Optimization control method and system for blade tip speed ratio of power generation fan

Abstract

The invention relates to the technical field of wind power generation, and discloses a power generation fan tip speed ratio optimization control method and system, and the method comprises the steps: carrying out the dynamic self-adaptive adjustment through the optimization of extreme value search control (ESC) based on dynamic disturbance and self-adaptive adjustment, enabling the disturbance intensity to automatically respond to the wind speed change, and carrying out the dynamic self-adaptive adjustment, and combining feed-forward compensation and ESC feedback search. The control targets of different wind speed intervals are automatically balanced, the extreme value is quickly searched in a low-wind-speed area, the power output is stabilized in a high-wind-speed area, robustness and time-varying adaptability under all working conditions are achieved, quick and self-adaptive response under dynamic change of wind speed and a strong noise environment is adapted, the response speed of the system is increased, meanwhile, power fluctuation is reduced, and the service life of the system is prolonged. The bottleneck that a traditional fixed strategy and a model depend on to solve fan tip speed ratio optimization control is solved.

Description

Technical Field

[0001] The present invention relates to the technical field of wind power generation, and in particular to a method and system for optimizing the tip speed ratio of a power generation wind turbine. Background Art

[0002] In the field of wind power generation, the core objective of tip speed ratio optimization control is to adjust the rotational speed (or pitch angle) of the wind turbine so that the tip speed ratio always corresponds to the peak value of the wind energy utilization coefficient C p (λ), thereby maximizing the energy capture efficiency. Traditional control methods are mainly based on model dependence or fixed strategies, such as the rated point optimization algorithm, the perturbation observation method, and the maximum power point tracking (MPPT) based on the aerodynamic model. The rated point optimization algorithm is a fixed tip speed ratio logic, assuming that there is a unique optimal tip speed ratio (such as the fixed value of 8.5 in the classical Betz theory) when the wind turbine is at the rated wind speed, and the reference rotational speed is obtained through direct calculation. The MPPT-based maximum power point tracking also utilizes the aerodynamic characteristic curve of the wind turbine to calculate the optimal rotational speed through the measured wind speed value. However, both of the above two traditional methods rely on the accurate Cp(λ) characteristic curve, and have a high tuning accuracy in small wind turbines and steady-state scenarios. However, in actual operation, the starting performance of the blades is significantly affected by aging, icing, and wind speed turbulence, resulting in a significant change in the optimal value. For example, after the blade wears, the Cp(λ) peak value decreases and the optimum will shift to the left, resulting in a significant decrease in the wind energy utilization efficiency when the wind speed is not at the rated value or the model is mismatched.

[0003] The traditional perturbation observation method (hill climbing method) is based on periodically perturbing the rotational speed (or tip speed ratio), comparing the power changes before and after the perturbation. If the power increases, continue to perturb in this direction, otherwise adjust in the opposite direction, thereby updating the rotational speed control. The current typical control strategy is to fix the perturbation step size. When the wind speed changes rapidly, it will cause the adjustment direction to be opposite to the moving direction of the actual extreme point, resulting in the "chasing the opposite direction" problem. When facing a sudden increase in wind speed, the hill climbing method requires multiple cycles to adjust the direction. This fixed perturbation strategy cannot balance the search speed and the steady-state accuracy. Using a small step size is likely to cause a response lag and requires more adjustment time, while using a large step size causes power oscillation. How to dynamically determine the perturbation step size is a difficult problem to be solved. The existing method cannot track the dynamic changes of the wind speed in time through the perturbation of a fixed step size. Especially in scenarios with high turbulence intensity, the control performance will deteriorate sharply. Summary of the Invention

[0004] The object of the present invention is to provide a method and system for optimizing the tip speed ratio control of a power generation wind turbine, to solve the bottleneck of traditional fixed strategies and model dependence in solving the tip speed ratio optimization control of wind turbines. Through the optimization of extremum seeking control (ESC) based on dynamic disturbance and adaptive regulation, the disturbance intensity automatically responds to wind speed changes and dynamically adapts. Combined with feedforward compensation and ESC feedback search, it automatically balances the control objectives in different wind speed intervals, focusing on quickly searching for the extremum in low wind speed areas and stable power output in high wind speed areas, achieving robustness and time-varying adaptability under all working conditions, and adapting to the fast and adaptive response under dynamic wind speed changes and strong noise environments.

