference between the first and third blades.

[0064] By performing statistical analysis on the angle differences of the three blades in the fault triggering interval, a statistical value of the angle differences of the three blades in the fault triggering interval is obtained. For example, the average value of the angle differences of the three blades is 3.8 degrees.

[0065] In this embodiment, the predetermined range of the fault trigger parameter is set to be greater than the fault trigger parameter and the difference from the fault trigger parameter is less than or equal to 1 degree. Therefore, for the fault trigger interval, the fault trigger parameter corresponding to the "pitch position comparison fault" can be updated to 3.8 degrees. That is, the fault trigger condition for the fault trigger interval is updated to "the angle difference between the three blades is greater than 3.8 degrees." In this way, the number of fault triggers in the fault trigger interval can be appropriately reduced. If the average angle difference between the three blades is less than or equal to 3 degrees or greater than 4 degrees, the fault trigger parameter remains unchanged.

[0066] The fault trigger conditions can be adjusted for each fault trigger interval, so that different fault trigger intervals correspond to different fault trigger conditions. This can appropriately reduce the number of fault triggers, especially for fault false trigger intervals, by adjusting the fault trigger conditions to avoid false fault triggers.

[0067] In addition, for each fault trigger condition, the fault trigger parameter may be adjusted according to a predetermined range of the corresponding fault trigger parameter.

[0068] According to an embodiment of the present disclosure, taking the "pitch minimum angle exceeding limit fault" as an example, its fault trigger condition is "the blade angle is less than -3 degrees", that is, the fault trigger parameter is initially set to -3 degrees. By performing a statistical analysis on the minimum angles of the three blades in the fault trigger interval, the statistical values of the minimum angles of the three blades in the fault trigger interval are obtained. For example, the average value of the minimum angles of the three blades is -3.6 degrees. The minimum angles of the three blades can be the minimum value, maximum value, median value, or average value of the minimum angle of the first blade, the minimum angle of the second blade, and the minimum angle of the third blade.

[0069] In this embodiment, the predetermined range of the fault trigger parameter is set to be less than the fault trigger parameter and the difference from the fault trigger parameter is less than or equal to 1 degree. Therefore, for the fault trigger interval, the fault trigger parameter corresponding to the "pitch minimum angle exceeding limit fault" can be updated to -3.6 degrees, that is, the fault trigger condition of the fault trigger interval is updated to "blade angle less than -3.6 degrees." In this way, the number of fault triggers in the fault trigger interval can be appropriately reduced. If the average value of the minimum angles of the three blades is greater than or equal to -3 degrees or less than -4 degrees, the fault trigger parameter remains unchanged.

[0070] According to embodiments of the present disclosure, a predetermined range for a fault trigger parameter can be set based on the fault trigger condition corresponding to the fault trigger interval. For example, the predetermined range for the fault trigger parameter can be set based on the judgment category of the fault trigger condition. If the judgment category of the fault trigger condition is "greater than," an upper limit greater than the fault trigger parameter is set, and the range between the fault trigger parameter and the upper limit is determined as the predetermined range for the fault trigger parameter. If the judgment category of the fault trigger condition is "less than," a lower limit less than the fault trigger parameter is set, and the range between the fault trigger parameter and the lower limit is determined as the predetermined range for the fault trigger parameter. According to embodiments of the present disclosure, the upper or lower limit can be set based on the magnitude of the fault trigger parameter. For example, if the fault trigger parameter is 3 and the corresponding judgment category is "less than," the lower limit for the fault trigger parameter less than the fault trigger parameter can be set to 2, meaning it is slightly less than the fault trigger parameter. If the fault trigger parameter is 30 and the corresponding judgment category is "less than," the lower limit for the fault trigger parameter less than the fault trigger parameter can be set to 20, meaning it is significantly less than the fault trigger parameter.

[0071] As mentioned above, by partitioning the operating data of the wind turbine generator set, the operating data can be distributed and counted, so as to obtain the fault triggering intervals with a large number of faults. Based on the analysis results, the number of fault triggering times in each fault triggering interval can be counted, and the fault triggering parameters for the intervals with a large number of fault triggering times can be appropriately optimized.

