Wind turbine yaw feedback control method and system with full wind condition gain scheduling

Abstract

The present disclosure provides a wind turbine full wind condition gain scheduling yaw feedback control method and system. The method comprises: obtaining the wind direction and wind speed measurement data of the nacelle of the wind turbine in a preset time period according to a preset sampling interval; establishing a probability density distribution curve of the wind speed and wind direction data, setting a wind direction sector division interval and a wind speed range division interval, and obtaining a plurality of wind condition domains; for a single wind condition domain, based on the wind direction and wind speed measurement data, using a kernel density estimation method, respectively counting the yaw error mean, the yaw error confidence interval range and the delay time of the wind turbine; in a single wind condition domain, designing and parameter setting a fractional order proportional-integral-derivative controller, and selecting appropriate yaw error threshold and / or delay time parameters for wind turbine yaw control. The method can make the unit timely to the wind when the wind speed is high and the wind direction changes quickly, improve the wind energy utilization efficiency, and improve the theoretical annual power generation of the wind turbine.

Description

TECHNICAL FIELD

[0001] The present disclosure belongs to the technical field of wind turbine control, and particularly relates to a wind turbine full-wind-condition gain scheduling yaw feedback control method and system. BACKGROUND

[0002] A wind turbine has optimal power generation efficiency when it is directly facing the wind direction, which is of great significance to improve the power generation of a single machine and a field. The yaw control loop of a wind turbine is a typical nonlinear controlled loop, which is difficult to establish an accurate mathematical description. Meanwhile, the yaw motion process has large inertia and large time delay dynamic characteristics. The yaw control parameters and controllers used by existing wind turbines cannot effectively cope with variable wind directions. In a mountain wind farm at low wind speed, variable terrain easily induces turbulent wind speed and variable wind direction. In a plain or plateau wind farm, the dominant wind direction is difficult to unify, and the wind direction between the wind turbines in the field is easily coupled and variable due to the influence of the operating characteristics of the wind turbines and the geographical location arrangement. In this case, the wind turbine is prone to have a large yaw control error, and the power generation is reduced due to the inaccurate wind direction. At present, the wind power operator can improve the power generation by obtaining the yaw angle correction value through the yaw error statistics. However, the above problems have not been effectively solved from the perspective of yaw control design. SUMMARY

[0003] The present disclosure aims to at least solve one of the technical problems existing in the prior art, and provides a wind turbine full-wind-condition gain scheduling yaw feedback control method and system.

[0004] In one aspect of the present disclosure, a wind turbine full-wind-condition gain scheduling yaw feedback control method is provided, which comprises:

[0005] According to a pre-set sampling interval, the wind direction and wind speed measurement data of the wind turbine in a pre-set time period are obtained;

[0006] A probability density distribution curve of the wind speed and wind direction data is established, a wind direction sector division interval and a wind speed range division interval are set, and a plurality of wind condition domains are obtained;

[0007] For a single wind condition domain, based on the wind direction and wind speed measurement data, a kernel density estimation method is used to respectively calculate the yaw error mean value, the yaw error confidence interval range, and the delay time of the wind turbine;

[0008] In a single wind condition domain, a fractional order proportional-integral-derivative controller is designed and parameterized, and appropriate yaw error threshold and / or delay time parameters are selected for wind turbine yaw control.

[0009] In some embodiments, the establishment of the probability density distribution curve of the wind speed and wind direction data, the setting of the wind direction sector division interval and the wind speed range division interval, and the obtaining of the plurality of wind condition domains comprise:

[0010] Taking the preset time period as the abscissa, the cumulative probability of the wind direction and wind speed data corresponding to each sampling interval is counted, and a probability density distribution curve of the wind speed and wind direction data is established;

[0011] According to the wind direction data probability density curve, a wind direction sector division interval is set;

[0012] According to the wind speed data probability density curve, the operating wind speed of the unit is divided into several intervals to obtain several wind condition domains.

