Wind generating set rotating speed stability control method and system based on far and near wind speeds

Abstract

The invention discloses a wind generating set rotating speed stability control method and system based on far and near wind speeds. The method comprises the steps that a near-point wind speed signal and a far-point wind speed signal in front of an impeller of a wind generating set are obtained through a laser radar; the measurement distance of the near-point wind speed signal is fixed, and the measurement distance of the far-point wind speed signal is dynamically and adaptively determined according to the real-time wind condition of the wind field; then dynamically calculating a near-point weight coefficient and a far-point weight coefficient, carrying out weighted fusion on the near-point wind speed signal and the far-point wind speed signal to obtain a fused wind speed, and solving a fused wind speed change rate; solving a feed-forward gain coefficient based on an empirical method, and calculating a blade angle feed-forward superposition amount; and superposing the paddle angle feed-forward superposition quantity with a traditional feedback control output signal to generate a paddle angle command for control. A precise wind field model is not needed, parameter setting is easy and convenient, rotating speed fluctuation can be effectively restrained, power pulsation is reduced, the pitch frequency is reduced, the unit operation stability is improved, and the method is suitable for complex turbulent wind conditions.

Description

Technical Field

[0001] This invention relates to the technical field of wind turbine generator speed stability control, and particularly to a method and system for wind turbine generator speed stability control based on near and far wind speeds, which is especially suitable for wind farms with high turbulence intensity and complex wind conditions. Background Technology

[0002] Currently, pitch control of wind turbine generator sets typically uses the generator speed as the control input. However, the wind farm flow has strong unsteady and turbulent characteristics, and the generator speed is lagging behind the wind speed, resulting in slow generator speed regulation response, large power pulsation, high pitch execution frequency, and increased structural fatigue. LiDAR technology can measure wind speed information at different distances in front of the generator unit, enabling wind speed prediction and feedforward control. However, existing methods mostly use fixed single-point or multi-point average wind measurement, which cannot adaptively adjust the weights of near and far measurement points according to the generator unit's operating status, making it difficult to balance predictability and responsiveness.

[0003] Furthermore, the gain of traditional feedforward control is usually obtained through complex theoretical derivation, which is not conducive to engineering applications.

[0004] Therefore, there is an urgent need for a speed stability control method that can adaptively match wind field characteristics, dynamically balance information from near and far measuring points, and has strong engineering applicability. Summary of the Invention

[0005] The purpose of this invention is to overcome the shortcomings of the prior art and provide a method and system for controlling the speed stability of wind turbine generator sets based on near and far wind speeds.

[0006] To achieve the above objectives, the technical solution provided by this invention is as follows: Wind turbine generator speed stability control methods based on near and far wind speeds include: The near-point wind speed signal and the far-point wind speed signal in front of the wind turbine rotor are acquired by lidar; wherein the measurement distance of the near-point wind speed signal is fixed, and the measurement distance of the far-point wind speed signal is dynamically and adaptively determined according to the real-time wind conditions of the wind field. The operating status parameters of the wind turbine generator are monitored in real time, and the operating status parameters are input into a two-dimensional fuzzy controller to dynamically calculate the near point weight coefficient and the far point weight coefficient. The near point wind speed signal and the far point wind speed signal are weighted and fused according to the near point weighting coefficient and the far point weighting coefficient to obtain the fused wind speed and calculate the rate of change of the fused wind speed. The feedforward gain coefficient is obtained based on empirical methods, and the blade angle feedforward superposition amount is calculated by combining the feedforward gain coefficient and the fused wind speed change rate. The blade angle feedforward superposition amount is superimposed with the traditional feedback control output signal to generate a blade angle command and output to the pitch actuator for wind turbine speed stability control.

[0007] Furthermore, the measurement distance of the far-point wind speed signal is dynamically and adaptively determined based on the real-time wind conditions of the wind field, including: Collect historical wind speed data sequences for past time periods from wind turbine generator sets; A Fast Fourier Transform was performed on the collected historical wind speed data sequence to extract the main energy components in the 0.01–0.1 Hz frequency band, and the frequency with the largest amplitude was identified. And calculate the corresponding wind speed fluctuation period. , ; Combined with the corresponding wind speed fluctuation cycle and average wind speed Determine the measurement distance of the distant wind speed signal : .

