Wind turbine maximum wind energy capturing method and system based on working point deviation degree

Abstract

The application discloses a kind of wind turbine maximum wind energy capture method and system based on working point deviation degree, method includes constructing transmission chain state equation and calibrating coefficient characteristic, synchronous acquisition unit torque and speed signal;Estimate aerodynamic torque relying on Kalman filter, obtain each item aerodynamic parameter by combining iterative algorithm and interpolation operation, compare benchmark parameter quantization working condition deviation degree;Nonlinear torque compensation is generated by fuzzy controller, and after superposition steady-state optimal torque, output control instruction is completed, and unit operation regulation is completed.This method combines state estimation, bias quantization and fuzzy compensation strategy, can improve the problem of speed response lag caused by inertia of large unit, adapt to nonlinear characteristics of wind energy, break through the limitation of traditional control condition, can dynamically adjust unit state with wind speed change, effectively improve wind energy capture efficiency and equipment operation stability under all conditions.

Description

Technical Field

[0001] This invention belongs to the field of wind power generation technology, specifically relating to a method and system for capturing maximum wind energy from a wind turbine based on operating point deviation. Background Technology

[0002] Under low wind speed conditions, wind turbines aim to maximize wind energy capture efficiency. The unit's power generation capacity is closely related to the tip speed ratio. Conventional control methods adjust the electromagnetic torque to make the rotor speed follow the wind speed changes, maintaining optimal tip speed ratio operation. Currently, the optimal torque method is widely used in engineering to achieve wind energy capture. This strategy relies on the ideal conditions of small rotational inertia and instantaneously adjustable speed to derive the control logic, and it has good control performance in small units.

[0003] As the capacity of individual wind turbine units and the size of their rotors continue to increase, the rotational inertia of the transmission system increases significantly, resulting in a noticeable lag in the rotor speed response. This makes it difficult to track fluctuating wind speeds in real time, leading to power generation losses. Furthermore, this method only applies to optimal operating conditions and cannot account for actual operating conditions where the unit deviates from its optimal operating point. Wind energy utilization characteristics exhibit strong nonlinear coupling, limiting the applicability of traditional linear control strategies. Existing improvement methods mostly optimize for small deviations from the optimal operating point, without quantifying the degree of deviation and implementing nonlinear compensation. This makes them unsuitable for large-disturbance, skewed operating scenarios, hindering further improvements in wind energy utilization efficiency. Summary of the Invention

[0004] The purpose of this invention is to provide a method and system for capturing maximum wind energy of wind turbines based on the deviation of the operating point, in order to solve the technical defects of large wind turbines, such as the large transmission inertia causing the speed response to lag and the inability to track wind speed changes in real time, the traditional optimal torque method only adapting to the ideal optimal operating condition without quantifying the deviation of the actual operating condition, and the difficulty of linear control adapting to the nonlinear characteristics of wind energy.

[0005] To achieve the above objectives, the present invention employs the following technical solutions.

[0006] In a first aspect, this application provides a method for maximum wind energy capture of wind turbines based on operating point deviation, comprising: The transmission chain state equation is constructed offline based on the structural parameters of the wind turbine, and the power coefficient characteristics and torque coefficient characteristics of the wind turbine are pre-calibrated. The generator electromagnetic torque signal and wind turbine speed signal are collected in real time through the unit monitoring unit. Based on the aforementioned speed signal, torque signal, and transmission chain state equation, Kalman filtering is used to estimate the wind turbine aerodynamic torque. Combining the torque coefficient characteristics, the Newton-Raphson iterative method is used to solve for the estimated value of the equivalent wind speed and tip speed ratio. Based on the power coefficient characteristics, the estimated value of the power coefficient is calculated by interpolation. Based on the estimated tip speed ratio, estimated power coefficient, preset optimal tip speed ratio and maximum power coefficient, the operating point deviation quantification index is calculated. The deviation quantification index is input into the fuzzy controller to generate a torque nonlinearity compensation amount. The compensation amount is superimposed with the steady-state optimal torque to determine the total electromagnetic torque setpoint. Based on the setpoint, the unit operation is regulated to achieve maximum wind energy capture.

[0007] Furthermore, the state equation of the transmission chain is specifically as follows: Based on the dynamic transmission relationship between the wind turbine, gearbox, generator, and transmission components, a state-space equation for the rotation direction of the wind turbine transmission chain is constructed.

[0008] Furthermore, the estimation of aerodynamic torque via Kalman filtering specifically includes: Based on the acquired generator electromagnetic torque signal and wind turbine speed signal, the Kalman filter state estimation algorithm is used to calculate the wind turbine aerodynamic torque value in real time.