[0013] In some embodiments, for a single wind condition domain, based on the wind direction and wind speed measurement data, a kernel density estimation method is used to count the yaw error mean, yaw error confidence interval range, and delay time of the wind turbine, including:

[0014] For a single wind condition domain, based on the wind direction and wind speed measurement data, a kernel density estimation method is used to calculate the yaw error mean of the wind turbine, and a yaw error probability density distribution curve of the wind turbine is established;

[0015] According to the yaw error probability density curve of the wind turbine, a yaw error confidence interval range is determined;

[0016] The yaw error probability density distribution curve of the wind turbine and the wind direction data probability density curve are compared in the same coordinate system, and the delay time between the yaw angle and the wind direction angle of the wind turbine is analyzed.

[0017] In some embodiments, the fractional order proportional-integral-derivative controller design principle is as follows:

[0018] When the wind direction error of the wind turbine is within the allowable range [-8°, 8°], it is considered to be a wind-against state, and the system does not perform yaw control;

[0019] When the wind-against error detected by the wind vane exceeds the allowable range, the system issues a yaw command, and the control action is realized through a fractional order PID yaw control system;

[0020] Different delay times T are set according to the wind speed d After the delay ends, the current wind-against error is detected, and when it returns to the set range [-4°, 4°], the yaw action is ended;

[0021] The transfer function form of the fractional order PID controller is:

[0022]

[0023] Wherein, P is the proportional coefficient of the controller, I is the integral time constant, D is the differential time constant, and λ and β are the integral order and the differential order of the controller, respectively;

[0024] Since the yaw system is a typical nonlinear system, it is difficult to establish an accurate mathematical model, so a simple mathematical model is used, and thus the transfer function of the yaw system is established as follows:

[0025]

[0026] Wherein, K m is the proportional constant, and T m is the inertial time constant.

[0027] In some embodiments, the parameter setting content is as follows:

[0028] Yaw control period: the control period of one yaw control process is 30 seconds;

[0029] Sampling time interval T: the wind direction data sampling interval is 1 minute, that is, the wind turbine performs yaw action every minute;

[0030] Delay time T d : obtained by comparing the probability density curve of the yaw error of the wind turbine with the probability density curve of the wind direction data;

[0031] Yaw error threshold: two thresholds of yaw angle error and wind duration;

[0032] Yaw error confidence interval range: the corresponding confidence interval is calculated according to the confidence degree of 95%.

[0033] In some embodiments, the method further comprises:

[0034] Establishing a power evaluation model of the wind turbine, recalculating the optimized power generation and yaw ratio using the wind direction and wind speed measurement data of the wind turbine, and comparing with the pre-optimization.

[0035] In some embodiments, the establishment of the power evaluation model of the wind turbine comprises:

[0036] In order to realize the preliminary comparison of the wind turbine data before and after optimization, the total power generation and yaw ratio of the original wind direction and wind speed measurement data of the target wind turbine are calculated, which are used for the preliminary evaluation and analysis of the yaw operation status of the wind turbine;

[0037] The theoretical power generation of the wind turbine is:

[0038]

[0039] Where, θ is the yaw error angle, i.e. the angle between the incoming wind direction and the vertical line of the wind turbine rotation plane; ρ is the air density; S is the swept area of the wind turbine; C P (β, λ) is the wind energy utilization coefficient, which is related to the pitch angle β and the tip speed ratio λ; v is the incoming wind speed in front of the wind turbine;

[0040] The power generation and the yaw ratio calculation formula are as follows:

[0041] W = ∑P·Δt

[0042]

[0043] Where, W is the total power generation of the wind turbine; P is the SCADA yaw power of the wind turbine; Δt is the SCADA data time interval; Y Ratio is the yaw ratio; T yaw is the wind turbine yaw time; T is the total operation time of the wind turbine;

[0044] According to the wind turbine momentum theory, the rule suitable for the yaw power evaluation can be obtained:

[0045]

[0046] Where, P a is the wind turbine absorbed power; R is the wind turbine radius; V is the wind speed in front of the wind turbine; ρ is the air density; θ is the yaw error angle; C P is the wind energy utilization coefficient;

[0047] Since the wind speed measurement data is less accurate, the pitch angle and the tip speed ratio function C P The above formula is simplified as follows:

[0048]

[0049] Where, ω g is the generator speed; G is the gear box speed ratio; λ is the tip speed ratio; β is the pitch angle.

