Control methods, devices, media, electronic equipment and software products for wind turbine generators

Abstract

This disclosure relates to a control method, device, medium, electronic equipment, and program product for a wind turbine generator. The wind turbine generator includes an actuator. The control method includes: acquiring operating data of the wind turbine generator; calculating risk data of the wind turbine generator and remaining capacity data of the actuator based on the operating data; determining risk mitigation data required to mitigate the risk based on the risk data; determining whether the current state of the wind turbine generator is a safe state based on the risk data, remaining capacity data, and risk mitigation data; if the current state of the wind turbine generator is a safe state, determining the control quantity of the actuator based on the risk data and remaining capacity data; and controlling the actuator based on the control quantity. This avoids secondary structural load impacts on the mechanical structure of the wind turbine generator, thus helping to ensure the structural safety of the wind turbine generator.

Description

Technical Field

[0001] This disclosure relates to the field of wind turbine control technology, specifically to a wind turbine control method, a wind turbine control device, a non-transitory computer-readable storage medium, electronic equipment, and computer program products. Background Technology

[0002] With the continuous expansion of wind power scale, the safe operation of wind turbines under extreme conditions such as high wind speeds, extreme gusts, and grid anomalies has become a key issue in ensuring grid reliability and turbine structural safety. In related technologies, wind turbines may blindly enter extreme condition control modes under extreme conditions, potentially leading to control strategies exceeding the capabilities of the actuators and causing secondary structural load impacts on the wind turbine's mechanical structure. Summary of the Invention

[0003] The purpose of this disclosure is to provide a control method for a wind turbine generator, a control device for a wind turbine generator, a non-transitory computer-readable storage medium, an electronic device, and a computer program product.

[0004] To achieve the above objectives, in one aspect, this disclosure provides a control method for a wind turbine generator, the wind turbine generator including an actuator, the method comprising: Obtain the operating data of the wind turbine; Based on the operational data, calculate the risk data of the wind turbine and the remaining capacity data of the actuator; Based on the aforementioned risk data, determine the risk mitigation data required to suppress the risk; Based on the risk data, the remaining capacity data, and the risk mitigation data, determine whether the current state of the wind turbine is a safe state; If the current state of the wind turbine is a safe state, the control quantity of the actuator is determined based on the risk data and the remaining capacity data; The actuator is controlled based on the control quantity.

[0005] Optionally, the operating data includes transmission data, structural load data, power grid data, wind condition data, and actuator data; The calculation of the risk data of the wind turbine and the remaining capacity data of the actuator based on the operating data includes: Calculate the overspeed risk value based on the transmission data; Calculate the load risk value based on the structural load data; Calculate the power grid risk value based on the power grid data; Calculate the yaw risk value based on the aforementioned wind condition data; The risk data is determined based on the overspeed risk value, load risk value, power grid risk value, and yaw risk value. The remaining capacity data is calculated based on the actuator data.

[0006] Optionally, determining whether the current state of the wind turbine is a safe state based on risk data, remaining capacity data, and risk mitigation data includes: The remaining capability data is mapped to suppression capability data based on the capability mapping matrix; If the risk suppression data is less than or equal to the suppression capability data, and the risk data is less than the risk threshold, then the current state of the wind turbine is determined to be a safe state.

[0007] Optionally, the method further includes: If the risk suppression data is greater than the suppression capacity data, and / or the risk data is greater than or equal to the risk threshold, the current state of the wind turbine is determined to be a risky state. If the current state of the wind turbine is considered a risky state, the wind turbine shall be shut down.

[0008] Optionally, determining the control amount of the actuator based on the risk data and the remaining capacity data includes: Calculate the normalized value corresponding to each risk value of the risk data; The risk type is determined based on the normalized value; The control level of the implementing agency is determined based on the risk type and the remaining capacity data.

[0009] Optionally, the remaining capacity data includes multiple remaining capacity values, and the number of actuators is multiple, with each remaining capacity value representing the risk mitigation capability of at least one actuator; Determining the control level of the actuator based on the risk type and the remaining capacity data includes: Based on the risk type, determine the allocation priority of control quantities for multiple implementing agencies; The allocation ratio of control quantities for multiple actuators is determined based on multiple remaining capacity values.

[0010] Optionally, determining the control quantity of the actuator based on the risk data and the remaining capacity data further includes: If the actuator with the current priority is unable to execute the assigned control quantity, at least a portion of the control quantity is transferred to the actuator with the next priority according to a preset substitution mapping relationship; The method further includes: After the control quantity transfer, the current state of the wind turbine is determined to be a safe state based on the updated risk data, the remaining capacity data, and the risk mitigation data.

[0011] Optionally, the method further includes: The predicted time period is determined based on the configuration parameters of the wind turbine. Based on the current state of the wind turbine and the control quantity of the actuator, the predicted state of the wind turbine during the predicted time period is determined. If the predicted state of the wind turbine is a safe state, the actuator continues to be controlled based on the control quantity. If the predicted state of the wind turbine is a risky state, the wind turbine will be shut down.

[0012] Optionally, the method further includes: Under preset conditions, the wind turbine unit is controlled to shut down; The preset conditions include at least one of the following: The risk data continues to increase within the first preset monitoring interval; The remaining capacity data continues to decrease within the second preset monitoring interval; The actuator malfunctioned.

[0013] In another aspect, this disclosure provides a control device for a wind turbine generator, the wind turbine generator including an actuator, the device comprising: The data acquisition module is used to acquire the operating data of the wind turbine. The data calculation module is used to calculate the risk data of the wind turbine and the remaining capacity data of the actuator based on the operating data. The first determining module is used to determine the risk suppression data required to suppress the risk based on the risk data; The second determining module is used to determine whether the current state of the wind turbine is a safe state based on the risk data, the remaining capacity data and the risk suppression data. The third determining module is used to determine the control quantity of the actuator based on the risk data and the remaining capacity data when the current state of the wind turbine is a safe state. An execution control module is used to control the actuator based on the control quantity.

[0014] Optionally, the second determining module is used to: The remaining capability data is mapped to suppression capability data based on the capability mapping matrix; If the risk suppression data is less than or equal to the suppression capability data, and the risk data is less than the risk threshold, then the current state of the wind turbine is determined to be a safe state.

[0015] Optionally, the device further includes a state prediction module, the state prediction module being used for: The predicted time period is determined based on the configuration parameters of the wind turbine. Based on the current state of the wind turbine and the control quantity of the actuator, the predicted state of the wind turbine during the predicted time period is determined. If the predicted state of the wind turbine is a safe state, the actuator continues to be controlled based on the control quantity. If the predicted state of the wind turbine is a risky state, the wind turbine will be shut down.

[0016] In another aspect, this disclosure provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the wind turbine control method described above.

[0017] In another aspect, this disclosure provides an electronic device, comprising: a memory storing a computer program thereon; and a processor for executing the computer program in the memory to implement the steps of the wind turbine control method described above.

[0018] In another aspect, this disclosure provides a computer program product, including a computer program that, when executed by a processor, implements the steps of the wind turbine control method described above.

[0019] The above technical solution acquires wind turbine operating data; calculates wind turbine risk data and remaining capacity data of the actuator based on the operating data; determines risk mitigation data required to suppress risks based on the risk data; determines whether the current state of the wind turbine is safe based on the risk data, remaining capacity data, and risk mitigation data; if the current state of the wind turbine is safe, determines the control quantity of the actuator based on the risk data and remaining capacity data; and controls the actuator based on the control quantity. This avoids secondary structural load impacts on the mechanical structure of the wind turbine, thus helping to ensure the structural safety of the wind turbine.

[0020] Other features and advantages of this disclosure will be described in detail in the following detailed description section. Attached Figure Description

[0021] The accompanying drawings are provided to further illustrate the present disclosure and form part of the specification. They are used together with the following detailed description to explain the present disclosure, but do not constitute a limitation thereof. In the drawings: Figure 1 This is a schematic diagram of a wind turbine module according to an exemplary embodiment; Figure 2 This is a flowchart illustrating a control method for a wind turbine generator according to an exemplary embodiment; Figure 3 It is based on Figure 1 The illustrated embodiment presents a flowchart of a control method for a wind turbine generator. Figure 4 It is based on Figure 1 The illustrated embodiment presents a flowchart of a control method for a wind turbine generator. Figure 5 This is a schematic diagram illustrating a process for allocating control quantities to an actuator based on risk type, according to an exemplary embodiment. Figure 6 This is a schematic diagram illustrating a process for allocating control quantities based on an alternative allocation mechanism, according to an exemplary embodiment. Figure 7 This is a flowchart illustrating a rollback protection mechanism according to an exemplary embodiment; Figure 8 This is a schematic diagram of a control device for a wind turbine generator according to an exemplary embodiment; Figure 9 This is a block diagram illustrating an electronic device according to an exemplary embodiment; Figure 10 This is a block diagram illustrating an electronic device according to another exemplary embodiment. Detailed Implementation

[0022] The specific embodiments of this disclosure will be described in detail below with reference to the accompanying drawings. It should be understood that the specific embodiments described herein are for illustration and explanation only and are not intended to limit this disclosure.