[0009] Furthermore, the calculation of the equivalent wind speed using the Newton-Raphson iterative method, based on the combined torque coefficient characteristics, specifically includes: The estimated aerodynamic torque of the wind turbine is combined with the pre-calibrated torque coefficient characteristics, and the Newton-Raphson iterative algorithm is used to complete the solution calculation.

[0010] Furthermore, the operating point deviation metric specifically includes: The indicators representing the degree of deviation from the optimal value of the tip speed ratio and the deviation from the maximum value of the power coefficient are used to characterize the degree of deviation from the actual operating conditions.

[0011] Furthermore, the fuzzy controller uses the quantized operating point deviation index as input and the torque nonlinearity compensation as output, and performs complete fuzzy inference calculations using the Mamdani fuzzy inference algorithm.

[0012] Furthermore, the fuzzy controller integrates all fuzzy inference results through union operation according to preset fuzzy control rules, and determines the output fuzzy set accordingly; The larger the value of the operating point deviation index, the higher the deviation of the actual operating condition of the unit from the optimal operating condition, and the greater the amplitude of the corresponding output torque nonlinear compensation.

[0013] Furthermore, the steady-state optimal torque is calculated by substituting the wind turbine's real-time operating speed, ambient air density, and gearbox transmission speed ratio structural parameters into the wind energy optimal torque calculation formula.

[0014] Furthermore, regulating the unit's operation based on the setpoint specifically includes: The total electromagnetic torque setpoint obtained by superposition is sent to the unit's electrical control unit to adjust the generator output torque and drive the wind turbine to stably approach the optimal operating condition.

[0015] A second aspect of this application provides a maximum wind energy capture system for wind turbines based on operating point deviation, comprising: The parameter modeling and calibration module is used to construct the transmission chain state equation offline based on the structural parameters of the wind turbine, and to pre-calibrate the power coefficient characteristics and torque coefficient characteristics of the wind turbine. The signal acquisition module is used to acquire the generator electromagnetic torque signal and the wind turbine speed signal in real time; The state estimation module connects the signal acquisition module and the parameter modeling and calibration module. It uses the acquired signals and the state equation of the transmission chain to estimate the aerodynamic torque of the wind turbine through Kalman filtering. It combines the torque coefficient characteristics to solve for the estimated values ​​of the equivalent wind speed and tip speed ratio, and interpolates to calculate the estimated value of the power coefficient. The deviation calculation module is connected to the state estimation module. Based on various estimation parameters and preset optimal parameters, it calculates the quantitative index of the deviation of the working point. The fuzzy control module, whose input end is connected to the deviation calculation module, generates torque nonlinear compensation amount according to the Mamdani fuzzy inference algorithm; The torque synthesis module is connected to the fuzzy control module and the parameter modeling and calibration module respectively. It superimposes the compensation amount and the steady-state optimal torque and outputs the total electromagnetic torque setpoint. The unit executes the control module and communicates with the torque synthesis module. Based on the torque setpoint, it adjusts the generator output torque and links it with the wind turbine through the transmission components, so that the unit's operating conditions approach the optimal wind energy capture state.

[0016] Compared with the prior art, the present invention has the following beneficial effects: 1. This method integrates state estimation, deviation quantification, and fuzzy compensation control. It uses the transmission chain equation and measured signals to accurately calculate aerodynamic parameters, quantifies the deviation between the actual and optimal operating conditions of the unit, and outputs nonlinear torque compensation in a targeted manner to complete torque synthesis and control. This effectively improves the speed response lag problem caused by the large inertia of large units, adapts to the nonlinear operating characteristics of wind energy, and breaks away from the limitation of traditional control that is only suitable for ideal operating conditions. It can adjust the unit state in real time according to wind speed fluctuations, significantly improving wind energy capture efficiency and operational stability across the entire operating range.

[0017] 2. Based on the dynamic correlation of wind turbine, gearbox, generator and transmission components, state-space equations are constructed to fully restore the power transmission law of the transmission system. This can accurately reflect the mechanical transmission operation characteristics of the unit and provide a model basis that fits the actual unit operating conditions for subsequent estimation of aerodynamic torque and wind speed parameters. This reduces the deviation between the theoretical model and the actual equipment operation and improves the accuracy of signal calculation and state observation.

[0018] 3. The Kalman filter algorithm is used to estimate the aerodynamic torque using measured electromagnetic torque and speed signals. This can effectively filter out interference noise from the field-acquired signals, restore the true aerodynamic force state of the wind turbine, and make up for the difficulty and low accuracy of directly detecting aerodynamic parameters. It obtains stable and reliable aerodynamic torque values, providing accurate raw data for solving key parameters such as equivalent wind speed and tip speed ratio, and ensuring the rationality of subsequent operating condition judgment and control adjustment.