[0005] According to the first aspect of the object of the present invention, a method for optimizing the tip speed ratio control of a power generation wind turbine is proposed, including the following steps:

[0006] Obtain the initial values of the wind turbine speed, the wind turbine output power, and the wind speed, and calculate the tip speed ratio in combination with the radius of the wind turbine blade;

[0007] Inject a dynamic sine wave disturbance based on the current tip speed ratio reference value to generate a tip speed ratio with disturbance; the dynamic sine wave disturbance is determined by the disturbance amplitude and the disturbance frequency, the disturbance amplitude is set to dynamically adjust according to the real-time wind speed change rate, and the disturbance frequency is preset according to the system bandwidth;

[0008] Based on the wind turbine output power, use a phase-locked loop to extract the component with the same frequency as the injected disturbance, and calculate the gradient estimation value of the power with respect to the tip speed ratio;

[0009] Optimize according to the gradient estimation value to obtain the current optimal tip speed ratio reference value;

[0010] Based on the Kalman filter, estimate the wind speed and its change rate in real time;

[0011] Based on the real-time wind speed estimation value and the current optimal tip speed ratio reference value, calculate the reference speed, generate the reference speed for feedforward control, and output the feedforward speed control command;

[0012] Superimpose the feedforward speed control command and the speed adjustment amount caused by the dynamic sine wave disturbance to generate the final wind turbine speed control command; and

[0013] Send the wind turbine speed control command to the converter through the CAN bus to adjust the generator torque and drive the wind turbine speed to track the command.

[0014] As an optional embodiment, the injecting a dynamic sine wave disturbance based on the current tip speed ratio reference value to generate a tip speed ratio with disturbance includes:

[0015] Inject a dynamic sine wave disturbance based on the current tip speed ratio reference value in the following manner:

[0016] λ(t) = λ0(t) + Δλ(t)·sin(ω pert t)

[0017] Wherein, λ(t) represents the tip speed ratio with perturbation, and λ0(t) represents the current reference value of the tip speed ratio, whose initial value is set according to the historical optimal value;

[0018] Δλ(t) represents the perturbation amplitude of adaptive adjustment, and the adjustment range is [0.2, 1.0], and its initial value is set to 0.5;

[0019] ω pert represents the perturbation frequency, which is set to 2π rad / s according to the system bandwidth, corresponding to a 1 s perturbation period.

[0020] As an optional embodiment, the perturbation amplitude of the adaptive adjustment is set to be dynamically adjusted according to the real-time wind speed change rate, including:

[0021] According to the real-time wind speed change rate dynamically adjust the perturbation amplitude Δλ(t):

[0022]

[0023] In the formula, Δλ min represents the minimum perturbation amplitude when the wind speed is stable, and Δλ max represents the maximum perturbation amplitude when the wind speed changes sharply, and kv represents the wind speed change rate sensitivity coefficient, which is used to control the response sensitivity of the perturbation amplitude to the wind speed change;

[0024] Wherein, represents the real-time wind speed change rate, which is set to obtain the real-time wind speed estimate value and the wind speed change rate based on the extended Kalman filter iterative update and the wind speed change rate

[0025] As an optional embodiment, based on the output power of the wind turbine, using a phase-locked loop to extract the component with the same frequency as the injected perturbation, and calculating the gradient estimate value of the power with respect to the tip speed ratio, including:

[0026] According to the collected wind turbine output power P(t), using a phase-locked loop PLL to extract the component with the same frequency as the injected perturbation, analyzing the correlation between the power and the perturbation signal according to the integral dP(t) / dλ, and calculating the gradient estimate value of the power with respect to the tip speed ratio

[0027]

[0028] Wherein, T represents the perturbation period, corresponding to the perturbation frequency, which is set to 1 s, and the window length of the integral operation is equal to the perturbation period;

[0029] Wherein, the gradient estimate value A positive value indicates that increasing the tip speed ratio λ(t) can improve power, and a negative value indicates the opposite.

[0030] As an alternative embodiment, the optimization based on the gradient estimate value to obtain the current optimal tip speed ratio reference value includes:

[0031] Optimizing and updating the tip speed ratio reference value according to the gradient estimate value, driving it to converge to the power extreme point, that is:

[0032]

[0033] where α(t) represents the optimization gain, which is used to control the update intensity of the gradient on the tip speed ratio reference value. Its initial value is set to 0.1 and is adaptively adjusted according to the power noise;

[0034] represents the estimated value of the tip speed ratio reference value;

[0035] Through the feedback search of converging to the power extreme point, the estimated value of the tip speed ratio reference value gradually approaches the maximum point of the wind energy utilization coefficient C p (λ), and the estimated value of the tip speed ratio reference value at this time is determined as the current optimal tip speed ratio reference value

[0036] As an alternative embodiment, the optimization gain α(t) is set to be adaptively adjusted according to the power noise, including:

[0037] Calculate the real-time standard deviation of the collected wind turbine output power P(t), and then adaptively adjust the optimization gain α(t) according to the noise level:

[0038]

[0039] where α0 represents the initial value of the optimization gain; N represents the number of points in the sliding window, which is used to calculate the power fluctuation in real time; σ P,nom represents the noise benchmark under the rated power; P i represents the wind turbine output power at the i-th moment, {P i} represents the real-time power sequence; σ P(t) represents the real-time power standard deviation;

[0040] According to the comparison between σ P(t) and σ P,nom , adaptively adjust the optimization gain α(t):

[0041] If σ P(t) > σ P,nom , then reduce α(t);

[0042] If σ P(t) < σP,nom , then increase α(t).

[0043] As an alternative embodiment, calculating a reference rotational speed based on the real-time wind speed estimate and the current optimal tip speed ratio reference value, generating a reference rotational speed for feedforward control, and outputting a feedforward rotational speed control command includes:

[0044] Based on the real-time wind speed estimate and the current optimal tip speed ratio reference value calculate the reference rotational speed ω for feedforward control ref :

[0045]

[0046] where R represents the radius of the wind turbine blade.

[0047] As an alternative embodiment, superimposing the feedforward rotational speed control command and the rotational speed adjustment amount caused by the dynamic sine wave disturbance to generate a final wind turbine rotational speed control command includes:

[0048] Superimpose the reference rotational speed ω for feedforward control ref and the rotational speed adjustment amount δω(t) caused by the dynamic sine wave disturbance to obtain the final control command ω cmd :

[0049] ω cmd = ω ref + δω(t)

[0050] where δω(t) represents the adjustment amount of the rotational speed mapped by the dynamic sine wave disturbance through the definition of the tip speed ratio:

[0051]

[0052] As an alternative embodiment, continuously collect the wind turbine rotational speed and the wind turbine output power in the next control cycle, iterate the reference rotational speed for feedforward control and the rotational speed adjustment amount caused by the dynamic sine wave disturbance, iteratively update the wind turbine rotational speed control command and apply it to the converter of the generator for torque adjustment, and dynamically track the wind turbine rotational speed in real time.

[0053] According to the second aspect of the object of the present invention, a computer system is proposed, including:

[0054] One or more processors;

[0055] A memory storing operable instructions, which when executed by the one or more processors cause the one or more processors to perform operations including the steps of the method for optimizing the tip speed ratio of a power generation wind turbine in the foregoing embodiments.

[0056] With the implementation of the tip speed ratio optimization control method and system of the power generation wind turbine according to the embodiments of the present invention above, by injecting a periodic disturbance signal into the tip speed ratio control quantity and using the gradient information of the system power output to iteratively update the control quantity, the maximum power point is gradually approached, thereby realizing automatic optimization without the need to establish an accurate aerodynamic model of the wind turbine (such as the Betz theory or the blade aerodynamic characteristic curve), and it is applicable to the actual scenarios where the model parameters are time-varying (such as blade aging, non-steady wind speed). Further combined with the injection of adaptive disturbance intensity for dynamic adjustment, the disturbance intensity and frequency are adjusted in real time according to the wind speed change. For example, the disturbance is increased at low wind speeds to quickly search for the extreme value, and the disturbance is reduced at high wind speeds to stably track, avoiding the response lag or oscillation caused by the fixed parameters of the traditional ESC, realizing the decoupling of the search speed and the steady-state accuracy, and improving the response and efficiency of the control system.

[0057] Compared with the prior art, the significant advantages of the tip speed ratio optimization control method of the power generation wind turbine of the present invention are as follows:

[0058] (1) The adaptive control updates the ESC parameters in real time, enabling the system to quickly adjust the tip speed ratio to a new optimal value when the wind speed undergoes a step change (such as a gust of wind), reducing the power loss time and improving the dynamic response speed;

[0059] (2) The dynamic design of the disturbance signal of the ESC adjusts the disturbance intensity and frequency in real time according to the wind speed change, thereby updating the disturbance injection, which can suppress the influence of small-amplitude wind speed fluctuations (such as turbulence), and the adaptive control compensates for large-amplitude wind speed changes (such as sudden changes in wind direction), realizing double-loop anti-interference. Under complex wind conditions (such as changes in the average wind speed, high-frequency pulsations), the control system can still stably track the optimal tip speed ratio, reducing the power fluctuation amplitude;

[0060] (3) The tip speed ratio directly affects the wind energy utilization coefficient. By using the ESC to search for the peak value of the wind energy utilization coefficient in real time, it is ensured that the wind turbine operates in the high-efficiency range within the full wind speed range and can still track the new extreme value when the curve changes (such as entering the stall state in the high wind speed area), avoiding the energy waste of the traditional fixed-speed control; at the same time, by adjusting the disturbance amplitude, the disturbance is reduced when approaching the extreme value point to reduce the periodic load fluctuations of the blades and the transmission chain, and the disturbance is increased when far from the extreme value point to accelerate the search speed. Combined with the rotational speed feedback control, a power-rotational speed double-loop regulation is formed to avoid the rotational speed overshoot caused by simple extreme value search and improve the smoothness of the generated power.

