WO2024045413A1 - 风力发电机组的变桨故障预测方法和装置 - Google Patents

风力发电机组的变桨故障预测方法和装置 Download PDF

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Publication number
WO2024045413A1
WO2024045413A1 PCT/CN2022/139082 CN2022139082W WO2024045413A1 WO 2024045413 A1 WO2024045413 A1 WO 2024045413A1 CN 2022139082 W CN2022139082 W CN 2022139082W WO 2024045413 A1 WO2024045413 A1 WO 2024045413A1
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Prior art keywords
pitch
wind turbine
blades
power
pitch angle
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PCT/CN2022/139082
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English (en)
French (fr)
Inventor
侠惠芳
田元兴
杜雪峰
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北京金风慧能技术有限公司
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Publication of WO2024045413A1 publication Critical patent/WO2024045413A1/zh

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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
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01D—MEASURING NOT SPECIALLY ADAPTED FOR A SPECIFIC VARIABLE; ARRANGEMENTS FOR MEASURING TWO OR MORE VARIABLES NOT COVERED IN A SINGLE OTHER SUBCLASS; TARIFF METERING APPARATUS; MEASURING OR TESTING NOT OTHERWISE PROVIDED FOR
    • G01D21/00—Measuring or testing not otherwise provided for
    • G01D21/02—Measuring two or more variables by means not covered by a single other subclass
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • 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

Definitions

  • the present application relates to the technical field of wind power generation, and more specifically, to a pitch fault prediction method and device for a wind turbine generator set.
  • Wind turbines usually perform pitching under the control of a main control logic. Under normal circumstances, the pitching actions of multiple blades of the wind turbine should be consistent. When there are components such as toothed belts, pitch motor brakes, etc. When the pitch failure is caused by damage, it will lead to inconsistent pitch movements, which may affect the power generation at least, or cause damage to the components of the wind turbine due to the aerodynamic imbalance of the wind turbine.
  • the purpose of this application is to provide a pitch fault prediction method and device for a wind turbine generator, so as to at least solve the above-mentioned problems in the related art, and may not solve any of the above-mentioned problems.
  • a pitch fault prediction method of a wind turbine generator set includes: during the pitch operation of the wind turbine generator set, acquiring at each sampling moment The pitch speed of each blade of the wind turbine generator set and the active power of the wind turbine generator set; determine the power interval in which the active power of the wind turbine generator set is located at each sampling moment, and based on the pitch of each two blades The mapping relationship between the speed difference range and the power interval determines whether the pitch speed difference of each two blades at each sampling moment exceeds the corresponding pitch speed difference range; and when the pitch speed difference of each two blades exceeds the corresponding pitch speed When the number of difference ranges is greater than the first preset number, the first pitch fault early warning information indicating the occurrence of a pitch fault is output.
  • a pitch fault prediction method of a wind turbine generator set wherein the pitch fault prediction method further includes: during the pitch pitch operation of the wind turbine generator set, The pitch angle of each blade of the wind turbine generator set and the active power of the wind turbine generator set are obtained at each sampling moment; the power interval in which the active power of the wind turbine generator set is located at each sampling moment is determined, and based on every two The mapping relationship between the pitch angle difference range of each blade and the power interval determines whether the pitch angle difference of each two blades at each sampling moment exceeds the corresponding pitch angle difference range; and when the number exceeds the corresponding pitch angle difference range When the number is greater than the second preset number, a second pitch fault warning message indicating the occurrence of a pitch fault is output.
  • a pitch fault prediction device of a wind turbine including: an acquisition unit configured to: during the pitch operation of the wind turbine , obtaining the pitch speed of each blade of the wind turbine generator set and the active power of the wind turbine generator set at each sampling moment; the determination unit is configured to: determine the active power of the wind turbine generator set at each sampling moment.
  • the unit is configured to: when the pitch speed difference of each two blades exceeds the corresponding pitch speed difference range by an amount greater than a first preset number, output a first pitch fault early warning message indicating that a pitch fault occurs.
  • a pitch fault prediction device of a wind turbine including: an acquisition unit configured to: during the pitch operation of the wind turbine , obtaining the pitch angle of each blade of the wind turbine generator set and the active power of the wind turbine generator set at each sampling moment; the determination unit is configured to: determine the active power of the wind turbine generator set at each sampling moment.
  • the unit is configured to: when the number exceeding the corresponding pitch angle difference range is greater than a second preset number, output a second pitch fault early warning message indicating that a pitch fault occurs.
  • a computer-readable storage medium storing a computer program
  • the computer program when executed by a processor, the wind turbine generator set according to the present disclosure is implemented. Pitch fault prediction method.
  • a controller including: a processor; and a memory storing a computer program.
  • the controller implements the steps described in the present disclosure.
  • the pitch fault prediction method according to the embodiment of the present disclosure is based on Pitch speed data can provide early warning of pitch failures.
  • the difference between the pitch speeds of different blades has different safety margins when the wind turbine performs pitch changes in different power ranges, the By using different judgment thresholds, the occurrence of pitch faults can be predicted more accurately, thereby reducing the probability of false pitch fault reports.
  • the pitch fault prediction method according to the embodiment of the present disclosure and The device can provide early warning of pitch failure based on pitch speed data.
  • the difference between the pitch speeds of different blades has different safety margins when the wind turbine performs pitch changes in different power ranges, the By using different judgment thresholds, the occurrence of pitch faults can be predicted more accurately, thereby reducing the probability of false pitch fault reports.
  • the pitch fault prediction method since the difference between the pitch angles of different blades has different safety margins when the wind turbine performs pitch in different power intervals, the pitch fault prediction method according to the embodiment of the present disclosure
  • the device adopts different judgment thresholds according to the power range of the wind turbine when pitching, which can more accurately predict the occurrence of pitch faults, thereby reducing the probability of false alarms of pitch faults.
  • Figure 1 shows a flow chart of a wind turbine pitch fault prediction method according to an embodiment of the present disclosure
  • Figure 2 is a graph showing pitch speed data during pitching of multiple wind turbines at a wind farm
  • Figure 3 shows a flow chart of a pitch fault prediction method of a wind turbine generator according to another embodiment of the present disclosure
  • Figure 4 is a graph showing pitch angle data during pitching of multiple wind turbines at a wind farm.
  • FIG. 5 is a block diagram illustrating the structure of a wind turbine pitch fault prediction device according to an embodiment of the present disclosure.
  • the expression “A or B”, or “at least one of A and/or B” may indicate A and B, A or B.
  • the expression “A or B” or “at least one of A and/or B” may indicate (1) A, (2) B, or (3) both A and B.
  • a component e.g., a first component
  • another component e.g., a second component
  • the component can be connected directly to the other component, or can be connected through another component (eg, a third component).
  • a component e.g., a first component
  • component e.g., a second component
  • component there is no other component (eg, a third component) between the component and the other component.
  • processor configured to perform A, B, and C may indicate a dedicated processor (eg, an embedded processor) for performing the corresponding operations or for executing at least one software stored in a memory device
  • a general-purpose processor for example, a central processing unit CPU or an application processor (AP) that programs a program to perform corresponding operations.
  • Modern wind turbines basically use variable pitch impellers.
  • the pitch angle is 0 degrees.
  • the pitch angle is 0 degrees.
  • the pitch angle is 90 degrees. Changing the pitch angle from 90 degrees to 0 degrees is called opening, and vice versa is called feathering.
  • the pitching systems of the blades of wind turbines are generally independent of each other, but are uniformly controlled by a central controller to ensure that the pitch angles of the blades are consistent.
  • the pitch system monitors the pitch angle voltage signal through position sensors deployed.
  • the sensing device will feedback abnormal information to the PLC system.
  • the control platform receives abnormal signals, and after analysis, reports specific fault information.
  • the relevant technology does not have a solution for early warning based on the blade pitch angle and pitch speed data during the pitching process of the wind turbine.
  • the current technical solution once the alarm is triggered, the fault has occurred.
  • monitoring the pitch angle of the blades through the position sensor cannot reflect abnormalities in the pitch speed. For example, although the pitch angle is consistent after the pitch change is completed, an abnormality in the pitch speed occurs during the pitch change process, which may This means that there may be a problem with the relevant components. Since there is no monitoring of the pitch speed, it is impossible to provide a timely warning.
  • the related technology determines whether a pitch fault occurs in a wind turbine
  • the same judgment criteria are used to determine whether the pitch angle exceeds a threshold for different active power ranges to determine whether a pitch fault occurs in the wind turbine.
  • the inventor found that when the wind turbine operates in different active power ranges and performs pitch changes, the pitch angle difference and/or the pitch angle difference between each two blades of the wind turbine is The pitch speed difference has different safe variation ranges. Therefore, it is thought to use different judgment criteria to determine whether the pitch angle difference and/or pitch speed difference are different in different active power ranges. exceeds the corresponding safety margin to reduce the probability of false alarm faults and more accurately predict the occurrence of pitch faults, so that maintenance personnel can eliminate and maintain wind turbines in a timely manner.
  • FIG. 1 shows a flowchart of a wind turbine pitch fault prediction method according to an embodiment of the present disclosure.
  • step S101 during the pitching operation of the wind turbine generator set, the pitch speed of each blade of the wind generator generator set and the active power of the wind generator generator set are obtained at each sampling moment.
  • the pitch speed of each blade of the wind turbine and the active power of the wind turbine can be obtained at each sampling moment through a supervisory control and data acquisition (SCADA) system.