[0050] Another aspect of the present disclosure provides a wind turbine full wind condition gain scheduling yaw feedback control system, which comprises:

[0051] The acquisition module is configured to acquire the nacelle wind direction and wind speed measurement data in a preset time period of the wind turbine according to a preset sampling interval;

[0052] The establishment module is configured to establish a probability density distribution curve of the wind speed and wind direction data, set a wind direction sector division interval and a wind speed range division interval, and obtain a plurality of wind condition domains;

[0053] The computing module is configured to, for a single wind condition domain, based on wind direction and wind speed measurement data, respectively count the yaw error mean value, the yaw error confidence interval range, and the delay time of the wind turbine by using a kernel density estimation method.

[0054] The control module is configured to, in the single wind condition domain, design and parameterize a fractional order proportional-integral-derivative controller, and select appropriate yaw error threshold and / or delay time parameters to perform yaw control on the wind turbine.

[0055] In another aspect of the present disclosure, an electronic device is provided, comprising:

[0056] One or more processors;

[0057] A storage unit configured to store one or more programs, which, when executed by the one or more processors, cause the one or more processors to implement the method described above.

[0058] In another aspect of the present disclosure, a computer-readable storage medium is provided, which stores a computer program, which, when executed by a processor, can implement the method described above.

[0059] The wind turbine full wind condition gain scheduling yaw feedback control method and system of the present disclosure can effectively improve the dynamic response characteristics of the yaw control of the wind turbine and reduce the steady-state error by parameterizing and gain scheduling the yaw feedback fractional order proportional-integral-derivative controller of the wind turbine under full working conditions. The control method and system of the present disclosure can make the wind turbine timely respond to the wind when the wind speed is high and the wind direction changes rapidly, improve the wind energy utilization efficiency, and improve the theoretical annual power generation of the wind turbine. BRIEF DESCRIPTION OF DRAWINGS

[0060] Figure 1 Flowchart of the wind turbine full wind condition gain scheduling yaw feedback control method of an embodiment of the present disclosure;

[0061] Figure 2 Flowchart of the wind turbine full wind condition gain scheduling yaw feedback control method of another embodiment of the present disclosure;

[0062] Figure 3 Schematic diagram of the optimized automatic yaw control strategy of another embodiment of the present disclosure;

[0063] Figure 4 Wind speed probability distribution curve of another embodiment of the present disclosure;

[0064] Figure 5 Comparison diagram of wind power curves before and after the optimization of the control strategy of another embodiment of the present disclosure;

[0065] Figure 6A yaw state comparison chart before and after optimization for another embodiment of the present disclosure;

[0066] Figure 7 A structure schematic diagram of a wind turbine yaw feedback control system with gain scheduling in all wind conditions for another embodiment of the present disclosure. DETAILED DESCRIPTION

[0067] In order for those skilled in the art to better understand the technical solutions of the present disclosure, the present disclosure will be further described in detail below in combination with the drawings and specific embodiments.

[0068] The yaw system has the effect of quickly and effectively aligning with the wind when there is a deviation between the wind direction and the nacelle angle, and ensuring maximum wind energy capture. The performance of the yaw system directly affects the economic benefits of the wind farm. At present, the yaw control of the wind turbine generally adopts the control method of setting a "yaw tolerance angle" in practical application. The wind direction signal is measured by a wind vane, and the measured wind direction signal is sent to the yaw controller for data processing. Then, whether to yaw and the direction of yaw are determined according to the control strategy, so that the wind wheel is aligned with the wind direction. Considering the measurement error of the wind vane and the wind direction turbulence, the average deviation between the direction of the nacelle axis and the actual wind direction within the delay time must be calculated. When the error of the nacelle alignment with the wind exceeds the set value of the yaw tolerance angle, the wind turbine performs yaw to align with the wind, thereby avoiding frequent movement of the nacelle and reducing the yaw error angle.

[0069] The current yaw control strategy of the wind farm is: when the wind speed is less than 8 m / s, the yaw delay time T D = 25 s; when the wind speed is greater than or equal to 8 m / s, T D = 115 s. This control strategy takes into account the differences in wind direction change characteristics at different wind speeds, but the division standard of small and large wind is too blind and lacks theoretical basis, which may lead to frequent movement of the wind turbine to align with the wind at small wind speed, and inaccurate alignment with the wind at large wind speed, thereby affecting the accuracy of the yaw control and the fatigue performance of the wind turbine.

