CN115045803A - Fault trigger adjusting method and device for wind generating set - Google Patents
Fault trigger adjusting method and device for wind generating set Download PDFInfo
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F03—MACHINES OR ENGINES FOR LIQUIDS; WIND, SPRING, OR WEIGHT MOTORS; PRODUCING MECHANICAL POWER OR A REACTIVE PROPULSIVE THRUST, NOT OTHERWISE PROVIDED FOR
- F03D—WIND MOTORS
- F03D17/00—Monitoring or testing of wind motors, e.g. diagnostics
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F03—MACHINES OR ENGINES FOR LIQUIDS; WIND, SPRING, OR WEIGHT MOTORS; PRODUCING MECHANICAL POWER OR A REACTIVE PROPULSIVE THRUST, NOT OTHERWISE PROVIDED FOR
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- Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
- Y02E10/00—Energy generation through renewable energy sources
- Y02E10/70—Wind energy
- Y02E10/72—Wind turbines with rotation axis in wind direction
Abstract
The disclosure provides a fault trigger adjusting method and device of a wind generating set. The fault trigger adjustment method comprises the following steps: partitioning the operation data of the wind generating set to generate operation data of a plurality of intervals; performing statistical analysis on the operating data of each interval to generate an operating data statistical value of each interval; determining a fault trigger interval in the plurality of intervals based on the running data statistic value of each interval; and adjusting the fault trigger condition corresponding to the fault trigger interval. The fault trigger adjustment method can optimize fault monitoring of the wind generating set.
Description
Technical Field
The disclosure relates to the field of wind power generation, in particular to a fault trigger adjusting method and device of a wind generating set.
Background
The wind generating set can realize the process of converting wind energy into mechanical energy and the process of converting mechanical energy into electric energy. For example, the impeller system can realize energy conversion from wind energy to mechanical energy, the generator and the control system can realize energy conversion from mechanical energy to electric energy, and the control system can realize functions of normal operation control, parameter monitoring and monitoring, safety protection and processing and the like when the control target of the control system of the wind generating set is considered.
The design and implementation of the control system of the wind generating set aim at meeting the requirements of unattended operation, automatic operation, state control and detection of the wind generating set. However, the fault monitoring of the wind generating set easily causes the number of times of the unit shutdown to be excessive, and even the unit shutdown is mistakenly caused by wrong fault identification, so that the problems of unstable operation, unsafe operation and the like of the wind generating set occur.
Disclosure of Invention
An object of the embodiments of the present disclosure is to provide a method and an apparatus for adjusting fault triggering of a wind turbine generator system, so as to overcome the deficiencies in the prior art, at least optimize fault monitoring of the wind turbine generator system, adjust fault triggering conditions of the wind turbine generator system in a partitioned manner, and ensure safe operation of the wind turbine generator system.
According to an embodiment of the present disclosure, there is provided a fault trigger adjustment method of a wind turbine generator system, the fault trigger adjustment method including: partitioning the operation data of the wind generating set to generate operation data of a plurality of intervals; performing statistical analysis on the operating data of each interval to generate an operating data statistic for each interval; determining a fault triggering interval in the plurality of intervals based on the running data statistic of each interval; and adjusting the fault trigger condition corresponding to the fault trigger interval.
According to an embodiment of the present disclosure, there is provided a computer-readable storage medium storing a computer program which, when executed by a processor, implements the fail-over adjustment method as described above.
According to an embodiment of the present disclosure, there is provided a computing device including: a processor; a memory storing a computer program which, when executed by the processor, implements the fail-over adjustment method as described above.
According to an embodiment of the present disclosure, there is provided a fault trigger adjustment device of a wind turbine generator system, the fault trigger adjustment device including: a data partitioning unit configured to partition the operation data of the wind turbine generator set to generate a plurality of intervals of operation data; a statistical analysis unit configured to perform statistical analysis on the operation data of each section to generate an operation data statistic for each section; a fault trigger section determination unit configured to determine a fault trigger section among the plurality of sections based on the operation data statistics for each section; a fault trigger condition adjustment unit configured to adjust a fault trigger condition corresponding to the fault trigger section.
According to an embodiment of the present disclosure, there is provided a computer-readable storage medium storing a computer program which, when executed by a processor, implements the fail-over adjustment method as described above.
According to an embodiment of the present disclosure, there is provided a computing device including: a processor; a memory storing a computer program which, when executed by the processor, implements the fail-over adjustment method as described above.
By adopting the fault trigger adjusting method and the fault trigger adjusting device for the wind generating set according to the embodiment of the disclosure, at least one of the following technical effects can be realized: the operation distribution condition of the wind generating set and each subsystem thereof can be given based on big data analysis, possible reason analysis is given by combining the data, and a data basis is provided for solving the operation condition, the operation characteristic, the load distribution and the like of the wind generating set and each subsystem thereof (such as a variable pitch system of the wind generating set); the method can perform statistical analysis on the operation data and the environment data of the wind generating set in different regions, and execute different fault triggering parameters (also called protection parameters) in different regions so as to adjust (for example, properly reduce) the fault triggering times of the wind generating set; in the interval with more fault triggering times, statistical analysis is carried out on the operation data related to the related fault triggering parameters so as to determine reasonable fault triggering parameters; the fault triggering adjustment can be carried out on the wind generating sets of the same type, so that the wind generating sets are prevented from being frequently started and stopped in a short time after the fault is triggered by mistake; by partitioning the operation data of the wind generating set, the distribution statistics can be carried out on the operation data, so that fault trigger intervals with more fault times are obtained, the fault trigger times of each fault trigger interval can be counted according to the analysis result, and fault trigger parameters can be optimized appropriately for the intervals with more fault trigger times; the operation data associated with the multiple fault triggering conditions can be simultaneously statistically analyzed, that is, if a fault triggering interval with a large number of fault triggering times exists and the corresponding statistical analysis results of the multiple fault triggering conditions are close to each other, the fault triggering parameters of each fault triggering condition can be optimized according to the statistical analysis results, so that the fault triggering times and/or the fault false triggering times of the wind driven generator can be appropriately reduced under the conditions that the program does not need to be updated in batches and the maintenance time is reduced.