[0019] 4. By using the Newton-Raphson iterative algorithm combined with the calibration torque coefficient to solve for the equivalent wind speed, the calculation convergence speed is fast and the solution accuracy is high. It can accurately back-calculate the real-time inflow wind speed value, and obtain the operating wind speed information without the need to deploy additional wind speed detection equipment, reducing hardware detection costs. At the same time, it accurately calculates the tip speed ratio parameter, objectively reflects the wind turbine operation matching status, and provides an accurate calculation basis for subsequent determination of the degree of deviation of the operating condition.

[0020] 5. The deviation of operating conditions is divided into two types of deviation indicators: tip speed ratio and power coefficient. This comprehensively measures the degree to which the actual operation of the unit deviates from the optimal state. The two types of indicators work together to clearly locate the causes of operating condition deviations, fully reflect the actual level of wind energy utilization, and avoid the one-sidedness of judging by a single parameter.

[0021] 6. Using the deviation index as input and the compensation torque as output, the Mamdani algorithm is used to perform fuzzy inference calculations. This can meet the nonlinear and non-fixed precise mathematical model operation characteristics of wind energy systems, break the linear control constraints, flexibly generate appropriate compensation torque according to the degree of operating condition deviation, adapt to complex and ever-changing wind conditions, and improve the adaptive adjustment capability of the control strategy to non-ideal operating conditions.

[0022] 7. By integrating the inference results with preset rules and union operations, the magnitude of the deviation is used to synchronously adjust the compensation torque amplitude. The larger the deviation, the stronger the compensation force. This can specifically correct large deviations in operating conditions, effectively reduce the operational disturbances caused by drastic wind speed fluctuations, dynamically offset the operating condition lag deviation caused by the unit's inertia, and allow the unit to quickly return to the optimal operating range, thereby enhancing the adjustment and correction effect under dynamic operating conditions.

[0023] 8. The steady-state optimal torque is calculated by combining multiple parameters such as rotational speed, air density, and transmission speed ratio. Taking into account the actual conditions of the unit structure and environment, the obtained optimal torque closely matches the actual operating limits of the equipment and the characteristics of the wind energy environment. This avoids the torque setting deviation caused by calculation of a single parameter, ensures that the basic torque reference value is reasonable and reliable, and provides an accurate and stable reference for subsequent superposition of nonlinear compensation.

[0024] 9. The generator control unit sets the synthetic torque value, adjusts the generator torque in a closed loop and links it with the wind turbine to achieve effective implementation of control commands to equipment actions, continuously corrects the unit's operating deviations, and promotes the wind turbine to maintain the optimal capture conditions. This forms a complete detection-calculation-regulation control closed loop, effectively transforming the parameter optimization results into actual wind energy capture benefits. Attached Figure Description

[0025] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of the present invention and should not be regarded as a limitation on the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0026] Figure 1 A flowchart of a method for capturing maximum wind energy of a wind turbine based on operating point deviation provided by the present invention; Figure 2 A schematic diagram illustrating the relationship between power coefficient and tip speed ratio in a wind turbine maximum wind energy capture method based on operating point deviation provided by the present invention. Figure 3 A schematic diagram illustrating the relationship between thrust coefficient and tip speed ratio in a wind turbine maximum wind energy capture method based on operating point deviation provided by the present invention. Figures 4-5 The quantitative index in the maximum wind energy capture method for wind turbines based on operating point deviation provided by this invention and A schematic diagram of the membership function; Figure 6 The output of the maximum wind energy capture method for wind turbines based on operating point deviation provided by this invention is... A schematic diagram of the membership function; Figures 7-11 This diagram illustrates the verification results of the control algorithm under step wind speed in a wind turbine maximum wind energy capture method based on operating point deviation provided by the present invention. Detailed Implementation

[0027] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. The components of the embodiments of the present invention described and shown in the accompanying drawings can generally be arranged and designed in various different configurations.

[0028] To address the technical deficiencies mentioned in the background section, this embodiment provides a method and system for maximum wind energy capture of wind turbines based on operating point deviation. The invention will be further described in detail below with reference to the accompanying drawings: In a first aspect, the present invention provides a method for maximum wind energy capture of wind turbines based on operating point deviation, such as... Figure 1 As shown, it includes: S101. Based on the structural parameters of the wind turbine, the transmission chain state equation is constructed offline, and the power coefficient characteristics and torque coefficient characteristics of the wind turbine are pre-calibrated. The generator electromagnetic torque signal and the wind turbine speed signal are collected in real time through the unit monitoring unit. For example, the state-space equations for the rotation direction of the wind turbine transmission chain are established offline, and the model is built based on the dynamic relationships of the wind turbine, gearbox, generator, and transmission components; the wind turbine power coefficient under different tip speed ratios is obtained through simulation or experimentation. With torque coefficient Characteristic curves; where, The maximum value is denoted as , The corresponding tip speed ratio is denoted as .