  • SCADA supervisory control and data acquisition
  • the pitch fault prediction method of the present disclosure is described by taking a wind turbine generator set with three blades as an example. Those skilled in the art will understand that the pitch fault prediction method and device described in the present disclosure are applicable to single-rotor (or impeller) wind turbines and multi-rotor (or impeller) wind turbines in which each rotor (or impeller) includes other numbers of blades. Pitch fault prediction of generator sets.
  • step S102 the power interval in which the active power of the wind turbine generator is located at each sampling moment is determined, and based on the mapping relationship between the pitch speed difference range of each two blades and the power interval, the power interval of each two blades at each sampling moment is determined. Whether the blade pitch speed difference exceeds the corresponding pitch speed difference range.
  • the active power range that the wind turbine can output can be divided into multiple power intervals according to the active power of the wind turbine.
  • the active power range that the wind turbine can output can be evenly divided into a plurality of power intervals.
  • mapping relationship between the pitch speed difference range of each two blades and the power interval may be stored in the form of a mapping table.
  • Table 1 shows an example of the mapping relationship between the pitch speed difference range of each two blades and the power interval.
  • SRi indicates the pitch speed difference range of each two blades corresponding to the i-th power interval (i.e., the pitch speed difference range of blade 1 and blade 2 corresponding to the i-th power interval, the pitch speed difference range of blade 1 and blade 3)
  • the pitch speed difference range and the pitch speed difference range of blade 2 and blade 2 are both SRi).
  • SRi for example, the pitch speed difference range [-5 degrees/s, 5 degrees/s]
  • i 1, 2, 3, 4, 5.
  • the power data of the wind turbine and the pitch speed of three blades are obtained. If the power belongs to the first power interval, calculate whether the difference in pitch speed of each two blades exceeds the range SR1. .
  • the number of times the pitch speed difference of each two blades exceeds the corresponding pitch speed difference range for each power interval can be recorded. For example, based on the pitch speed of the blades of a wind turbine operating in a certain power range obtained at a certain sampling time, if it is determined that the difference in pitch speed between blade 1 and blade 2 exceeds the corresponding pitch speed range, then blade 1 The number of times the pitch speed difference with the blade 2 exceeds the pitch speed difference range corresponding to a certain power interval is increased by one.
  • the mapping relationship is obtained in the following manner: binning data on the pitching speeds of blades during the pitching operations of multiple wind turbine generators according to power intervals, and calculating the power generation of the multiple wind turbines for any one power interval.
  • the pitch speed difference of every two blades of each wind turbine in the unit is determined based on the pitch speed difference of every two blades of each wind turbine for any one of the power intervals.
  • the plurality of wind turbines may be multiple wind turbines of the same type as the wind turbine in the same wind farm.
  • the plurality of wind turbines may include the wind turbine.
  • the pitch speed data of multiple wind turbines can be collected through the SCADA system, and then the collected pitch speed data can be processed.
  • the pitch angle data and/or pitch speed data acquired by the SCADA system during the pitching of each wind turbine can be read as follows:
  • BLADE_ANGLE2 'WTPS_Ang_Ra_F32_blade2' #Pitch angle of blade 2
  • the step of binning data on the pitching speeds of blades during the pitching operations of multiple wind turbines may include: classifying the acquired pitching speeds of blades during the pitching operations of multiple wind turbines according to a preset rule Filter to remove abnormal or erroneous data, and then bin the remaining pitch speed data according to power intervals.
  • the pitch speed data that meets at least one of the following conditions can be determined as the pitch speed data that needs to be eliminated: the active power corresponding to the pitch speed data is not greater than a% (for example, 60%) of the rated power;
  • the propeller speed data is not within the range of b degrees/second (for example, -5 degrees/second) to c (for example, 5 degrees/second) degrees;
  • the pitch speed difference of the two blades is greater than the preset value (for example, 3 degrees /second), where a, b, c are preset values, which can be determined according to the fan model.
  • the pitch speeds of the three blades of a wind turbine generator at a certain sampling time include pitch speeds beyond the range of [-5 degrees/second, 5 degrees/second], then the wind force collected at that sampling time will be Genset pitch speed data can be eliminated.
  • the pitch speed data may be eliminated.
  • an alarm message indicating that the wind turbine has a fault may be output, for example, the wind turbine
  • the sensor used by the unit to collect data may have malfunctioned.
  • the step of determining the pitch speed difference range of every two blades corresponding to the any one power interval based on the pitch speed difference of every two blades of each wind turbine includes: : Calculate the average value of the pitch speed difference of every two blades of each wind turbine; add the first preset increment to the average value as the pitch value of every two blades corresponding to any one of the power intervals.
  • the average value minus the first preset increment is used as the lower limit of the pitch speed difference range of each two blades corresponding to any one of the power intervals.
  • sampling data of three wind turbines in the same wind farm for one day will be used as an example.
  • the first wind turbine has, for example, 1,000 pieces of pitch speed data
  • the second wind turbine has, for example, 1,000 pieces of pitch speed data
  • the third wind turbine has, for example, 1,000 pieces of pitch speed data.
  • the unit has, for example, 1,000 pieces of data, where each piece of data indicates the pitch speed data of the three blades of the wind turbine unit corresponding to a certain sampling time.
  • the average of the 9000 differences calculates the average of the 9000 differences, and add the average to the first preset increment (for example, 0.15) as the pitch speed difference range of each two blades corresponding to the first power interval (i.e., blade 1 and The upper limit of the pitch speed difference range of blade 2, the pitch speed difference range of blade 1 and blade 3, and the pitch speed difference range of blade 2 and blade 3), subtract the first preset increment from the average value as the The lower limit of the pitch speed difference range of each two blades corresponding to a power range.
  • the first preset increment for example, 0.15
  • the pitch speed difference range of each two blades corresponding to each power interval is determined based on the average value of the pitch speed differences of each two blades of each wind turbine, this is only an example.
  • the corresponding pitch speed difference range can also be obtained in other ways based on the pitch speed difference of every two blades of each wind turbine generator set.
  • the minimum value of the pitch speed difference of every two blades of each wind turbine generator set plus an increment can be used as the lower limit of the corresponding pitch speed difference range, and the minimum value of each two blades of each wind turbine generator set can be The maximum value of the pitch speed difference minus an increment is used as the upper limit of the corresponding pitch speed difference range.
  • the first preset increment may be an empirical value.
  • the first preset increment may be the absolute value of the maximum value among the 9000 difference values and the average value, or the absolute value minus a certain value.
  • step S103 when the pitch speed difference of each two blades exceeds the corresponding pitch speed difference range by an amount greater than a first preset number (for example, 3), a first signal indicating that a pitch failure occurs is output. Pitch fault warning information.
  • a first preset number for example, 3
  • the first pitch fault warning information output at this time may be warning information indicating that blade 1 and blade 2 may have a pitch fault.
  • the system when the number of pitch speed differences between blades 1 and 3 corresponding to the second power interval exceeds the range of the pitch speed differences between blades 1 and 3 corresponding to the second power interval, the system will also The first pitch fault early warning information indicating the occurrence of a pitch fault is output.
  • the first pitch fault information may be early warning information indicating that blade 1 and blade 3 may have faults.
  • the first preset increment and the first preset number may be obtained based on big data. For example, based on the obtained pitch speed data and pitch fault data of multiple wind turbine units, the pitch speed data before the pitch fault occurs is determined, and the pitch speed data before the pitch fault occurs is determined based on the distribution pattern of the pitch speed data before the pitch fault occurs.
  • the preset increment and the first preset quantity may be determined with prediction accuracy as an optimization target.
  • Figure 2 shows a graph of pitch speed data during pitching of multiple wind turbines at a wind farm.
  • the horizontal axis represents the power of the wind turbine
  • the vertical axis represents the pitch difference of every two blades of each wind turbine.
  • a large number of points in Figure 2 represent the pitch of every two blades of the wind turbine.
  • Speed difference the solid line represents the pitch speed difference data of a certain problem unit. It can be seen from Figure 2 that the pitch speed difference of each two blades of most wind turbines without pitch faults is around 0 degrees/second. (located between the two dotted lines in the figure), and the average pitch speed difference is close to 0 degrees/second, while the pitch speed difference between blade 1 of the problem unit and the other two blades reaches 0.2 degrees/second. And compared with the healthy units, the pitch speed difference data of the problem units deviated far away from 0 degrees/second and the deviation value was greater than 0.15 degrees/second. After the shutdown, it was confirmed that the electromagnetic brake of the pitch motor of blade 1 was completely stuck.
  • the first preset increment corresponding to a certain power interval can be set to, for example, 0.15 degrees/s ( ⁇ 0.2 degrees/s), and the first preset number can be set to, for example, 3.
  • an early warning message can be output to prompt the user to perform maintenance on related components in a timely manner.
  • FIG. 3 shows a flowchart of a pitch fault prediction method of a wind turbine generator according to another embodiment of the present disclosure.
  • step S301 during the wind turbine pitching operation, the pitch angle of each blade of the wind turbine and the active power of the wind turbine are acquired at each sampling moment.
  • step S302 the power interval in which the active power of the wind turbine generator is located at each sampling moment is determined, and based on the mapping relationship between the pitch angle difference range of each two blades and the power interval, the power interval of each two blades at each sampling moment is determined. Whether the pitch angle difference of the blade exceeds the corresponding pitch angle difference range.
  • the power interval may be divided in the same or similar manner as that described with reference to FIG. 1 .