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The above and other objects and features of the present disclosure will become more apparent from the following description when taken in conjunction with the accompanying drawings.
FIG. 1 is a control block diagram of a pitch system of a wind park according to an embodiment of the present disclosure;
FIG. 2 is a flow chart of a method of fault triggered regulation of a wind park according to an embodiment of the present disclosure;
FIG. 3 is another flow chart of a method of fault-triggered regulation of a wind park according to an embodiment of the present disclosure;
FIG. 4 is an example table of operating data statistics for multiple intervals of a wind turbine generator set according to an embodiment of the present disclosure;
FIG. 5 is a graph of a mean value of pitch angle of a wind park according to an embodiment of the present disclosure versus an ambient wind speed interval;
FIG. 6 is a graph of an average impeller speed versus an interval of ambient wind speeds for a wind turbine generator set according to an embodiment of the present disclosure;
FIG. 7 is another flow chart of a method of fault-triggered regulation of a wind park according to an embodiment of the present disclosure;
FIG. 8 is a schematic view of a fault triggered regulating device of a wind park according to an embodiment of the present disclosure;
fig. 9 is a schematic diagram of a computing device according to an embodiment of the present disclosure.
Detailed Description
The normal operation control of the wind generating set comprises set starting control, stop control, grid connection control, variable speed control, constant power operation control and the like. The parameter monitoring and controlling comprises the monitoring and controlling of parameters such as power grid parameters (such as power grid voltage and frequency, generator output current, power and power factor and the like), environment parameters (such as wind speed, wind direction, environment temperature and the like), component temperature (including generator winding temperature, gear bearing temperature, control cabinet temperature, cabin temperature and the like), converter parameters (such as converter torque, converter ventilation opening temperature and the like), cabin vibration conditions, cable winding conditions, pressure and the like. When a fault occurs inside or outside the wind turbine, or when an emergency situation occurs due to the monitored parameters exceeding limit values, or when the control system fails, or when the wind turbine cannot be kept working within a normal operating range, the safety protection system should be activated to maintain the wind turbine in a safe state. The wind generating set can be stopped when the fault is caused by the problem of the non-set, and after the fault phenomenon is eliminated, the wind generating set executes the automatic software resetting function and enters a standby state.
The basic method and steps for parameter optimization of a wind turbine generator set may generally comprise: 1) counting the frequency of field faults, and performing information combing on the conditions of multiple triggering and multi-unit triggering; 2) checking a unit fault file, or manually acquiring data, confirming a fault reason, or modifying a test program so as to capture fault data; 3) after reasons are found according to the data, the test program is modified and perfected; 4) and sending the new test program to a project site, and updating the test programs in batches.
Among the above steps, especially the time consumed by the step 2) and the step 4) is long, and the total time is about 1 to 2 weeks; for step 2), on one hand, partial data cannot be captured immediately (if the duration of abnormal conditions is short), sometimes the data needs to be captured for a long time manually, and time is consumed for capturing the data and analyzing reasons; in the process, workload caused by analyzing, checking and checking data is generated; on the other hand, the test procedure is modified for testing, and the test period has uncertainty, generally at least 3-5 days; for step 4), it also takes a long time and a large amount of personnel work to issue program processes and update programs in batches in the wind power plant, and during this period, the wind generating set may trigger similar faults and stop.
In order to overcome the defects of the prior art (for example, fault detection of the wind generating set is optimized), the disclosure provides a fault trigger adjusting method and device of the wind generating set, which can provide the operation distribution condition of the wind generating set and each subsystem thereof based on big data analysis, provide possible reason analysis by combining data, and provide data basis for solving the operation condition, operation characteristic, load distribution and the like of the wind generating set and each subsystem thereof (for example, a variable pitch system of the wind generating set).
According to the embodiment of the disclosure, the fault trigger adjusting method and device can perform inter-partition statistical analysis on the operation data and the environmental data of the wind generating set, and execute different fault trigger parameters (also referred to as protection parameters) in different intervals so as to adjust (for example, appropriately reduce) the fault trigger times of the wind generating set; in the interval with more fault triggering times, statistical analysis is carried out on the operation data related to the related fault triggering parameters so as to determine reasonable fault triggering parameters.
The fault trigger adjusting method and the fault trigger adjusting device can be used for carrying out fault trigger adjustment on the wind generating sets of the same type, and the wind generating sets are prevented from being frequently started and stopped in a short time after faults are triggered by mistake.
Fig. 1 is a control block diagram of a pitch system of a wind park according to an embodiment of the present disclosure. The control logic of the control block diagram of the pitch system shown in fig. 1 is: in a normal pitch adjustment mode, the main control system 101 sends a required pitch speed command to the pitch controller 103 through the communication unit 102 (e.g., 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 sends an enable signal at the same time, so as to drive the pitch motor 105 to operate and realize a blade pitch adjustment function. The relevant operation mechanism of the variable pitch system is as follows: when the wind speed is higher than the rated rotating speed (or the wind speed is increased in the limited power operation state), a variable propeller system of the wind generating set can execute propeller adjustment, and when the variable propeller motor 105 operates to adjust the propeller, the temperature of the motor can be increased, and the voltage of a backup power supply of the variable propeller system can be reduced; meanwhile, after the wind speed is increased, the rotating speed of an impeller of the wind generating set is increased, and the propeller adjusting speed is also increased.