[0023] It should be noted that all actions involving the acquisition of signals, information, or data in this disclosure are carried out in compliance with the relevant data protection laws and policies of the country where the location is situated, and with authorization from the owner of the relevant device.

[0024] Before detailing the specific implementation methods of this disclosure, the application scenarios of this disclosure are explained below. This disclosure can be applied to the control system of wind turbine units.

[0025] With the continuous expansion of wind power scale, the safe operation of wind turbines under extreme conditions such as high wind speeds, extreme gusts, and grid anomalies has become a key issue in ensuring grid reliability and turbine structural safety. In some related technologies in this field, wind turbines may blindly enter extreme condition control modes under extreme conditions, potentially leading to control strategies exceeding the capabilities of the actuators and causing secondary structural load impacts on the wind turbine's mechanical structure.

[0026] To address the aforementioned issues, the present disclosure provides a solution that acquires wind turbine operating data; calculates risk data and remaining capacity data of the actuator based on the operating data; determines risk mitigation data required to suppress the risk based on the risk data; determines whether the current state of the wind turbine is safe based on the risk data, remaining capacity data, and risk mitigation data; if the current state of the wind turbine is safe, determines the control input of the actuator based on the risk data and remaining capacity data; and controls the actuator based on the control input. This avoids secondary structural load impacts on the mechanical structure of the wind turbine, thus contributing to the structural safety of the wind turbine.

[0027] Figure 1 This is a schematic diagram of a wind turbine generator 200 according to an exemplary embodiment. The wind turbine generator 200 includes a control system 210 and an actuator 220, and the control system 210 and the actuator 220 are communicatively connected.

[0028] Figure 2 This is a flowchart illustrating a control method for a wind turbine generator 200 according to an exemplary embodiment, as shown below. Figure 2 As shown, this disclosure provides a control method for a wind turbine generator 200, the method including: S110: Obtain the operating data of wind turbine 200; S120: Calculate the risk data of the wind turbine 200 and the remaining capacity data of the actuator 220 based on the operating data; S130: Determine the risk mitigation data required to mitigate risk based on risk data; S140: Determine whether the current state of wind turbine 200 is a safe state based on risk data, remaining capacity data, and risk mitigation data; S150: If the current state of the wind turbine 200 is a safe state, determine the control quantity of the actuator 220 based on risk data and remaining capacity data; S160: Control the actuator 220 based on the control quantity.

[0029] For example, the operating data of the wind turbine 200 may include various data from multiple components within the wind turbine 200 to characterize the operating status of the wind turbine 200. Based on the obtained operating data, risk data of the wind turbine 200 can be calculated, representing the degree of risk of the wind turbine 200 under current extreme operating conditions. Based on the operating data, the remaining capacity data of the actuator 220 of the wind turbine 200 can also be calculated, representing the control margin that the actuator 220 can provide at the current moment.

[0030] Based on the risk data, the corresponding risk suppression data can be determined. The risk suppression data represents the amount of risk suppression that the actuator 220 needs to provide to control the operating status of the wind turbine 200 within the safety boundary by suppressing the risk of the wind turbine 200.

[0031] Next, the remaining capacity data is compared with the risk mitigation data, and the safety status of the wind turbine 200 is determined by combining the magnitude of the risk data. The safety status refers to the state in which the actuator 220 can maintain or bring back the operating state of the wind turbine 200 within the safety boundary. Determining whether the current state of the wind turbine 200 is a safe state determines whether the actuator 220 has the ability to perform risk mitigation, and thus whether the wind turbine 200 has limit ride capability. Limit ride refers to the control process by which the actuator 220, when the wind turbine 200 is approaching but has not yet exceeded the structural safety limit, electrical safety limit, or actuator 220 control limit, allows the wind turbine 200 to continue operating within the safety boundary or smoothly transition under operating conditions.

[0032] If the current state of the wind turbine 200 is considered safe, it indicates that the wind turbine 200 possesses extreme ride capability, and the wind turbine 200 can enter the extreme ride control mode. In the extreme ride control mode, the control quantity of the actuator 220 can be determined based on risk data and remaining capacity data, and the actuator 220 can be controlled based on the control quantity.

[0033] In the above technical solution, it is first determined whether the wind turbine 200 has extreme ride capability. If the current state of the wind turbine 200 is determined to be a safe state, i.e., possessing extreme ride capability, then the extreme ride control mode is entered. Based on risk data and remaining capacity data, the control quantity of the actuator 220 is determined, and the actuator 220 is controlled. In this way, the capability limitations of the actuator 220 under extreme operating conditions are taken into account, avoiding secondary structural load impacts on the mechanical structure of the wind turbine 200 due to blindly entering the extreme operating condition control mode, thus helping to ensure the structural safety of the wind turbine 200.

[0034] In some implementations, under normal operating conditions, the control system 210 of the wind turbine 200 operates in normal control mode. In this mode, the control system 210 can periodically collect operating data and calculate risk data. If the risk data exceeds a preset warning threshold, the control system 210 enters the limit crossing determination process, that is, it executes the control method of the wind turbine 200 disclosed herein.

[0035] In some embodiments, the actuator 220 includes a pitch system, a converter, a yaw system, and a power controller; the control system 210 can issue pitch commands to the pitch system, generator torque commands to the converter, yaw suppression commands to the yaw system, and active power derating commands to the power controller according to the control quantity, so as to control the pitch system, the converter, the yaw system, and the power controller.

[0036] In some cases, slope limitations can be applied to output commands. For example, pitch commands can be limited in terms of pitch angle change rate and pitch acceleration; generator torque commands can be limited in terms of torque drop slope and torque fluctuation amplitude; yaw suppression commands can be limited in terms of yaw start / stop frequency and yaw rate; and active power derating commands can be limited in terms of power drop slope. By limiting slopes, the wind turbine 200 can reduce risk while preventing control switching itself from becoming a new source of load excitation.

[0037] In some embodiments, the operational data includes transmission data, structural load data, power grid data, wind condition data, and actuator data. Calculating the risk data of the wind turbine 200 and the remaining capacity data of the actuator 220 based on the operational data (i.e., S120) includes: Calculate overspeed risk value based on transmission data; Calculate load risk values ​​based on structural load data; Calculate power grid risk values ​​based on power grid data; Calculate the yaw risk value based on wind condition data; Risk data is determined based on overspeed risk value, load risk value, power grid risk value, and yaw risk value; Remaining capacity data is calculated based on the data from the implementing agency.

[0038] For example, the control system 210 of the wind turbine 200 is communicatively connected to the transmission status acquisition module, the structural load acquisition module, the power grid status acquisition module, the wind condition acquisition module, and the actuator status acquisition module.

[0039] The transmission status acquisition module can obtain transmission data from the main shaft encoder, generator encoder, converter torque feedback, and torsional vibration observer of the wind turbine 200. The transmission data includes rotor speed, generator speed, generator torque, and transmission chain torsional vibration amplitude. Specifically, rotor speed is used to calculate overspeed risk; generator speed is used to assist in judging the transmission chain speed status; generator torque is used to calculate the converter's remaining torque capacity; and transmission chain torsional vibration amplitude is used to determine whether rapid torque changes may cause transmission chain shock.

[0040] The structural load acquisition module may include blade root strain sensors, tower acceleration sensors, tower base bending moment sensors, and load observers. The structural load acquisition module is used to collect structural load data, including blade root flapping bending moment, blade root flaring bending moment, tower top forward and backward acceleration, tower top lateral acceleration, tower base bending moment, and nacelle pitch angle. Specifically, the blade root flapping bending moment primarily reflects the ultimate load on the wind turbine blades in the wind direction; the blade root flaring bending moment primarily reflects the tangential load and the coupling effect of the transmission chain; the tower top forward and backward acceleration and tower base bending moment are used to assess the risk of first-order mode vibration of the tower; and the tower top lateral acceleration is used to assess the risk of lateral load caused by yaw error. If the wind turbine is not equipped with all load sensors, the corresponding representative load values ​​can also be estimated using the state observer based on wind speed, rotational speed, pitch angle, power, and acceleration signals.