[0029] Real-time acquisition of generator electromagnetic torque using existing sensors in the unit With wind turbine speed The signal is transmitted without the need for additional detection devices.

[0030] S102. Based on the speed signal, torque signal and transmission chain state equation, Kalman filter is used to estimate the wind turbine aerodynamic torque. The Newton-Raphson iteration method is used to solve for the estimated value of the equivalent wind speed and tip speed ratio, and the estimated value of the power coefficient is obtained by interpolation based on the power coefficient characteristics. For example, the real-time acquired wind turbine speed signal and generator electromagnetic torque signal are input into the constructed transmission chain state space equation; wherein, the Kalman filter estimates the aerodynamic torque based on the two types of signals, and uses the Kalman filter state estimation algorithm to complete data processing and state observation, and calculates the wind turbine aerodynamic torque value in real time.

[0031] Using the estimated aerodynamic torque as input, the equivalent wind speed is calculated by combining the pre-calibrated torque coefficient characteristic curve. This process relies on the obtained aerodynamic torque parameters to match the corresponding coefficient characteristics, and uses the Newton-Raphson iterative algorithm to complete the iterative solution calculation, quickly converge to obtain the equivalent wind speed of the wind turbine, and then combine the equivalent wind speed, wind turbine speed and wind turbine radius parameters to obtain the estimated tip speed ratio.

[0032] Finally, the calibrated power coefficient characteristic curve is retrieved, and linear interpolation is performed based on the estimated tip speed ratio to calculate the estimated power coefficient value corresponding to the current operating condition, providing accurate parameter basis for subsequent operating point deviation calculation.

[0033] Specifically, using the obtained torque coefficient Generator electromagnetic torque signal Wind turbine speed signal Real-time calculation of tip speed ratio estimate The state-space equations for the rotation direction of the wind turbine drive chain are established offline.

[0034] Based on the obtained generator electromagnetic torque signal Wind turbine speed signal By combining the state-space equations of the transmission chain rotation direction established offline, the estimated value of the wind turbine aerodynamic torque is obtained using the Kalman filter state estimation algorithm. Then, based on the obtained torque coefficient Estimated value of wind turbine aerodynamic torque The equivalent wind speed of the wind turbine was obtained using the Newton-Raphson method. Using the wind turbine speed signal Wind turbine equivalent wind speed Calculate the estimated tip speed ratio :

[0035] in, Where is the radius of the wind turbine.

[0036] Next, utilizing the power coefficient characteristics of the wind turbine Tip speed ratio estimate Real-time interpolation calculation of power coefficient estimate .

[0037] S103. Based on the estimated tip speed ratio, estimated power coefficient, preset optimal tip speed ratio and maximum power coefficient, calculate the quantification index of operating point deviation. For example, the estimated tip speed ratio and power coefficient obtained from the calculation are retrieved and matched with the optimal tip speed ratio and maximum power coefficient benchmark parameters pre-stored by the system; the quantification index of operating point deviation specifically includes the tip speed ratio deviation from the optimal value index and the power coefficient deviation from the maximum value index, which characterize the degree of deviation of the actual operating conditions.

[0038] By comparing the real-time operating parameters with the optimal baseline parameters one by one, the differences are calculated for both types of deviations, and combined to form a complete quantitative index of operating point deviation. The quantitative results allow for a direct assessment of the extent to which the unit's operating conditions deviate from the ideal capture state, clarifying the gap between the actual wind energy utilization level and the theoretical optimal operating condition. This provides a precise calculation basis for generating appropriate torque compensation in subsequent fuzzy control stages.

[0039] During implementation, quantitative indicators of the degree of deviation from the optimal operating point are calculated in real time. and :

[0040]

[0041] in, Characterizing the deviation of the tip speed ratio from the optimal value To what extent, Characterizing the deviation of the power coefficient from the maximum value To what extent, It is a symbolic function.

[0042] S104. Input the deviation quantification index into the fuzzy controller to generate the torque nonlinearity compensation amount, superimpose the compensation amount with the steady-state optimal torque to determine the total electromagnetic torque set value, and regulate the unit operation based on the set value to achieve maximum wind energy capture.

[0043] For example, the calculated deviation quantification index of the operating point is input into the fuzzy controller. The controller uses the quantified deviation index as input and the torque nonlinear compensation amount as output, and completes the full calculation using the Mamdani fuzzy inference algorithm. The controller integrates all inference results through union operation according to the preset fuzzy control rules and determines the corresponding output fuzzy set; and the larger the deviation index value, the higher the degree of deviation of the operating condition from the optimal state, and the larger the amplitude of the output torque nonlinear compensation amount.

[0044] By combining the real-time rotor speed, air density, and gearbox transmission speed ratio parameters, the steady-state optimal torque is calculated using the formula. The compensation amount is then superimposed on the steady-state optimal torque to obtain the total electromagnetic torque setpoint. This torque setpoint is then sent to the unit's electrical control unit, which adjusts the generator output torque to drive the rotor towards its optimal operating condition, continuously correcting operational deviations, and ultimately achieving the control objective of efficient wind energy capture under all operating conditions.