The fault trigger adjusting method and device disclosed by the invention can be used for carrying out big data analysis on the operation data of the wind generating set based on the operation mechanism of the variable pitch system so as to obtain the operation state of the wind generating set and a subsystem (such as the variable pitch system) of the wind generating set, and carrying out proper optimization on related parameters (such as fault trigger parameters) based on the analysis result.
The fault trigger adjustment method and apparatus of a wind turbine generator set according to the present disclosure will be illustrated below with reference to the accompanying drawings, but the present disclosure is not limited to the described embodiments.
Fig. 2 is a flow chart of a method of fault triggered regulation of a wind park according to an embodiment of the present disclosure. In embodiments of the present disclosure, operational data may be obtained for a wind park, for example, from a data acquisition and monitoring control system (SCADA system) of the wind park. In addition, environmental data of the wind generating set can be obtained. The operational data may include, but is not limited to, impeller speed, pitch angle, generator speed, backup power supply voltage of the pitch system; environmental data may include, but is not limited to, ambient wind speed, ambient temperature, ambient humidity. According to the embodiment of the disclosure, fault trigger adjustment can be performed for the wind generating sets of the same model (for example, MW-class wind generating sets), and therefore, operation data can be acquired for the wind generating sets of the same model.
As shown in fig. 2, the fault trigger adjustment method may include: partitioning the operation data of the wind generating set to generate a plurality of intervals of operation data (step S11); performing statistical analysis on the operation data of each interval to generate an operation data statistical value of each interval (step S12); determining a fault triggering interval among the plurality of intervals based on the operation data statistics of each interval (step S13); the fail-over condition corresponding to the fail-over interval is adjusted (step S14).
In step S11, the operating data of the wind turbine generator set may be partitioned by time interval to generate operating data for a plurality of time intervals.
The fault trigger adjustment method may further include: the environmental data of the wind turbine generator set is partitioned to generate a plurality of intervals of environmental data (e.g., ambient wind speed, ambient temperature, etc.). Alternatively, in step S11, the operation data may be partitioned based on the plurality of sections of environment data to generate a plurality of sections of operation data respectively corresponding to the plurality of sections of environment data.
According to embodiments of the present disclosure, the operational data of the wind turbine generator system may include: first operational data and second operational data. Optionally, in step S11, the first operation data may be partitioned to generate a plurality of intervals of the first operation data; the second operation data is partitioned based on the first operation data of the plurality of sections to generate second operation data of the plurality of sections corresponding to the first operation data of the plurality of sections, respectively.
As described above, by dividing the operation data into sections, the operation data of each section can be statistically analyzed, and the statistical result can be approximated to the real data by analyzing the mass data, ignoring the accidental sample value. For example, when the maximum value of the three-blade angle difference is counted, a large jump of the encoder may occur, for example, a jump from 10 degrees to 30 degrees, but in the case of a large amount of data statistical analysis, the accidental jump value may be averaged, and thus the final statistical result approaches to an accurate and reliable result. For example, the data jump variable is 20 degrees, but in 10 ten thousand data files, the average value influence generated by the equivalent jump is only 20/100000-0.0002, which is basically negligible, and the influence has almost no influence on the regulation of the fault triggering condition and the operation safety of the wind driven generator.
According to an embodiment of the present disclosure, the ambient wind speed of the wind turbine generator set may be partitioned to generate a plurality of intervals of ambient wind speed. Then, the operational data is partitioned based on the plurality of sections of ambient wind speeds to generate a plurality of sections of operational data corresponding to the plurality of sections of ambient wind speeds, respectively. For example, the operation data may be partitioned into corresponding ambient wind speed intervals according to the ambient wind speed intervals corresponding to the operation data in time series.
At step S12, a statistical analysis is performed on the operating data for each interval to generate operating data statistics for each interval. In an embodiment of the present disclosure, the method of statistical analysis includes, but is not limited to, one of averaging, median, maximum, minimum, variance, standard deviation. Thus, one of averaging, median, maximum, minimum, variance, and standard deviation may be performed on the operational data for each interval to generate operational data statistics for each interval. In this way, the actual value of the operation data of each interval can be approximately estimated by using the operation data statistical value of each interval of the massive operation data, and the operation condition of each interval is analyzed from a plurality of data dimensions. For example, a maximum pitch angle value of each section, a minimum ambient temperature value of each section, and a minimum backup power supply voltage value of the pitch system of each section may be obtained.
Optionally, before performing the statistical analysis, zero operating data in the operating data of each interval may be removed to generate the zero-removed operating data of each interval. Then, one of averaging, median, maximum, minimum, variance, and standard deviation is performed on the zeroed running data of each interval to generate a running data statistic for each interval. In embodiments of the present disclosure, the run data is zero, which may indicate that the run data is meaningless. Therefore, the zero operation data can be removed. In addition, missing operational data may also be ignored.
According to the embodiment of the disclosure, for the operation data of each interval, the operation data can be sequentially read to obtain the operation data statistic value of each interval. Fig. 3 is another flow chart of a method of fault-triggered regulation of a wind park according to an embodiment of the present disclosure.