[0061] It should be understood that all combinations of the foregoing concepts and additional concepts described in more detail below can be regarded as part of the inventive subject matter of the present disclosure as long as such concepts do not conflict with each other. In addition, all combinations of the claimed subject matter are regarded as part of the inventive subject matter of the present disclosure.

[0062] The foregoing and other aspects, embodiments, and features of the teachings of the present invention can be more fully understood from the following description in conjunction with the accompanying drawings. Other additional aspects of the present invention, such as the features and / or beneficial effects of exemplary embodiments, will be apparent from the following description or learned through the practice of specific embodiments according to the teachings of the present invention. Description of the Drawings

[0063] The drawings are not intended to be drawn to scale. In the drawings, each identical or nearly identical component shown in various figures may be represented by the same reference numeral. For clarity, not every component is labeled in each figure. Now, embodiments of various aspects of the present invention will be described by way of example and with reference to the drawings.

[0064] Figure 1 It is a schematic flowchart of a method for optimizing the tip speed ratio control of a power generation wind turbine according to an embodiment of the present invention.

[0065] Figure 2 It is a control logic block diagram of a method for optimizing the tip speed ratio control of a power generation wind turbine according to an embodiment of the present invention. Detailed Embodiments

[0066] In order to better understand the technical content of the present invention, specific embodiments are hereby given and described in conjunction with the accompanying drawings as follows.

[0067] {Embodiment 1}

[0068] The method for optimizing the tip speed ratio control of a power generation wind turbine according to an embodiment of the present invention is a model - independent, extremum - seeking control (ESC) - based tip speed ratio optimization control method. While achieving fast - response tip speed ratio optimization control, it improves the wind energy capture efficiency and ensures system stability. It is applicable to complex environments with frequently changing wind speeds and also applicable to optimization control in steady - state wind speed environments.

[0069] Combined with the attached Figure 1 、 Figure 2 As shown, the implementation of the method for optimizing the tip speed ratio control of a power generation wind turbine according to an example of the present invention includes the following steps:

[0070] S1. Obtain the initial values of the wind turbine speed, the wind turbine output power, and the wind speed, and calculate the tip speed ratio in combination with the radius of the wind turbine blade;

[0071] S2. Inject a dynamic sine - wave perturbation based on the current tip speed ratio reference value to generate a perturbed tip speed ratio; the aforementioned dynamic sine - wave perturbation is determined by the perturbation amplitude and the perturbation frequency. The perturbation amplitude is set to be dynamically adjusted according to the real - time wind speed change rate, and the perturbation frequency is preset according to the system bandwidth;

[0072] S3. Based on the output power of the wind turbine, use a phase-locked loop to extract the component with the same frequency as the injected perturbation, and calculate the gradient estimate of the power with respect to the tip speed ratio.

[0073] S4. Optimize according to the gradient estimate to obtain the current optimal tip speed ratio reference value.

[0074] S5. Based on the Kalman filter, estimate the wind speed and its change rate in real time.

[0075] S6. Calculate the reference speed based on the real-time wind speed estimate and the aforementioned current optimal tip speed ratio reference value, generate the reference speed for feedforward control, and output the feedforward speed control command.

[0076] S7. Superimpose the feedforward speed control command and the speed adjustment amount caused by the dynamic sine wave perturbation to generate the final wind turbine speed control command; and

[0077] S8. Send the wind turbine speed control command to the converter through the CAN bus to adjust the generator torque and drive the wind turbine speed to track the command.

[0078] It should be understood that the signal acquisition of the wind turbine speed ω (unit: rad / s) comes from speed sensors such as photoelectric encoders, which is used to characterize the current rotational speed of the blade to calculate the real-time tip speed ratio.

[0079] The signal acquisition of the wind turbine output power P(t) comes from a power transmitter, which is used to judge the wind energy utilization efficiency and drive the extremum search.

[0080] The initial wind speed input comes from wind speed measurement sensors such as ultrasonic anemometers, and combines with the EKF estimation to accurately iterate the real-time wind speed estimate.