[0072] According to an embodiment of the present disclosure, the operating data associated with multiple fault trigger conditions can be statistically analyzed simultaneously, that is, if there is a fault trigger interval with a large number of fault trigger times, and the corresponding statistical analysis results of multiple fault trigger conditions are close, the fault trigger parameters of each fault trigger condition can be optimized based on these statistical analysis results, thereby appropriately reducing the number of fault trigger times and / or the number of false fault triggers of the wind turbine without having to update the program in batches and reducing maintenance time.

[0073] Conversely, if we count the intervals corresponding to each fault moment, such as ambient wind speed, it is difficult to obtain accurate statistical intervals because ambient wind speed is transient. For example, at the moment of fault zero, the ambient wind speed could be 5m / s, 8m / s, or even reach 10m / s. Therefore, it is impossible to set the fault parameters based on the ambient wind speed interval.

[0074] However, the fault trigger adjustment method according to the embodiment of the present disclosure can achieve accurate data interval statistics based on big data statistics. In this way, when performing fault analysis, it is possible to avoid analyzing the interval corresponding to the historical fault (for example, the ambient wind speed interval) based on the triggered historical fault. Instead, big data statistics are performed based on the fault trigger conditions. This makes the statistical operating data more accurate and reliable, allowing for the setting of reasonable fault trigger parameters, thereby effectively ensuring the operational safety of the wind turbine generator set.

[0075] According to an embodiment of the present disclosure, a fault-triggered adjustment device capable of executing each operation in the fault-triggered adjustment method is also provided.

[0076] Figure 8 FIG2 is a schematic diagram of a fault-triggered adjustment device 1 for a wind turbine generator set according to an embodiment of the present disclosure. According to an embodiment of the present disclosure, the fault-triggered adjustment device 1 can be provided in a central control system (e.g., a central monitoring terminal) of a wind farm. The central control system is used to monitor and control the operation of each wind turbine generator set in the wind farm. The various units in the fault-triggered adjustment device 1 can be implemented using hardware or software modules in the central control system. Alternatively, the fault-triggered adjustment device 1 can be implemented by a control device (e.g., a master control system) in the wind turbine generator set.

[0077] like Figure 8As shown, the fault trigger adjustment device 1 may include a data partitioning unit 11, which is configured to partition the operating data of the wind turbine generator set to generate operating data of multiple intervals. The fault trigger adjustment device 1 may also include a statistical analysis unit 12, which is configured to perform statistical analysis on the operating data of each interval to generate a statistical value of the operating data of each interval. The fault trigger adjustment device 1 may also include a fault trigger interval determination unit 13, which is configured to determine a fault trigger interval among the multiple intervals based on the statistical value of the operating data of each interval. The fault trigger adjustment device 1 may also include a fault trigger condition adjustment unit 14, which is configured to adjust the fault trigger condition corresponding to the fault trigger interval.

[0078] Please refer to the above combination Figures 2 to 7 The fault-triggered adjustment method described is used to understand the specific details of the corresponding processing performed by the fault-triggered adjustment device 1 and its various units, which will not be described in detail here.

[0079] According to an embodiment of the present disclosure, there is also provided a computer-readable storage medium having a computer program stored thereon, which can realize reference to Figures 2 to 7 The fault trigger adjustment method described herein may, for example, perform the following steps: partitioning the operating data of a wind turbine generator set to generate operating data of multiple intervals; performing statistical analysis on the operating data of each interval to generate a statistical value of the operating data of each interval; determining a fault trigger interval among the multiple intervals based on the statistical value of the operating data of each interval; and adjusting the fault trigger condition corresponding to the fault trigger interval.

[0080] Computer-readable storage media can be, for example, but not limited to, electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices or components, or any combination thereof. More specific examples of computer-readable storage media can include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof. In embodiments of the present disclosure, a computer-readable storage medium can be any tangible medium that contains or stores a computer program that can be used by or in conjunction with an instruction execution system, device or component. The computer program contained on the computer-readable storage medium can be transmitted using any suitable medium, including but not limited to: wires, optical cables, RF (radio frequency), etc., or any suitable combination thereof. The computer-readable storage medium can be contained in any device; it can also exist independently without being incorporated into the device.