[0045] In implementation, quantitative indicators of the degree of deviation from the optimal operating point are based on real-time calculations. and The torque nonlinearity compensation amount, considering the degree of deviation from the optimal operating point, is calculated using a fuzzy control strategy. ; Quantitative indicators of the degree of deviation from the optimal operating point and Defined as two inputs to fuzzy control, the torque compensation quantity to be determined is... Defined as the sole output of fuzzy control; input and The universe of discourse is defined as the range [-0.6, 0.6]; the output will be... The domain of discourse is defined as the range ,in, This is the rated electromagnetic torque of the unit.

[0046] Then, the membership functions of the input and output are defined, and fuzzy rules are established based on the Mamdani inference algorithm, where... and The output of the fuzzy set is derived using the union operation rule. A fuzzy set is used to ensure that when one of the tip speed ratio and power coefficient deviates significantly, a larger torque compensation is generated.

[0047] Input within the domain and The process involves fuzzification, calculation of the output fuzzy set based on fuzzy rules, and then calculation of the torque nonlinearity compensation amount based on the output fuzzy set. Based on the real-time monitored wind turbine speed Calculate the steady-state optimal torque control quantity:

[0048] in, air density; This represents the gearbox speed ratio.

[0049] The calculated torque nonlinearity compensation amount takes into account the degree of deviation from the optimal operating point. and the calculated steady-state optimal torque control quantity Calculate the total electromagnetic torque setpoint of the generator:

[0050] Next, using the DTU 10MW reference wind turbine model as the application object, the invention is simulated and verified. The rotor radius of this unit is... The length is 178.3 meters, and the moment of inertia of the wind turbine is 156,243,120 kg·m. 2 The generator's moment of inertia is 1500.5 kg·m. 2 Gearbox speed ratio The rated torque of the generator is 50. The power coefficient is 200,000 M·N. Speed ​​ratio with leaf tip The correspondence is as follows Figure 2As shown, thrust coefficient Speed ​​ratio with leaf tip The correspondence is as follows Figure 3 As shown.

[0051] Quantitative indicators of the degree of deviation from the optimal operating point and The membership function is defined using a triangle, such as... Figures 4-5 As shown.

[0052] Fuzzy controller output The membership function is defined using a triangle, such as... Figure 6 As shown, based on Figures 4-6 The membership function is defined, and the fuzzy rules are formulated as shown in Table 1 below.

[0053] Table 1 Fuzzy Rule Table ( and Derived using the union method )

[0054] in, and The output of the fuzzy set is derived using the union operation rule. A fuzzy set is used to ensure that when one of the tip speed ratio and power coefficient deviates significantly, a larger torque compensation is generated.

[0055] The control algorithm proposed in this invention is compared and verified with the commonly used steady-state optimal torque method in engineering under step wind speeds. Figures 7-11 As shown, the results indicate that: The proposed method for maximum wind energy capture of wind turbines based on operating point deviation enables wind turbines to adjust rotor speed more quickly when wind speed changes, allowing the tip speed ratio and power coefficient to recover to their optimal values ​​more quickly, thereby reducing power loss.

[0056] A second aspect of this invention provides a maximum wind energy capture system for wind turbines based on operating point deviation, applicable to large-scale wind turbines with high inertia, capable of achieving efficient wind energy capture and control under complex low-wind-speed conditions, comprising: The parameter modeling and calibration module, as the basic modeling and data calibration unit of the entire capture and control system, is mainly used to retrieve various fixed structural parameters of the wind turbine itself. Based on the obtained unit structural parameters, it completes offline modeling work and stably builds the transmission chain state equation that conforms to the actual transmission operation characteristics of the unit.

[0057] After completing the model construction, the module further combines real machine operation tests and simulation calculations to fully cover the normal operating conditions of the unit, and pre-calibrates and obtains the power coefficient characteristics and torque coefficient characteristics of the wind turbine under different operating conditions, and stores the calibrated characteristic data in a unified manner.

[0058] The completed transmission chain state equations and the two types of coefficient characteristic data obtained from calibration can provide reliable model support and standard data benchmarks for all subsequent signal processing, parameter estimation, operating condition determination, and control quantity calculation links of the system. This ensures that the overall control calculation process conforms to the actual mechanical transmission law of the unit and the characteristics of wind energy action, reduces the calculation deviation between the theoretical model and the actual equipment operation from the source, and ensures that the calculation results of subsequent functional modules have practical reference value.