  • mapping relationship between the pitch angle difference range of each two blades and the power interval can be stored in the form of a mapping table.
  • Table 2 shows an example of the mapping relationship between the pitch angle difference range of each two blades and the power interval.
  • ARi indicates the pitch angle difference range of each two blades corresponding to the i-th power interval (i.e., the pitch angle difference range of blade 1 and blade 2 corresponding to the i-th power interval, the pitch angle difference range of blade 1 and blade 3)
  • the pitch angle difference range and the pitch angle difference range of blade 2 and blade 3 are both ARi).
  • the power data of the wind turbine and the pitch angles of three blades are obtained. If the power belongs to the first power interval, calculate whether the pitch angle difference of each two blades exceeds the range AR1. .
  • the mapping relationship between the pitch angle difference range and the power interval is obtained in the following way: according to the power interval, the data of the blade pitch angles during the pitching operation of multiple wind turbine generators are binned, and for any one power interval, calculate the pitch angle difference of every two blades of each wind turbine among the plurality of wind turbines, and for any one of the power intervals, based on the pitch of every two blades of each wind turbine The angle difference determines the pitch angle difference range of each two blades corresponding to any one of the power intervals.
  • the number of times the pitch angle difference of each two blades exceeds the corresponding pitch angle difference range in each power interval can be recorded. For example, based on the pitch angle of the blades of the wind turbine operating in a certain power range obtained at a certain sampling time, if it is determined that the pitch angle difference between blade 1 and blade 2 exceeds the corresponding pitch angle difference range, the blade The number of times the pitch angle difference between 1 and blade 2 exceeds the pitch angle difference range corresponding to a certain power interval is increased by 1.
  • the pitch angle data during pitch changes of multiple wind turbines can be collected through the SCADA system, and the collected pitch angle data can be processed.
  • the plurality of wind turbines may be a plurality of wind turbines of the same type in the same wind farm as the wind turbine.
  • the step of binning data on the pitch angles of the blades during the pitching operations of multiple wind turbines may include: filtering the acquired pitch angles of the blades during the pitching operations of the multiple wind turbines according to preset rules. , to remove abnormal or erroneous data, and then bin the remaining pitch angle data according to power intervals.
  • the pitch angle data that meets at least one of the following conditions can be determined as the pitch angle data that needs to be eliminated: the active power corresponding to the pitch angle data is not greater than d% (for example, 20%) of the rated power; the blades The pitch angle of the blade is not between e (for example, -5) degrees and f (for example, 100) degrees; the pitch angle difference of the two blades is greater than the preset value (for example, 3 degrees), where d, e, f It is a preset value and can be determined according to the fan model.
  • this set of pitch angle data can be eliminated.
  • the pitch angle data of the wind turbine collected at that sampling time can be eliminated.
  • the pitch angle data can be eliminated.
  • an alarm message indicating that the wind turbine has a fault can be output, for example, the wind turbine
  • the sensor used by the unit to collect data may have malfunctioned.
  • the step of determining the pitch angle range corresponding to the any one power interval based on the pitch angle difference of each two blades of each wind turbine includes: calculating each wind power generation The average value of the pitch angle difference of each two blades of the unit; add the second preset increment to the average value as the upper limit of the pitch angle difference range of each two blades corresponding to any one of the power intervals, The average value minus the second preset increment is used as the lower limit of the pitch angle difference range of each two blades corresponding to any one of the power intervals.
  • the description is also based on the pitch angle data collected in one day from three wind turbines in the same wind farm as an example.
  • the first wind turbine has 1000 pieces of pitch angle data
  • the second wind turbine has 1000 pieces of pitch angle data
  • the second wind turbine has 1000 pieces of pitch angle data.
  • the three wind turbines have 1,000 pieces of pitch angle data, where each piece of pitch angle data indicates the pitch angle data of the three blades of the wind turbine corresponding to a certain sampling time.
  • the pitch angle difference range of each two blades corresponding to each power interval is determined based on the average value of the pitch angle differences of each two blades of each wind turbine, this is only an example.
  • the corresponding pitch angle difference range can also be obtained in other ways based on the pitch angle difference of every two blades of each wind turbine generator set.
  • the minimum value of the pitch angle difference of every two blades of each wind turbine set can be added to an increment as the lower limit of the corresponding pitch angle difference range, and the minimum value of each two blades of each wind turbine set can be The maximum value of the pitch angle difference minus an increment serves as the upper limit of the corresponding pitch angle difference range.
  • the second preset increment may be an empirical value.
  • the second preset increment may be the absolute value of the maximum value among the 9000 difference values and the average value minus a certain numerical value.
  • step S303 when the number exceeding the corresponding pitch angle difference range is greater than the second preset number, second pitch fault warning information indicating the occurrence of a pitch fault is output.
  • second pitch fault warning information indicating that a pitch fault occurs is output.
  • the second pitch fault information indicating the occurrence of a pitch fault is output.
  • the second pitch fault information output at this time may be early warning information indicating that blade 1 and blade 2 may have a pitch fault. This allows the user to easily determine which two blades to inspect.
  • the second preset increment and the second preset number may be obtained based on big data. For example, based on the obtained pitch angle data during the pitching period of multiple wind turbine generators and the pitching fault data, the pitch angle data during the pitching period before the pitching fault occurs is determined, and based on the pitch angle data before the pitching fault occurs, The second preset increment and the second preset number are determined based on the distribution law of the data. For example, the second preset increment and the second preset number may be determined based on a large amount of pitch angle data acquisition during the pitch period before the pitch failure occurs, with pitch failure prediction accuracy as an optimization target.
  • Figure 4 is a graph showing pitch angle data during pitching of multiple wind turbines at a certain wind farm.
  • the horizontal axis represents the power of the wind turbine
  • the vertical axis represents the pitch angle difference of each two blades during pitching.
  • a large number of points in the figure represent the pitch angle difference data of each two blades, and the solid line represents a certain
  • the pitch angle difference data of the problematic unit can be seen from Figure 4.
  • the pitch angle difference of each two blades of most wind turbines without pitch failure is near 0 degrees (located between the two dotted lines in the figure).
  • the second preset increment can be set to, for example, 0.5 degrees ( ⁇ 0.75 degrees), and the second preset number can be set to, for example, 3, so that when the toothed belt of the blade of the wind turbine is loose but does not report In the event of a fault, early warning information can be output as early as possible to prompt users to perform timely maintenance on related components.
  • the pitch failure can be predicted based on both the pitch speed data and the pitch angle data during the pitching of the wind turbine, that is, the methods shown in Figures 1 and 3 can be combined to predict the pitch of the wind turbine. Fault.
  • a pitch fault prediction method of a wind turbine includes: during the pitch operation of the wind turbine, acquiring each parameter of the wind turbine at each sampling moment. The pitch speed of each blade and the active power of the wind turbine set; determine the power interval in which the active power of the wind turbine set is located at each sampling moment, and based on the pitch speed difference range of each two blades and the power interval The mapping relationship determines whether the pitch speed difference of each two blades at each sampling moment exceeds the corresponding pitch speed difference range; and when the pitch speed difference of each two blades exceeds the corresponding pitch speed difference range by an amount greater than the first When the preset number is reached, the first pitch fault early warning information indicating the occurrence of a pitch fault is output, wherein the method further includes: during the pitch operation of the wind turbine generator, acquiring the wind turbine generator at each sampling moment the pitch angle of each blade; and based on the mapping relationship between the pitch angle difference range of each two blades and the power interval, determine whether the pitch angle difference of each two blades at
  • a pitch fault alarm message is output.
  • a pitch fault has already occurred at this time, for example, the toothed belt is severely loose or fallen off, or the electromagnetic brake is completely stuck. die.
  • the pitch fault is predicted based on the pitch speed data and/or the pitch angle data during the pitch period. An early warning message will be issued when the belt does not fall off and the electromagnetic brake is not completely stuck, which can reduce losses caused by pitch failure.
  • the pitch fault prediction method of the wind turbine generator set according to the embodiment of the present disclosure has been described above with reference to FIGS. 1 to 4 .
  • the pitch fault prediction device of the wind turbine generator set according to the embodiment of the present disclosure will be described below with reference to FIG. describe.
  • FIG. 5 is a diagram illustrating the structure of a pitch failure prediction device 500 of a wind turbine generator according to an embodiment of the present disclosure.
  • the pitch fault prediction device 500 may include an acquisition unit 501 , a determination unit 502 and an output unit 503 .
  • the pitch fault prediction device 500 may additionally include other components, and at least one of the components included in the pitch fault prediction device 500 may be omitted, combined, or split.
  • the acquisition unit 501 may be configured to: acquire the pitching speed of each blade of the wind turbine and the active power of the wind turbine at each sampling moment during the pitching operation of the wind turbine. .
  • the determining unit 502 may be configured to: determine the power interval in which the active power of the wind turbine generator is located at each sampling moment, and determine each time based on the mapping relationship between the pitch speed difference range of each two blades and the power interval. Whether the pitch speed difference of each two blades at each sampling moment exceeds the corresponding pitch speed difference range.
  • the output unit 503 may be configured to: when the pitch speed difference of each two blades exceeds the corresponding pitch speed difference range by an amount greater than a first preset number, output a first pitch fault indicating that a pitch fault occurs. Early warning information.
  • the acquisition unit 501 may also be configured to: during the pitching operation of the wind turbine, acquire the pitch angle of each blade of the wind turbine and the active power of the wind turbine at each sampling moment. power.
