Rotor kinetic energy-based wind power converter output power smoothing control method and system

Abstract

The present application relates to the field of wind power converter control, and provides a wind power converter output power smoothing control method and system based on rotor kinetic energy, which is started when the speed needs to be restored, and the deviation between the current speed, i.e. the rotor angular velocity, and the optimal target speed is calculated in real time, the deviation is sent to an adaptive PID controller, and the controller dynamically sets the proportional, integral and differential gain parameters according to the current speed deviation, the grid frequency and the wind speed turbulence intensity and other real-time working conditions. Through this adaptive mechanism, the controller intelligently generates a power correction quantity matched with the real-time working condition, the power correction quantity is superimposed with the conventional maximum power point tracking instruction to form a smooth final power reference instruction, and the final power reference instruction is converted into an electromagnetic torque instruction for execution by the wind power converter. In this way, the speed recovery process is changed from passive hard switching to active and adaptive closed-loop feedback regulation process, which ensures the power generation efficiency and eliminates the impact on the grid during the recovery phase.

Description

Technical Field

[0001] This invention relates to the field of wind power converter control technology, and in particular to a method and system for smooth control of the output power of a wind power converter based on rotor kinetic energy. Background Technology

[0002] Wind power, as a clean and renewable energy source, plays an increasingly important role in the global energy structure transformation. However, the inherent randomness and volatility of wind energy lead to significant uncertainty in the output power of wind turbine generators. When large-scale wind farms are connected to the grid, this drastic power fluctuation poses a serious challenge to the grid's frequency stability, voltage quality, and system security.

[0003] Although the concept of power smoothing using rotor kinetic energy has been widely adopted, shortcomings remain in specific control strategies, especially in a critical stage—the speed recovery phase after a disturbance. Existing technologies exhibit significant deficiencies in this area. After a wind turbine successfully uses rotor kinetic energy to cope with a sudden drop in wind speed and compensate for the power drop, its speed is in a suboptimal state, deviating from the optimal power point. At this point, the control system must restore the speed to the optimal value under the current wind speed to ensure subsequent power generation efficiency. However, traditional recovery strategies often have a single objective: to return to the operating point with maximum output power as quickly as possible. This recovery method causes the controller to generate a drastic change in torque or power command, essentially allowing the turbine to rapidly absorb energy to increase its speed. This inevitably leads to another sharp drop in output power, creating a secondary impact on the power grid, which contradicts the original intention of power smoothing. Existing control logic generally lacks planning and constraints on the power trajectory of the recovery process itself, focusing only on the final speed point while ignoring the disturbance to the power grid caused by the process. While some improved methods attempt to limit the recovery rate by using a fixed slope, this open-loop control method with fixed parameters cannot adapt to complex and ever-changing real-time operating conditions such as wind conditions and grid status. It lacks flexibility and optimality, and it is difficult to ensure both grid friendliness and recovery efficiency. Summary of the Invention

[0004] The present invention aims to solve at least one of the problems existing in the prior art, and provides a method and system for smooth control of the output power of a wind power converter based on rotor kinetic energy.

[0005] One aspect of the present invention provides a method for smoothing the output power of a wind power converter based on rotor kinetic energy, comprising: In response to the power smoothing control activation flag being inactive, the real-time rotor angular velocity of the wind turbine generator is obtained, and the real-time rotor angular velocity is compared with the optimal angular velocity under the current wind speed to determine whether to set the speed recovery mode trigger flag to 1. In response to the speed recovery mode trigger flag being set to 1, the real-time wind speed and rotor real-time angular velocity at the current time point are collected, and the PID input is calculated to obtain the target angular velocity and speed deviation signals; The speed deviation signal, real-time grid frequency, and wind speed turbulence intensity are adaptively tuned using PID parameters to obtain the proportional gain, integral gain, and derivative gain. The power correction is calculated based on the speed deviation signal, proportional gain, integral gain, and derivative gain. The power correction amount, the current optimal power, and the real-time rotor angular velocity are combined and executed to obtain the electromagnetic torque reference command.