[0008] Furthermore, the real-time monitored operating status parameters of the wind turbine generator set include the wind turbine generator set speed acceleration and acceleration change rate; The wind turbine generator's rotational speed acceleration and acceleration change rate are input into a two-dimensional fuzzy controller, which then performs dynamic analysis using fuzzy rules and outputs the nearest point weight coefficients. The fuzzy rules used by the two-dimensional fuzzy controller are as follows: When the wind turbine generator's rotational acceleration is negatively large, and the rate of change of acceleration is negatively large, negatively small, zero, positively small, and positively large, the corresponding near-point weighting coefficients are 1.0, 0.9, 0.8, 0.9, and 1.0, respectively. When the wind turbine generator's rotational acceleration is negatively small, and the rate of change of acceleration is negatively large, negatively small, zero, positively small, and positively large, the corresponding near-point weighting coefficients are 0.9, 0.7, 0.5, 0.7, and 0.9, respectively. When the wind turbine generator's rotational acceleration is zero, and the rate of change of acceleration is negative large, negative small, zero, positive small, and positive large, the corresponding near point weighting coefficients are 0.8, 0.5, 0.1, 0.5, and 0.8, respectively. When the wind turbine generator's rotational acceleration is positively small, and the rate of change of acceleration is negatively large, negatively small, zero, positively small, and positively large, the corresponding near-point weighting coefficients are 0.9, 0.7, 0.5, 0.7, and 0.9, respectively. When the wind turbine generator's rotational acceleration is positive, and the rate of change of acceleration is negative, negative, zero, positive, and positive, the corresponding near-point weighting coefficients are 1.0, 0.9, 0.8, 0.9, and 1.0, respectively.

[0009] Furthermore, the near-point wind speed signal and the far-point wind speed signal are weighted and fused according to the near-point weighting coefficient and the far-point weighting coefficient to obtain the fused wind speed. The formula is as follows:

[0010] in, For near-point weighting coefficients, For near-point wind speed, For the far point weighting coefficient, For the wind speed at the far point, It is a time variable; When the wind turbine is in the acceleration or fluctuation phase, the two-dimensional fuzzy controller automatically increases the near point weight coefficient to improve the response speed; when the wind turbine is running stably, the far point weight coefficient is increased to enhance the prediction capability.

[0011] Furthermore, the feedforward gain coefficient is obtained based on an empirical method. The formula is as follows: ; in, This refers to the maximum allowable pitch angle change of a wind turbine generator when typical wind speed disturbances occur. It is determined based on the limits of the pitch drive system and the safety requirements of the blade structure. The reference wind speed change rate is taken from the maximum wind speed change rate during the wind farm wind resource testing phase.

[0012] Furthermore, the blade angle feedforward superposition amount is superimposed with the traditional feedback control output signal to generate a blade angle command. The formula is as follows: ; in, For the blade angle feedforward superposition: ; This is the feedforward gain coefficient. To incorporate the rate of change of wind speed: ; To incorporate wind speed; It is a time variable; This is the output signal for traditional feedback control.

[0013] Furthermore, after the pitch actuator executes the blade angle command, it collects the actual rotational speed feedback signal, adjusts the fuzzy rules of the two-dimensional fuzzy controller in real time, and optimizes the near-point weight coefficient. ， This outputs the final blade angle command, enabling stable control of the wind turbine generator speed.

[0014] Furthermore, to achieve the above objectives, the present invention also provides a wind turbine generator speed stability control system based on near and far wind speeds, used to implement the above-mentioned wind turbine generator speed stability control method based on near and far wind speeds, which includes a lidar and a main control PLC. The lidar is used to acquire near-point wind speed signals and far-point wind speed signals in front of the wind turbine rotor. The main control PLC has a built-in adaptive calculation module, a two-dimensional fuzzy controller, a weighted fusion module, a feedforward gain calculation module, a blade angle feedforward superposition calculation module, and a superposition operation module. The adaptive calculation module is used to calculate the measurement distance of the distant wind speed signal; The two-dimensional fuzzy controller is used to dynamically calculate the near point weight coefficient and the far point weight coefficient; The weighted fusion module is used to calculate the fused wind speed and the rate of change of the fused wind speed; The feedforward gain calculation module is used to determine the feedforward gain coefficient based on an empirical method. The blade angle feedforward superposition calculation module is used to calculate the blade angle feedforward superposition by combining the feedforward gain coefficient and the fused wind speed change rate. The superposition calculation module is used to superimpose the blade angle feedforward superposition amount with the traditional feedback control output signal to generate a blade angle command.