  • the determining unit 502 may also be configured to: determine the power interval in which the active power of the wind turbine generator is located at each sampling moment, and determine based on the mapping relationship between the pitch angle difference range of each two blades and the power interval. Whether the pitch angle difference of each two blades at each sampling moment exceeds the corresponding pitch angle difference range.
  • the output unit 503 may also be configured to: when the number exceeding the corresponding pitch angle difference range is greater than the second preset number, output second pitch fault warning information indicating that a pitch fault occurs.
  • a computer-readable storage medium storing instructions may also be provided, wherein when the instructions are executed by at least one processor, the at least one processor is caused to execute the wind turbine generator according to the embodiment of the present disclosure. Pitch fault prediction method.
  • Examples of computer readable storage media include: read only memory (ROM), random access programmable read only memory (PROM), electrically erasable programmable read only memory (EEPROM), random access memory (RAM) , dynamic random access memory (DRAM), static random access memory (SRAM), flash memory, non-volatile memory, CD-ROM, CD-R, CD+R, CD-RW, CD+RW, DVD-ROM , DVD-R, DVD+R, DVD-RW, DVD+RW, DVD-RAM, BD-ROM, BD-R, BD-R LTH, BD-RE, Blu-ray or optical disk storage, hard disk drive (HDD), solid state Hard disk (SSD), card storage (such as multimedia card, secure digital (SD) card or extreme digital (XD) card), magnetic tape, floppy disk, magneto-optical data storage device, optical data storage device, hard disk, solid state disk and any Other devices, any other device configured to store and make available a computer program and any associated data, data files and data structures in a non
  • the computer program in the above computer-readable storage medium can run in an environment deployed in computer equipment such as a client, a host, a proxy device, a server, etc.
  • the computer program and any associated data, data files and data structures are distributed over networked computer systems such that computer programs and any associated data, data files and data structures are stored, accessed and executed in a distributed fashion by one or more processors or computers.
  • a controller may also be provided, and the controller may include a processor and a memory.
  • the memory stores a computer program.
  • the computer program is executed by the processor, the pitch fault prediction method of the wind turbine generator set as described above is implemented.
  • a computer program product may also be provided, and instructions in the computer program product may be executed by a processor of a computer device to complete the pitch fault prediction method of a wind turbine generator described herein.

Abstract

一种风力发电机组的变桨故障预测方法，所述变桨故障预测方法包括：在所述风力发电机组变桨操作期间，在每个采样时刻获取所述风力发电机组的每个叶片的变桨速度以及所述风力发电机组的有功功率；确定每个采样时刻的风力发电机组的有功功率所处的功率区间，并基于每两个叶片的变桨速度差范围与功率区间的映射关系，确定每个采样时刻的每两个叶片的变桨速度差是否超出对应变桨速度差范围；以及当每两个叶片的变桨速度差超出对应变桨速度差范围的数量大于第一预设数量时，输出指示发生变桨故障的第一变桨故障预警信息。一种风力发电机组的变桨故障预测装置，包括采集单元（501）、确定单元（502）和输出单元（503）。提前预测变桨故障的发生，从而使用户及时采取维护措施以避免产生大的损失。

Description

风力发电机组的变桨故障预测方法和装置
相关申请的交叉引用
本申请要求享有于2022年8月31日提交的申请号为202211062730.5、发明名称为“风力发电机组的变桨故障预测方法和装置”的中国专利申请的优先权，该申请的全部内容通过引用并入本文中。
技术领域
本申请涉及风力发电技术领域，更具体地，涉及一种风力发电机组的变桨故障预测方法和装置。
背景技术
风力发电机组通常在一个主控逻辑的控制下执行变桨，在正常情况下，风力发电机组的多个叶片的变桨动作应该一致，当出现由例如齿形带、变桨电机刹车等零部件的损坏而导致的变桨故障时，则会导致变桨动作不一致，轻则影响发电量，重则由于风力发电机组的气动不平衡导致风力发电机组的部件损坏。
相关技术中，通过检测不同叶片的桨距角之间的差值是否超过阈值来确定是否出现了变桨故障，然而当确定所述差值超过了阈值时，故障已然发生，可见其无法预测是否将发生变桨故障以便通知用户及时采取维护措施。
因此亟需一种风力发电机组的变桨故障预测方法和装置以提前预测变桨故障的发生，从而使用户及时采取维护措施以避免产生大的损失。
发明内容
本申请的目的在于提供一种风力发电机组的变桨故障预测方法和装置，以至少解决上述相关技术中的问题，也可以不解决任何上述问题。
根据本公开的实施例的一个方面，提供了一种风力发电机组的变桨故障预测方法，所述变桨故障预测方法包括：在所述风力发电机组变桨操作期间，在每个采样时刻获取所述风力发电机组的每个叶片的变桨速度以及所述风力 发电机组的有功功率；确定每个采样时刻的风力发电机组的有功功率所处的功率区间，并基于每两个叶片的变桨速度差范围与功率区间的映射关系，确定每个采样时刻的每两个叶片的变桨速度差是否超出对应变桨速度差范围；以及当每两个叶片的变桨速度差超出对应变桨速度差范围的数量大于第一预设数量时，输出指示发生变桨故障的第一变桨故障预警信息。