[0006] Another aspect of the present invention provides a wind power converter output power smoothing control system based on rotor kinetic energy, comprising: The data acquisition and flag generation module is used to acquire the real-time angular velocity of the wind turbine rotor in response to the power smoothing control activation flag being inactive, and compare the real-time angular velocity of the rotor with the optimal angular velocity under the current wind speed to determine whether to set the speed recovery mode trigger flag to 1. The target angular velocity and rotational speed deviation signal acquisition module is used to acquire the real-time wind speed and rotor real-time angular velocity at the current time point in response to the rotational speed recovery mode trigger flag being 1, and to calculate the PID input to obtain the target angular velocity and rotational speed deviation signals; The PID parameter adaptive tuning module is used to adaptively tune the PID parameters of the speed deviation signal, the real-time frequency of the power grid and the wind speed turbulence intensity to obtain the proportional gain, integral gain and derivative gain. The power correction calculation module is used to calculate the power correction based on the speed deviation signal, proportional gain, integral gain, and derivative gain. The instruction synthesis and execution module is used to synthesize and execute the final power instruction from the power correction amount, the current optimal power, and the real-time rotor angular velocity to obtain the electromagnetic torque reference instruction.

[0007] Compared with existing technologies, this invention provides a wind power converter output power smoothing control method and system based on rotor kinetic energy. It activates when a speed recovery is detected, calculates in real time the deviation between the current speed (rotor angular velocity) and the optimal target speed, and uses this deviation signal as the core input. This deviation signal is fed into an adaptive PID controller, which dynamically adjusts its proportional, integral, and derivative gain parameters based on real-time operating conditions such as the magnitude of the current speed deviation, grid frequency, and wind speed turbulence intensity. Through this adaptive mechanism, the controller can intelligently generate a power correction amount that matches the current system state. This power correction amount is not a direct command but is superimposed with a conventional maximum power point tracking command to form a smoothed final power reference command, which is then converted into an electromagnetic torque command executed by the wind power converter. In this way, the speed recovery process is transformed from a passive, abrupt hard switching into an active, controlled, and condition-adaptive closed-loop feedback regulation process, thereby ensuring power generation efficiency while eliminating the impact on the grid during the recovery phase. Attached Figure Description

[0008] One or more embodiments are illustrated by way of example with the corresponding pictures in the accompanying drawings. These illustrations do not constitute a limitation on the embodiments. Elements with the same reference numerals in the drawings are denoted as similar elements. Unless otherwise stated, the figures in the drawings are not to be limited by scale.

[0009] Figure 1 A flowchart of a wind power converter output power smoothing control method based on rotor kinetic energy according to an embodiment of the present invention; Figure 2 This is a data flow diagram illustrating the wind power converter output power smoothing control method based on rotor kinetic energy according to an embodiment of the present invention. Figure 3 This is a flowchart illustrating the process of acquiring real-time wind speed and real-time rotor angular velocity at the current time point and calculating PID input to obtain target angular velocity and speed deviation signals in the wind power converter output power smoothing control method based on rotor kinetic energy according to an embodiment of the present invention. Figure 4 The flowchart illustrates the process of adaptively tuning PID parameters for the output power smoothing control method of a wind power converter based on rotor kinetic energy according to an embodiment of the present invention to obtain the proportional gain, integral gain, and derivative gain by means of the speed deviation signal, the real-time grid frequency, and the wind speed turbulence intensity. Figure 5 This is a block diagram of a wind power converter output power smoothing control system based on rotor kinetic energy according to an embodiment of the present invention. Detailed Implementation

[0010] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the various embodiments of the present invention will be described in detail below with reference to the accompanying drawings. However, those skilled in the art will understand that many technical details are presented in the various embodiments of the present invention to facilitate a better understanding of the invention. However, the technical solutions claimed in the present invention can be implemented even without these technical details and with various variations and modifications based on the following embodiments. The division of the various embodiments below is for ease of description and should not constitute any limitation on the specific implementation of the present invention. The various embodiments can be combined with and referenced by each other without contradiction.

[0011] As shown in the specification and claims of this invention, unless the context clearly indicates otherwise, the words "a," "an," "an," and / or "the" do not specifically refer to the singular and may also include the plural. Generally speaking, the terms "comprising" and "including" only indicate the inclusion of explicitly identified steps and elements, which do not constitute an exclusive list, and the method or apparatus may also include other steps or elements.

[0012] While this invention makes various references to certain modules in systems according to embodiments of the invention, any number of different modules can be used and run on user terminals and / or servers. The modules described are merely illustrative, and different aspects of the systems and methods may use different modules.

[0013] This invention uses flowcharts to illustrate the operations performed by the system according to embodiments of the invention. It should be understood that the preceding or following operations are not necessarily performed precisely in sequence. Instead, various steps can be processed in reverse order or simultaneously, as needed. Furthermore, other operations can be added to these processes, or one or more steps can be removed from them.