In the example shown in fig. 3, the statistical analysis by averaging is taken as an example for explanation. The average of the initial running data may be set to 0. As shown in fig. 3, the operation data of the current section may be sequentially read (step S31), for example, the next operation data of the current section (i.e., the newly read operation data) may be read. Then, it is determined whether the previous average (i.e., the average before reading the new operation data) is 0 (step S32). If the previous average value is 0, setting the current average value to the value of the new read operation data (step S37); otherwise, it is determined whether the newly read operation data is missing (step S33). If it is determined that the newly read operation data is missing, step S36 is performed to determine whether the reading of all operation data of the current span is completed. If it is determined that the newly read operation data is not missing, it is determined whether the value of the newly read operation data is 0 (step S34). If the value of the newly read operation data is 0, performing step S36; otherwise, step S35 is executed to set the current average value as: the current average value is (previous average value + value of new read run data) ÷ 2. In step S36, if it is determined that all the operation data of the current interval are completely read, the statistical analysis of the current interval is ended, and the statistical analysis of the operation data of the next interval may be continued. If it is determined that all the operation data reading of the current section has not been completed, step S31 is performed.
The example of fig. 3 may be understood in conjunction with the example table of fig. 4. FIG. 4 is an example table of operating data statistics for multiple intervals of a wind turbine generator set according to an embodiment of the present disclosure. As shown in fig. 4, the average of the zeroed operation data of each interval may be used as the operation data statistic for each interval. The more the statistical analysis is performed, the more accurate the statistical value is obtained.
Referring again to fig. 2, according to an embodiment of the present disclosure, in step S13, a shutdown interval of a plurality of intervals may be identified based on the operation data statistics of each interval; and determining the shutdown interval as a fault triggering interval. The fault trigger interval may represent an interval in which the wind park triggers a fault protection mechanism (e.g. shuts down the wind park) due to the detection of a fault.
How to determine the fault trigger interval is explained in detail below in connection with the examples shown in fig. 5 and 6.
FIG. 5 is a graph of a mean value of pitch angle of a wind park according to an embodiment of the disclosure versus an ambient wind speed interval. The ordinate is the variable pitch angle (unit: degree), the abscissa is the environment wind speed interval (unit: m/s), and each node on the curve represents the average value of the variable pitch angle of the corresponding environment wind speed interval.
As shown in FIG. 5, the maximum value of the pitch angle is about 86 degrees, and when the pitch angle reaches 86 degrees, the wind generating set is stopped. When the ambient wind speed is 4m/s to 8m/s, the average value of the variable pitch angle is larger, and the maximum value reaches 86 degrees. When the ambient wind speed exceeds 22m/s, the average value of the pitch angle is about 86 degrees, and the wind generating set is in a stop state.
In the embodiment shown in fig. 5, the ambient wind speed interval 4m/s to 8m/s, 22m/s to 32m/s is a shutdown interval of the wind turbine generator set, which may be identified as a fault triggering interval of the wind turbine generator set.
According to embodiments of the present disclosure, the fault trigger interval may comprise a fault false trigger interval. When the fault trigger interval in the plurality of intervals is determined, the shutdown abnormal interval can be identified from the shutdown intervals, and the shutdown abnormal interval is determined as the fault false trigger interval.
According to the normal working state, in the fault triggering interval of 4m/s to 8m/s, the wind generating set is in the working state and is not switched off and stopped. Therefore, in the fault triggering interval of 4m/s to 8m/s, the fault triggering frequency of the wind generating set is likely to be more, so that the wind generating set stops abnormally, namely, the fault is triggered by mistake. Therefore, the fault trigger interval of 4m/s to 8m/s can be identified as a fault false trigger interval.
FIG. 6 is a graph of an average impeller speed versus an interval of ambient wind speeds for a wind turbine generator set, according to an embodiment of the disclosure. The ordinate is the impeller rotation speed (unit: m/s), the abscissa is the ambient wind speed interval (unit: m/s), and each node on the curve represents the impeller rotation speed average value of the corresponding ambient wind speed interval.
As shown in FIG. 6, when the ambient wind speed is 4m/s to 8m/s, the average value of the impeller rotation speed is small, close to 0. When the ambient wind speed exceeds 22m/s, the impeller speed rapidly decreases to approach 0. When the impeller rotating speed becomes 0, the wind generating set can be indicated to be in a stop state.
In the embodiment shown in fig. 6, the ambient wind speed interval 4m/s to 8m/s, 22m/s to 36m/s is a shutdown interval of the wind generating set, which may be identified as a fault triggering interval of the wind generating set.
According to embodiments of the present disclosure, the fault trigger interval may comprise a fault false trigger interval. When the fault trigger interval in the plurality of intervals is determined, the shutdown abnormal interval can be identified from the shutdown intervals, and the shutdown abnormal interval is determined as the fault false trigger interval.
Similar to the situation shown in fig. 5, in terms of normal operating conditions, within a fault triggering interval of 4m/s to 8m/s, the wind park should be in operation and should not be switched out of operation. Therefore, in the fault triggering interval of 4m/s to 8m/s, the fault triggering times of the wind generating set are likely to be more, so that the wind generating set stops abnormally, namely, faults are triggered mistakenly. Therefore, the fault trigger interval of 4m/s to 8m/s can be identified as a fault false trigger interval.
Referring again to fig. 2, in step S14, a fault trigger condition corresponding to the fault trigger interval may be acquired, and the fault trigger condition may include a fault trigger parameter; performing statistical analysis on the operation data of the wind generating set, which are associated with the fault triggering condition, so as to generate an operation data statistical value of a fault triggering interval; and adjusting the fault trigger parameters corresponding to the fault trigger intervals based on the running data statistic values of the fault trigger intervals.