根据本公开的实施例另一方面，提供了一种风力发电机组的变桨故障预测方法，其特征在于，所述变桨故障预测方法还包括：在所述风力发电机组变桨操作期间，在每个采样时刻获取所述风力发电机组的每个叶片的桨距角以及所述风力发电机组的有功功率；确定每个采样时刻的风力发电机组的有功功率所处的功率区间，并基于每两个叶片的桨距角差范围与功率区间的映射关系，确定每个采样时刻的每两个叶片的桨距角差是否超出对应桨距角差范围；以及当超出对应桨距角差范围的数量大于第二预设数量时，输出指示发生变桨故障的第二变桨故障预警信息。
根据本公开的实施例另一方面，提供了一种风力发电机组的变桨故障预测装置，所述变桨故障预测装置包括：采集单元，被配置为：在所述风力发电机组变桨操作期间，在每个采样时刻获取所述风力发电机组的每个叶片的变桨速度以及所述风力发电机组的有功功率；确定单元，被配置为：确定每个采样时刻的风力发电机组的有功功率所处的功率区间，并基于每两个叶片的变桨速度差范围与功率区间的映射关系，确定每个采样时刻的每两个叶片的变桨速度差是否超出对应变桨速度差范围；以及输出单元，被配置为：当每两个叶片的变桨速度差超出对应变桨速度差范围的数量大于第一预设数量时，输出指示发生变桨故障的第一变桨故障预警信息。
根据本公开的实施例另一方面，提供了一种风力发电机组的变桨故障预测装置，所述变桨故障预测装置包括：采集单元，被配置为：在所述风力发电机组变桨操作期间，在每个采样时刻获取所述风力发电机组的每个叶片的桨距角以及所述风力发电机组的有功功率；确定单元，被配置为：确定每个采样时刻的风力发电机组的有功功率所处的功率区间，并基于每两个叶片的桨距角差范围与功率区间的映射关系，确定每个采样时刻的每两个叶片的桨距角差是否超出对应桨距角差范围；以及输出单元，被配置为：当超出对应桨距角差范围的数量大于第二预设数量时，输出指示发生变桨故障的第二变桨故障预警信息。
根据本公开的实施例另一方面，提供了一种存储有计算机程序的计算机可读存储介质，其特征在于，当所述计算机程序被处理器执行时，实现如本公开所述的风力发电机组的变桨故障预测方法。
根据本公开的实施例另一方面，提供了一种控制器，所述控制器包括：处理器；和存储器，存储有计算机程序，当所述计算机程序被处理器执行时，实现如本公开所述的风力发电机组的变桨故障预测方法。
相关技术中，并不存在通过变桨速度数据预测变桨故障的方案，而变桨速度出现大的差异可提前反映将会出现的故障，而根据本公开的实施例的变桨故障预测方法基于变桨速度数据可提前预警变桨故障。另外，由于风力发电机组在不同功率区间执行变桨时，不同叶片的变桨速度之间的差值具有不同的安全裕度，因此，根据变桨时风力发电机组所处的功率区间的不同而采用不同的判定阈值，可以更准确地预测变桨故障的发生，从而降低误报变桨故障的概率。
相关技术中，并不存在通过变桨速度数据预测变桨故障的方案，而变桨速度出现大的差异可提前反映将会出现的故障，而根据本公开的实施例的变桨故障预测方法和装置基于变桨速度数据可提前预警变桨故障。另外，由于风力发电机组在不同功率区间执行变桨时，不同叶片的变桨速度之间的差值具有不同的安全裕度，因此，根据变桨时风力发电机组所处的功率区间的不同而采用不同的判定阈值，可以更准确地预测变桨故障的发生，从而降低误报变桨故障的概率。
根据本公开的实施例，由于风力发电机组在不同功率区间执行变桨时，不同叶片的桨距角之间的差值具有不同的安全裕度，根据本公开的实施例的变桨故障预测方法和装置根据变桨时风力发电机组所处的功率区间的不同而采用不同的判定阈值，可以更准确地预测变桨故障的发生，从而降低误报变桨故障的概率。
附图说明
通过下面结合示例性地示出本公开的实施例的附图进行的描述，本公开的上述和其他目的和特点将会变得更加清楚，其中：
图1示出了根据本公开的实施例的风力发电机组变桨故障预测方法的流程图；
图2是示出某一风电场的多个风力发电机组变桨期间的变桨速度数据的曲线图；
图3示出了根据本公开的另一实施例的风力发电机组的变桨故障预测方法的流程图；
图4是示出某一风电场的多个风力发电机组变桨期间的桨距角数据的曲线图；以及
图5是示出根据本公开的实施例的风力发电机组变桨故障预测装置的结构的框图。
具体实施方式
在下文中，参照附图对本公开的各种实施例进行描述，其中，相同的标号用于表示相同或相似的元件、特征和结构。然而，不旨在由本文所述的各种实施例将本公开限制于具体实施例，并且旨在于：本公开覆盖本公开的所有修改、等同物和/或替代物，只要它们在所附权利要求及其等同物的范围内。在以下说明书和权利要求书中使用的术语和词语不限于它们的词典含义，而是仅被用于使得能够清楚和一致地理解本公开。因此，对于本领域技术人员应显而易见的是：提供本公开的各种实施例的以下描述仅用于说明的目的，而不是为了限制由所附权利要求和它们的等同物限定的本公开的目的。
应理解，除非上下文另外明确指出，否则单数形式包括复数形式。本文使用的术语“包括”、“包含”和“具有”指示公开的功能、操作或元件的存在，但不排除其它功能、操作或元件。
例如，表述“A或B”、或“A和/或B中的至少一个”可指示A和B、A或者B。例如，表述“A或B”或“A和/或B中的至少一个”可指示(1)A、(2)B或(3)A和B两者。
在本公开的各种实施例中，意图是：当组件(例如，第一组件)被称为与另一组件(例如，第二组件)“耦接”或“连接”或者被“耦接”或者“连接”到另一组件(例如，第二组件)时，所述组件可被直接连接到所述另一组件，或者可通过另一组件(例如，第三组件)被连接。相比之下，当组件(例如，第一组件)被称为与另一组件(例如，第二组件)“直接耦接”或“直接连接”或者被直接耦接到或直接连接到另一组件(例如，第二组件)时，在所述组件和所述另一组件之间不存在另一组件(例如，第三组件)。
在描述本公开的各种实施例中使用的表述“被配置为”可以例如根据情况与诸如“适用于”、“具有…的能力”、“被设计为”、“适合于”、“被制造为”和“能够”的表述互换使用。术语“被配置为”可不一定指示按照硬件“被专门设计为”。相反，在一些情况下的表述“被配置为...的装置”可指示所述装置和另一装置或者部分“能够…”。例如，表述“被配置为执行A、B和C的处理器”可指示用于执行相应操作的专用处理器(例如，嵌入式处理器)或用于通过执行存储在存储器装置中的至少一个软件程序来执行相应的操作的通用处理器(例如，中央处理单元CPU或应用处理器(AP))。
本文使用的术语在于描述本公开的某些实施例，但并不旨在限制其它实施例的范围。除非本文另外指出，否则本文使用的所有术语(包括技术或科学术语)可具有与本领域技术人员通常理解的含义相同含义。通常，词典中定义的术语应被视为具有与相关领域中的上下文含义相同的含义，并且，除非本文明确地定义，否则不应被不同地理解或被理解为具有过于正式的含义。在任何情况下，本公开中定义的术语也不旨在被解释为排除本公开的实施例。
现代风力发电机组基本都是采用可变桨距的叶轮，当执行变桨时，风力发电机组的叶片在地面的垂直投影发生变化，当叶片与地面垂直时为桨距角为0度，当叶片与地面平行时，桨距角为90度。桨距角从90度朝0度变桨叫做开桨，反之叫做顺桨。风力发电机组的叶片的变桨系统一般是相互独立的，但是由一个中控来统一控制，以保证叶片的桨距角一致。
相关技术中，变桨系统通过布设的位置传感器，对桨距角电压信号进行监测，当叶片的桨距角差最大差值超过例如4°时，传感装置会将异常信息反馈到PLC系统中。控制平台接受异常信号，经由分析后，报出具体的故障信息。
然而相关技术没有基于风力发电机组变桨过程叶片桨距角和变桨速度数据进行提前预警的方案，根据现由技术方案，一旦触发报警，即是故障已经发生。另外，通过位置传感器监测叶片的桨距角，无法反映变桨速度的异常，例如，虽然，变桨结束后，桨距角一致，但是在变桨过程中，变桨速度出现了异常，这可能意味着相关部件可能产生了问题，由于没有对变桨速度的监控，因此无法及时进行预警。
另外，相关技术在判断风力发电机组是否出现变桨故障时，针对不同的有功功率区间，采用同样的判定标准来判断桨距角是否超过了阈值以确定风 力发电机组是否出现了变桨故障。
然而，发明人基于采集的大量的风力发电机组的工况数据发现，当风力发电机组运行在不同的有功功率区间执行变桨时，风力发电机组的每两个叶片的桨距角差和/或变桨速度差具有不同的安全变化范围，因此想到针对不同有功功率区间的桨距角差和/或变桨速度差，利用不同的判定标准来判断桨距角差和/或变桨速度差是否超出了对应的安全裕度，以降低误报故障的概率，从而更准确地预测变桨故障的发生，这样维修人员能够时对风力发电机组进行消缺和维护。
图1示出了根据本公开的实施例的风力发电机组变桨故障预测方法的流程图。
参照图1，在步骤S101,在所述风力发电机组变桨操作期间，在每个采样时刻获取所述风力发电机组的每个叶片的变桨速度以及所述风力发电机组的有功功率。
作为示例，可通过监控和数据采集(supervisory control and data acquisition，SCADA)系统在每个采样时刻获取风力发电机组的每个叶片的变桨速度和风力发电机组的有功功率。
为了便于描述本公开，以具有三个叶片的风力发电机组为例来描述本公开的变桨故障预测方法。本领域技术人员应当理解，本公开描述的变桨故障预测方法和装置适用于每个转子(或叶轮)包括其它数量的叶片的单转子(或叶轮)风力发电机组和多转子(或叶轮)风力发电机组的变桨故障预测。
在步骤S102,确定每个采样时刻的风力发电机组的有功功率所处的功率区间，并基于每两个叶片的变桨速度差范围与功率区间的映射关系，确定每个采样时刻的每两个叶片的变桨速度差是否超出对应变桨速度差范围。