[0014] In existing wind power converter power smoothing control based on rotor kinetic energy, a common technical problem exists: after the wind turbine uses rotor kinetic energy to cope with a power fluctuation, its rotational speed is in a non-optimal state. Traditional control strategies often use a single-target hard switching method to guide the rotational speed back to the optimal operating point, resulting in drastic changes in torque and power, thus causing secondary impacts on the power grid, which contradicts the original intention of power smoothing. Therefore, the technical solution of this invention proposes a wind power converter output power smoothing control method based on rotor kinetic energy. First, the response power smoothing control activation flag is deactivated. When a significant deviation is detected between the real-time angular velocity of the wind turbine rotor and its optimal angular velocity at the current wind speed, a speed recovery mode is triggered. In this mode, the controller does not directly switch back to Maximum Power Point Tracking (MPPT) control, but enters a specific closed-loop adjustment stage. In this stage, the controller uses the real-time wind speed to calculate the current optimal target angular velocity through the MPPT module and compares it with the real-time rotor angular velocity to obtain a dynamic speed deviation signal. Next, the speed deviation signal is fed into a PID controller and adjusted online through an adaptive tuning module. The PID controller then uses these adaptive parameters and the speed deviation signal to calculate a power correction for speed recovery, reflecting the additional power reduction or increase required for smooth speed recovery. Finally, this power correction is superimposed on the current optimal power calculated by the conventional MPPT algorithm to form a smooth, coordinated final power reference command. This power reference command, combined with the real-time speed, is converted into an electromagnetic torque reference command, which is then precisely executed by the wind power converter. In this way, utilizing the closed-loop feedback characteristics of the adaptive PID controller, the speed and power smoothly converge to the target value, effectively suppressing secondary shocks during the recovery process.

[0015] Figure 1 This is a flowchart of a wind power converter output power smoothing control method based on rotor kinetic energy according to an embodiment of the present invention. Figure 2 This is a schematic diagram of the data flow in a wind power converter output power smoothing control method based on rotor kinetic energy according to an embodiment of the present invention. (In conjunction with...) Figure 1 and Figure 2According to an embodiment of the present invention, a wind power converter output power smoothing control method based on rotor kinetic energy includes the following steps: S100, in response to the power smoothing control activation flag being inactive, acquiring the real-time rotor angular velocity of the wind turbine generator set, comparing the real-time rotor angular velocity with the optimal angular velocity under the current wind speed to determine whether to set the speed recovery mode trigger flag to 1; S200, in response to the speed recovery mode trigger flag being 1, acquiring the real-time wind speed and the real-time rotor angular velocity at the current time point, and calculating the PID input to obtain the target angular velocity and speed deviation signal; S300, performing PID parameter adaptive tuning on the speed deviation signal, the real-time grid frequency, and the wind speed turbulence intensity to obtain the proportional gain, integral gain, and derivative gain; S400, calculating the power correction amount based on the speed deviation signal, the proportional gain, the integral gain, and the derivative gain; S500, synthesizing and executing the final power command by combining the power correction amount, the current optimal power, and the real-time rotor angular velocity to obtain the electromagnetic torque reference command.

[0016] Specifically, in step S100, in response to the power smoothing control activation flag being deactivated, the real-time rotor angular velocity of the wind turbine generator is acquired, and compared with the optimal angular velocity at the current wind speed to determine whether to set the speed recovery mode trigger flag to 1. It is understood that after the wind turbine generator performs an active power smoothing action, such as by releasing or absorbing rotor kinetic energy to cope with power fluctuations, its rotor angular velocity will deviate from the optimal operating point at the current wind speed. If the system immediately switches back to conventional maximum power point tracking control after the power smoothing action ends, the control system will attempt to quickly correct this deviation, which may lead to drastic changes in output power, i.e., secondary impact. Therefore, in the technical solution of this invention, in response to the power smoothing control activation flag being deactivated, the real-time rotor angular velocity of the wind turbine generator is acquired, and compared with the optimal angular velocity at the current wind speed to determine whether to set the speed recovery mode trigger flag to 1, in order to determine whether the speed deviation has reached a level requiring special intervention. This ensures that the subsequent collaborative recovery control process is activated only when the speed deviation truly exceeds a reasonable range, avoiding unnecessary control switching and providing the necessary triggering conditions for subsequent smoothing recovery.