[0015] Furthermore, the main control PLC also has a built-in feedback correction module, which adjusts the fuzzy rules of the two-dimensional fuzzy controller in real time based on the actual rotational speed feedback signal, and optimizes the near-point weight coefficient. .

[0016] Furthermore, the sampling frequency of the lidar is not less than 1Hz, and the wind measurement accuracy is not less than 0.1m / s.

[0017] Compared with existing technologies, the principles and advantages of this technical solution are as follows: 1. During the process of controlling the speed stability of wind turbine generator sets, near-point wind speed signals and far-point wind speed signals are collected. The near-point wind speed signal can ensure responsiveness, while the far-point wind speed signal can provide predictability, thereby achieving the suppression of speed fluctuations and the improvement of power stability.

[0018] 2. The measurement distance of the far-point wind speed signal is dynamically and adaptively determined according to the real-time wind conditions of the wind field, so as to automatically adapt to different wind field scales and turbulence characteristics, thereby improving the accuracy of wind turbine speed stability control.

[0019] 3. The fuzzy weighted fusion of wind speeds at near and far distances does not require an accurate model and can dynamically balance prediction and response.

[0020] 4. The feedforward gain is obtained by empirical method, which is simple, reliable, easy to implement on site and standardize. Attached Figure Description

[0021] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the services required in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings in the following description are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0022] Figure 1 This is a flowchart illustrating the principle of the wind turbine generator speed stability control method based on near and far wind speeds in Embodiment 1 of the present invention. Figure 2 This is a connection block diagram of the wind turbine generator speed stability control system based on near and far wind speeds in Embodiment 1 of the present invention; Figure 3 This is a flowchart illustrating the principle of the wind turbine generator speed stability control method based on near and far wind speeds in Embodiment 2 of the present invention. Figure 4 This is a connection block diagram of the wind turbine generator speed stability control system based on near and far wind speeds in Embodiment 2 of the present invention. Detailed Implementation

[0023] The present invention will be further described below with reference to specific embodiments: Example 1 like Figure 1 As shown in this embodiment, the wind turbine generator speed stability control method based on near and far wind speeds includes the following steps: S1. Obtain near-point wind speed signals and far-point wind speed signals in front of the wind turbine rotor using lidar; wherein the measurement distance of the near-point wind speed signal is fixed, and the measurement distance of the far-point wind speed signal is dynamically and adaptively determined according to the real-time wind conditions of the wind field. In this step, the measurement distance for the near-point wind speed signal is set to 50 meters, that is, the wind speed signal is measured 50 meters in front of the wind turbine rotor. If the measurement distance is too close, the lidar laser beam may be blocked by the rotor, resulting in signal distortion. The 50-meter measurement distance can ensure that the wind speed lead is sufficient for feedforward prediction, and can also avoid blade shading interference.

[0024] The measurement distance for the distant wind speed signal is dynamically and adaptively determined based on the real-time wind conditions of the wind field. The specific process is as follows: Collect historical wind speed data sequences for past time periods from wind turbine generator sets; A Fast Fourier Transform was performed on the collected historical wind speed data sequence to extract the main energy components in the 0.01–0.1 Hz frequency band, and the frequency with the largest amplitude was identified. And calculate the corresponding wind speed fluctuation period. , ; Combined with the corresponding wind speed fluctuation cycle and average wind speed Determine the measurement distance of the distant wind speed signal : .