[0081] According to an embodiment of the present disclosure, a computing device is also provided. Figure 9 is a schematic diagram of a computing device 5 according to an embodiment of the present disclosure. Figure 9 is a schematic diagram of a computing device according to an embodiment of the present disclosure.

[0082] Reference Figure 9 According to an embodiment of the present disclosure, the computing device 5 may include a memory 51 and a processor 52. A computer program 53 is stored in the memory 51. When the computer program 53 is executed by the processor 52, the yaw control method according to an embodiment of the present disclosure is implemented. For example, the following steps may be performed: partitioning the operating data of the wind turbine generator set to generate operating data of multiple intervals; performing statistical analysis on the operating data of each interval to generate a statistical value of the operating data of each interval; determining a fault trigger interval among the multiple intervals based on the statistical value of the operating data of each interval; and adjusting the fault trigger condition corresponding to the fault trigger interval.

[0083] Figure 9 The computing device shown is only an example and should not bring any limitation to the functions and usage scope of the embodiments of the present disclosure.

[0084] The above has been referred to Figures 2 to 9 The present invention describes a method and apparatus for fault-triggered regulation of a wind turbine generator set, a computer-readable storage medium, and a computing device according to an embodiment of the present invention. However, it should be understood that: Figure 8 The fault trigger adjustment device and its various units shown in the figure can be configured as software, hardware, firmware or any combination of the above items to perform specific functions. Figure 9 The computing device shown in is not limited to including the components shown above, but some components may be added or deleted as needed, and the above components may also be combined.

[0085] By adopting the fault trigger adjustment method and device of a wind turbine generator set according to the embodiments of the present disclosure, at least one of the following technical effects can be achieved: based on big data analysis, the operating distribution of the wind turbine generator set and its various subsystems can be given, and possible cause analysis can be given in combination with the data, providing a data basis for understanding the operating conditions, operating characteristics, load distribution, etc. of the wind turbine generator set and its various subsystems (for example, the variable pitch system of the wind turbine generator set); the operating data and environmental data of the wind turbine generator set can be statistically analyzed in intervals, and different fault trigger parameters (also called protection parameters) can be executed in different intervals to adjust (for example, appropriately reduce) the number of fault triggers of the wind turbine generator set; among them, for intervals with a large number of fault triggers, the operating data related to the fault trigger parameters involved are statistically analyzed to obtain accurate and reliable statistical analysis data, so as to set reasonable fault trigger parameters, thereby effectively ensuring the operation of the wind turbine generator set. Safety; fault triggering adjustment can be performed for wind turbines of the same model to avoid frequent startup and shutdown of the wind turbines in a short period of time after a fault is falsely triggered; by partitioning the operating data of the wind turbines, the operating data can be distributed and statistically analyzed to obtain fault triggering intervals with a large number of faults. Based on the analysis results, the number of fault triggering intervals can be counted, and the fault triggering parameters can be appropriately optimized for the intervals with a large number of fault triggering intervals; the operating data associated with multiple fault triggering conditions can be statistically analyzed at the same time, that is, if there is a fault triggering interval with a large number of fault triggering times, and the corresponding statistical analysis results of multiple fault triggering conditions are close, the fault triggering parameters of each fault triggering condition can be optimized based on these statistical analysis results, thereby appropriately reducing the number of fault triggering times and / or the number of false fault triggering times of the wind turbines without having to update the program in batches and reducing maintenance time.

[0086] The control logic or function executed by each component or controller in the control system can be represented by a flow chart or similar diagram in one or more accompanying drawings. These drawings provide representative control strategies and / or logic, which can be implemented using one or more processing strategies (such as, event-driven, interrupt-driven, multi-tasking, multi-threading, etc.). Therefore, the various steps or functions shown can be executed in the order shown, performed in parallel, or omitted in some cases. Although not always clearly shown, it will be appreciated by those of ordinary skill in the art that the one or more steps or functions shown can be repeatedly executed according to the specific processing strategy used.

[0087] While the present disclosure has been shown and described with reference to preferred embodiments, it will be understood by those skilled in the art that various modifications and variations can be made in these embodiments without departing from the spirit and scope of the disclosure as defined by the appended claims.