作为示例，可按照风力发电机组的有功功率大小将风力发电机组能够输出的有功功率范围划分多个功率区间。
作为示例，可将风力发电机组能够输出的有功功率范围平均划分为多个功率区间。
作为示例，每两个叶片的变桨速度差范围与功率区间的映射关系可以以映射表的形式被存储。
表1示出了每两个叶片的变桨速度差范围与功率区间的映射关系的示例。参照表1，SRi指示与第i功率区间对应的每两个叶片的变桨速度差范围(即 与第i功率区间对应的叶片1和叶片2的变桨速度差范围、叶片1和叶片3的变桨速度差范围以及叶片2和叶片2的变桨速度差范围均为SRi)，当风力发电机组在第i功率区间变桨时，如果不存在变桨故障，则每两个叶片的变桨速度差一般不会超出SRi(例如，变桨速度差范围[-5度/s，5度/秒])，i＝1、2、3、4、5。
Figure PCTCN2022139082-appb-000001
例如，在某一采样时刻，获取到风力发电机组的功率数据以及三个叶片的变桨速度，如果该功率属于第一功率区间，则计算每两个叶片的变桨速度差是否超出了范围SR1。
作为示例，可记录每个功率区间每两个叶片的变桨速度差超出对应变桨速度差范围的次数。例如，基于某一采样时刻获取的在某一功率区间运行的风力发电机组的叶片的变桨速度，如果确定叶片1与叶片2的变桨速度差超出了对应的变桨速度范围，则叶片1与叶片2的变桨速度差超出与所述某一功率区间对应的变桨速度差范围的次数增加1。
作为示例，所述映射关系通过以下方式获得：按照功率区间，对多个风力发电机组变桨操作期间的叶片的变桨速度进行数据分仓，针对任意一个功率区间，计算所述多个风力发电机组中的每个风力发电机组的每两个叶片的变桨速度差，针对所述任意一个功率区间，基于每个风力发电机组的每两个叶片的变桨速度差确定与所述任意一个功率区间对应的每两个叶片的变桨速度差范围，其中，所述多个风力发电机组与所述风力发电机组的类型相同。
作为示例，所述多个风力发电机组可以是同一风电场的与所述风力发电机组同类型多个风力发电机组。
作为示例，所述多个风力发电机组可包括所述风力发电机组。
作为示例，可通过SCADA系统采集多个风力发电机组的变桨速度数据， 然后对采集的变桨速度数据进行处理。
作为示例，可按照以下字段读取SCADA系统获取的每个风力发电机组变桨期间的桨距角数据和/或变桨速度数据：
RECTIME＝'rectime'                         #时间
POWER＝'WTUR_PwrAt_Ra_F32'               #有功功率
BLADE_ANGLE1＝'WTPS_Ang_Ra_F32_blade1'   #叶片1的桨距角
BLADE_ANGLE2＝'WTPS_Ang_Ra_F32_blade2'   #叶片2的桨距角
BLADE_ANGLE3＝'WTPS_Ang_Ra_F32_blade3'   #叶片3的桨距角
BLADE_SPEED1＝'WTPS_Spd_Ra_F32_blade1'   #叶片1的变桨速度
BLADE_SPEED2＝'WTPS_Spd_Ra_F32_blade2'   #叶片2的变桨速度
BLADE_SPEED3＝'WTPS_Spd_Ra_F32_blade3'   #叶片3的变桨速度
作为示例，对多个风力发电机组变桨操作期间的叶片的变桨速度进行数据分仓的步骤可包括：对获取的多个风力发电机组变桨操作期间的叶片的变桨速度按照预设规则进行筛选，以去除异常或者错误数据，然后按照功率区间对剩余的变桨速度数据进行数据分仓。
作为示例，可将满足以下条件中的至少一个的变桨速度数据确定为需要剔除的变桨速度数据：变桨速度数据对应的有功功率不大于额定功率的a％(例如，60％)；变桨速度数据不在b度/秒(例如，-5度/秒)度至c(例如，5度/秒)度的范围内；两个叶片的变桨速度差大于预设值(例如，3度/秒)，其中，a、b、c为预设值，可根据风机型号被确定。
作为示例，如果某一风力发电机组的需要剔除的变桨速度数据的数量大于预设值(例如，10)，可将该风力发电机组的所有变桨速度数据剔除。
例如，对于在某一采样时刻采集的某一风力发电机组的三个叶片的变桨速度，如果每两个叶片的变桨速度差中的至少一个大于3度/秒(例如，叶片1与叶片2的变桨速度差大于3度/秒)，则可将该组变桨速度数据剔除。
例如，如果某一采样时刻的某一风力发电机组的三个叶片的变桨速度中存在超出[-5度/秒，5度/秒]范围的变桨速度，则该采样时刻采集的该风力发电机组的变桨速度数据可被剔除。
例如，如果变桨速度数据对应的风力发电机组的有功功率小于风力发电机组的额定功率的60％，则该变桨速度数据可被剔除。
例如，如果某一风力发电机组的变桨速度数据按照上述剔除规则被剔除 了超出预设数量的变桨速度数据，则与该风力发电机组相应的变桨速度数据全部被剔除。
作为示例，如果确定某一风力发电机组的变桨速度数据按照上述剔除规则被剔除了超出预设数量的变桨速度数据，可输出指示该风力发电机组存在故障的报警信息，例如，该风力发电机组采集数据的传感器可能出现了故障。
作为示例，针对所述任意一个功率区间，基于每个风力发电机组的每两个叶片的变桨速度差确定与所述任意一个功率区间对应的每两个叶片的变桨速度差范围的步骤包括：计算每个风力发电机组的每两个叶片的变桨速度差的平均值；将所述平均值加上第一预设增量作为所述任意一个功率区间对应的每两个叶片的变桨速度差范围的上限，将所述平均值减去第一预设增量作为所述任意一个功率区间对应的每两个叶片的变桨速度差范围的下限。
为了便于描述，以同一风电场的3个风力发电机组的1天的采样数据为例进行说明。
例如，在根据功率区间进行数据分仓后，对于第一功率区间，第一风力发电机组有例如1000条变桨速度数据，第二风力发电机组有例如1000条变桨速度数据，第三风力发电机组有例如1000条数据，其中每条数据据指示在某一采样时刻对应的风力发电机组的三个叶片的变桨速度数据。
针对与第一功率区间对应的每条数据，计算每两个叶片的变桨速度差，即计算叶片1与叶片2的变桨速度的差值、叶片1与叶片3的变桨速度的差值、叶片2与叶片3的变桨速度的差值。这样可获得9000个差值。
然后，计算9000差值的平均值，并将该平均值加上第一预设增量(例如，0.15)作为第一功率区间对应的每两个叶片的变桨速度差范围(即叶片1和叶片2的变桨速度差范围、叶片1和叶片3的变桨速度差范围和叶片2和叶片3的变桨速度差范围)的上限，将该平均值减去第一预设增量作为第一功率区间对应的每两个叶片的变桨速度差范围的下限。
类似地，可获得与其它功率区间对应的每两个叶片的变桨速度差范围。
虽然上文中描述了基于每个风力发电机组的每两个叶片的变桨速度差的平均值来确定每个功率区间对应的每两个叶片的变桨速度差范围，但这仅是示例。
作为示例，还可以基于每个风力发电机组的每两个叶片的变桨速度差，以其它方式获取对应的变桨速度差范围。
例如，可以将每个风力发电机组的每两个叶片的变桨速度差中的最小值加上一个增量作为对应的变桨速度差范围的下限，将每个风力发电机组的每两个叶片的变桨速度差中的最大值减去一个增量作为对应的变桨速度差范围的上限。
作为示例，第一预设增量可以是经验值。
作为示例，第一预设增量可以是9000个差值与所述平均值的差值中的最大值的绝对值,或者该绝对值减去某一数值。
返回参照图1，在步骤S103，当每两个叶片的变桨速度差超出对应变桨速度差范围的数量大于第一预设数量(例如，3)时，输出指示发生变桨故障的第一变桨故障预警信息。
例如，与第一功率区间对应的叶片1与叶片2的变桨速度差超出第一功率区间对应的叶片1与叶片2的变桨速度差范围的数量超过了第一预设数量时，输出指示发生变桨故障的第一变桨故障信息，此时输出的第一变桨故障预警信息可以是指示叶片1与叶片2可能存在变桨故障的预警信息。
例如，与第二功率区间对应的叶片1与叶片3的变桨速度差超出第二功率区间对应的叶片1与叶片3的变桨速度差范围的数量超过了第一预设数量时，也会输出指示发生变桨故障的第一变桨故障预警信息，此时，第一变桨故障信息可以是指示叶片1和叶片3可能存在故障的预警信息。
作为示例，所述第一预设增量和第一预设数量可以基于大数据获得。例如，根据获得的多个风力发电机组的变桨速度数据以及变桨故障数据，确定变桨故障出现之前的变桨速度数据，并根据出现变桨故障之前变桨速度数据的分布规律来确定所述预设增量和第一预设数量。例如，可以以预测准确性作为优化目标来确定所述预设增量和所述第一预设数量。
图2示出了某一风电场的多个风力发电机组变桨期间的变桨速度数据的曲线图。
参照图2，横轴表示风力发电机组的功率，纵轴表示每个风力发电机组的每两个叶片的变桨速度差，图2中大量的点表示风力发电机组的每两个叶片的变桨速度差，实线表示某一问题机组的变桨速度差数据，从图2中可以看出，大部分无变桨故障风力发电机组的每两个叶片的变桨速度差在0度/秒附近(位于图中两条虚线之间)，并且变桨速度差的均值接近于0度/秒，而问题机组的叶片1与其它两个叶片之间的变桨速度差达到了0.2度/秒，并且 相对于健康机组，问题机组较多的变桨速度差数据偏离0度/秒较远且偏离值大于0.15度/秒，停机后登机确认叶片1的变桨电机电磁刹车完全卡死。
例如，可将某一功率区间对应的所述第一预设增量设置为例如0.15度/s(<0.2度/s)，将第一预设数量设置为例如3。这样当风力发电机组的电磁刹车未报出故障或者未完全损坏时，可以输出预警信息，以提示用户及时对相关部件进行维护。
图3示出了根据本公开的另一实施例的风力发电机组的变桨故障预测方法的流程图。
参照图3，在步骤S301,在所述风力发电机组变桨操作期间，在每个采样时刻获取所述风力发电机组的每个叶片的桨距角以及所述风力发电机组的有功功率。