[0070] Therefore, the present disclosure aims to provide a wind turbine yaw feedback control method with gain scheduling in all wind conditions. The SCADA (Supervisory Control and Data Acquisition) data of the specific wind turbine are statistically analyzed. Under the condition of meeting the yaw ratio requirement, the wind turbine yaw control interval is reasonably divided and the controller parameters are set, so that the yaw parameters have higher self-adaptation level and pertinence, thereby improving the dynamic response characteristics of the wind turbine yaw control and reducing the steady-state error.

[0071] An aspect of the present embodiment, as shown in Figure 1 and Figure 2 , relates to a wind turbine yaw feedback control method S100 with gain scheduling in all wind conditions, the method S100 comprising:

[0072] S110, obtain wind direction and wind speed measurement data of the wind turbine generator set in a preset time period according to a preset sampling interval.

[0073] Specifically, in this step, a certain peninsula wind farm in the coastal area of Jiangsu Province is selected as the research object. The wind farm is installed with 50 WD77-1500S units, with a single unit capacity of 1.5 MW. According to the statistical results of the wind farm feasibility study report, the wind speed in the field area is mainly concentrated in the 4-12 m / s wind speed range.

[0074] S120, establish a probability density distribution curve of wind speed and wind direction data, set a wind direction sector division interval and a wind speed range division interval, and obtain a plurality of wind condition domains.

[0075] Specifically, in this step, the preset time period is taken as the horizontal coordinate, and the cumulative probability of the wind direction and wind speed data corresponding to each sampling interval is counted to establish a probability density distribution curve of the wind speed and wind direction data. According to the wind direction data probability density curve, a wind direction sector division interval is set. According to the wind speed data probability density curve, the operating wind speed of the unit is divided into a plurality of intervals to obtain a plurality of wind condition domains.

[0076] S130, for a single wind condition domain, based on wind direction and wind speed measurement data, a kernel density estimation method is used to respectively count the yaw error mean, yaw error confidence interval range, and delay time of the wind turbine generator set.

[0077] Specifically, in this step, for a single wind condition domain, based on SCADA wind measurement data, a kernel density estimation method is used to calculate the yaw error mean of the wind turbine generator set, and a yaw error probability density distribution curve of the wind turbine generator set is established. According to the yaw error probability density curve of the wind turbine generator set, the yaw error confidence interval range is determined. The yaw error probability density distribution curve of the wind turbine generator set and the wind direction data probability density curve are compared in the same coordinate system to analyze the delay time between the yaw angle and the wind direction angle of the wind turbine generator set.

[0078] S140, in a single wind condition domain, a fractional order proportional-integral-derivative controller is designed and parameterized, and appropriate yaw error threshold and / or delay time parameters are selected for wind turbine generator set yaw control.

[0079] The wind turbine gain scheduling yaw feedback control method of this embodiment effectively improves the dynamic response characteristics of wind turbine yaw control and reduces steady-state error by tuning the parameters of the fractional-order proportional-integral-derivative controller and scheduling the gain of the yaw feedback under all operating conditions of the wind turbine. The control method of this embodiment can enable the unit to adjust to the wind in time when the wind speed is high and the wind direction changes rapidly, thereby improving the wind energy utilization efficiency and increasing the theoretical annual power generation of the wind turbine.

[0080] In some implementations, the fractional proportional-integral-derivative controller is designed based on the following principles:

[0081] When the wind direction error of the wind turbine is within the allowable range of [-8°, 8°], it is considered to be in a windy state, and the system does not perform yaw control.

[0082] When the wind vane detects a wind error exceeding the allowable range, the system issues a yaw command and implements the control behavior through a fractional-order PID yaw control system.

[0083] Different delay times T are set according to wind speed. d After the delay ends, the current wind error is detected, and the yaw action ends when it returns to the set range [-4°, 4°].

[0084] The transfer function of the fractional-order PID controller is as follows:

[0085]

[0086] Where P is the proportional gain of the controller, I is the integral time constant, D is the derivative time constant, and λ and β are the integral and derivative orders of the controller, respectively.