[0081] In the embodiment of the present invention, the basic sensing data of the power generation wind turbine system is set to be collected at a high frequency according to a unified sampling frequency. Taking 100 Hz as an example.

[0082] As an optional embodiment, in step S2, injecting a dynamic sine wave perturbation on the basis of the current tip speed ratio reference value to generate a perturbed tip speed ratio includes:

[0083] Inject a dynamic sine wave perturbation on the basis of the current tip speed ratio reference value in the following manner:

[0084] λ(t) = λ0(t) + Δλ(t)·sin(ω pert t)

[0085] where λ(t) represents the perturbed tip speed ratio, λ0(t) represents the current tip speed ratio reference value, and its initial value is set according to the historical optimal value, for example, set to 8.5;

[0086] Δλ(t) represents the amplitude of the perturbation adjusted adaptively, with the adjustment range being [0.2, 1.0], and its initial value is set to 0.5;

[0087] ω pert represents the perturbation frequency and is configured with a preset fixed value. For example, it is set to 2π rad / s according to the system bandwidth, corresponding to a 1s perturbation period, to avoid coincidence with the mechanical resonance frequency of the wind turbine.

[0088] Thus, through the perturbation injection of S1, the system output power response is excited according to the periodic perturbation to provide dynamic data for gradient estimation.

[0089] Among them, the aforementioned adaptively adjusted perturbation amplitude Δλ(t) is set to be dynamically adjusted using an S-shaped function according to the real-time wind speed change rate, including:

[0090] According to the real-time wind speed change rate dynamically adjust the perturbation amplitude Δλ(t):

[0091]

[0092] Thus, by inputting the real-time estimated wind speed change rate, the perturbation amplitude after real-time dynamic adaptive adjustment is obtained, thereby balancing the search speed and system stability, automatically enhancing the perturbation when the wind speed changes rapidly to accelerate the localization of the extreme point; reducing the perturbation when it is stable to reduce the power fluctuation.

[0093] In the formula, Δλ min represents the minimum perturbation amplitude when the wind speed is stable, and Δλ max represents the maximum perturbation amplitude when the wind speed changes drastically. kv represents the wind speed change rate sensitivity coefficient, which is used to control the response sensitivity of the perturbation amplitude to the wind speed change;

[0094] Among them, represents the real-time wind speed change rate and is set to obtain the real-time wind speed estimate based on the extended Kalman filter iterative update and the wind speed change rate

[0095] In the embodiment of the present invention, the settings of Δλ min and Δλ max are as follows:

[0096] Δλ min = 0.2, the minimum perturbation amplitude (reduce the power fluctuation when the wind speed is stable);

[0097] Δλ max = 1.0, the maximum perturbation amplitude (accelerate the search speed when the wind speed changes drastically).

[0098] The sensitivity coefficient $k_v$ of the wind speed change rate is set to 5 according to empirical values and is used to control the steepness of the amplitude change.

[0099] As an example, the dynamic adjustment logic of $\Delta\lambda(t)$ is as follows:

[0100] When the real-time wind speed change rate increases (such as a sudden change in wind speed), the denominator approaches 0, and $\Delta\lambda(t)$ approaches the maximum value, enhancing the search ability;

[0101] When the real-time wind speed change rate increases (steady wind speed), $\Delta\lambda(t)$ decreases, reducing the impact of disturbances on power.

[0102] In step S3, based on the wind turbine output power, a phase-locked loop is used to extract the component with the same frequency as the injected disturbance, and the gradient estimate value of the power with respect to the tip speed ratio is calculated, including:

[0103] According to the collected wind turbine output power $P(t)$, a phase-locked loop PLL is used to extract the component with the same frequency as the injected disturbance, and the correlation between the power and the disturbance signal is analyzed according to the integral $dP(t) / d\lambda$, and the gradient estimate value of the power with respect to the tip speed ratio is calculated

[0104]

[0105] Among them, $T$ represents the disturbance period, corresponding to the disturbance frequency, and is set to 1 s, and the window length of the integral operation is equal to the disturbance period;

[0106] Among them, the aforementioned gradient estimate value being positive indicates that increasing the tip speed ratio $\lambda(t)$ can increase the power, and vice versa for negative values.

[0107] Thus, by calculating the correlation between the power and the disturbance signal through the above integral, high-frequency noise is filtered out, the approximate gradient direction is obtained, and thus the position of the current operating point relative to the extreme point is determined, providing a direction for the adjustment of the tip speed ratio.