[0041] The power grid status acquisition module can obtain power grid data from converters, power quality monitoring devices, and grid-connected protection devices. Power grid data includes grid connection point voltage, grid frequency, frequency change rate, voltage sag duration, and converter DC bus voltage. Specifically, the grid connection point voltage is used to determine whether there is a low-voltage or high-voltage disturbance; the grid frequency and frequency change rate are used to determine frequency support requirements; the DC bus voltage is used to determine whether the converter's energy absorption capacity is approaching its limit; and the voltage sag duration is used to distinguish between short-term ride-through and persistent faults.

[0042] The wind condition acquisition module may include a nacelle anemometer, a lidar feedforward anemometer unit, a wind direction sensor, and a turbulence estimation unit. The module collects wind condition data, including nacelle wind speed, wind speed change rate, turbulence intensity, nacelle wind direction, and yaw error. The nacelle wind speed can be provided by the nacelle anemometer or the lidar feedforward anemometer; the wind speed change rate can be calculated from the wind speed difference between adjacent sampling periods; the turbulence intensity can be determined by the ratio of the standard deviation of wind speed to the average wind speed within a preset time window; and the yaw error can be determined by the angle between the nacelle orientation and the inflow wind direction. For wind speed signals affected by wind measurement obstruction or nacelle wake, equivalent wind speeds derived from engine speed, power, and pitch angle can be used for correction.

[0043] The actuator status acquisition module can acquire actuator data from the pitch control cabinet, yaw control cabinet, converter, and temperature sensors. Actuator data includes pitch angle, pitch rate, pitch acceleration, pitch motor temperature, converter available torque margin, yaw rate, yaw brake status, yaw drive availability status, and converter temperature. All actuator data is used to determine whether actuator 220 has the capability to continue performing risk mitigation tasks. For example, if the pitch rate is close to the maximum allowable value, or the pitch angle is close to the maximum allowable pitch angle, the remaining pitch capability is low; if the converter temperature is close to the upper temperature limit, its ability to continue performing torque regulation or active power derating tasks is reduced; if the yaw brake is locked or the yaw drive is unavailable, the remaining yaw capability should be reduced or even zero.

[0044] When collecting operational data through multiple acquisition modules, a unified timestamp synchronization mechanism can be used for data acquisition. For example, for data with high sampling frequencies such as blade root load, tower acceleration, and drive train speed, representative values ​​within the control cycle can be obtained using sliding window averaging, peak hold, or bandpass filtering. For data with low sampling frequencies such as wind speed, yaw error, and grid frequency, alignment can be achieved using linear interpolation or holding the previous valid value. This ensures that various types of data can enter the same control cycle for subsequent risk calculations and remaining capacity calculations. After acquiring the operational data, data preprocessing can be performed according to the actual application requirements. Data preprocessing can include data filtering, outlier removal, etc.

[0045] Then, the overspeed risk value can be calculated based on the transmission data. The calculation formula is as follows: ; in, This is the risk value for speeding. This represents the current rotor speed. This refers to the rated rotor speed; To allow for the maximum possible rotor speed. If the current rotor speed is lower than the rated rotor speed, the overspeed risk value can be limited to zero, thus avoiding negative risk values ​​under low-speed conditions.

[0046] Load risk values ​​can be calculated based on structural load data. These load risk values ​​include blade load risk values ​​and tower load risk values. The formula for calculating the blade load risk value is as follows: ; in, This represents the blade load risk value. The blade represents the load; This represents the allowable ultimate load on the blade. The representative load on the blade can be the maximum absolute value of the bending moment in the flapping direction at the blade root of the three blades of the 200 wind turbine unit, or it can be the combined load calculated by weighting the bending moment in the flapping direction and the bending moment in the shimmy direction at the blade root.

[0047] The formula for calculating the tower load risk value is as follows: ; in, This represents the risk value of the tower load. The tower represents the load; The tower's allowable ultimate load is given. The representative load on the tower can be obtained directly from the bending moment at the bottom of the tower, or it can be estimated by a load observer based on the forward and backward acceleration at the top of the tower, the lateral acceleration at the top of the tower, and the pitch angle of the nacelle.

[0048] The power grid risk value can be calculated based on power grid data. The formula for calculating the power grid risk value is as follows: ; in, This represents the power grid risk value. This refers to the voltage at the grid connection point. This is the rated grid connection point voltage; To allow for voltage deviation; The power grid frequency; The rated power grid frequency; To allow for frequency deviation; This refers to the DC bus voltage of the converter. This is the DC bus reference voltage; To allow for DC bus voltage deviation; This indicates taking the maximum value. Using the maximum value method allows the most severe disturbance among voltage, frequency, and DC bus voltage to be used as the representative value of grid-side risk, avoiding the underestimation of grid risk due to averaging of various disturbances.

[0049] Based on wind condition data, the yaw risk value can be calculated. The formula for calculating the yaw risk value is as follows: ; in, Yaw risk value; This refers to yaw error; To allow for yaw error, yaw risk is considered as an independent risk value in risk assessment during extreme crossing scenarios. Yaw risk affects power generation efficiency and can cause cyclic loads on blades, lateral loads on the tower, and yaw system impacts.

[0050] Risk data can be determined based on overspeed risk values, load risk values, grid risk values, and yaw risk values. Risk data can be represented in vector form, where overspeed risk values, load risk values, grid risk values, and yaw risk values ​​are all risk components of the risk vector. The expression for risk data can be as follows: ; in, This is the risk vector, also known as risk data.

[0051] The remaining capacity data can be calculated based on the actuator data, and the calculation process is as follows.

[0052] Based on actuator data, the remaining pitch capability, torque capability, yaw capability, and thermal safety capability can be calculated. Among these, the remaining pitch capability characterizes the pitch system's ability to mitigate risks, and the calculation formula is as follows: ; in, This represents the remaining pitch capability value. This is the current pitch rate; To allow the maximum pitch rate; This is the current pitch angle; To the maximum allowable pitch angle; This is the reference pitch angle for normal operation. This indicates taking the minimum value. The calculation formula considers both pitch rate margin and pitch angle travel margin, avoiding capability judgment biases caused by focusing solely on either rate or angle. For example, when the pitch angle is close to its maximum value, even if there is still a margin in the pitch rate, the actual usable space for further pitch increases is limited.

[0053] The residual torque capacity value characterizes the converter's ability to mitigate risks, and the calculation formula is as follows: ; in, This represents the remaining torque capacity value. This represents the current generator torque. This is the maximum allowable generator torque. Torque reserve capacity characterizes the torque space available when the converter continues to regulate torque, limit torque slope, or perform active power derating.

[0054] The yaw residual capacity value characterizes the yaw system's ability to mitigate risk, and the calculation formula is as follows: ; in, This represents the remaining yaw capacity value. This is the yaw availability value; it is set to 1 when yaw is available and 0 when yaw is unavailable. The current yaw rate; The maximum permissible yaw rate is determined by this formula. This calculation incorporates the availability of yaw drive, the permissibility of yaw brake release, and the current yaw rate margin into the assessment of remaining yaw capacity, making the determination of remaining yaw capacity more accurate.

[0055] The formula for calculating the residual thermal safety capacity is as follows: ; in, This represents the remaining thermal safety capacity value. The temperature of the pitch motor; This refers to the upper limit of the allowable temperature for the pitch motor; Reference temperature for the pitch motor; For converter temperature; This refers to the upper limit of the allowable temperature for the converter; This is the converter reference temperature. Thermal safety margin can be used to prevent actuator 220 from failing due to thermal overload during extreme ride-through.

[0056] Subsequently, the remaining capacity data can be determined based on the pitch remaining capacity value, torque remaining capacity value, yaw remaining capacity component, and thermal safety remaining capacity value. The remaining capacity data can be represented in vector form, where the pitch remaining capacity value, torque remaining capacity value, yaw remaining capacity component, and thermal safety remaining capacity value are all remaining capacity components of the remaining capacity vector. The expression for the remaining capacity data can be as follows: ; in, This is the remaining capacity vector, also known as the remaining capacity data. In some cases, the weights of each remaining capacity component in the remaining capacity vector can be adjusted based on the specific values ​​of the actuator data. For example, when the voltage drop duration exceeds a preset time threshold, the weight of the grid disturbance risk component can be increased.