Claims

1. A fault triggering adjustment method for a wind turbine generator set, characterized in that: The fault trigger adjustment method includes: Partitioning the operating data of the wind turbine generator set to generate operating data of multiple intervals; performing statistical analysis on the running data of each interval to generate a statistical value of the running data of each interval; determining a fault triggering interval among the plurality of intervals based on the operating data statistics of each interval; Adjust the fault trigger conditions corresponding to the fault trigger interval; The step of determining a fault triggering interval among the multiple intervals based on the operation data statistics of each interval includes: identifying a downtime interval among the plurality of intervals based on the operation data statistics of each interval; The shutdown interval is determined as a fault triggering interval.

2. The fault-triggered adjustment method according to claim 1, characterized in that: Adjusting the fault trigger conditions corresponding to the fault trigger interval includes: Acquire a fault trigger condition corresponding to the fault trigger interval, wherein the fault trigger condition includes a fault trigger parameter; Performing statistical analysis on operating data of the wind turbine generator set associated with the fault triggering condition to generate a statistical value of the operating data in the fault triggering interval; Based on the operating data statistics of the fault trigger interval, the fault trigger parameters corresponding to the fault trigger interval are adjusted.

3. The fault-triggered adjustment method according to claim 2, characterized in that: Based on the running data statistics of the fault trigger interval, adjusting the fault trigger parameters corresponding to the fault trigger interval includes: In response to the operating data statistics of the fault trigger interval being within a predetermined range of the fault trigger parameter, setting the operating data statistics of the fault trigger interval to a new fault trigger parameter corresponding to the fault trigger interval; and / or, In response to the running data statistic value of the fault trigger interval being outside the predetermined range of the fault trigger parameter, the fault trigger parameter of the fault trigger interval is kept unchanged.

4. The fault-triggered adjustment method according to claim 3, characterized in that: The fault trigger adjustment method further includes: setting a predetermined range of the fault trigger parameter based on a fault trigger condition corresponding to a fault trigger interval.

5. The fault-triggered adjustment method according to any one of claims 1 to 4, characterized in that: Statistical analysis is performed on the running data of each interval to generate running data statistics for each interval including: One of averaging, median, maximum, minimum, variance, and standard deviation is performed on the running data of each interval to generate a statistical value of the running data of each interval.

6. The fault-triggered adjustment method according to claim 5, characterized in that: Performing one of averaging, median, maximum, minimum, variance, and standard deviation on the running data of each interval to generate a statistical value of the running data of each interval includes: Removing zero running data from the running data of each interval to generate zero-removed running data of each interval; One of averaging, median, maximum, minimum, variance, and standard deviation is performed on the zero-deleted running data of each interval to generate a running data statistic value of each interval.

7. The fault-triggered adjustment method according to any one of claim 1, characterized in that: The fault triggering interval includes the fault false triggering interval, Determining the fault triggering interval among the multiple intervals based on the operation data statistics of each interval further includes: identifying an abnormal shutdown interval from the shutdown intervals; and determining the abnormal shutdown interval as a fault false triggering interval.

8. The fault-triggered adjustment method according to any one of claims 1 to 4, characterized in that: The fault trigger adjustment method further includes: partitioning the environmental data of the wind turbine generator set to generate environmental data of multiple intervals, Partitioning the operation data of the wind turbine generator set to generate operation data of multiple intervals includes partitioning the operation data based on the environmental data of the multiple intervals to generate operation data of multiple intervals respectively corresponding to the environmental data of the multiple intervals.

9. The fault-triggered adjustment method according to any one of claims 1 to 4, characterized in that: The operation data includes: first operation data and second operation data, Partitioning the operation data of the wind turbine generator set to generate operation data of multiple intervals includes: partitioning the first operation data to generate first operation data of multiple intervals; The second operating data is partitioned based on the first operating data of the plurality of intervals to generate second operating data of a plurality of intervals respectively corresponding to the first operating data of the plurality of intervals.

10. The fault-triggered adjustment method according to any one of claims 1 to 4, characterized in that: Partitioning the operating data of the wind turbine generator set to generate operating data of multiple intervals includes partitioning the operating data of the wind turbine generator set according to time intervals to generate operating data of multiple time intervals.

11. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the fault-triggered adjustment method according to any one of claims 1 to 10 is implemented.

12. A computing device, characterized in that: The computing device comprises: processor; The memory stores a computer program, and when the computer program is executed by the processor, the fault triggering adjustment method according to any one of claims 1 to 10 is implemented.

13. A fault triggering regulating device for a wind turbine generator set, characterized in that: The fault triggering adjustment device comprises: a data partitioning unit configured to partition the operation data of the wind turbine generator set to generate operation data of a plurality of intervals; a statistical analysis unit configured to perform statistical analysis on the operation data of each interval to generate a statistical value of the operation data of each interval; a fault trigger interval determining unit configured to determine a fault trigger interval among the plurality of intervals based on a statistical value of the operation data of each interval; a fault trigger condition adjustment unit, configured to adjust a fault trigger condition corresponding to the fault trigger interval; The fault triggering interval determining unit is configured to: identify a shutdown interval among the multiple intervals based on the operation data statistics of each interval; and determine the shutdown interval as the fault triggering interval.

14. The fault-triggered adjustment device according to claim 13, characterized in that: The fault trigger condition adjustment unit is configured as follows: Acquire a fault trigger condition corresponding to the fault trigger interval, wherein the fault trigger condition includes a fault trigger parameter; Performing statistical analysis on operating data of the wind turbine generator set associated with the fault triggering condition to generate a statistical value of the operating data in the fault triggering interval; Based on the operating data statistics of the fault trigger interval, the fault trigger parameters corresponding to the fault trigger interval are adjusted.

15. The fault-triggered adjustment device according to claim 14, characterized in that: The fault trigger condition adjustment unit is configured as follows: In response to a statistical value of the operating data of the fault trigger interval being within a predetermined range of the fault trigger parameter, setting the statistical value of the operating data of the fault trigger interval as a new fault trigger parameter corresponding to the fault trigger interval; and / or, In response to the running data statistic value of the fault trigger interval being outside the predetermined range of the fault trigger parameter, the fault trigger parameter of the fault trigger interval is kept unchanged.

16. The fault-triggered adjustment device according to claim 15, characterized in that: The fault trigger condition adjustment unit is further configured to set a predetermined range of the fault trigger parameter based on a fault trigger condition corresponding to a fault trigger interval.

17. The fault-triggered adjustment device according to any one of claims 13 to 16, characterized in that: The statistical analysis unit is configured to: One of averaging, median, maximum, minimum, variance, and standard deviation is performed on the running data of each interval to generate a statistical value of the running data of each interval.

18. The fault-triggered adjustment device according to claim 17, characterized in that: The statistical analysis unit is configured to: Removing zero running data from the running data of each interval to generate zero-removed running data of each interval; One of averaging, median, maximum, minimum, variance, and standard deviation is performed on the zero-deleted running data of each interval to generate a running data statistic value of each interval.

19. The fault-triggered adjustment device according to any one of claims 13, characterized in that: The fault triggering interval includes the fault false triggering interval, The fault triggering interval determining unit is configured to: identify an abnormal shutdown interval from the shutdown interval; and determine the abnormal shutdown interval as a fault false triggering interval.

20. The fault-triggered adjustment device according to any one of claims 13 to 16, characterized in that: The data partitioning unit is further configured to: partition the environmental data of the wind turbine generator set to generate environmental data of multiple intervals; partition the operating data based on the environmental data of the multiple intervals to generate operating data of multiple intervals corresponding to the environmental data of the multiple intervals respectively.

21. The fault-triggered adjustment device according to any one of claims 13 to 16, characterized in that: The operation data includes: first operation data and second operation data, The data partitioning unit is configured to: partition the first operating data to generate first operating data of multiple intervals; partition the second operating data based on the first operating data of the multiple intervals to generate second operating data of multiple intervals respectively corresponding to the first operating data of the multiple intervals.

22. The fault-triggered adjustment device according to any one of claims 13 to 16, characterized in that: The data partitioning unit is configured to partition the operation data of the wind turbine generator set according to time intervals to generate operation data of multiple time intervals.

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