[0108] In step S4, optimization is performed according to the gradient estimate value to obtain the current optimal tip speed ratio reference value, including:

[0109] The tip speed ratio reference value is optimized and updated according to the gradient estimate value, driving it to converge to the power extreme point, that is:

[0110]

[0111] Among them, represents the estimated value of the tip speed ratio reference value.

[0112] Thus, through the feedback search converging to the power extreme point, the estimated value of the tip speed ratio reference value gradually approaches the maximum point of the wind energy utilization coefficient C p (λ), and the estimated value of the tip speed ratio reference value at this time is determined as the current optimal tip speed ratio reference value

[0113] Through a model-free direct search method, the tip speed ratio converges to the extreme point of the wind energy utilization coefficient C p (λ), avoiding dependence on an accurate aerodynamic model, thus adapting to uncertainties such as fan aging and environmental changes, with strong robustness, especially suitable for complex wind change scenarios.

[0114] Among them, α(t) represents the optimization gain, which is used to control the update intensity of the gradient on the tip speed ratio reference value. Its initial value is set to 0.1 (about 10% of the optimal tip speed ratio to avoid excessive power fluctuations), and it is adaptively adjusted according to the power noise.

[0115] As an optional embodiment, the optimization gain α(t) is set to be adaptively adjusted according to the power noise, including:

[0116] Calculate the real-time standard deviation of the collected fan output power P(t), and then adaptively adjust the optimization gain α(t) according to the noise level:

[0117]

[0118] Among them, α0 represents the initial value of the optimization gain (as the reference gain); N represents the number of points in the sliding window, for example, taking the value of 100, which is used to calculate the power fluctuation in real time; σ P,nom represents the noise benchmark under the rated power, which can be calibrated through historical data, for example, calibrated to 100W;

[0119] P i represents the fan output power at the i-th moment, {P i} represents the real-time power sequence; σ P(t) represents the real-time power standard deviation.

[0120] As an example, the logic of the optimization gain α(t) following the adaptive adjustment of the power noise is as follows:

[0121] According to the comparison between σ P(t) and σ P,nom , adaptively adjust the optimization gain α(t):

[0122] If σ P(t) > σ P,nom , then reduce α(t) to avoid misjudging the gradient direction due to signal fluctuations;

[0123] If σ P(t) < σP,nom , then increase α(t) to accelerate the convergence speed and enhance the robustness.

[0124] Thus, by adaptively adjusting the perturbation amplitude (with the wind speed change rate) and dynamically adjusting the optimization gain (with the power noise), the search speed and system stability are balanced, the adaptability is enhanced when the wind speed changes rapidly, and misregulation is avoided in a noisy environment, thereby solving the defect of fixed perturbation parameters in the traditional ESC algorithm, improving the system response speed, and reducing the power fluctuation.

[0125] It should be understood that in step S5, based on the Kalman filter, the wind speed and its change rate are estimated in real time, especially referring to fusing the measurement equations of the rotational speed and power to estimate the wind speed and its change rate in real time, and using the extended Kalman filter (EKF algorithm) to iteratively update for real-time estimation of the wind speed and filter out the measurement noise.

[0126] In the embodiment of the present invention, the real-time estimation of the wind speed can be based on the existing EKF algorithm.

[0127] As an example, the real-time estimation process of the wind speed is as follows:

[0128] Define the state vector of the wind speed and its change rate, and establish the measurement equation by fusing the rotational speed and power:

[0129]

[0130] Among them, the radius R of the fan blade is a known parameter (such as calibrated by the manufacturer, unit: m); ρ represents the air density, which can be calculated through the atmospheric pressure, air temperature, and ideal gas constant; n ω , n P respectively represent the measurement noises of the rotational speed and power (from sensor calibration).

[0131] Thus, through the EKF algorithm, the wind speed and its change rate are iteratively updated, the measurement noise is filtered out, and accurate estimated values of the wind speed and its change rate are provided.

[0132] As an optional embodiment, in step S6, based on the real-time wind speed estimated value and the aforementioned current optimal tip speed ratio reference value, calculate the reference rotational speed, generate the reference rotational speed for feedforward control, and output the feedforward rotational speed control instruction, including:

[0133] According to the real-time wind speed estimated value and the current optimal tip speed ratio reference value calculate the reference rotational speed ω for feedforward control ref :

[0134]

[0135] Among them, R represents the radius of the fan blade.