[0057] In some embodiments, risk data and remaining capacity data can be subjected to amplitude limiting, so that each component is within a range of 0 to 1 or other preset ranges. When the risk component is greater than 1, it indicates that the corresponding risk has reached or exceeded the allowable limit; when the remaining capacity component is close to 0, it indicates that the corresponding actuator 220 can no longer provide effective control capability.

[0058] The above technical solutions realize the transformation of physical quantities with different dimensions, units, and dynamic characteristics into a unified vector expression that is comparable, matchable, and usable for control decision-making. They fully consider the multidimensional risks of the wind turbine 200 under extreme operating conditions, incorporating overspeed risk, blade load risk, tower load risk, grid risk, and yaw risk into the risk data. This effectively avoids the biased judgment problems caused by relying solely on sudden speed changes, single blade root loads, or single yaw states to trigger load reduction control in related technologies. Simultaneously, the remaining pitch capacity, torque capacity, yaw capacity, and thermal safety capacity are all included in the remaining capacity data, serving as an important basis for whether the wind turbine 200 is allowed to enter the extreme ride control mode. This takes into account the various capability limitations of each actuator 220, further avoiding secondary structural load impacts on the mechanical structure of the wind turbine 200 caused by blindly entering the extreme operating condition control mode, thus contributing to the structural safety of the wind turbine 200.

[0059] After determining the risk data, risk mitigation data can be determined based on the risk data. Risk mitigation data can also be represented in vector form, and the formula for calculating risk mitigation data is as follows: ; in, This is the risk suppression vector, also known as risk suppression data; This is a risk-inhibition transformation matrix, used to characterize the demand relationship between each risk component and the control quantity of each actuator 220; For risk data; This serves as the baseline vector for normal operation risk.

[0060] The risk-suppression transformation matrix may include a first suppression demand coefficient, a second suppression demand coefficient, a third suppression demand coefficient, a fourth suppression demand coefficient, and a fifth suppression demand coefficient. Specifically, the first suppression demand coefficient represents the suppression requirement of overspeed risk on pitch and generator torque; the second suppression demand coefficient guarantees the suppression requirement of blade load risk on independent pitch; the third suppression demand coefficient represents the suppression requirement of tower load risk on tower damping-added pitch; the fourth suppression demand coefficient represents the suppression requirement of grid risk on active power derating and torque slope limitation; and the fifth suppression demand coefficient represents the suppression requirement of yaw risk on yaw suppression.

[0061] When the overspeed risk value increases significantly, the demand for strain gauge pitch increase and torque limitation in the risk suppression data increases; when the blade load risk value increases significantly, the demand for independent pitch reduction in the risk suppression data increases; when the tower load risk value increases significantly, the demand for tower damping additional pitch in the risk suppression data increases; when the grid risk value increases significantly, the demand for active power derating, torque slope limitation, and DC bus energy control in the risk suppression data increases; when the yaw risk value increases significantly, the demand for yaw suppression or speed-limited yaw in the risk suppression data increases.

[0062] The following describes in detail the process of determining whether the current state of wind turbine 200 has the capability to overcome extreme risks based on risk data, remaining capacity data, and risk mitigation data.

[0063] Figure 3 It is based on Figure 1 The illustrated embodiment presents a flowchart of a control method for a wind turbine generator 200, as shown below. Figure 3 As shown, determining whether the current state of wind turbine 200 is a safe state (i.e., S140) based on risk data, remaining capacity data, and risk mitigation data includes: S141: Map the remaining capability data to suppression capability data based on the capability mapping matrix; S142: If the risk suppression data is less than or equal to the suppression capacity data and the risk data is less than the risk threshold, the current state of wind turbine 200 is determined to be a safe state.

[0064] For example, using This represents the remaining capability data corresponding to the current state. Suppression capability data can be represented as... ,in, This represents the capability mapping matrix. In practical applications, the capability mapping matrix can be determined based on the turbine parameters of the wind turbine 200, the parameters of the control system 210, the dynamic performance of the actuators 220, simulation calibration results, and experimental data. Through the capability mapping matrix, the remaining pitch capability value can be mapped to the capability to suppress overspeed risk, blade load risk, and tower load risk; the remaining converter capability value can be mapped to the capability to suppress overspeed risk and grid risk; the remaining yaw capability value can be mapped to the capability to suppress yaw risk and tower load risk; and the remaining thermal safety capability value can be used as a reduction factor for the continuous output capability of all actuators 220.

[0065] After determining the suppression capability data, the safety status of wind turbine 200 can be determined. The expression for the safety status is as follows: ; in, Indicates a safe state; This is the current state vector of wind turbine 200; This refers to the risk suppression data corresponding to the current state vector; This refers to the risk data corresponding to the current state vector; The risk threshold represents the maximum permissible risk that a wind turbine unit 200 can withstand. "" indicates that each corresponding component in the vector is less than or equal to " "" indicates that each corresponding component in the vector is less than the others.

[0066] If the risk mitigation data is less than or equal to the mitigation capacity data, it indicates that the risk mitigation capacity required by wind turbine 200 is less than the risk mitigation capacity of actuator 220, and actuator 220 is sufficient to complete the risk mitigation. Conversely, if the risk mitigation data is greater than the mitigation capacity data, it indicates that actuator 220 is insufficient to complete the risk mitigation. If the risk data is less than the risk threshold, it indicates that the current risk of wind turbine 200 does not exceed the allowable range of wind turbine 200. Conversely, if the risk data is greater than or equal to the risk threshold, it indicates that the current risk of wind turbine 200 exceeds the allowable range of wind turbine 200.

[0067] Based on the expression for the safe state, it can be seen that when the risk suppression data is less than or equal to the suppression capability data and the risk data is less than the risk threshold, the current state of the wind turbine 200 is a safe state, which means that the wind turbine 200 has the ability to overcome extreme risks and can enter the extreme risk crossing control mode.

[0068] If the risk suppression data is greater than the suppression capacity data, and / or the risk data is greater than or equal to the risk threshold, the current state of wind turbine 200 is determined to be a risky state, which means that wind turbine 200 does not have the ability to overcome extreme risks.

[0069] It is understandable that a safe state is not a fixed threshold range, but rather a state that dynamically changes with current operating data. For example, under the same blade load risk, if the pitch motor temperature is low, the pitch angle stroke is sufficient, and the pitch rate margin is large, then the remaining pitch capacity is strong, and the current state is more likely to be a safe state. Conversely, if the pitch system is already close to the rate limit or temperature limit, then even if the blade load risk value is the same, the current state may not be a safe state. Through this judgment method in this embodiment, the problem of traditional threshold control continuing to attempt load reduction when the actuator 220's capacity is insufficient can be effectively avoided, ensuring the safety of the actuator 220.

[0070] In some embodiments, if the current state of the wind turbine 200 is a risky state, it is not suitable to perform extreme crossing, and the wind turbine 200 is put into a controlled shutdown mode to shut down the wind turbine 200.

[0071] Upon entering controlled shutdown mode, the control system 210 executes shutdown actions according to a preset sequence. In some cases, firstly, it limits new yaw actions or maintains yaw braking to prevent yaw actions from introducing lateral loads during shutdown; subsequently, it increases the pitch angle according to a preset pitch rate, gradually reducing the power absorbed by the rotor; simultaneously, it reduces the generator torque according to a preset torque reduction slope to avoid transmission chain impact; under grid disturbance conditions, it can also perform active power derating or grid disconnection protection according to grid connection specifications and converter status. During shutdown, the control system 210 continuously monitors structural load data, transmission data, and grid data, and corrects the shutdown slope according to load changes. In this way, the wind turbine 200 is taken out of operation under controllable structural loads and electrical shocks.

[0072] The process of determining the control quantity of actuator 220 is described in detail below.

[0073] Figure 4 It is based on Figure 1 The illustrated embodiment presents a flowchart of a control method for a wind turbine generator 200, as shown below. Figure 4 As shown, determining the control quantity (i.e., S150) of actuator 220 based on risk data and remaining capacity data includes: S151: Calculate the normalized value corresponding to each risk value of the risk data; S152: Determining risk type based on normalized values; S153: Determine the control amount of the implementing agency 220 based on risk type and remaining capacity data.

[0074] For example, the overspeed risk value, load risk value, grid risk value, and yaw risk value in the risk data are normalized to obtain normalized overspeed risk values, load risk values, grid risk values, and yaw risk values. Based on the magnitude of each normalized value, the risk type of wind turbine 200 can be determined. In one example, the risk type of wind turbine 200 is determined based on the risk value with the largest normalized value.