According to an embodiment of the present disclosure, the fault triggering condition may include a fault triggering parameter, and may further have a plurality of judgment categories, as shown in table 1. Referring to table 1, the operational data (e.g., the angular differences of the three blades) of the wind turbine generator set associated with the fault triggering condition (e.g., the angular differences of the three blades are greater than 3 degrees) may be statistically analyzed (e.g., averaged) to generate a statistical value (e.g., an average value) of the operational data for a fault triggering interval (e.g., a fault triggering interval of 4m/s to 8m/s or 22m/s to 36 m/s). Then, the fault trigger parameter corresponding to the fault trigger interval may be adjusted based on the running data statistics for the fault trigger interval (e.g., the current fault trigger parameter is 3 degrees).
TABLE 1
Fig. 7 is another flow chart of a method of fault-triggered regulation of a wind park according to an embodiment of the present disclosure. According to the embodiment of the present disclosure, the fault trigger parameter corresponding to the fault trigger interval may be adjusted according to whether the operation data statistic of the fault trigger interval is within the predetermined range of the fault trigger parameter (step S21).
For example, the operation data statistics for the failed trigger interval may be set to the new failed trigger parameters corresponding to the failed trigger interval in response to the operation data statistics for the failed trigger interval being within the predetermined range of the failed trigger parameters (step S22). Alternatively, the failure trigger parameters of the failure trigger interval may be kept unchanged in response to the operating data statistics of the failure trigger interval being outside of the predetermined range of the failure trigger parameters (step S23).
In addition, the corresponding fault trigger parameters can be adjusted in real time according to the current fault trigger interval in which the wind generating set operates. For example, in the case that the operation data statistic value of the fault trigger interval with the ambient wind speed of 4m/s to 8m/s is determined to be within the preset range of the fault trigger parameter, if the wind generating set is determined to be currently in the fault trigger interval with the ambient wind speed of 4m/s to 8m/s, the operation data statistic value of the fault trigger interval is set as a new fault trigger parameter corresponding to the fault trigger interval. Therefore, the fault triggering times and/or fault false triggering times of the wind generating set are/is properly reduced by adjusting the fault triggering parameters of the fault triggering interval in real time.
According to the embodiment of the disclosure, taking "comparing the pitch position with the fault" as an example, the fault triggering condition is that "the angle difference between the three blades is greater than 3 degrees", that is, the fault triggering parameter is initially set to 3 degrees. The angular difference value of the three blades may be an average value, a maximum value, a minimum value, or a median value among the angular difference absolute values of the first blade and the second blade, the angular difference absolute value of the third blade and the second blade, the angular difference absolute values of the first blade and the third blade.
The statistical values of the angle differences of the three blades in the fault triggering interval are obtained by performing statistical analysis on the angle differences of the three blades in the fault triggering interval, for example, the average value of the angle differences of the three blades is 3.8 degrees.
In this embodiment, the predetermined range of the false trigger parameter is set to be greater than the false trigger parameter and less than or equal to 1 degree from the false trigger parameter. 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 of the fault trigger interval is updated to "the angle difference between the three blades is greater than 3.8 degrees". Thus, the number of times of false triggering in the false triggering section can be appropriately reduced. If the average of the angular differences of the three blades is less than or equal to 3 degrees or greater than 4 degrees, the fault triggering parameter is kept unchanged.
The fault trigger conditions may be adjusted for each fault trigger interval such that different fault trigger intervals correspond to different fault trigger conditions. The fault triggering frequency can be properly reduced, and particularly for a fault false triggering interval, the fault false triggering can be avoided by adjusting a fault triggering condition.
In addition, the fault trigger parameters may also be adjusted for each fault trigger condition according to a predetermined range of the corresponding fault trigger parameter.
According to the embodiment of the disclosure, taking the pitch minimum angle overrun fault as an example, the fault triggering condition is that the blade angle is smaller than-3 degrees, that is, the fault triggering parameter is initially set to-3 degrees. Through statistical analysis of the minimum angles of the three blades in the fault triggering interval, a statistical value of the minimum angles of the three blades in the fault triggering interval is obtained, for example, the average value of the minimum angles of the three blades is-3.6 degrees. Wherein the minimum angle of the three blades may be a minimum value, a maximum value, a median value, or an average value among the minimum angle of the first blade, the minimum angle of the second blade, and the minimum angle of the third blade.
In this embodiment, the predetermined range of the false trigger parameter is set to be less than the false trigger parameter and less than or equal to 1 degree from the false trigger parameter. Therefore, for the fault trigger interval, the fault trigger parameter corresponding to the "minimum pitch angle overrun fault" can be updated to-3.6 degrees, that is, the fault trigger condition of the fault trigger interval is updated to "the blade angle is smaller than-3.6 degrees". Thus, the number of times of false triggering in the false triggering section can be appropriately reduced. If the average of the minimum angles of the three blades is greater than or equal to-3 degrees or less than-4 degrees, the fault triggering parameter is kept unchanged.
According to an embodiment of the present disclosure, a predetermined range of the fail-over parameter may be set based on a fail-over condition corresponding to the fail-over interval. For example, the predetermined range of the false trigger parameter may be set according to the judgment category of the false trigger condition. If the judgment category of the fault triggering condition is 'greater than', an upper limit greater than the fault triggering parameter is set, and the range between the fault triggering parameter and the upper limit is determined as a preset range of the fault triggering parameter. If the judgment category of the fault triggering condition is 'less than', setting a lower limit less than the fault triggering parameter, and determining the range between the fault triggering parameter and the lower limit as the preset range of the fault triggering parameter. According to embodiments of the present disclosure, the upper or lower limit may be set according to the size of the fault triggering parameter. For example, if the fault triggering parameter is 3 and the corresponding judgment category is "less than", the lower limit less than the fault triggering parameter may be set to 2, that is, slightly less than the fault triggering parameter; if the false trigger parameter is 30 and the corresponding decision category is "less than", the lower limit less than the false trigger parameter may be set to 20, i.e. more than the false trigger parameter.