[0087] Since the yaw system is a typical nonlinear system, it is difficult to establish an accurate mathematical model. Therefore, a simplified mathematical model is adopted, and the transfer function of the yaw system is established as follows:

[0088]

[0089] Among them, K m T is a proportionality constant. m is the inertial time constant.

[0090] Based on the inherent characteristics of wind resources, it is known that the lower the wind speed, the more frequently the wind direction changes; conversely, the higher the wind speed, the weaker the turbulence effect, and the more stable the wind direction.

[0091] like Figure 3 and Figure 4 As shown, based on this characteristic, the wind speed probability distribution curve of a single wind turbine in a wind farm is identified, and the wind speed corresponding to the "peak" of the wind speed probability curve is V. m , with Vm The rated wind speed V e The following wind speed interval is divided into two intervals.

[0092] The wind speed between the cut-in wind speed V Cut-in and the peak wind speed V Peak is the first interval, the yaw deviation threshold and the delay time can be greater than the original low wind speed segment parameter value of the unit, considering that the delay time is taken in the minimum and maximum range [T min , T max ], generally [5, 120] min, the first interval delay time can be taken in the range [T L , T max ], wherein T L is the original low wind speed segment delay time of the unit, reducing the yaw frequency of the unit in this range will not significantly reduce the power generation;

[0093] The wind speed between the peak wind speed V Peak and the rated speed wind speed V ωrated is the second interval, the wind speed is relatively high, the sampling accuracy of the wind direction is also high, and the vibration caused by the lateral force acting on the wind turbine body is also large, so a relatively small yaw deviation threshold and a short delay time should be selected, and the value range is [T min , T H ], wherein T H is the original high wind speed segment delay time threshold, and the smaller delay time value obtained in this range is applied to improve the wind direction accuracy in the second interval, thereby improving the power generation;

[0094] The wind speed after reaching the rated speed wind speed V ωrated is the third interval, the unit can realize constant power output through pitch control, and the original high wind speed segment delay time can be appropriately increased to reduce the yaw control accuracy and thereby reduce the pitch burden, and the yaw frequency can also be reduced, considering the limitation of the load of the unit at high wind speed, [T H , T max ] can be the new delay time value range of the third interval.

[0095] In some embodiments, the parameter setting content is as follows:

[0096] Yaw control cycle: the control cycle of one yaw control process is 30 seconds;

[0097] Sampling time interval T: the wind direction data sampling interval is 1 minute, that is, the unit performs a yaw action every minute;

[0098] Delay time T d : obtained by comparing the probability density curve of the unit yaw error with the probability density curve of the wind direction data;

[0099] Yaw error threshold: two thresholds for yaw angle error and wind duration;

[0100] Yaw error confidence interval range: the corresponding confidence interval is calculated at 95% confidence level.

[0101] In some embodiments, the method further comprises:

[0102] Establish a power evaluation model of the wind turbine, and recalculate the optimized power generation and yaw ratio using the wind direction and wind speed measurement data of the wind turbine, and compare with the pre-optimization.

[0103] Specifically, in this step, in order to realize the preliminary comparison of wind turbine data before and after optimization, the total power generation and yaw ratio of the target wind turbine original wind direction and wind speed measurement data are calculated, which are used for preliminary evaluation and analysis of the yaw operation status of the wind turbine;

[0104] The theoretical power generation of the wind turbine is:

[0105]

[0106] In the formula, θ is the yaw error angle, i.e. the angle between the incoming wind direction and the vertical line of the wind wheel rotation plane; ρ is the air density; S is the swept area of the wind wheel; C P (β,λ) is the wind energy utilization coefficient, which is related to the pitch angle β and the tip speed ratio λ; v is the incoming wind speed in front of the wind wheel;

[0107] The power generation and yaw ratio calculation formula is as follows:

[0108] W=∑P·Δt

[0109]

[0110] In the formula, W is the total power generation of the wind turbine; P is the SCADA yaw power of the wind turbine; Δt is the SCADA data time interval; Y Ratio is the yaw ratio; T yaw is the yaw time of the wind turbine; T is the total running time of the wind turbine;

[0111] According to the momentum theory of wind turbine, the following formula applicable to yaw power evaluation can be obtained:

[0112]

[0113] In the formula, P a is the wind wheel absorbed power; R is the wind wheel radius; V is the wind speed in front of the wind wheel; ρ is the air density; θ is the yaw deviation angle; C P is the wind energy utilization coefficient;

[0114] Because wind speed measurement data is not very accurate, the blade pitch angle and tip speed ratio function C are selected. P Simplify the above formula:

[0115]

[0116] In the formula, ω g λ is the generator speed; G is the gearbox speed ratio; λ is the tip speed ratio; β is the propeller pitch angle.