[0025] S2. Real-time monitoring of the wind turbine generator's speed acceleration and acceleration change rate, and inputting the wind turbine generator's speed acceleration and acceleration change rate into a two-dimensional fuzzy controller, which then performs dynamic analysis using fuzzy rules and outputs the nearest point weight coefficients. The fuzzy rules used by the two-dimensional fuzzy controller are as follows: When the wind turbine generator's rotational acceleration is negatively large, and the rate of change of acceleration is negatively large, negatively small, zero, positively small, and positively large, the corresponding near-point weighting coefficients are 1.0, 0.9, 0.8, 0.9, and 1.0, respectively. When the wind turbine generator's rotational acceleration is negatively small, and the rate of change of acceleration is negatively large, negatively small, zero, positively small, and positively large, the corresponding near-point weighting coefficients are 0.9, 0.7, 0.5, 0.7, and 0.9, respectively. When the wind turbine generator's rotational acceleration is zero, and the rate of change of acceleration is negative large, negative small, zero, positive small, and positive large, the corresponding near point weighting coefficients are 0.8, 0.5, 0.1, 0.5, and 0.8, respectively. When the wind turbine generator's rotational acceleration is positively small, and the rate of change of acceleration is negatively large, negatively small, zero, positively small, and positively large, the corresponding near-point weighting coefficients are 0.9, 0.7, 0.5, 0.7, and 0.9, respectively. When the wind turbine generator's rotational acceleration is positive, and the rate of change of acceleration is negative, negative, zero, positive, and positive, the corresponding near-point weighting coefficients are 1.0, 0.9, 0.8, 0.9, and 1.0, respectively. The weighting coefficient of the far point is obtained by subtracting the weighting coefficient of the near point from 1.

[0026] S3. The near point wind speed signal and the far point wind speed signal are weighted and fused according to the near point weighting coefficient and the far point weighting coefficient to obtain the fused wind speed and calculate the rate of change of the fused wind speed. Among them, the fusion wind speed is obtained. The formula is as follows: ; in, For near-point weighting coefficients, For near-point wind speed, For the far point weighting coefficient, For the wind speed at the far point, It is a time variable; When the wind turbine is in the acceleration or fluctuation phase, the two-dimensional fuzzy controller automatically increases the near point weight coefficient to improve the response speed; when the wind turbine is running stably, the far point weight coefficient is increased to enhance the prediction capability.

[0027] Find the rate of change of the combined wind speed of The formula is as follows: .

[0028] S4. Calculate the feedforward gain coefficient based on empirical methods, and calculate the blade angle feedforward superposition amount by combining the feedforward gain coefficient and the fused wind speed change rate. In this step, The feedforward gain coefficient is obtained using an empirical method. The formula is as follows: ; in, This refers to the maximum allowable pitch angle change of a wind turbine generator when typical wind speed disturbances occur. It is determined based on the limits of the pitch drive system and the safety requirements of the blade structure. The reference wind speed change rate is taken from the maximum wind speed change rate during the wind farm wind resource testing phase.

[0029] The blade angle feedforward superposition amount is calculated by combining the feedforward gain coefficient and the fused wind speed change rate. The formula is as follows: .

[0030] S5. The blade angle feedforward superposition amount is superimposed with the traditional feedback control output signal to generate a blade angle command and output to the pitch actuator for wind turbine generator speed stability control.

[0031] In this step, the blade angle command is generated. The formula is as follows: ； This is the output signal for traditional feedback control.

[0032] Furthermore, this embodiment also includes, as follows: Figure 2The wind turbine generator speed stability control system shown is used to implement the above-mentioned wind turbine generator speed stability control method based on near and far wind speeds. It includes a lidar and a main control PLC. The system includes a lidar unit for acquiring near-point and far-point wind speed signals in front of the wind turbine rotor; a main control PLC with built-in adaptive calculation module, two-dimensional fuzzy controller, weighted fusion module, feedforward gain calculation module, blade angle feedforward superposition calculation module, and superposition operation module; the adaptive calculation module calculates the measurement distance of the far-point wind speed signal; the two-dimensional fuzzy controller dynamically calculates the near-point and far-point weighting coefficients; the weighted fusion module calculates the fused wind speed and the fused wind speed change rate; the feedforward gain calculation module calculates the feedforward gain coefficient based on empirical methods; the blade angle feedforward superposition calculation module calculates the blade angle feedforward superposition amount by combining the feedforward gain coefficient and the fused wind speed change rate; and the superposition operation module superimposes the blade angle feedforward superposition amount with the traditional feedback control output signal to generate a blade angle command.