[0136] Further, in step S7, the foregoing superposition of the feedforward speed control command and the speed adjustment amount caused by the dynamic sine wave disturbance to generate the final fan speed control command includes:

[0137] Superpose the reference speed ω of the feedforward control ref with the speed adjustment amount δω(t) caused by the dynamic sine wave disturbance to obtain the final control command ω cmd :

[0138] ω cmd = ω ref + δω(t)

[0139] wherein, δω(t) represents the adjustment amount of the speed mapped by the dynamic sine wave disturbance through the tip speed ratio definition:

[0140]

[0141] Thus, in this embodiment, by utilizing the forward-looking nature of wind speed estimation, the speed is adjusted in advance to respond to wind speed changes, reducing the hysteresis of ESC feedback regulation, forming a complement to the feedback search of ESC, shortening the extreme point convergence time, closing the loop feedback to correct the residual error, forming a feedforward-feedback composite control strategy, improving the overall response speed, and significantly enhancing the dynamic tracking ability.

[0142] Further, in the next control cycle, continuously collect the fan speed and the fan output power, estimate the wind speed estimation value in real time and input it into the feedforward module, iteratively calculate the reference speed of the feedforward control; dynamically generate a disturbance signal injection according to the power noise feedback, and update the current optimal tip speed ratio based on the ESC extreme value search optimization module; fuse the reference speed of the feedforward control and the speed adjustment amount caused by the dynamic disturbance injection, iteratively update the fan speed control command and apply it to the converter of the generator for torque adjustment, and dynamically track the fan speed in real time.

[0143] {Embodiment 2}

[0144] Combined with the embodiments of the method for optimizing the tip speed ratio of a power generation fan based on ESC extreme value search in the above embodiments, according to the present invention, a computer system is further disclosed, including:

[0145] One or more processors;

[0146] A memory, and this storage area is used to store operable instructions.

[0147] Wherein, when the foregoing instructions are executed by the foregoing one or more processors, the foregoing one or more processors are caused to execute operations, and the foregoing operations include the steps of the method for optimizing the tip speed ratio of a power generation fan in the foregoing embodiments.

[0148] Although the present invention has been disclosed above in its preferred embodiments, it is not intended to limit the present invention. Those of ordinary skill in the technical field to which the present invention pertains can make various modifications and refinements without departing from the spirit and scope of the present invention. Therefore, the scope of protection of the present invention shall be determined by the claims.

Claims

1. A method for optimizing the tip speed ratio control of a power generation wind turbine, characterized in that, It includes the following steps: Obtain the initial values of the fan speed, the fan output power, and the wind speed, and calculate the tip speed ratio in combination with the fan blade radius; Inject a dynamic sine wave perturbation based on the current tip speed ratio reference value to generate a perturbed tip speed ratio; the dynamic sine wave perturbation is determined by the perturbation amplitude and the perturbation frequency, the perturbation amplitude is set to be dynamically adjusted according to the real-time wind speed change rate, and the perturbation frequency is preset according to the system bandwidth; Based on the fan output power, use a phase-locked loop to extract the component with the same frequency as the injected perturbation, and calculate the gradient estimation value of the power with respect to the tip speed ratio; Optimize according to the gradient estimation value to obtain the current optimal tip speed ratio reference value; Based on the Kalman filter, estimate the wind speed and its change rate in real time; Calculate the reference speed based on the real-time wind speed estimation value and the current optimal tip speed ratio reference value, generate the reference speed for feedforward control, and output the feedforward speed control instruction; Superimpose the feedforward speed control instruction and the speed adjustment amount caused by the dynamic sine wave perturbation to generate the final fan speed control instruction; And Send the fan speed control instruction to the converter through the CAN bus to adjust the generator torque and drive the fan speed to track the instruction.

2. The tip speed ratio optimization control method for a power generation wind turbine according to claim 1, wherein The injecting a dynamic sine wave perturbation based on the current tip speed ratio reference value to generate a perturbed tip speed ratio includes: Inject a dynamic sine wave perturbation based on the current tip speed ratio reference value in the following manner: λ(t) = λ0(t) + Δλ(t)·sin(ω pert t) where λ(t) represents the perturbed tip speed ratio, λ0(t) represents the current tip speed ratio reference value, and its initial value is set according to the historical optimal value; Δλ(t) represents the adaptively adjusted perturbation amplitude, and the adjustment range is [0.2, 1.0], and its initial value is set to 0.5; ω pert represents the disturbance frequency, which is set to 2π rad / s according to the system bandwidth, corresponding to a disturbance period of 1 s.