As described above, by partitioning the operation data of the wind turbine generator system, the operation data can be subjected to distribution statistics, so that fault trigger intervals with a large number of fault times can be obtained, the fault trigger times of each fault trigger interval can be counted according to the analysis result, and fault trigger parameters can be optimized appropriately for the intervals with the large number of fault trigger times.
According to the embodiment of the disclosure, the operation data associated with the 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 the multiple fault triggering conditions are close to each other, the fault triggering parameters of each fault triggering condition can be optimized according to the statistical analysis results, so that the number of fault triggering times and/or the number of fault false triggering times of the wind turbine generator can be appropriately reduced without updating programs in batch and reducing maintenance time.
On the contrary, if the interval corresponding to each fault time is counted, for example, the ambient wind speed, since the ambient wind speed is transient, it is difficult to obtain an accurate counted interval. For example, when the fault is 0, the ambient wind speed may be 5m/s, may be 8m/s, or may reach an instantaneous value of 10m/s, and therefore, the setting of the fault parameter in the sub-regions according to the ambient wind speed region cannot be realized.
However, according to the fault trigger adjustment method of the embodiment of the present disclosure, accurate data interval statistics may be implemented based on big data statistics. Therefore, when fault analysis is carried out, the condition that the interval (for example, the environment wind speed interval) corresponding to the historical fault is analyzed according to the triggered historical fault can be avoided, big data statistics is carried out according to the fault triggering condition, the statistical operation data can be more accurate and reliable, reasonable fault triggering parameters are set, and the operation safety of the wind generating set is effectively guaranteed.
According to an embodiment of the present disclosure, there is also provided a fail-safe adjusting apparatus capable of performing each operation in the fail-safe adjusting method.
Fig. 8 is a schematic view of a fault-triggered adjusting device 1 of a wind park according to an embodiment of the present disclosure. According to an embodiment of the present disclosure, the fail-safe regulating device 1 may be provided in a central control system (e.g., a central monitoring terminal) of the wind farm. The central control system is used for monitoring and controlling the operation of each wind generating set in the wind power plant. The individual units in the fail-safe regulating device 1 can be implemented by means of hardware or software modules in a central control system. Alternatively, the fault triggering adjustment device 1 may be implemented by a control device (e.g., a master control system) in the wind turbine generator set.
As shown in fig. 8, the fault triggering adjustment device 1 may comprise a data partitioning unit 11 configured to partition the operational data of the wind park to generate operational data of a plurality of intervals. The fault-triggered adjusting device 1 may further comprise a statistical analysis unit 12 configured to perform a statistical analysis on the operation data of each interval to generate an operation data statistic for each interval. The fault trigger adjustment device 1 may further comprise a fault trigger interval determination unit 13 configured to determine a fault trigger interval of the plurality of intervals based on the running data statistics for each interval. The fault trigger adjusting device 1 may further comprise a fault trigger condition adjusting unit 14 configured to adjust a fault trigger condition corresponding to the fault trigger interval.
The specific details of the corresponding processing performed by the fault trigger adjusting apparatus 1 and each unit thereof can be understood by referring to the fault trigger adjusting method described above with reference to fig. 2 to 7, which are not described herein again.
There is also provided, in accordance with an embodiment of the present disclosure, a computer-readable storage medium having stored thereon a computer program which, when executed, may implement the fail-over adjustment method described with reference to fig. 2 to 7, for example, the following steps may be performed: partitioning the operation data of the wind generating set to generate operation data of a plurality of intervals; performing statistical analysis on the operating data of each interval to generate an operating data statistic for each interval; determining a fault trigger interval in the plurality of intervals based on the running data statistic value of each interval; and adjusting fault trigger conditions corresponding to the fault trigger intervals.
A computer readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples of the computer readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer diskette, 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 disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In embodiments of the disclosure, a computer readable storage medium may be any tangible medium that can contain, or store a computer program for use by or in connection with an instruction execution system, apparatus, or device. The computer program embodied on the computer readable storage medium may be transmitted using any appropriate medium, including but not limited to: electrical wires, optical cables, RF (radio frequency), etc., or any suitable combination of the foregoing. The computer readable storage medium may be embodied in any device; or may be present alone without being assembled into the device.
According to an embodiment of the present disclosure, there is also provided a computing device, and fig. 9 is a schematic diagram of the computing device 5 according to an embodiment of the present disclosure. Fig. 9 is a schematic diagram of a computing device according to an embodiment of the present disclosure.
Referring to fig. 9, the computing device 5 according to an embodiment of the present disclosure may comprise a memory 51 and a processor 52, on the memory 51 a computer program 53 is stored, which computer program 53, when executed by the processor 52, implements a yaw control method according to an embodiment of the present disclosure, e.g. may perform the following steps: partitioning the operation data of the wind generating set to generate operation data of a plurality of intervals; performing statistical analysis on the operating data of each interval to generate an operating data statistic for each interval; determining a fault triggering interval in the plurality of intervals based on the running data statistic of each interval; and adjusting fault trigger conditions corresponding to the fault trigger intervals.