[0017] More specifically, in a specific example of the present invention, comparing the real-time angular velocity of the rotor with the optimal angular velocity under the current wind speed to determine whether to set the speed recovery mode trigger flag to 1 includes: calculating the absolute deviation between the real-time angular velocity of the rotor and the optimal angular velocity under the current wind speed to obtain the angular velocity deviation; comparing the angular velocity deviation with a preset speed dead zone threshold, and setting the speed recovery mode trigger flag to 1 when the angular velocity deviation is greater than the preset speed dead zone threshold.

[0018] In other words, more specifically, the power smoothing control activation flag is first continuously monitored. When the flag changes from 1 (active) to 0 (inactive), it indicates that the previous power smoothing operation has ended. At this point, the real-time angular velocity of the wind turbine generator rotor is immediately collected. For example, the collected The value is Simultaneously, based on the current wind speed, the maximum power point tracking characteristic curve is queried to obtain the optimal angular velocity under the current wind speed. ,For example, The value is Then, the absolute deviation between the two is calculated to obtain the angular velocity deviation. ,Right now The angular velocity deviation With a preset speed dead zone threshold Compare the preset speed dead zone threshold. An acceptable range of deviations is defined that does not require initiating a special recovery, for example, Set as .because Greater than Therefore, the angular velocity deviation at this time Greater than the preset speed dead zone threshold This indicates a significant speed deviation, requiring controlled recovery. Therefore, the speed recovery mode trigger flag is set to 1, initiating the subsequent adaptive PID speed recovery control steps. Assuming the calculated angular velocity deviation... Less than or equal to the preset speed dead zone threshold If the speed recovery mode trigger flag is not set to 1, control is returned to the regular MPPT control.

[0019] In step S200, in response to the speed recovery mode trigger flag being set to 1, the real-time wind speed and rotor real-time angular velocity at the current time point are collected, and the PID input is calculated to obtain the target angular velocity and speed deviation signal. It can be understood that once the speed recovery mode is triggered, it indicates that a closed-loop feedback control process needs to be initiated to replace the conventional MPPT control, in order to smoothly guide the speed return. The core of any closed-loop control is based on the deviation between a clear control target (setpoint) and a real-time controlled variable (feedback value), and this control target changes in real-time with wind speed in wind power applications. Therefore, in the technical solution of this invention, in response to the speed recovery mode trigger flag being set to 1, the real-time wind speed and rotor real-time angular velocity at the current time point are collected, and the PID input is calculated to obtain the target angular velocity and speed deviation signal. This allows the real-time collected wind speed to be used by the maximum power point tracking module to determine the optimal angular velocity under the current operating condition as the control target, and it is compared with the real-time collected rotor angular velocity to calculate the core input of the PID controller, namely the speed deviation signal. This ensures that the input of the PID controller accurately reflects the real-time degree to which the rotor angular velocity deviates from the dynamic optimal target. This speed deviation signal is not only the direct basis for subsequent calculation of power correction, but also serves as one of the key state parameters for adaptive tuning of the PID gain, thereby ensuring that the entire recovery process is based on closed-loop regulation with real-time feedback.

[0020] Figure 3 This is a flowchart illustrating the process of acquiring real-time wind speed and rotor angular velocity at the current time point, and calculating PID inputs to obtain target angular velocity and speed deviation signals in a wind power converter output power smoothing control method based on rotor kinetic energy according to an embodiment of the present invention. Figure 3 As shown, step S200 includes: S210, obtaining the target angular velocity by passing the real-time wind speed through the maximum power point tracking module; S220, calculating the difference between the target angular velocity and the real-time angular velocity of the rotor to obtain the rotational speed deviation signal.

[0021] In step S210, the real-time wind speed is processed by the maximum power point tracking module to obtain the target angular velocity. It is understood that since the wind speed may still be changing during speed recovery, the target speed for recovery control should not be a fixed value, but must be the optimal operating point that is updated in real time according to wind conditions. Therefore, in the technical solution of this invention, the real-time wind speed is further processed by the maximum power point tracking module to obtain the target angular velocity, thereby providing the speed recovery PID controller with a precise control setpoint that is updated in real time according to wind conditions. This ensures that the final target of the recovery process is always aligned with the highest wind energy capture efficiency point under the current operating conditions, avoiding control deviations or efficiency losses caused by outdated targets due to wind speed changes, and providing a benchmark for subsequent calculation of accurate speed deviation signals.