[0059] The signal acquisition module operates without requiring external detection devices, relying on the various sensors and monitoring equipment pre-installed in the wind turbine at the factory to collect data. During operation, the module maintains a continuous data acquisition mode, acquiring real-time electromagnetic torque signals from the generator and real-time rotor speed signals, accurately capturing the unit's power output and the rotor's dynamic operating conditions. The acquired raw operating signals are real-time and accurate, directly reflecting the unit's current operating level. This provides stable, complete, and effective real-time operating data to the connected state estimation module. The entire acquisition process is simple and convenient, utilizing existing hardware to complete data acquisition, effectively controlling the overall system setup and maintenance costs, while ensuring the data acquisition frequency and accuracy meet the computational requirements of wind energy capture and control.

[0060] The state estimation module establishes communication connections with both the signal acquisition module and the parameter modeling and calibration module. It can synchronously retrieve real-time operating signals acquired from the front end and the pre-built transmission chain state equations within the module. First, using the speed and torque signals transmitted from the signal acquisition module, state observation calculations are performed in conjunction with the transmission chain state equations. The Kalman filter algorithm is then used to perform noise reduction and optimization processing on the original acquired signals, effectively eliminating data interference errors caused by external environmental factors such as natural wind conditions and circuit fluctuations, and accurately estimating the aerodynamic torque value of the wind turbine under the current actual operating conditions.

[0061] After obtaining the aerodynamic torque estimation results, the module retrieves the torque coefficient characteristic data stored in the parameter modeling and calibration module. Based on this, the Newton-Raphson iterative algorithm is used to gradually converge the calculation, and the real-time equivalent wind speed value and tip speed ratio estimate are obtained in sequence.

[0062] Finally, by matching the pre-calibrated power coefficient characteristic curve and performing interpolation calculations based on the calculated tip speed ratio estimate, the power coefficient estimate corresponding to the current operating condition is accurately derived. This module fully realizes the calculation of key aerodynamic parameters related to the unit, providing comprehensive and accurate parameter basis for subsequent judgment of the unit's operating status.

[0063] The deviation calculation module maintains electrical connectivity with the state estimation module, enabling it to receive all estimated parameters, including the tip speed ratio and power coefficient estimates, from the state estimation module in real time. Simultaneously, the module pre-stores optimal reference parameters such as the system-defined best tip speed ratio and maximum power coefficient. Upon receiving real-time estimated parameters, the module compares and analyzes each real-time operating condition parameter with the preset optimal reference parameters, calculating the deviation of the tip speed ratio from the optimal value and the deviation of the power coefficient from the maximum value. Based on the comprehensive calculation of these two core deviation values, a quantitative index for operating point deviation is finally generated, comprehensively characterizing the degree to which the actual operating conditions of the unit deviate from the ideal optimal state. This quantitative index clearly quantifies the magnitude of wind energy utilization deviation, objectively reflecting the gap between the current operating conditions and the optimal capture conditions, accurately locating operating condition deviation problems, and providing a quantitative judgment standard for the subsequent fuzzy control module to generate targeted compensation control quantities, ensuring that control and regulation work has clear data references.

[0064] The fuzzy control module has its input end directly connected to the deviation calculation module. It can obtain the calculated deviation quantification index of the working point in real time and use this quantification index as the input of the module. At the same time, the final generated torque nonlinear compensation quantity is set as the output of the module.

[0065] The fuzzy control module performs calculations according to pre-set fuzzy control rules. The entire process uses the Mamdani fuzzy inference algorithm to complete the fuzzy inference calculation. During the calculation, the results of all branches are integrated through union operation to accurately determine the corresponding output fuzzy set.

[0066] The fuzzy control module has adaptive adjustment capabilities. Its operating logic satisfies the rule that the larger the deviation index value of the operating point, the greater the deviation of the actual operating condition of the unit from the optimal operating condition, and the corresponding torque nonlinear compensation amplitude increases synchronously. The compensation torque is flexibly adjusted according to the deviation of the operating condition, effectively adapting to the nonlinear operating characteristics of the wind power system. It outputs a compensation amount with a high degree of matching for operating conditions with different deviation levels, improving the adaptive adaptability of the control system to complex and fluctuating wind conditions.

[0067] The torque synthesis module is connected to the fuzzy control module and the parameter modeling and calibration module. It receives various computational data and basic parameters transmitted from the front end. The module retrieves structural and environmental parameters such as the real-time operating speed of the wind turbine, the ambient air density, and the gearbox transmission speed ratio. These parameters are then substituted into the optimal wind energy torque calculation formula to accurately calculate the steady-state optimal torque suitable for the current operating conditions. After obtaining the reference torque value, the module receives the torque nonlinearity compensation amount from the fuzzy control module. It then superimposes and integrates the compensation amount with the steady-state optimal torque to offset operating deviations caused by the unit's large inertia and deviations from the operating conditions. Finally, it calculates and outputs the total electromagnetic torque setpoint that the unit can execute.