[0075] For example, when the normalized value of overspeed risk is the largest, the risk type of wind turbine 200 is determined to be overspeed dominant; when the normalized value of blade load risk is the largest, the risk type of wind turbine 200 is determined to be blade load dominant; when the normalized value of tower load risk is the largest, the risk type of wind turbine 200 is determined to be tower load dominant; when the normalized value of grid risk is the largest, the risk type of wind turbine 200 is determined to be grid dominant; when the normalized value of yaw risk is the largest, the risk type of wind turbine 200 is determined to be yaw dominant. If two or more normalized values ​​reach their maximum values ​​simultaneously, the risk dominant type can be determined in the following order: blade load risk, tower load risk, overspeed risk, grid risk, and yaw risk. This priority order is determined based on the control principle of prioritizing structural safety, that is, when blade load and tower load are close to their limits, structural safety is prioritized.

[0076] After determining the risk type, the control quantities of actuator 220 can be determined based on the risk type and remaining capacity data. The control quantities of actuator 220 include the pitch control quantity of the pitch system, the generator torque control quantity of the converter, the yaw suppression control quantity of the yaw system, and the active power derating control quantity of the power controller.

[0077] In some embodiments, each remaining capability value characterizes the ability of at least one actuator 220 to mitigate risk; Based on risk type and remaining capacity data, the control quantities of the implementing agency 220 are determined as follows: The allocation priority of control quantities for multiple implementing agencies 220 is determined based on risk type; The allocation ratio of control quantities for multiple actuators 220 is determined based on multiple remaining capacity values.

[0078] For example, different risk types represent different main risks that need to be suppressed, and the corresponding implementing agencies 220 that need to be controlled are different. Therefore, when determining the control amount of the implementing agency 220, the allocation priority of the control amounts of multiple implementing agencies 220 can be determined first based on the risk type.

[0079] For example, such as Figure 5 As shown, when the risk type is overspeed-dominant, priority is given to allocating pitch control to the pitch system and generator torque control to the converter. Specifically, the pitch control can be a unified pitch increase control, and the generator torque control can be a generator torque limit control. The unified pitch increase control can be used to reduce the power absorbed by the wind turbine, and the generator torque limit control can be used to avoid drivetrain impact caused by excessive torque. If grid disturbances prevent the effective transmission of generated power, an active power derating control can be allocated to the power controller to reduce wind energy capture.

[0080] When the risk type is blade load dominant, pitch control should be prioritized for the pitch system. In this case, the pitch control can specifically be an independent pitch load reduction control. Independent pitch load reduction can generate periodic pitch compensation based on the differences in blade root loads among the three blades, appropriately adjusting the pitch angle of the high-load blades to reduce blade cyclic loads and hub unbalanced loads. If the risk type is blade load dominant and accompanied by an increased overspeed risk value, a unified pitch increase control can be superimposed on the independent pitch load reduction control.

[0081] When the risk type is tower load-dominated, priority should be given to allocating pitch control parameters to the pitch system. In this case, the pitch control parameters can be tower damping-added pitch control parameters. These tower damping-added pitch control parameters can be generated based on the phase relationship between the forward and backward acceleration at the top of the tower or the bending moment at the bottom of the tower. This creates a damping effect between the aerodynamic thrust change and the tower vibration direction, thereby reducing the peak forward and backward vibration of the tower. If the tower lateral load is significant, a yaw suppression control parameter can also be applied to the yaw system to prevent yaw actions from further amplifying the tower lateral vibration.

[0082] When the risk type is grid disturbance-dominated, priority should be given to allocating active power derating control to the power controller and generator torque control to the converter. Specifically, the generator torque control can be a torque slope limiting control. Active power derating control can reduce the active power output of the wind turbine to the grid, while torque slope limiting control can prevent drivetrain impacts and DC bus voltage fluctuations caused by rapid torque changes under grid disturbances. If a voltage drop leads to a decrease in the converter's power delivery capacity, pitch control can be allocated to the pitch system to reduce wind energy capture by increasing the pitch, while generator torque control can be allocated to the converter to reduce DC bus energy accumulation by limiting the torque.

[0083] When the risk type is yaw-dominant, yaw suppression control should be prioritized for the yaw system. Yaw suppression does not necessarily mean immediate yaw, but rather preventing additional load impacts caused by wind-chasing actions under extreme operating conditions. If the yaw error is large but the blade and tower loads are already close to their limits, the yaw system should be kept at a speed limit or locked first, and yaw correction should be gradually restored after the structural loads have subsided. It should be noted that yaw action can be resumed after both the blade load risk value and the tower load risk value are lower than the corresponding preset recovery threshold. The preset recovery threshold is the threshold used to determine when the wind turbine returns to normal operating conditions.

[0084] Thus, for different types of risks, the appropriate execution mechanism 220 is selected to prioritize risk suppression, thereby improving the reliability of the risk suppression process and avoiding secondary structural load impacts caused by blindly suppressing risks.

[0085] During the control quantity allocation process, the control ratio that each actuator 220 can undertake can be determined based on the remaining capacity value, thereby determining the allocation ratio of control quantities for multiple actuators 220, and allocating control quantities to multiple actuators 220 according to the allocation ratio. For example, when the pitch remaining capacity value is high, the allocation ratio of pitch control quantity can be increased; when the pitch remaining capacity value is low but the torque remaining capacity value is high, the allocation ratio of generator torque control quantity and active power derating control quantity can be increased; when the yaw remaining capacity component is zero, yaw control quantity can be discontinued, and instead, a method of prohibiting new yaw actions can be adopted to prevent the risk from escalating.

[0086] In this way, the control quantity is allocated according to the remaining capacity of each actuator 220, which avoids some actuators 220 being overused and damaged, helps to extend the service life of the actuators 220, and at the same time ensures that the risks are effectively suppressed.

[0087] When allocating control quantities, allocation can also be based on a pre-set alternative allocation mechanism. The alternative allocation mechanism is described in detail below.

[0088] In some embodiments, determining the control quantity of actuator 220 based on risk data and remaining capacity data further includes: If the executor 220 with the current allocation priority is unable to execute the allocated control quantity, at least part of the control quantity will be transferred to the executor 220 with the next allocation priority according to the preset substitution mapping relationship; The method also includes: After the control volume is transferred, the current state of wind turbine 200 is determined to be safe based on the updated risk data, remaining capacity data, and risk mitigation data.

[0089] For example, such as Figure 6 As shown, after allocating control quantities according to allocation priorities, if the remaining capacity value of the currently allocated actuator 220 is less than the corresponding preset capacity lower limit, it means that the actuator 220 cannot execute the allocated control quantity. In this case, part of the control quantity allocated to the actuator 220 can be transferred to other actuators 220 with lower allocation priorities. During the transfer, the control quantity to be transferred from the actuator 220 can be mapped to the corresponding control quantity of the receiving actuator 220 based on a preset substitution mapping relationship.

[0090] The preset lower limit of capability can be set according to the actual configuration of the actuator 220, representing the minimum allowable remaining capability value of the actuator 220, in order to prevent the actuator 220 from being damaged due to overuse.

[0091] For example, when the risk type is overspeed dominant, but the remaining pitch capacity is less than the preset pitch capacity lower limit, some pitch control quantities can be mapped to generator torque control quantities and active power derating control quantities through a preset substitution mapping relationship, thereby transferring the overspeed risk suppression requirement from the pitch system to the converter and power controller.

[0092] When the risk type is tower load dominant, but the remaining pitch capacity is less than the preset pitch capacity lower limit, some pitch control variables can be mapped to yaw suppression control variables and active power derating control variables through a preset substitution mapping relationship. This transfers the tower load risk suppression requirement from the pitch system to the yaw system and power controller, thereby reducing aerodynamic thrust and lateral load.

[0093] When the risk type is yaw-dominant, but the remaining yaw capacity is less than the preset lower limit of yaw capacity, some yaw suppression control quantities can be mapped to pitch control quantities and active power derating control quantities through a preset substitution mapping relationship. This transfers the yaw risk suppression requirement from the yaw system to the pitch system and power controller, thereby reducing the structural load caused by yaw error.

[0094] This avoids damage to the actuator 220 due to overuse, while ensuring that risk mitigation tasks can be performed reliably.

[0095] It should be noted that transferring at least part of the control quantity to the actuator 220 with the next allocation priority means transferring the control quantity that exceeds the safety capacity range. The actuator 220 may retain basic safety actions or irreplaceable actions. For example, even if the pitch system has insufficient remaining pitch capacity, it may still need to retain basic pitch increase or safety shutdown actions; generator torque and active power derating cannot completely replace the full load reduction effect of independent blade pitch.