[0117] To verify the effectiveness of the optimization algorithm, a case study analysis was conducted using second-level operating data recorded in the SCADA system of a 1.5MW wind turbine generator in the aforementioned wind farm.

[0118] Before optimization, the yaw ratio was 0.0946, and the total number of yaws was 105,963; after optimization, the yaw ratio was 0.0962, and the total number of yaws was 108,917. These data indicate that the new yaw control parameters not only meet the yaw ratio requirements but also do not significantly increase the number of yaw maneuvers, thus avoiding excessive fatigue damage to the crew.

[0119] The power curves of the unit before and after the yaw control strategy optimization can be plotted using SCADA data before and after the power update, and the comparison results are as follows: Figure 5 As shown. From Figure 5 The simulation results show that the wind power curve of the unit is significantly improved in the wind speed range of 6 to 11 m / s. The new yaw control parameters enable the unit to adjust to the wind in time when the wind speed is high and the wind direction changes rapidly, thereby improving the wind energy utilization efficiency.

[0120] Simulation calculations show that the annual power generation of this 1.5MW wind turbine was approximately 834,000 kWh before optimization and approximately 870,000 kWh after optimization. The new yaw control parameters can increase the theoretical annual power generation of the turbine by about 3%.

[0121] The yaw feedback control method for all wind conditions of the wind turbine generator, adopted in this embodiment, is compared with the yaw state before and after optimization, as shown in the figure below. Figure 6 As shown in the figure, it can be seen that due to the relatively small yaw deviation threshold and delay time adopted after optimization, the unit significantly improved yaw accuracy in the second wind speed range. Throughout the simulation, the global optimization effect of the yaw parameters was significant.

[0122] Another aspect of this disclosure, such as Figure 7 As shown, a wind turbine all-wind-condition gain-scheduled yaw feedback control system 100 is provided. This system 100 is applicable to the methods described above, and details can be found in the relevant previous descriptions, which will not be repeated here. The system 100 includes:

[0123] The acquisition module 110 is configured to acquire the wind direction and wind speed measurement data of the wind turbine in a preset time period according to a preset sampling interval.

[0124] The establishment module 120 is configured to establish a probability density distribution curve of the wind speed and wind direction data, set a wind direction sector division interval and a wind speed range division interval, and obtain a plurality of wind condition domains.

[0125] The calculation module 130 is configured to, for a single wind condition domain, based on the wind direction and wind speed measurement data, respectively calculate the yaw error mean value, the yaw error confidence interval range and the delay time of the wind turbine by using a kernel density estimation method.

[0126] The control module 140 is configured to, in the single wind condition domain, design and parameterize a fractional order proportional-integral-derivative controller, and select an appropriate yaw error threshold value and / or delay time parameter to perform the yaw control of the wind turbine.

[0127] The wind turbine full wind condition gain scheduling yaw feedback control system of the embodiment can effectively improve the dynamic response characteristics and reduce the steady-state error of the yaw control of the wind turbine by parameterizing and gain scheduling the yaw feedback fractional order proportional-integral-derivative controller of the wind turbine under full working conditions. The control system of the embodiment can make the wind turbine timely face the wind when the wind speed is high and the wind direction changes rapidly, improve the wind energy utilization efficiency, and improve the theoretical annual power generation of the wind turbine.

[0128] In another aspect of the present disclosure, an electronic device is provided, comprising:

[0129] one or more processors;

[0130] a storage unit configured to store one or more programs, which, when executed by the one or more processors, cause the one or more processors to implement the method as described above.