[0068] The steady-state optimal torque provides a basic torque reference for the normal operation of the unit, and the nonlinear compensation completes the deviation correction. The torque setpoint formed by the combination of the two takes into account both steady-state operation stability and dynamic deviation correction capability, ensuring that the torque output value is scientific and reasonable.

[0069] The unit's execution control module establishes a stable communication connection with the torque synthesis module, enabling it to receive the total electromagnetic torque setting command output by the torque synthesis module in real time. Based on the received torque setting value, the unit's execution control module adjusts the actual output torque of the generator accordingly. After the generator torque changes, power is transmitted and linked through various transmission components such as the gearbox, synchronously driving the wind turbine to adjust its rotational operating state. In the continuous process of torque adjustment and power linkage, the unit's execution control module continuously corrects the existing operating condition deviations of the unit, gradually narrowing the gap between the actual operating state and the optimal wind energy capture state, continuously optimizing the unit's operating mode, and ensuring that the overall operating condition of the unit continuously approaches the optimal wind energy capture standard state. The entire system forms a complete closed-loop control process from data acquisition, parameter estimation, deviation judgment, compensation calculation to terminal execution and control, effectively improving the problems of large transmission inertia and lag in speed response of large units, overcoming the shortcomings of traditional control strategies that are only suitable for ideal operating conditions, and comprehensively improving the unit's wind energy capture efficiency and operational stability under complex wind conditions.

[0070] In summary, this system addresses the pain points of large-scale generator units, such as large transmission inertia, slow speed response, insufficient adaptability of traditional optimal torque control conditions, and inability to adapt to the nonlinear characteristics of wind energy. It establishes an integrated closed-loop system covering parameter modeling and calibration, signal acquisition, state estimation, deviation calculation, fuzzy control, torque synthesis, and generator unit execution control, demonstrating significant technical advantages.

[0071] First, the transmission chain state equation is constructed through the parameter modeling and calibration module, and the wind turbine coefficient characteristics are calibrated. This serves as the benchmark for subsequent calculation models, replicating the power transmission mechanism of the transmission system and reducing the error between the theoretical model and the actual operating conditions. In conjunction with the signal acquisition module, the original sensors of the unit are used to acquire operating data without the need to install additional detection devices. This ensures the sampling quality while reducing hardware investment and maintenance costs, providing reliable raw data support for the subsequent state estimation stage.

[0072] The state estimation module uses the acquired signals and preset models, and Kalman filtering to suppress signal clutter interference, to accurately estimate the aerodynamic torque of the wind turbine. It then combines iterative algorithms and interpolation methods to solve for various key aerodynamic parameters, effectively addressing the difficulty of directly detecting aerodynamic parameters. The calculation results can be directly supplied to the deviation calculation module. The deviation calculation module compares the deviation with the benchmark parameters from the dual dimensions of tip speed ratio and power coefficient, quantifying the deviation, avoiding the limitations of a single evaluation standard, objectively measuring the degree of deviation of the unit's operating conditions, and providing quantitative input basis for the fuzzy control module.

[0073] The fuzzy control module employs the Mamdani inference algorithm, adaptively outputting nonlinear torque compensation based on the magnitude of deviation. The larger the deviation, the stronger the compensation, adapting to the nonlinear operating characteristics of wind power systems. The torque synthesis module integrates the compensation with the steady-state optimal torque, balancing the unit's steady-state operating foundation with dynamic deviation correction requirements, to generate the final torque setpoint.

[0074] The unit's execution control module adjusts the generator's output torque according to the set value, and the linkage transmission structure optimizes the wind turbine's operating state. This breaks the limitation of traditional linear control, which is only suitable for small deviation conditions. It effectively solves the problems of slow speed tracking and high power generation losses in large units, and can adapt to complex and variable low wind speed conditions. It significantly expands the unit's operating range, effectively improves wind energy utilization efficiency and equipment operating stability, and has high practical engineering application value.

[0075] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit its scope of protection. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that after reading the present invention, they can still make various changes, modifications or equivalent substitutions to the specific implementation of the invention, but these changes, modifications or equivalent substitutions are all within the scope of protection of the pending claims of the invention.

Claims

1. A method for maximum wind energy capture of wind turbines based on operating point deviation, characterized in that, include: The transmission chain state equation is constructed offline based on the structural parameters of the wind turbine, and the power coefficient characteristics and torque coefficient characteristics of the wind turbine are pre-calibrated. The generator electromagnetic torque signal and wind turbine speed signal are collected in real time through the unit monitoring unit. Based on the aforementioned speed signal, torque signal, and transmission chain state equation, Kalman filtering is used to estimate the wind turbine aerodynamic torque. Combining the torque coefficient characteristics, the Newton-Raphson iterative method is used to solve for the estimated value of the equivalent wind speed and tip speed ratio. Based on the power coefficient characteristics, the estimated value of the power coefficient is calculated by interpolation. Based on the estimated tip speed ratio, estimated power coefficient, preset optimal tip speed ratio and maximum power coefficient, the operating point deviation quantification index is calculated. The deviation quantification index is input into the fuzzy controller to generate a torque nonlinearity compensation amount. The compensation amount is superimposed with the steady-state optimal torque to determine the total electromagnetic torque setpoint. Based on the setpoint, the unit operation is regulated to achieve maximum wind energy capture.