[0096] After the control transfer, the actuator 220 is controlled based on the newly allocated control variables. This allows for the acquisition of the latest operating data of the wind turbine 200, re-determining risk data, remaining capacity data, and risk mitigation data. Based on the updated risk data, remaining capacity data, and risk mitigation data, it is determined again whether the current state of the wind turbine 200 is safe. If the current state of the wind turbine 200 is safe, the actuator 220 can be controlled according to the transferred control variable allocation, continuing operation in the extreme ride control mode. If the current state of the wind turbine 200 is risky, it enters the controlled shutdown mode.

[0097] Thus, the alternative allocation mechanism in this embodiment is not a blind transfer, but a reasonable control of resource scheduling under the constraints of a safe state, ensuring that the risk suppression task can be executed reliably and avoiding structural load impact.

[0098] In some embodiments, a rollback protection mechanism may also be set, which will be described in detail below.

[0099] Figure 7 It is based on Figure 1 The illustrated embodiment presents a flowchart of a control method for a wind turbine generator 200, as shown below. Figure 7 As shown, the method also includes: The forecast period is determined based on the configuration parameters of the wind turbine unit 200; Based on the current state of the wind turbine 200 and the control input of the actuator 220, the predicted state of the wind turbine 200 within the predicted time period is determined. If the predicted state of the wind turbine 200 is a safe state, continue to control the actuator 220 based on the control quantity; If the predicted state of wind turbine 200 is a risky state, control the wind turbine to shut down.

[0100] For example, the prediction time period can be determined based on the wind turbine 200 capacity, rotor inertia, pitch response time, converter response time, and tower natural period. For a large-megawatt wind turbine 200, the prediction time period can be selected to cover the response period of at least one major structure or several control cycles of the control system 210.

[0101] Based on the current state of the wind turbine 200 and the determined control input of the actuator 220, combined with the rate of change of operating data in historical control cycles, the state of the wind turbine 200 in the future prediction period can be predicted. The predicted state is the forecast state. In one example, the forecast data obtained through prediction can include the future trend of wind turbine speed change, the trend of blade-represented load change, the trend of tower-represented load change, the trend of grid disturbance persistence, and the trend of actuator 220 capacity decay. The forecast state is characterized by the forecast data.

[0102] Based on the forecast data, forecast operation data can be obtained; forecast risk data and forecast remaining capacity data can be determined based on the forecast operation data; forecast risk mitigation data required to mitigate risks can be determined based on the forecast risk data; and the forecast status of wind turbine 200 can be determined as a safe state based on the forecast risk data, forecast remaining capacity data, and forecast risk mitigation data.

[0103] If the predicted state of the wind turbine 200 is a safe state, it means that the wind turbine 200 will be in a safe state for the entire predicted period of time, and the actuator 220 can continue to be controlled based on the control quantity.

[0104] If the predicted state of wind turbine 200 is a risky state, it means that wind turbine 200 will change from a safe state to a risky state within the predicted future time period. Wind turbine 200 can be controlled to enter a controlled shutdown mode. Before it changes to a risky state, a backoff protection mechanism can be implemented to control the wind turbine shutdown. In this way, early intervention and defense are carried out before the risk exceeds the limit, avoiding damage to the components of wind turbine 200 caused by shutting down only after the risk has actually exceeded the limit.

[0105] In some embodiments, the method further includes: Under the condition that the preset conditions are met, the wind turbine unit 200 is shut down; The preset conditions include at least one of the following: Risk data continued to increase within the first preset monitoring interval; The remaining capacity data continued to decrease within the second preset monitoring interval; Actuator 220 malfunctioned.

[0106] For example, in the extreme crossing control mode, risk data can be continuously monitored. If the risk data continues to increase within a first preset monitoring interval, the wind turbine 200 is controlled to enter a controlled shutdown mode. The first preset monitoring interval is determined based on the actual application and can be a time period or any other form. In some examples, the wind turbine 200 may enter a controlled shutdown mode when the risk data continues to increase within the first preset monitoring interval and exceeds a risk threshold. This avoids secondary structural load impacts caused by blindly suppressing risks.

[0107] In the extreme crossing control mode, the remaining capacity data can be continuously monitored. If the remaining capacity data continues to decrease within a second preset monitoring interval, the wind turbine 200 is controlled to enter a controlled shutdown mode. The second preset monitoring interval is determined based on the actual application and can be a time period or any other form. In some examples, the wind turbine 200 may enter a controlled shutdown mode when at least one remaining capacity value in the remaining capacity data continuously decreases within the second preset monitoring interval and falls below a preset lower capacity limit. This avoids overuse and damage to the actuator 220, improving the reliability of the risk mitigation process.

[0108] In some cases, the actuator 220 is monitored, and in the event of a failure of at least one actuator 220, the wind turbine 200 can be controlled into a controlled shutdown mode. This protects the equipment safety of the wind turbine 200 and prevents further malfunctions.

[0109] In addition, the duration of grid disturbances can be monitored. If the duration of the grid disturbance exceeds a preset duration, the wind turbine 200 will be controlled to enter a controlled shutdown mode. The preset duration is a preset allowable ride-through time. If the duration of the grid disturbance exceeds the preset duration, it indicates that the grid risk exceeds the tolerance range of the wind turbine 200, and the wind turbine 200 needs to be shut down to protect the equipment safety of the wind turbine 200 and avoid causing greater damage to the grid.

[0110] In some implementations, when the risk data falls below the risk recovery threshold and the remaining capacity data recovers to above the capacity recovery threshold, the control system 210 can switch from the extreme crossing control mode to the recovery control mode. It should be noted that the recovery control mode does not immediately revert to the normal control mode; instead, it gradually removes additional pitch, torque limiting, yaw suppression, and active power derating control parameters to avoid severe electromechanical shocks caused by sudden changes in control commands. Normal control mode is then entered only after the speed, load, grid status, and actuator 220 temperature have all returned to normal ranges.

[0111] In some embodiments, during the determination of a safe state, an reachability margin for entering the cross-travel control mode and a recovery margin for exiting the cross-travel control mode can be set. The reachability margin refers to setting stricter conditions when determining whether the current state is safe. For example, adding a difference judgment condition: only when the risk suppression data is less than the suppression capability data and the difference is greater than a first preset value, and the risk data is less than the risk threshold and the difference is greater than a second preset value, can the current state of the wind turbine 200 be determined to be safe.

[0112] Recovery margin refers to setting stricter conditions when determining whether to switch from extreme crossing mode to recovery control mode. For example, adding a preset recovery time condition. If the risk data falls below the risk recovery threshold and the remaining capacity data recovers to above the capacity recovery threshold, and the preset recovery time is maintained, then switch to normal recovery control mode.

[0113] Thus, by setting the buffer boundary through hysteresis processing, the current state can be prevented from frequently fluctuating between safe and risky states, thereby reducing the secondary load impact caused by frequent mode switching.

[0114] The technical effects of this disclosure are described below with reference to three embodiments.

[0115] Example 1 is a blade load suppression experiment under extreme gusts.

[0116] The local extreme gust conditions of the wind turbine 200 were simulated in a laboratory wind tunnel environment: the wind speed suddenly increased to 20% above the rated wind speed, and the turbulence intensity was 5%. A three-bladed turbine model was used, which included blades, hub, nacelle, and tower. Experimental data were acquired through wind condition acquisition modules and structural load acquisition modules. The pitch system, yaw system, and generator operated according to commands issued by the control system 210.

[0117] During the experiment, risk data and remaining capacity data were first calculated; the risk type was determined to be blade load dominant; risk suppression data was determined and the current state was judged to be a safe state; the allocated control quantities included pitch control quantities, which included independent pitch load reduction control quantities and small-amplitude unified pitch increase control quantities, with the pitch rate controlled within 2° per second and the generator torque reduction slope limited to 0.5% / s.

[0118] The experimental results are as follows: the maximum bending moment of the blade decreased from the original simulated peak value of 500kNm to 395kNm, a reduction of 21%; the peak forward and backward acceleration at the top of the tower decreased from 2.8m / s² to 2.1m / s²; the peak rotor speed decreased from 13rpm to 11.5rpm; the yaw error remained within ±1.5°; the average response delay of the control action was 0.05s, and no overshoot or secondary load impact occurred.

[0119] This embodiment verifies that the control method disclosed herein can effectively reduce blade load and tower acceleration under extreme gusts, while maintaining stable operation of the wind turbine unit.