3. The optimization control method for the tip speed ratio of a power generation wind turbine according to claim 2, characterized in that, The adaptively adjusted perturbation amplitude is set to be dynamically adjusted according to the real-time wind speed change rate, including: According to the real-time wind speed change rate Dynamically adjust the disturbance amplitude Δλ(t): where, Δλ min represents the minimum disturbance amplitude when the wind speed is stable, and Δλ max represents the maximum disturbance amplitude when the wind speed changes suddenly. kv represents the sensitivity coefficient of the wind speed change rate, which is used to control the response sensitivity of the disturbance amplitude to the wind speed change; Wherein, represents the real-time wind speed change rate and is set to iteratively update and obtain the real-time wind speed estimated value based on the extended Kalman filter and the wind speed change rate 4. The optimal control method for the tip speed ratio of a power generation wind turbine according to claim 1, characterized in that, The based on the fan output power, using a phase-locked loop to extract the component with the same frequency as the injected perturbation, and calculating the gradient estimation value of the power with respect to the tip speed ratio, includes: According to the collected wind turbine output power P(t), the phase-locked loop PLL is used to extract the component with the same frequency as the injected disturbance, and the correlation between the power and the disturbance signal is analyzed according to the integral dP(t) / dλ, and the gradient estimation value of the power with respect to the tip speed ratio is calculated. where T represents the perturbation period, corresponding to the perturbation frequency, set to 1 s, and the window length of the integral operation is equal to the perturbation period; Among them, the gradient estimation value is a positive value, indicating that increasing the tip speed ratio λ(t) can increase the power, and vice versa for negative values.

5. The optimal control method for the tip speed ratio of a power generation wind turbine according to claim 4, wherein, The optimizing according to the gradient estimation value to obtain the current optimal tip speed ratio reference value, includes: Optimize and update the tip speed ratio reference value according to the gradient estimation value, and drive it to converge to the power extreme point, that is: where α(t) represents the optimization gain, which is used to control the update intensity of the gradient on the tip speed ratio reference value, and its initial value is set to 0.1 and is adaptively adjusted according to the power noise; Indicates the estimated value of the tip speed ratio reference value; By means of a feedback search that converges to the power extreme point, the estimated value of the tip speed ratio reference value gradually approaches the maximum point of the wind energy utilization coefficient C p (λ), and thus the estimated value of the tip speed ratio reference value at this time is determined as the current optimal tip speed ratio reference value 6. The optimization control method for the tip speed ratio of a power generation wind turbine according to claim 5, wherein The optimization gain α(t) is set to be adaptively adjusted according to the power noise, including: Calculate the real-time standard deviation of the collected fan output power P(t), and then adaptively adjust the optimization gain α(t) according to the noise level: Among them, α0 represents the initial value of the optimized gain; N represents the number of points in the sliding window, which is used to calculate the power fluctuation in real time; σ P,nom represents the noise reference under the rated power; P i represents the wind turbine output power at the i-th moment, {P i} represents the real-time power sequence; σ P(t) represents the real-time power standard deviation; According to σ P(t) Compared with σ P,nom Adaptively adjust the optimization gain α(t): If σ P(t) > σ P,nom , then reduce α(t); If σ P(t) <σ P,nom , then increase α(t).

7. The optimization control method for the tip speed ratio of a power generation wind turbine according to any one of claims 1-6, characterized in that, The calculating the reference speed based on the real-time wind speed estimation value and the current optimal tip speed ratio reference value, generating the reference speed for feedforward control, and outputting the feedforward speed control instruction, includes: According to the estimated value of the real-time wind speed and the current optimal tip speed ratio reference value calculate the reference rotational speed ω of the feedforward control ref : where R represents the fan blade radius.

8. The optimal control method for the tip speed ratio of a power generation wind turbine according to claim 1, characterized in that, The superimposing the feedforward speed control instruction and the speed adjustment amount caused by the dynamic sine wave perturbation to generate the final fan speed control instruction, includes: The reference speed ω of the feedforward control ref is superimposed with the rotational speed adjustment amount δω(t) caused by the dynamic sine wave disturbance to obtain the final control command ω cmd : ω cmd = ω ref + δω(t) Among them, δω(t) represents the adjustment amount of the rotational speed mapped by the dynamic sine wave disturbance through the tip speed ratio definition:

9. The optimization control method for the tip speed ratio of a power generation wind turbine according to claim 1, characterized in that In the next control period, continuously collect the rotational speed of the wind turbine and the output power of the wind turbine, and iterate the reference rotational speed of the feedforward control and the rotational speed adjustment amount caused by the dynamic sine wave disturbance, iteratively update the wind turbine rotational speed control command and apply it to the converter of the generator for torque adjustment, and dynamically track the rotational speed of the wind turbine in real time.

10. A computer system, characterized in that, It includes: One or more processors; A memory storing operable instructions that, when executed by the one or more processors, cause the one or more processors to perform operations including the steps of the optimal control method for the tip speed ratio of the wind turbine generator as described in any one of claims 1-9.

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