上文中描述了风力发电机组变桨期间，风力发电机组叶片的变桨速度的采样方法，变桨期间叶片的桨距角可以以类似方式获取，故在此不做赘述。
在步骤S302，确定每个采样时刻的风力发电机组的有功功率所处的功率区间，并基于每两个叶片的桨距角差范围与功率区间的映射关系，确定每个采样时刻的每两个叶片的桨距角差是否超出对应桨距角差范围。
功率区间的划分方式可以与参照图1描述的功率区间划分方式相同或相似。
作为示例，每两个叶片的桨距角差范围与功率区间的映射关系可以以映射表的形式被存储。
表2示出了每两个叶片的桨距角差范围与功率区间的映射关系的示例。参照表2，ARi指示与第i功率区间对应的每两个叶片的桨距角差范围(即与第i功率区间对应的叶片1和叶片2的桨距角差范围、叶片1和叶片3的桨距角差范围以及叶片2和叶片3的桨距角差范围均为ARi)，当风力发电机组在第i功率区间变桨时，如果不存在变桨故障，则变桨期间每两个叶片的桨距角差一般不会超出ARi，i＝1、2、3、4、5。
Figure PCTCN2022139082-appb-000002
Figure PCTCN2022139082-appb-000003
例如，在某一采样时刻，获取到风力发电机组的功率数据以及三个叶片的桨距角，如果该功率属于第一功率区间，则计算每两个叶片的桨距角差是否超出了范围AR1。
作为示例，所述桨距角差范围与功率区间的映射关系通过以下方式获得：按照功率区间，对多个风力发电机组变桨操作期间的叶片的桨距角进行数据分仓，针对任意一个功率区间，计算所述多个风力发电机组中的每个风力发电机组的每两个叶片的桨距角差，针对所述任意一个功率区间，基于每个风力发电机组的每两个叶片的桨距角差确定与所述任意一个功率区间对应的每两个叶片的桨距角差范围。
作为示例，可记录每个功率区间每两个叶片的桨距角差超出对应桨距角差范围的次数。例如，基于某一采样时刻获取的在某一功率区间运行的风力发电机组的叶片的桨距角，如果确定叶片1与叶片2的桨距角差超出了对应的桨距角差范围，则叶片1与叶片2的桨距角差超出与所述某一功率区间对应的桨距角差范围的次数增加1。
作为示例，可通过SCADA系统采集多个风力发电机组的变桨期间的桨距角数据，并对采集的桨距角数据进行处理。
作为示例，所述多个风力发电机组可以是与所述风力发电机组同一风电场的相同类型的多个风力发电机组。
作为示例，对多个风力发电机组变桨操作期间叶片的桨距角进行数据分仓的步骤可包括：对获取的多个风力发电机组变桨操作期间叶片的桨距角按照预设规则进行筛选，以去除异常或者错误数据，然后按照功率区间对剩余的桨距角数据进行数据分仓。
作为示例，可将满足以下条件中的至少一个的桨距角数据确定为需要剔除的桨距角数据：桨距角数据对应的有功功率不大于额定功率的d％(例如，20％)；叶片的桨距角不在e(例如，-5)度至f(例如，100)度之间；两个叶片的桨距角差大于预设值(例如，3度)，其中，d、e、f为预设值，可根据风机型号被确定。
作为示例，如果某一风力发电机组的需要剔除的桨距角数据的数量大于 预设值(例如，10)，可将该风力发电机组的所有桨距角数据剔除。
例如，对于在某一采样时刻采集的某一风力发电机组变桨期间的三个叶片的桨距角，如果每两个叶片的桨距角差中的至少一个大于3度(例如，叶片1和叶片2的桨距角的差值大于3度)，则可将该组桨距角数据剔除。
例如，如果某一采样时刻某一风力发电机组的三个叶片的桨距角中存在超出-5度至100度范围的桨距角，则该采样时刻采集的该风力发电机组的桨距角数据可被剔除。
例如，如果桨距角数据对应的风力发电机组的有功功率小于额定功率的20％，则该桨距角数据可被剔除。
例如，如果某一风力发电机组的桨距角数据按照上述剔除规则被剔除了超出预设数量的桨距角数据，则与该风力发电机组相应的桨距角数据全部被剔除。
作为示例，如果确定某一风力发电机组的桨距角数据按照上述剔除规则被剔除了超出预设数量的桨距角数据，可输出指示该风力发电机组存在故障的报警信息，例如，该风力发电机组采集数据的传感器可能出现了故障。
作为示例，针对所述任意一个功率区间，基于每个风力发电机组的每两个叶片的桨距角差确定与所述任意一个功率区间对应的桨距角范围的步骤包括：计算每个风力发电机组的每两个叶片的桨距角差的平均值；将所述平均值加上第二预设增量作为所述任意一个功率区间对应的每两个叶片的桨距角差范围的上限，将所述平均值减去第二预设增量作为所述任意一个功率区间对应的每两个叶片的桨距角差范围的下限。
为了便于描述，同样基于同一风电场的3个风力发电机组1天的采集的桨距角数据为例进行说明。
例如，在根据功率区间对桨距角数据进行数据分仓后，对于第一功率区间，第一风力发电机有1000条桨距角数据，第二风力发电机组有1000条桨距角数据，第三风力发电机组有1000条桨距角数据，其中每条桨距角数据指示某一采样时刻对应的风力发电机组的三个叶片的桨距角数据。
针对与第一功率区间对应的每条桨距角数据，计算每两个叶片的桨距角的差值，即计算叶片1与叶片2的桨距角差值、叶片1与配片3的桨距角差值、叶片2与叶片3的桨距角差值。这样可获得9000个差值。
然后，计算9000差值的平均值，并将该平均值加上第二预设增量作为第 一功率区间对应的每两个叶片的桨距角差范围(即叶片1与叶片2的桨距角差范围、叶片1与叶片3的桨距角差范围以及叶片2与叶片3的桨距角差范围和)的上限，将该平均值减去第二预设增量(例如，0.15)作为第一功率区间对应的每两个叶片的桨距角差范围的下限。
类似地，可获得与其它功率区间对应的每两个叶片的桨距角差范围。
虽然上文中描述了基于每个风力发电机组的每两个叶片的桨距角差的平均值来确定每个功率区间对应的每两个叶片的桨距角差范围，但这仅是示例。
作为示例，还可以基于每个风力发电机组的每两个叶片的桨距角差，以其它方式获取对应的桨距角差范围。
例如，可以将每个风力发电机组的每两个叶片的桨距角差中的最小值加上一个增量作为对应的桨距角差范围的下限，将每个风力发电机组的每两个叶片的桨距角差中的最大值减去一个增量作为对应的桨距角差范围的上限。
作为示例，第二预设增量可以是经验值。
作为示例，第二预设增量可以是9000个差值与所述平均值的差值中的最大值的绝对值减去某一数值。
返回参照图3，在步骤S303，当超出对应桨距角差范围的数量大于第二预设数量时，输出指示发生变桨故障的第二变桨故障预警信息。
例如，当每两个叶片的变桨速度差超出对应变桨速度差范围的数量大于第二预设数量(例如，3)时，输出指示发生变桨故障的第二变桨故障预警信息。
例如，风力发电机组在第一功率区间变桨期间，如果叶片1与叶片2的变桨速度差超出第一功率区间对应的叶片1与叶片2的变桨速度差范围的数量超过了第二预设数量，则输出指示发生变桨故障的第二变桨故障信息。作为示例，此时输出的第二变桨故障信息可以是指示叶片1与叶片2可能存在变桨故障的预警信息。这样用户可方便地确定对哪两个叶片进行检查。
作为示例，所述第二预设增量和第二预设数量可以基于大数据获得。例如，根据获得的多个风力发电机组变桨期间的桨距角数据以及变桨故障数据，确定变桨故障出现之前的变桨期间的桨距角数据，并根据出现变桨故障之前桨距角数据的分布规律来确定所述第二预设增量和第二预设数量。例如，可以基于出现变桨故障之前变桨期间的大量桨距角数据获取，以变桨故障预测准确性作为优化目标来确定所述第二预设增量和所述第二预设数量。
图4是示出某一风电场的多个风力发电机组变桨期间的桨距角数据的曲线图。
参照图4，横轴表示风力发电机组的功率，纵轴表示变桨期间每两个叶片的桨距角差，图中大量点表示每两个叶片的桨距角差数据，实线表示某一问题机组的桨距角差数据，从图4中可以看出，大部分无变桨故障的风力发电机组的每两个叶片的桨距角差在0度附近(位于图中两条虚线之间)，并且桨距角差的均值接近于0度，且不超出-0.5度至0.5度之间的范围，而问题机组的叶片3与其它两个叶片之间的桨距角差达到了-0.75度，并且问题机组较多桨距角差数据偏离0度较远且偏离大于0.5度，停机后登机确认叶片3的齿形带产生了松动。
因此，可将所述第二预设增量设置为例如0.5度(<0.75度)，将第二预设数量设置为例如3，这样当风力发电机组的叶片的齿形带松动但未报出故障时，可以尽早输出预警信息，以提示用户及时对相关部件进行维护。
作为示例，可以基于风力发电机组变桨期间的变桨速度数据和桨距角数据两者来预测变桨故障，即可将图1和图3所示的方法结合来预测风力发电机组的变桨故障。
作为示例，提供了一种风力发电机组的变桨故障预测方法，所述变桨故障预测方法包括：在所述风力发电机组变桨操作期间，在每个采样时刻获取所述风力发电机组的每个叶片的变桨速度以及所述风力发电机组的有功功率；确定每个采样时刻的风力发电机组的有功功率所处的功率区间，并基于每两个叶片的变桨速度差范围与功率区间的映射关系，确定每个采样时刻的每两个叶片的变桨速度差是否超出对应变桨速度差范围；以及当每两个叶片的变桨速度差超出对应变桨速度差范围的数量大于第一预设数量时，输出指示发生变桨故障的第一变桨故障预警信息，其中，所述方法还包括：在所述风力发电机组变桨操作期间，在每个采样时刻获取所述风力发电机组的每个叶片的桨距角；并基于每两个叶片的桨距角差范围与功率区间的映射关系，确定每个采样时刻的每两个叶片的桨距角差是否超出对应桨距角差范围；以及当超出对应桨距角差范围的数量大于第二预设数量时，输出指示发生变桨故障的第二变桨故障预警信息。
根据相关技术的变桨故障判断方法，当桨距角差值超过阈值时，输出变桨故障报警信息，然而此时变桨故障已然发生，例如，齿形带严重松动或脱 落、电磁刹车完全卡死。而根据本申请的实施例，由于齿形带脱落、电磁刹车完全卡死属于渐变式故障，因此，基于变桨速度数据和/或变桨期间的桨距角数据来预测变桨故障，当齿形带未脱落、电磁刹车未完全卡死时即发出预警信息，可以降低变桨故障导致的损失。