[0033] In this embodiment, the sampling frequency of the lidar is not less than 1Hz, and the wind measurement accuracy is not less than 0.1m / s.

[0034] Example 2 like Figure 3 As shown, compared with Example 1, the wind turbine generator speed stability control method based on near and far wind speeds described in this example further includes the following steps: After the pitch actuator executes the blade angle command, it collects the actual rotational speed feedback signal, adjusts the fuzzy rules of the two-dimensional fuzzy controller in real time, and optimizes the near-point weight coefficient. ， This outputs the final blade angle command, enabling stable control of the wind turbine generator speed.

[0035] like Figure 4 As shown, compared with Embodiment 1, in the wind turbine generator speed stability control system based on near and far wind speeds described in this embodiment, the main control PLC also has a built-in feedback correction module. This feedback correction module adjusts the fuzzy rules of the two-dimensional fuzzy controller in real time based on the actual speed feedback signal, and optimizes the near point weight coefficient. .

[0036] The above-described embodiments are merely preferred embodiments of the present invention and are not intended to limit the scope of the present invention. Therefore, any changes made in accordance with the shape and principle of the present invention should be covered within the protection scope of the present invention.

Claims

1. A wind turbine rotor speed stability control method based on the distance of the wind speed, characterized in that, The method comprises the following steps: acquiring a near-point wind speed signal and a far-point wind speed signal in front of a wind turbine impeller through a laser radar; wherein the measurement distance of the near-point wind speed signal is fixed, and the measurement distance of the far-point wind speed signal is dynamically and adaptively determined according to real-time wind conditions of a wind farm; real-time monitoring of a wind turbine operating state parameter and inputting the operating state parameter into a two-dimensional fuzzy controller to dynamically calculate a near-point weight coefficient and a far-point weight coefficient; weighting and fusing the near-point wind speed signal and the far-point wind speed signal according to the near-point weight coefficient and the far-point weight coefficient to obtain a fused wind speed and calculate a fused wind speed change rate; calculating a feedforward gain coefficient based on an empirical method, and combining the feedforward gain coefficient and the fused wind speed change rate to calculate a blade angle feedforward superimposition amount; superimposing the blade angle feedforward superimposition amount and a traditional feedback control output signal to generate a blade angle command and output the blade angle command to a variable pitch actuator for wind turbine speed stability control.

2. The wind turbine rotor speed stability control method based on the relative wind speed according to claim 1, characterized by, The measurement distance of the far-point wind speed signal is dynamically and adaptively determined according to real-time wind conditions of a wind farm, comprising: collecting historical wind speed data sequences of a wind turbine in a past time period; The historical wind speed data sequence collected is subjected to fast Fourier transform, main energy components in 0.01~0.1 Hz frequency band are extracted, and the frequency with the largest amplitude is found And the corresponding wind speed fluctuation period is calculated , ; combining the corresponding wind speed fluctuation periods and the average wind speed to determine the measuring distance of the remote point wind speed signal : 。 3. The wind turbine rotor speed stability control method based on the relative wind speed according to claim 1, characterized by, the real-time monitored wind turbine operating state parameters include wind turbine speed acceleration and acceleration change rate; inputting the wind turbine speed acceleration and acceleration change rate into a two-dimensional fuzzy controller, and dynamically analyzing and outputting a near-point weight coefficient by the two-dimensional fuzzy controller using fuzzy rules; the fuzzy rules used by the two-dimensional fuzzy controller are as follows: when the wind turbine speed acceleration is negative large and the acceleration change rate is negative large, negative small, zero, positive small and positive large, the corresponding near-point weight coefficients are 1.0, 0.9, 0.8, 0.9 and 1.0, respectively; when the wind turbine speed acceleration is negative small and the acceleration change rate is negative large, negative small, zero, positive small and positive large, the corresponding near-point weight coefficients are 0.9, 0.7, 0.5, 0.7 and 0.9, respectively; when the wind turbine speed acceleration is zero and the acceleration change rate is negative large, negative small, zero, positive small and positive large, the corresponding near-point weight coefficients are 0.8, 0.5, 0.1, 0.5 and 0.8, respectively; when the wind turbine speed acceleration is positive small and the acceleration change rate is negative large, negative small, zero, positive small and positive large, the corresponding near-point weight coefficients are 0.9, 0.7, 0.5, 0.7 and 0.9, respectively; when the wind turbine speed acceleration is positive large and the acceleration change rate is negative large, negative small, zero, positive small and positive large, the corresponding near-point weight coefficients are 1.0, 0.9, 0.8, 0.9 and 1.0, respectively.