[0022] Specifically, in one particular example of the present invention, firstly, the filtered real-time wind speed is acquired from the wind turbine's anemometer system, such as the nacelle anemometer. Subsequently, this real-time wind speed is used as an address index and input into a maximum power point tracking lookup table (MPPT) pre-stored in the controller's memory. This MPPT lookup table stores the optimal rotor angular velocity corresponding to the wind turbine achieving maximum wind energy capture efficiency at different wind speeds. The controller retrieves the optimal angular velocity value corresponding to the real-time wind speed through a lookup operation and sets it as the target angular velocity for this PID control. For example, if the rotor angular velocity at the optimal operating point corresponding to the acquired real-time wind speed is... Then the That is, it is determined to be the target angular velocity.

[0023] In step S220, the difference between the target angular velocity and the real-time rotor angular velocity is calculated to obtain the speed deviation signal. It is understood that since speed recovery control is a closed-loop feedback process, the execution of its core control law depends on a definite error signal, which quantifies the instantaneous difference between the controlled variable and the desired target. Therefore, in the technical solution of this invention, the difference between the target angular velocity and the real-time rotor angular velocity is further calculated to obtain the speed deviation signal, thereby generating a dynamic, quantified input value, which is directly fed to the subsequent adaptive PID controller. This provides a precise calculation basis for the proportional, integral, and derivative components of the PID controller, ensuring that the subsequently generated power correction accurately responds and strives to eliminate the current speed deviation.

[0024] Specifically, in a specific example of the present invention, after obtaining the target angular velocity at the current wind speed and collecting the current real-time rotor angular velocity of the wind turbine, a subtraction operation is performed in the controller to calculate the difference between the two, and the difference is defined as the speed deviation signal, which is transmitted to the PID parameter adaptive tuning process and the power correction calculation process as a key control input.

[0025] Specifically, in step S300, the speed deviation signal, the real-time grid frequency, and the wind speed turbulence intensity are adaptively tuned using PID parameters to obtain the proportional gain, integral gain, and derivative gain. It is understood that a PID controller with fixed parameters cannot simultaneously achieve both rapid speed recovery and smooth power output, and its control effect is easily deteriorated by real-time operating conditions. For example, a high gain set to pursue rapid recovery may cause oscillations when the grid is vulnerable, while a conservative low gain may sacrifice power generation efficiency. Therefore, in the technical solution of this invention, the speed deviation signal, the real-time grid frequency, and the wind speed turbulence intensity are further adaptively tuned using PID parameters to obtain the proportional gain, integral gain, and derivative gain. This allows the PID controller's gain to be optimized online in real-time based on the magnitude of the speed deviation that determines the urgency of recovery, the grid frequency reflecting the grid's acceptance capacity, and the wind speed turbulence affecting control stability. This ensures that the control system uses more aggressive parameters to quickly restore the rotational speed when the power grid is stable but has large deviations, while automatically switching to more conservative parameters to ensure power smoothness when the power grid fluctuates or the wind speed is unstable. This intelligently balances recovery efficiency and grid friendliness under different operating conditions, improving the robustness and adaptability of the control strategy.

[0026] In a specific example of this invention, the proportional gain, integral gain, and derivative gain are obtained by adaptively tuning the PID parameters of the speed deviation signal, the real-time grid frequency, and the wind speed turbulence intensity. This includes using the speed deviation signal, the real-time grid frequency, and the wind speed turbulence intensity as address indices to query and output the corresponding proportional gain, integral gain, and derivative gain from a multidimensional lookup table. It is understood that real-time online calculation of optimal PID parameters using fuzzy logic or complex optimization algorithms places high demands on the controller's computing power. In engineering practice, the real-time performance and determinism of control are crucial. Therefore, in the technical solution of this invention, the speed deviation signal, the real-time grid frequency, and the wind speed turbulence intensity are further used as address indices to query and output the corresponding proportional gain, integral gain, and derivative gain from a multidimensional lookup table. This utilizes a data table that pre-calculates and stores complex nonlinear mapping relationships to achieve rapid acquisition of adaptive parameters. In this way, a set of PID parameters, optimized offline, corresponding to the current operating condition (represented by three input quantities), can be deterministically retrieved with minimal online computational overhead, ensuring the rapid response and engineering practicality of adaptive control.