2. The method for maximum wind energy capture of wind turbines based on operating point deviation as described in claim 1, characterized in that, The specific state equation of the transmission chain is as follows: Based on the dynamic transmission relationship between the wind turbine, gearbox, generator, and transmission components, a state-space equation for the rotation direction of the wind turbine transmission chain is constructed.

3. The method for maximum wind energy capture of wind turbines based on operating point deviation as described in claim 1, characterized in that, The estimation of aerodynamic torque via Kalman filtering specifically includes: Based on the acquired generator electromagnetic torque signal and wind turbine speed signal, the Kalman filter state estimation algorithm is used to calculate the wind turbine aerodynamic torque value in real time.

4. The method for maximum wind energy capture of wind turbines based on operating point deviation as described in claim 1, characterized in that, The combined torque coefficient characteristic is used to calculate the equivalent wind speed using the Newton-Raphson iterative method, specifically including: The estimated aerodynamic torque of the wind turbine is combined with the pre-calibrated torque coefficient characteristics, and the Newton-Raphson iterative algorithm is used to complete the solution calculation.

5. The method for maximum wind energy capture of wind turbines based on operating point deviation as described in claim 1, characterized in that, The specific metrics for measuring deviation from the operating point include: The indicators representing the degree of deviation from the optimal value of the tip speed ratio and the deviation from the maximum value of the power coefficient are used to characterize the degree of deviation from the actual operating conditions.

6. The method for maximum wind energy capture of wind turbines based on operating point deviation as described in claim 1, characterized in that, The fuzzy controller uses the quantized operating point deviation index as input and the torque nonlinearity compensation as output, and performs complete fuzzy inference calculations using the Mamdani fuzzy inference algorithm.

7. The method for maximum wind energy capture of wind turbines based on operating point deviation as described in claim 6, characterized in that, The fuzzy controller integrates all fuzzy inference results through union operation according to preset fuzzy control rules, and determines the output fuzzy set accordingly; The larger the value of the operating point deviation index, the higher the deviation of the actual operating condition of the unit from the optimal operating condition, and the greater the amplitude of the corresponding output torque nonlinear compensation.

8. The method for maximum wind energy capture of wind turbines based on operating point deviation as described in claim 1, characterized in that, The steady-state optimal torque is calculated by substituting the wind turbine's real-time operating speed, ambient air density, and gearbox transmission speed ratio structural parameters into the wind energy optimal torque calculation formula.

9. The method for maximum wind energy capture of wind turbines based on operating point deviation as described in claim 1, characterized in that, The operation of the generating unit is regulated based on the setpoint, specifically including: The total electromagnetic torque setpoint obtained by superposition is sent to the unit's electrical control unit to adjust the generator output torque and drive the wind turbine to stably approach the optimal operating condition.

10. A maximum wind energy capture system for wind turbines based on operating point deviation, characterized in that, include: The parameter modeling and calibration module is used to construct the transmission chain state equation offline based on the structural parameters of the wind turbine, and to pre-calibrate the power coefficient characteristics and torque coefficient characteristics of the wind turbine. The signal acquisition module is used to acquire the generator electromagnetic torque signal and the wind turbine speed signal in real time. The state estimation module connects the signal acquisition module and the parameter modeling and calibration module. It uses the acquired signals and the state equation of the transmission chain to estimate the aerodynamic torque of the wind turbine through Kalman filtering. It combines the torque coefficient characteristics to solve for the estimated values ​​of the equivalent wind speed and tip speed ratio, and interpolates to calculate the estimated value of the power coefficient. The deviation calculation module is connected to the state estimation module. Based on various estimation parameters and preset optimal parameters, it calculates the quantitative index of the deviation of the working point. The fuzzy control module, with its input end connected to the deviation calculation module, generates torque nonlinear compensation based on the Mamdani fuzzy inference algorithm. The torque synthesis module is connected to the fuzzy control module and the parameter modeling and calibration module respectively. It superimposes the compensation amount and the steady-state optimal torque and outputs the total electromagnetic torque setpoint. The unit executes the control module and communicates with the torque synthesis module. Based on the torque setpoint, it adjusts the generator output torque and links it with the wind turbine through the transmission components, so that the unit's operating conditions approach the optimal wind energy capture state.

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