4. The wind turbine rotor speed stability control method based on the relative wind speed according to claim 1, characterized by, The near-point wind speed signal and the far-point wind speed signal are fused by weighting according to the near-point weight coefficient and the far-point weight coefficient to obtain a fused wind speed The formula is as follows: ； wherein, is a near point weight coefficient, is a near point wind speed, is a far point weight coefficient, is a far point wind speed, is a time variable; When the wind turbine is in an acceleration or fluctuation stage, the two-dimensional fuzzy controller automatically increases the near-point weight coefficient to improve the response speed; when the wind turbine is in a stable operation stage, the far-point weight coefficient is increased to enhance the prediction ability.

5. The wind turbine generator set rotational speed stability control method based on the relative wind speed according to claim 1, characterized by, Determination of feedforward gain coefficient based on empirical method The formula is as follows: ； wherein, is the maximum pitch angle variation allowed by the wind turbine when a typical disturbance in wind speed occurs, which is determined according to the pitch drive system limits and blade structural safety requirements; is the reference wind speed variation rate, which is taken from the maximum wind speed variation rate during the wind farm wind resource testing phase.

6. The wind turbine generator set rotational speed stability control method based on the relative wind speed according to claim 1, characterized by, The blade angle feed forward overlay quantity is superimposed with a conventional feedback control output signal to generate a blade angle command The formula is as follows: ； wherein, is the blade angle feedforward superimposition amount: ； is a feed forward gain coefficient, is a fusion wind speed rate of change: ； is the fusion wind speed; is the time variable; is a conventional feedback control output signal.

7. The wind turbine rotor speed stability control method based on the relative wind speed according to any one of claims 1-6, characterized in that, After the pitch actuator executes the blade angle command, the actual rotating speed feedback signal is collected, the fuzzy rules of the two-dimensional fuzzy controller are adjusted in real time, and the near-point weight coefficient is optimized Thus, the final blade angle command is output, and the rotating speed stability control of the wind turbine generator system is realized.

8. A wind turbine rotor speed stability control system based on far and near wind speed for implementing the wind turbine rotor speed stability control method based on far and near wind speed according to any one of claims 1-6, characterized in that, The method comprises the following steps: a laser radar and a main control PLC are included; the laser radar is used to acquire a near-point wind speed signal and a far-point wind speed signal in front of a wind turbine impeller; The master PLC is internally provided with an adaptive calculation module, a two-dimensional fuzzy controller, a weighted fusion module, a feedforward gain calculation module, a paddle angle feedforward superposition amount calculation module and a superposition operation module; The adaptive calculation module is used for calculating a measurement distance of a far-point wind speed signal; The two-dimensional fuzzy controller is used for dynamically calculating a near-point weight coefficient and a far-point weight coefficient; The weighted fusion module is used for calculating a fused wind speed and a fused wind speed change rate; The feedforward gain calculation module is used for calculating a feedforward gain coefficient based on an empirical method; The paddle angle feedforward superposition amount calculation module is used for calculating a paddle angle feedforward superposition amount in combination with the feedforward gain coefficient and the fused wind speed change rate; The superposition operation module is used for superimposing the paddle angle feedforward superposition amount and a traditional feedback control output signal to generate a paddle angle command.

9. The wind turbine generator set rotational speed stability control system based on the relative wind speed according to claim 8, characterized by, The master PLC is also internally provided with a feedback correction module, which adjusts the fuzzy rules of the two-dimensional fuzzy controller in real time based on the actual speed feedback signal, and optimizes the near-point weight coefficient .

10. The wind turbine generator set rotational speed stability control system based on the relative wind speed according to claim 8, characterized by, The sampling frequency of the laser radar is not less than 1 Hz, and the wind measurement accuracy is not less than 0.1 m / s.

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