The computing device illustrated in fig. 9 is only one example and should not impose any limitations on the functionality or scope of use of embodiments of the disclosure.
The fault trigger adjustment method and apparatus, the computer readable storage medium, and the computing apparatus of the wind turbine generator set according to the embodiments of the present disclosure have been described above with reference to fig. 2 to 9. However, it should be understood that: the fail-safe apparatus shown in fig. 8 and its respective units may be respectively configured as software, hardware, firmware, or any combination thereof to perform a specific function, the computing apparatus shown in fig. 9 is not limited to including the above-illustrated components, but some components may be added or deleted as necessary, and the above components may also be combined.
By adopting the fault trigger adjusting method and device of the wind generating set according to the embodiment of the disclosure, at least one of the following technical effects can be realized: the operation distribution condition of the wind generating set and each subsystem thereof can be given based on big data analysis, possible reason analysis is given by combining the data, and a data basis is provided for solving the operation condition, the operation characteristic, the load distribution and the like of the wind generating set and each subsystem thereof (such as a variable pitch system of the wind generating set); the method can perform statistical analysis on the operation data and the environment data of the wind generating set in different regions, and execute different fault triggering parameters (also called protection parameters) in different regions so as to adjust (for example, properly reduce) the fault triggering times of the wind generating set; in the interval with more fault triggering times, the operation data related to the related fault triggering parameters are subjected to statistical analysis to obtain accurate and reliable statistical analysis data so as to set reasonable fault triggering parameters, thereby effectively ensuring the operation safety of the wind generating set; the fault triggering adjustment can be carried out on the wind generating sets of the same type, so that the wind generating sets are prevented from being frequently started and stopped in a short time after the fault is triggered by mistake; by partitioning the operation data of the wind generating set, the operation data can be subjected to distribution statistics, so that fault trigger intervals with more fault times are obtained, the fault trigger times of each fault trigger interval can be counted according to an analysis result, and fault trigger parameters can be optimized appropriately for the intervals with more fault trigger times; the operation data associated with the multiple fault triggering conditions can be simultaneously statistically analyzed, that is, if a fault triggering interval with a large number of fault triggering times exists and the corresponding statistical analysis results of the multiple fault triggering conditions are close to each other, the fault triggering parameters of each fault triggering condition can be optimized according to the statistical analysis results, so that the number of fault triggering times and/or the number of fault false triggering times of the wind driven generator can be appropriately reduced under the condition that the program does not need to be updated in batches and the maintenance time is reduced.
The control logic or functions performed by the various components or controllers in the control system may be represented by flowcharts or the like in one or more of the figures. These figures provide representative control strategies and/or logic that may be implemented using one or more processing strategies such as event-driven, interrupt-driven, multi-tasking, multi-threading, and the like. As such, various steps or functions illustrated may be performed in the sequence illustrated, in parallel, or in some cases omitted. Although not always explicitly illustrated, one of ordinary skill in the art will recognize that one or more of the illustrated steps or functions may be repeatedly performed depending on the particular processing strategy being used.
While the disclosure has been shown and described with reference to the preferred embodiments, it will be understood by those skilled in the art that various changes and modifications may be made to these embodiments without departing from the spirit and scope of the disclosure as defined by the claims.
Claims (24)
1. A fault trigger adjustment method of a wind generating set is characterized by comprising the following steps:
partitioning the operation data of the wind generating set to generate operation data of a plurality of intervals;
performing statistical analysis on the operating data of each interval to generate an operating data statistic for each interval;
determining a fault triggering interval in the plurality of intervals based on the running data statistic of each interval;
and adjusting the fault trigger condition corresponding to the fault trigger interval.
2. The method of claim 1, wherein adjusting the fault trigger condition corresponding to the fault trigger interval comprises:
acquiring a fault trigger condition corresponding to a fault trigger interval, wherein the fault trigger condition comprises a fault trigger parameter;
performing statistical analysis on the operation data of the wind generating set, which are associated with the fault triggering condition, so as to generate an operation data statistical value of a fault triggering interval;
and adjusting the fault trigger parameters corresponding to the fault trigger intervals based on the running data statistic values of the fault trigger intervals.
3. The method of claim 2, wherein adjusting the fault trigger parameters corresponding to the fault trigger interval based on the running data statistics of the fault trigger interval comprises:
setting the operation data statistic value of the fault trigger interval as a new fault trigger parameter corresponding to the fault trigger interval in response to the operation data statistic value of the fault trigger interval being within the preset range of the fault trigger parameter; and/or the presence of a gas in the gas,
and keeping the fault trigger parameters of the fault trigger interval unchanged in response to the fact that the running data statistic value of the fault trigger interval is out of the preset range of the fault trigger parameters.
4. The method of claim 3, further comprising: and setting a preset range of the fault triggering parameter based on a fault triggering condition corresponding to the fault triggering interval.
5. The method of any of claims 1 to 4, wherein performing a statistical analysis on the operating data for each interval to generate operating data statistics for each interval comprises:
one of averaging, median, maximum, minimum, variance, and standard deviation is performed on the operating data for each interval to generate an operating data statistic for each interval.
6. The method of claim 5, wherein performing one of averaging, median, maximum, minimum, variance, and standard deviation on the operational data for each interval to generate the operational data statistics for each interval comprises:
removing zero operating data in the operating data of each interval to generate zero-removed operating data of each interval;
one of averaging, median, maximum, minimum, variance, and standard deviation is performed on the zeroed operational data of each interval to generate an operational data statistic for each interval.