[0131] In another aspect of the present disclosure, a computer-readable storage medium is provided, which stores a computer program, and the computer program, when executed by a processor, can implement the method as described above.

[0132] The computer-readable medium can be included in the device, the apparatus, or the system of the present disclosure, or can exist independently.

[0133] The computer-readable storage medium may be any tangible medium that contains or stores a program, and may be an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device. More specific examples include, but are not limited to, electrical connections having one or more wires, portable computer disks, hard disks, optical fibers, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof.

[0134] The computer-readable storage medium may also include data signals propagated in baseband or as part of a carrier wave, carrying computer-readable program code, specific examples of which include, but are not limited to, electromagnetic signals, optical signals, or any suitable combination thereof.

[0135] It is understood that the above embodiments are merely exemplary embodiments used to illustrate the principles of this disclosure, and this disclosure is not limited thereto. For those skilled in the art, various modifications and improvements can be made without departing from the spirit and substance of this disclosure, and these modifications and improvements are also considered to be within the scope of protection of this disclosure.

Claims

1. A method for all-wind-condition gain-based yaw feedback control of wind turbine units, characterized in that, The method includes: According to the preset sampling interval, the wind direction and wind speed measurement data of the wind turbine nacelle within a preset time period are obtained; Establish probability density distribution curves for wind speed and wind direction data, set wind direction sector division intervals and wind speed range division intervals, and obtain several wind condition domains. For a single wind condition domain, based on wind direction and wind speed measurement data, the kernel density estimation method is used to statistically analyze the mean yaw error, confidence interval range of yaw error, and delay time of the wind turbine. Within a single wind condition domain, a fractional proportional-integral-derivative controller is designed and its parameters are tuned. Appropriate yaw error thresholds and / or delay time parameters are selected for yaw control of the wind turbine. The process of establishing probability density distribution curves for wind speed and direction data, setting wind direction sector division intervals and wind speed range division intervals, and obtaining several wind condition domains includes: using the preset time period as the horizontal axis, calculating the cumulative probability of wind direction and wind speed data corresponding to each sampling interval, and establishing probability density distribution curves for the wind speed and wind direction data; setting wind direction sector division intervals based on the wind direction data probability density curves; and dividing the unit operating wind speed into several intervals based on the wind speed data probability density curves to obtain several wind condition domains. For a single wind condition domain, based on wind direction and wind speed measurement data, a kernel density estimation method is used to statistically analyze the mean yaw error, confidence interval range, and delay time of the wind turbine. This includes: calculating the mean yaw error of the wind turbine based on wind direction and wind speed measurement data using a kernel density estimation method, and establishing the yaw error probability density distribution curve of the wind turbine; determining the yaw error confidence interval range based on the yaw error probability density curve of the wind turbine; comparing the yaw error probability density distribution curve of the wind turbine with the wind direction data probability density curve in the same coordinate system, and analyzing the delay time between the yaw angle and the wind direction angle of the wind turbine.

2. The method according to claim 1, characterized in that, The method further includes: A power evaluation model for wind turbines was established. The optimized power generation and yaw ratio were recalculated using the wind direction and wind speed measurement data of the wind turbines and compared with those before optimization.

3. A wind turbine all-wind-condition gain-based yaw feedback control system, characterized in that, The system for implementing the method according to claim 1 or 2 comprises: The acquisition module is used to acquire the wind direction and wind speed measurement data of the nacelle of the wind turbine within a preset time period according to the preset sampling interval; A module is established to create probability density distribution curves for wind speed and wind direction data, set the interval for dividing wind direction sectors and the interval for dividing wind speed ranges, and obtain several wind condition domains. The calculation module is used to calculate the mean yaw error, confidence interval range of yaw error, and delay time of the wind turbine based on wind direction and wind speed measurement data for a single wind condition domain using the kernel density estimation method. The control module is used to design and tune the parameters of a fractional-order proportional-integral-derivative controller within a single wind condition domain, and to select appropriate yaw error thresholds and / or delay time parameters for yaw control of the wind turbine.

4. An electronic device, characterized in that, include: One or more processors; A storage unit for storing one or more programs that, when executed by one or more processors, enable the one or more processors to implement the method according to claim 1 or 2.

5. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it can implement the method according to claim 1 or 2.

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