[0120] Example 2 is an experiment on the coordinated control of low voltage disturbances in the power grid and tower loads. The experiment employed a coordinated control mode for the converter and yaw system, simulating a sudden drop in grid connection voltage to 85% of the rated value for 3 seconds at a wind speed of 12 m / s. The forward and backward acceleration at the top of the tower was measured using an accelerometer, simultaneously monitoring the blade load.

[0121] During the experiment, the calculated risk data showed high grid risk values ​​and tower load risk values. The remaining capacity data showed that the pitch remaining capacity was 80% and the converter torque remaining capacity was 60%. The current state is a safe state. The allocated control quantities include active power derating control quantity (controlling active power derating by 20%), generator torque control quantity (controlling torque reduction by 0.3%), and pitch control quantity (controlling the pitch action amplitude of the tower damping to within 1°).

[0122] The experimental results are as follows: the peak acceleration at the top of the tower decreased from the original 4.2 m / s² to 3.3 m / s², a reduction of about 21%; the unit maintained a controllable power output range during grid disturbances, and no grid disconnection occurred; the fluctuations in rotor speed and yaw error were within the allowable range; and the predicted state remained safe within the predicted time period.

[0123] This embodiment demonstrates that the control method of this disclosure can comprehensively consider tower load risk and grid risk under low voltage disturbance conditions of the power grid. Through the coordinated allocation of multiple control quantities and safety status judgment, it can achieve safe crossing and grid connection compatibility.

[0124] Example 3 is an experiment on the joint control of yaw error and thermal safety constraints. Under conditions of wind speed of 10 m / s and moderate tower vibration, the simulated yaw error is ±4°, the pitch motor temperature is 75℃ (rated allowable temperature is 80℃), and the converter temperature is 70℃ (rated allowable temperature is 75℃).

[0125] During the experiment, the calculated yaw risk and blade load risk values ​​were too high, the pitch residual capacity decreased to 30%, the converter residual torque capacity was 50%, and the yaw residual capacity was 0 (yaw system locked). Based on the preset substitution mapping relationship, some overspeed suppression and load suppression requirements were transferred to the converter and power controller. The converter implemented generator torque limiting, and the power controller implemented active power derating; the current state was still considered safe. During the predicted time period, the predicted state transitioned to a risk state, triggering a controlled shutdown mode.

[0126] The experimental results are as follows: the peak blade load decreased from 400kNm to 330kNm; the peak tower acceleration decreased from 2.5m / s² to 2.0m / s²; during the yaw system lockout, the converter and power controller shared part of the load suppression requirements; the rotor speed fluctuation remained within ±5% of the rated value; the controlled shutdown mode was successfully triggered within the predicted time period, and the wind turbine safely exited the extreme operating condition without structural overruns.

[0127] This embodiment demonstrates the effectiveness of the alternative allocation mechanism and the backoff protection mechanism when the actuator 220 has limited capabilities, and realizes extreme crossing safety control under conditions of multiple risk coupling and local capability insufficiency.

[0128] Figure 8 This is a schematic diagram of a control device 300 for a wind turbine generator 200, as shown in an exemplary embodiment. Figure 8As shown, this disclosure provides a control device 300 for a wind turbine generator 200. The wind turbine generator 200 includes an actuator 220. The device includes a data acquisition module 310, a data calculation module 320, a first determination module 330, a second determination module 340, a third determination module 350, and an execution control module 360. The data acquisition module 310 acquires the operating data of the wind turbine generator 200. The data calculation module 320 calculates the risk data of the wind turbine generator 200 and the remaining capacity data of the actuator 220 based on the operating data. The first determination module 330 determines the risk suppression data required to suppress the risk based on the risk data. The second determination module 340 determines whether the current state of the wind turbine generator 200 is a safe state based on the risk data, remaining capacity data, and risk suppression data. The third determination module 350 determines the control quantity of the actuator 220 based on the risk data and remaining capacity data if the current state of the wind turbine generator 200 is a safe state. The execution control module 360 ​​controls the actuator 220 based on the control quantity.

[0129] In some embodiments, the operational data includes transmission data, structural load data, power grid data, wind condition data, and actuator data. The data calculation module 320 is specifically used to calculate the overspeed risk value based on the transmission data; calculate the load risk value based on the structural load data; calculate the power grid risk value based on the power grid data; calculate the yaw risk value based on the wind condition data; determine risk data based on the overspeed risk value, load risk value, power grid risk value, and yaw risk value; and calculate the remaining capacity data based on the actuator data.

[0130] In some embodiments, the second determining module 340 is specifically used to map the remaining capacity data to suppression capacity data based on the capacity mapping matrix; and to determine that the current state of the wind turbine 200 is a safe state when the risk suppression data is less than or equal to the suppression capacity data and the risk data is less than the risk threshold.

[0131] In some embodiments, the second determining module 340 is further configured to determine that the current state of the wind turbine 200 is a risky state when the risk suppression data is greater than the suppression capability data and / or the risk data is greater than or equal to the risk threshold. The third determining module 350 is further configured to control the wind turbine 200 to shut down when the current state of the wind turbine 200 is a risky state.

[0132] In some embodiments, the third determining module 350 is specifically used to calculate the normalized value corresponding to each risk value of the risk data; determine the risk type based on the normalized value; and determine the control quantity of the actuator 220 based on the risk type and the remaining capacity data.

[0133] In some embodiments, the remaining capacity data includes multiple remaining capacity values, and the number of actuators 220 is multiple, with each remaining capacity value representing the risk mitigation capability of at least one actuator 220. The third determining module 350 is specifically used to determine the allocation priority of the control quantities of the multiple actuators 220 based on the risk type; and to determine the allocation ratio of the control quantities of the multiple actuators 220 based on the multiple remaining capacity values.

[0134] In some embodiments, the third determining module 350 is further configured to transfer at least a portion of the control quantity to the next-priority executing mechanism 220 according to a preset substitution mapping relationship when the currently assigned priority executor 220 is unable to execute the assigned control quantity. The second determining module 340 is further configured to determine whether the current state of the wind turbine 200 is a safe state based on updated risk data, remaining capacity data, and risk mitigation data after the control quantity transfer.

[0135] In some embodiments, the control device 300 of the wind turbine 200 further includes a state prediction module. The state prediction module is used to determine a prediction time period based on the configuration parameters of the wind turbine 200; determine the predicted state of the wind turbine 200 within the prediction time period based on the current state of the wind turbine 200 and the control quantity of the actuator 220; if the predicted state of the wind turbine 200 is a safe state, continue to control the actuator 220 based on the control quantity; if the predicted state of the wind turbine 200 is a risky state, control the wind turbine to shut down.

[0136] In some embodiments, the third determining module 350 is further configured to control the wind turbine 200 to shut down when preset conditions are met; wherein the preset conditions include at least one of the following: Risk data continued to increase within the first preset monitoring interval; The remaining capacity data continued to decrease within the second preset monitoring interval; Actuator 220 malfunctioned.

[0137] Regarding the apparatus in the above embodiments, the specific manner in which each module performs its operation has been described in detail in the embodiments related to the method, and will not be elaborated upon here.

[0138] Figure 9 This is a block diagram illustrating an electronic device 400 according to an exemplary embodiment. Figure 9 As shown, the electronic device 400 may include a processor 401 and a memory 402. The electronic device 400 may also include one or more of a multimedia component 404, an input / output (I / O) interface 404, and a communication component 405.

[0139] The processor 401 controls the overall operation of the electronic device 400 to complete all or part of the steps in the control method of the wind turbine 200 described above. The memory 402 stores various types of data to support the operation of the electronic device 400. This data may include, for example, instructions for any application or method operating on the electronic device 400, and application-related data such as contact data, sent and received messages, pictures, audio, video, etc. The memory 402 can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as Static Random Access Memory (SRAM), Electrically Erasable Programmable Read-Only Memory (EEPROM), Erasable Programmable Read-Only Memory (EPROM), Programmable Read-Only Memory (PROM), Read-Only Memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk. Multimedia component 403 may include a screen and an audio component. The screen may be, for example, a touchscreen, and the audio component is used to output and / or input audio signals. For example, the audio component may include a microphone for receiving external audio signals. The received audio signals may be further stored in memory 402 or transmitted via communication component 405. The audio component also includes at least one speaker for outputting audio signals. I / O interface 404 provides an interface between processor 401 and other interface modules, such as a keyboard, mouse, buttons, etc. These buttons may be virtual or physical buttons. Communication component 405 is used for wired or wireless communication between the electronic device 400 and other devices. Wireless communication, such as Wi-Fi, Bluetooth, Near Field Communication (NFC), 2G, 3G, 4G, NB-IoT, eMTC, or other 5G technologies, or combinations thereof, is not limited here. Therefore, the corresponding communication component 405 may include: a Wi-Fi module, a Bluetooth module, an NFC module, etc.