以上参照图1至图4对根据本公开的实施例的风力发电机组的变桨故障预测方法进行了描述，下面参照图5对根据本公开的实施例的风力发电机组的变桨故障预测装置进行描述。
图5是示出根据本公开的实施例的风力发电机组的变桨故障预测装置500的结构的示图。
参照图5，变桨故障预测装置500可包括采集单元501、确定单元502和输出单元503。本领域技术人员应当理解，变桨故障预测装置500还可另外包括其它组件，并且变桨故障预测装置500包括的组件中的至少一个可被省略、组合或者拆分。
作为示例，采集单元501可被配置为：在所述风力发电机组变桨操作期间，在每个采样时刻获取所述风力发电机组的每个叶片的变桨速度以及所述风力发电机组的有功功率。
作为示例，确定单元502可被配置为：确定每个采样时刻的风力发电机组的有功功率所处的功率区间，并基于每两个叶片的变桨速度差范围与功率区间的映射关系，确定每个采样时刻的每两个叶片的变桨速度差是否超出对应变桨速度差范围。
作为示例，输出单元503可被配置为：当每两个叶片的变桨速度差超出对应变桨速度差范围的数量大于第一预设数量时，输出指示发生变桨故障的第一变桨故障预警信息。
作为示例，采集单元501还可被配置为：在所述风力发电机组变桨操作期间，在每个采样时刻获取所述风力发电机组的每个叶片的桨距角以及所述风力发电机组的有功功率。
作为示例，确定单元502还可被配置为：确定每个采样时刻的风力发电机组的有功功率所处的功率区间，并基于每两个叶片的桨距角差范围与功率区间的映射关系，确定每个采样时刻的每两个叶片的桨距角差是否超出对应桨距角差范围。
作为示例，输出单元503还可被配置为：当超出对应桨距角差范围的数 量大于第二预设数量时，输出指示发生变桨故障的第二变桨故障预警信息。
根据本公开的实施例，还可提供一种存储指令的计算机可读存储介质，其中，当指令被至少一个处理器运行时，促使至少一个处理器执行根据本公开的实施例的风力发电机组的变桨故障预测方法。这里的计算机可读存储介质的示例包括：只读存储器(ROM)、随机存取可编程只读存储器(PROM)、电可擦除可编程只读存储器(EEPROM)、随机存取存储器(RAM)、动态随机存取存储器(DRAM)、静态随机存取存储器(SRAM)、闪存、非易失性存储器、CD-ROM、CD-R、CD+R、CD-RW、CD+RW、DVD-ROM、DVD-R、DVD+R、DVD-RW、DVD+RW、DVD-RAM、BD-ROM、BD-R、BD-R LTH、BD-RE、蓝光或光盘存储器、硬盘驱动器(HDD)、固态硬盘(SSD)、卡式存储器(诸如，多媒体卡、安全数字(SD)卡或极速数字(XD)卡)、磁带、软盘、磁光数据存储装置、光学数据存储装置、硬盘、固态盘以及任何其他装置，所述任何其他装置被配置为以非暂时性方式存储计算机程序以及任何相关联的数据、数据文件和数据结构并将所述计算机程序以及任何相关联的数据、数据文件和数据结构提供给处理器或计算机使得处理器或计算机能执行所述计算机程序。上述计算机可读存储介质中的计算机程序可在诸如客户端、主机、代理装置、服务器等计算机设备中部署的环境中运行，此外，在一个示例中，计算机程序以及任何相关联的数据、数据文件和数据结构分布在联网的计算机系统上，使得计算机程序以及任何相关联的数据、数据文件和数据结构通过一个或多个处理器或计算机以分布式方式存储、访问和执行。
根据本公开的实施例，还可提供一种控制器，所述控制器可包括处理器和存储器。存储器存储有计算机程序，当计算机程序被处理器执行时，实现如上所述的风力发电机组的变桨故障预测方法。
根据本公开的实施例，还可提供一种计算机程序产品，该计算机程序产品中的指令可由计算机设备的处理器执行以完成本文所述的风力发电机组的变桨故障预测方法。
本领域技术人员在考虑说明书及实践这里公开的实施例后，将容易想到本公开的其它实施方案。本申请旨在涵盖本公开的任何变型、用途或者适应性变化，这些变型、用途或者适应性变化遵循本公开的一般性原理并包括本公开未公开的本技术领域中的公知常识或惯用技术手段。说明书和实施例仅被视为示例性的，本公开的真正范围和精神由下面的权利要求指出。

Claims (10)

  1. 一种风力发电机组的变桨故障预测方法，其特征在于，所述变桨故障预测方法包括：
    在所述风力发电机组变桨操作期间，在每个采样时刻获取所述风力发电机组的每个叶片的变桨速度以及所述风力发电机组的有功功率；
    确定每个采样时刻的风力发电机组的有功功率所处的功率区间，并基于每两个叶片的变桨速度差范围与功率区间的映射关系，确定每个采样时刻的每两个叶片的变桨速度差是否超出对应变桨速度差范围；以及
    当每两个叶片的变桨速度差超出对应变桨速度差范围的数量大于第一预设数量时，输出指示发生变桨故障的第一变桨故障预警信息。
  2. 如权利要求1所述的变桨故障预测方法，其特征在于，所述映射关系通过以下方式获得：
    按照功率区间，对多个风力发电机组变桨操作期间的叶片的变桨速度进行数据分仓，
    针对任意一个功率区间，计算所述多个风力发电机组中的每个风力发电机组的每两个叶片的变桨速度差，
    针对所述任意一个功率区间，基于每个风力发电机组的每两个叶片的变桨速度差确定与所述任意一个功率区间对应的每两个叶片的变桨速度差范围，
    其中，所述多个风力发电机组与所述风力发电机组的类型相同。
  3. 如权利要求2所述的变桨故障预测方法，其特征在于，针对所述任意一个功率区间，基于每个风力发电机组的每两个叶片的变桨速度差确定与所述任意一个功率区间对应的每两个叶片的变桨速度差范围的步骤包括：
    计算每个风力发电机组的每两个叶片的变桨速度差的平均值；
    将所述平均值加上第一预设增量作为所述任意一个功率区间对应的每两个叶片的变桨速度差范围的上限，将所述平均值减去第一预设增量作为所述任意一个功率区间对应的每两个叶片的变桨速度差范围的下限。
  4. 一种风力发电机组的变桨故障预测方法，其特征在于，所述变桨故障预测方法包括：
    在所述风力发电机组变桨操作期间，在每个采样时刻获取所述风力发电 机组的每个叶片的桨距角以及所述风力发电机组的有功功率；
    确定每个采样时刻的风力发电机组的有功功率所处的功率区间，并基于每两个叶片的桨距角差范围与功率区间的映射关系，确定每个采样时刻的每两个叶片的桨距角差是否超出对应桨距角差范围；以及
    当超出对应桨距角差范围的数量大于第二预设数量时，输出指示发生变桨故障的第二变桨故障预警信息。
  5. 如权利要求4所述的变桨故障预测方法，其特征在于，所述桨距角差范围与功率区间的映射关系通过以下方式获得：
    按照功率区间，对多个风力发电机组变桨操作期间的叶片的桨距角进行数据分仓，
    针对任意一个功率区间，计算所述多个风力发电机组中的每个风力发电机组的每两个叶片的桨距角差，
    针对所述任意一个功率区间，基于每个风力发电机组的每两个叶片的桨距角差确定与所述任意一个功率区间对应的每两个叶片的桨距角差范围。
  6. 如权利要求5所述的变桨故障预测方法，其特征在于，针对所述任意一个功率区间，基于每个风力发电机组的每两个叶片的桨距角差确定与所述任意一个功率区间对应的桨距角范围的步骤包括：
    计算每个风力发电机组的每两个叶片的桨距角差的平均值；
    将所述平均值加上第二预设增量作为所述任意一个功率区间对应的每两个叶片的桨距角差范围的上限，将所述平均值减去第二预设增量作为所述任意一个功率区间对应的每两个叶片的桨距角差范围的下限。
  7. 一种风力发电机组的变桨故障预测装置，其特征在于，所述变桨故障预测装置包括：
    采集单元，被配置为：在所述风力发电机组变桨操作期间，在每个采样时刻获取所述风力发电机组的每个叶片的变桨速度以及所述风力发电机组的有功功率；
    确定单元，被配置为：确定每个采样时刻的风力发电机组的有功功率所处的功率区间，并基于每两个叶片的变桨速度差范围与功率区间的映射关系，确定每个采样时刻的每两个叶片的变桨速度差是否超出对应变桨速度差范围；以及
    输出单元，被配置为：当每两个叶片的变桨速度差超出对应变桨速度差范围的数量大于第一预设数量时，输出指示发生变桨故障的第一变桨故障预警信息。
  8. 一种风力发电机组的变桨故障预测装置，其特征在于，所述变桨故障预测装置包括：
    采集单元，被配置为：在所述风力发电机组变桨操作期间，在每个采样时刻获取所述风力发电机组的每个叶片的桨距角以及所述风力发电机组的有功功率；
    确定单元，被配置为：确定每个采样时刻的风力发电机组的有功功率所处的功率区间，并基于每两个叶片的桨距角差范围与功率区间的映射关系，确定每个采样时刻的每两个叶片的桨距角差是否超出对应桨距角差范围；以及
    输出单元，被配置为：当超出对应桨距角差范围的数量大于第二预设数量时，输出指示发生变桨故障的第二变桨故障预警信息。
  9. 一种存储有计算机程序的计算机可读存储介质，其特征在于，当所述计算机程序被处理器执行时，实现如权利要求1至6中任意一项所述的风力发电机组的变桨故障预测方法。
  10. 一种控制器，其特征在于，所述控制器包括：
    处理器；和
    存储器，存储有计算机程序，当所述计算机程序被处理器执行时，实现如权利要求1至6中任意一项所述的风力发电机组的变桨故障预测方法。
PCT/CN2022/139082 2022-08-31 2022-12-14 风力发电机组的变桨故障预测方法和装置 WO2024045413A1 (zh)

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