7. The method of any of claims 1-4, wherein determining the fault-triggering interval of the plurality of intervals based on the operating data statistics for each interval comprises:
identifying a shutdown interval of the plurality of intervals based on the operational data statistics for each interval;
and determining the shutdown interval as a fault triggering interval.
8. The method of any of claim 7, wherein the false trigger intervals comprise false trigger intervals,
determining a fault trigger interval of the plurality of intervals based on the operating data statistics for each interval further comprises: identifying a shutdown abnormal interval from the shutdown intervals; and determining the shutdown abnormal interval as a fault false triggering interval.
9. The fail-over adjustment method according to any one of claims 1 to 4, characterized in that the fail-over adjustment method further comprises: partitioning the environmental data of the wind generating set to generate environmental data of a plurality of intervals,
partitioning the operational data of the wind turbine generator set to generate a plurality of intervals of operational data comprises: partitioning the operational data based on the environmental data of the plurality of intervals to generate operational data of a plurality of intervals respectively corresponding to the environmental data of the plurality of intervals.
10. The method of any of claims 1 to 4, wherein the operational data comprises: the first operational data and the second operational data,
partitioning the operational data of the wind turbine generator system to generate a plurality of intervals of operational data comprises: partitioning the first operating data to generate a plurality of intervals of first operating data;
partitioning the second operation data based on the first operation data of the plurality of sections to generate second operation data of the plurality of sections corresponding to the first operation data of the plurality of sections, respectively.
11. The method of any of claims 1 to 4, wherein partitioning the operational data of the wind turbine generator set to generate the operational data for a plurality of intervals comprises: and partitioning the operation data of the wind generating set according to the time intervals to generate operation data of a plurality of time intervals.
12. A computer-readable storage medium, in which a computer program is stored which, when being executed by a processor, carries out the method of fault-triggered tuning according to any one of claims 1 to 11.
13. A computing device, wherein the computing device comprises:
a processor;
memory storing a computer program which, when executed by a processor, implements a fail-over adjustment method as claimed in any one of claims 1 to 11.
14. A wind turbine generator set fault trigger adjustment device, characterized in that the fault trigger adjustment device comprises:
a data partitioning unit configured to partition operation data of the wind turbine generator set to generate a plurality of intervals of operation data;
a statistical analysis unit configured to perform statistical analysis on the operation data of each section to generate an operation data statistic for each section;
a failure trigger section determination unit configured to determine a failure trigger section among the plurality of sections based on the operation data statistics for each section;
and a fault trigger condition adjusting unit configured to adjust a fault trigger condition corresponding to the fault trigger section.
15. The false trigger adjustment device of claim 14, wherein the false trigger condition adjustment unit is configured to:
acquiring a fault trigger condition corresponding to a fault trigger interval, wherein the fault trigger condition comprises a fault trigger parameter;
performing statistical analysis on the operation data of the wind generating set, which are associated with the fault triggering condition, so as to generate an operation data statistical value of a fault triggering interval;
and adjusting the fault trigger parameters corresponding to the fault trigger intervals based on the running data statistic values of the fault trigger intervals.
16. The fail-over adjustment device of claim 15, wherein the fail-over condition adjustment unit is configured to:
setting the operation data statistic value of the fault trigger interval as a new fault trigger parameter corresponding to the fault trigger interval in response to the operation data statistic value of the fault trigger interval being within the preset range of the fault trigger parameter; and/or the presence of a gas in the gas,
and responding to the fact that the running data statistic value of the fault triggering interval is out of the preset range of the fault triggering parameter, and keeping the fault triggering parameter of the fault triggering interval unchanged.
17. The device according to claim 16, wherein the false trigger condition adjustment unit is further configured to set the predetermined range of the false trigger parameter based on a false trigger condition corresponding to a false trigger interval.
18. The fail-safe apparatus of any of claims 14 to 17, wherein the statistical analysis unit is configured to:
one of averaging, median, maximum, minimum, variance, and standard deviation is performed on the operating data for each interval to generate an operating data statistic for each interval.
19. The fail-safe apparatus of claim 18, wherein the statistical analysis unit is configured to:
removing zero operating data in the operating data of each interval to generate zero-removed operating data of each interval;
one of averaging, median, maximum, minimum, variance, and standard deviation is performed on the zeroed operational data of each interval to generate an operational data statistic for each interval.
20. The device according to any one of claims 14 to 17, characterized in that the false trigger interval determination unit is configured to:
identifying a shutdown interval of the plurality of intervals based on the operational data statistics for each interval;
and determining the shutdown interval as a fault triggering interval.
21. The device of any of claims 20, wherein the false trigger intervals comprise false trigger intervals,
the failure trigger interval determination unit is configured to: identifying a shutdown abnormal interval from the shutdown intervals; and determining the shutdown abnormal interval as a fault false triggering interval.
22. The device of any of claims 14 to 17, wherein the data partitioning unit is further configured to: partitioning the environmental data of the wind generating set to generate environmental data of a plurality of intervals; partitioning the operation data based on the environment data of the plurality of sections to generate operation data of the plurality of sections corresponding to the environment data of the plurality of sections, respectively.
23. The device of any of claims 14 to 17, wherein the operational data comprises: the first operational data and the second operational data,
the data partitioning unit is configured to: partitioning the first operating data to generate a plurality of intervals of first operating data; partitioning the second operation data based on the first operation data of the plurality of sections to generate second operation data of the plurality of sections corresponding to the first operation data of the plurality of sections, respectively.
24. The device of any of claims 14 to 17, wherein the data partitioning unit is configured to: and partitioning the operation data of the wind generating set according to the time intervals to generate operation data of a plurality of time intervals.
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