[0140] In an exemplary embodiment, the electronic device 400 may be implemented by one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field programmable gate arrays (FPGAs), controllers, microcontrollers, microprocessors, or other electronic components to execute the control method of the wind turbine 200 described above.

[0141] In another exemplary embodiment, a computer-readable storage medium including program instructions is also provided, which, when executed by a processor, implement the steps of the wind turbine 200 control method described above. For example, the computer-readable storage medium may be the memory 402 including program instructions, which may be executed by the processor 401 of the electronic device 400 to complete the wind turbine 200 control method described above.

[0142] Figure 10 This is a block diagram illustrating an electronic device 500 according to an exemplary embodiment. For example, the electronic device 500 may be provided as a server. (Refer to...) Figure 10 The electronic device 500 includes a processor 522, which may be one or more, and a memory 532 for storing computer programs executable by the processor 522. The computer program stored in the memory 532 may include one or more modules, each corresponding to a set of instructions. Furthermore, the processor 522 may be configured to execute the computer program to perform the control method of the wind turbine 200 described above.

[0143] Additionally, the electronic device 500 may also include a power supply component 526 and a communication component 550. The power supply component 526 can be configured to perform power management of the electronic device 500, and the communication component 550 can be configured to enable communication of the electronic device 500, such as wired or wireless communication. Furthermore, the electronic device 500 may also include an input / output (I / O) interface 558. The electronic device 500 can operate on an operating system stored in the memory 532.

[0144] In another exemplary embodiment, a computer-readable storage medium including program instructions is also provided, which, when executed by a processor, implement the steps of the wind turbine 200 control method described above. For example, the non-transitory computer-readable storage medium may be the memory 532 including program instructions, which may be executed by the processor 522 of the electronic device 500 to complete the wind turbine 200 control method described above.

[0145] In another exemplary embodiment, a computer program product is also provided, which includes a computer program executable by a programmable device, the computer program having a code portion for performing the control method of the wind turbine 200 described above when executed by the programmable device.

[0146] The preferred embodiments of this disclosure have been described in detail above with reference to the accompanying drawings. However, this disclosure is not limited to the specific details of the above embodiments. Within the scope of the technical concept of this disclosure, various simple modifications can be made to the technical solutions of this disclosure, and these simple modifications all fall within the protection scope of this disclosure.

[0147] It should also be noted that the various specific technical features described in the above embodiments can be combined in any suitable manner without contradiction. To avoid unnecessary repetition, this disclosure will not describe the various possible combinations separately.

[0148] Furthermore, various different embodiments of this disclosure can be combined in any way, as long as they do not violate the spirit of this disclosure, they should also be regarded as the content disclosed in this disclosure.

Claims

1. A control method for a wind turbine generator set, characterized in that, The wind turbine generator includes an actuator, and the method includes: Obtain the operating data of the wind turbine; Based on the operational data, calculate the risk data of the wind turbine and the remaining capacity data of the actuator; Based on the aforementioned risk data, determine the risk mitigation data required to suppress the risk; Based on the risk data, the remaining capacity data, and the risk mitigation data, determine whether the current state of the wind turbine is a safe state; If the current state of the wind turbine is a safe state, the control quantity of the actuator is determined based on the risk data and the remaining capacity data; The actuator is controlled based on the control quantity.

2. The method according to claim 1, characterized in that, The operational data includes transmission data, structural load data, power grid data, wind condition data, and actuator data; The calculation of the risk data of the wind turbine and the remaining capacity data of the actuator based on the operating data includes: Calculate the overspeed risk value based on the transmission data; Calculate the load risk value based on the structural load data; Calculate the power grid risk value based on the power grid data; Calculate the yaw risk value based on the aforementioned wind condition data; The risk data is determined based on the overspeed risk value, load risk value, power grid risk value, and yaw risk value. The remaining capacity data is calculated based on the actuator data.

3. The method according to claim 1, characterized in that, The step of determining whether the current state of the wind turbine is in a safe state based on risk data, remaining capacity data, and risk mitigation data includes: The remaining capability data is mapped to suppression capability data based on the capability mapping matrix; If the risk suppression data is less than or equal to the suppression capability data, and the risk data is less than the risk threshold, then the current state of the wind turbine is determined to be a safe state.

4. The method according to claim 3, characterized in that, The method further includes: If the risk suppression data is greater than the suppression capacity data, and / or the risk data is greater than or equal to the risk threshold, the current state of the wind turbine is determined to be a risky state. If the current state of the wind turbine is considered a risky state, the wind turbine shall be shut down.

5. The method according to claim 2, characterized in that, Determining the control level of the actuator based on the risk data and the remaining capacity data includes: Calculate the normalized value corresponding to each risk value of the risk data; The risk type is determined based on the normalized value; The control level of the implementing agency is determined based on the risk type and the remaining capacity data.

6. The method according to claim 5, characterized in that, The remaining capacity data includes multiple remaining capacity values, and the number of actuators is multiple. Each remaining capacity value represents the ability of at least one actuator to mitigate risk. Determining the control level of the actuator based on the risk type and the remaining capacity data includes: Based on the risk type, determine the allocation priority of control quantities for multiple implementing agencies; The allocation ratio of control quantities for multiple actuators is determined based on multiple remaining capacity values.

7. The method according to claim 6, characterized in that, The determination of the control quantity of the actuator based on the risk data and the remaining capacity data further includes: If the actuator with the current priority is unable to execute the assigned control quantity, at least a portion of the control quantity is transferred to the actuator with the next priority according to a preset substitution mapping relationship; The method further includes: After the control quantity transfer, the current state of the wind turbine is determined to be a safe state based on the updated risk data, the remaining capacity data, and the risk mitigation data.

8. The method according to claim 1, characterized in that, The method further includes: The predicted time period is determined based on the configuration parameters of the wind turbine. Based on the current state of the wind turbine and the control quantity of the actuator, the predicted state of the wind turbine during the predicted time period is determined. If the predicted state of the wind turbine is a safe state, the actuator continues to be controlled based on the control quantity. If the predicted state of the wind turbine is a risky state, the wind turbine will be shut down.

9. The method according to any one of claims 1-8, characterized in that, The method further includes: Under preset conditions, the wind turbine unit is controlled to shut down; The preset conditions include at least one of the following: The risk data continues to increase within the first preset monitoring interval; The remaining capacity data continues to decrease within the second preset monitoring interval; The actuator malfunctioned.

10. A control device for a wind turbine generator set, characterized in that, The wind turbine generator includes an actuator, and the device includes: The data acquisition module is used to acquire the operating data of the wind turbine. The data calculation module is used to calculate the risk data of the wind turbine and the remaining capacity data of the actuator based on the operating data. The first determining module is used to determine the risk suppression data required to suppress the risk based on the risk data; The second determining module is used to determine whether the current state of the wind turbine is a safe state based on the risk data, the remaining capacity data and the risk suppression data. The third determining module is used to determine the control quantity of the actuator based on the risk data and the remaining capacity data when the current state of the wind turbine is a safe state. An execution control module is used to control the actuator based on the control quantity.

11. The apparatus according to claim 10, characterized in that, The second determining module is used for: The remaining capability data is mapped to suppression capability data based on the capability mapping matrix; If the risk suppression data is less than or equal to the suppression capability data, and the risk data is less than the risk threshold, then the current state of the wind turbine is determined to be a safe state.

12. The apparatus according to claim 10, characterized in that, The device further includes a state prediction module, the state prediction module being used for: The predicted time period is determined based on the configuration parameters of the wind turbine. Based on the current state of the wind turbine and the control quantity of the actuator, the predicted state of the wind turbine during the predicted time period is determined. If the predicted state of the wind turbine is a safe state, the actuator continues to be controlled based on the control quantity. If the predicted state of the wind turbine is a risky state, the wind turbine will be shut down.

13. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the steps of the control method for the wind turbine generator as described in any one of claims 1 to 9.

14. An electronic device, characterized in that, include: A memory on which computer programs are stored; A processor for executing the computer program in the memory to implement the steps of the control method for the wind turbine generator as described in any one of claims 1 to 9.

15. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by the processor, it implements the steps of the control method for the wind turbine generator as described in any one of claims 1 to 9.

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