[0027] More specifically, in one embodiment of the invention, the multidimensional lookup table is constructed as a data matrix pre-stored in the controller's memory. During adaptive tuning, the controller first senses the current system state, acquiring the speed deviation signal, the real-time grid frequency, and the wind speed turbulence intensity. For example, if the detected speed deviation signal is within a moderate deviation range, it indicates that the speed has just begun to recover and the deviation is relatively significant. Simultaneously, the real-time grid frequency obtained from the grid monitoring unit shows that the grid is very stable, with minimal frequency deviation. Furthermore, the wind speed turbulence intensity analyzed from the wind measurement system indicates that the current wind condition is low turbulence, i.e., stable wind speed. At this point, the controller quantizes and converts the descriptions of these three operating conditions—moderate deviation, very stable, and low turbulence—into address indices for a multi-dimensional lookup table. These three address indices are then combined into a unique address pointer. The controller uses this address pointer to access the multi-dimensional lookup table and directly reads a pre-stored set of PID parameters from the address cell corresponding to that pointer. This set of PID parameters, such as a relatively high proportional gain, a moderate integral gain, and a small derivative gain, is offline optimized for rapid and stable recovery under conditions of moderate deviation, stable grid, and stable wind. Finally, the retrieved proportional gain, integral gain, and derivative gain are output for subsequent power correction calculations.

[0028] Alternatively, in another specific embodiment of the present invention, the PID parameter adaptive tuning step can also be implemented by fuzzy logic control. Figure 4 This document describes a flowchart illustrating the process of adaptively tuning PID parameters (proportional gain, integral gain, and derivative gain) for a wind power converter output power smoothing control method based on rotor kinetic energy, according to an embodiment of the present invention. The method involves using the speed deviation signal, real-time grid frequency, and wind speed turbulence intensity. Figure 4 As shown, step S300 includes: S310, after fuzzifying the speed deviation signal, the real-time frequency of the power grid and the wind speed turbulence intensity, performing fuzzy logic reasoning based on fuzzy rules to obtain fuzzy logic; S320, defuzzifying the fuzzy logic to obtain the proportional gain, integral gain and differential gain.

[0029] In step S310, the speed deviation signal, real-time grid frequency, and wind speed turbulence intensity are fuzzified, and then fuzzy logic reasoning is performed based on fuzzy rules to obtain fuzzy logic. It is understood that the relationship between the wind turbine operating conditions and the optimal PID parameters is highly nonlinear and difficult to model precisely. While multidimensional lookup tables are simple to implement, their parameters change abruptly between quantization intervals, lacking smoothness, and the table creation relies heavily on offline simulations. Therefore, in this invention, the speed deviation signal, real-time grid frequency, and wind speed turbulence intensity are further fuzzified, and then fuzzy logic reasoning is performed based on fuzzy rules to obtain fuzzy logic. This simulates expert control experience, transforming precise numerical inputs into linguistic fuzzy concepts for reasoning, thereby handling the uncertainty and nonlinear relationship of the input signal. This allows the PID parameter tuning process to smoothly adapt to continuous changes in operating conditions, rather than jumping between discrete points, improving the robustness and intelligence of the control system under complex operating conditions.

[0030] More specifically, in a specific example of the present invention, the PID parameter adaptive tuning process performed in steps S310 and S320 is an optional alternative to the PID parameter adaptive tuning method using a multidimensional lookup table. In this embodiment, the three precise input quantities, namely the speed deviation signal, the real-time grid frequency, and the wind speed turbulence intensity, are first subjected to fuzzification processing. For example, the current speed deviation signal is fuzzified as follows: Based on its membership function, its degree of belonging to the small deviation is determined to be 0.7, and its degree of belonging to the medium deviation is determined to be 0.3. Simultaneously, the real-time frequency of the power grid is considered to have an extremely small deviation. The mapping to frequency stability is 0.9, and the wind speed turbulence intensity is as follows: The mapping to low turbulence is 0.8. Subsequently, logical reasoning is performed based on a pre-set fuzzy rule base in the controller, which includes expert rules such as "if the speed deviation is large and the power grid is stable, then the proportional gain is large" or "if the power grid is unstable, then the proportional gain is small." The controller calculates the activation strength of each relevant rule based on the input membership degree. For example, a rule such as small deviation, stable frequency, and low turbulence is activated with a strength of 0.7, while another rule such as medium deviation, stable frequency, and low turbulence is activated with a strength of 0.3. Finally, the results of all activated rules are merged to obtain a comprehensive fuzzy logic output set, which is defuzzified in step S320 to obtain precise parameter values. The fuzzy logic outputs of integral gain and derivative gain are also obtained in parallel through a similar process.

[0031] In step S320, the fuzzy logic is defuzzified to obtain the proportional gain, integral gain, and derivative gain. It is understood that since the fuzzy logic generated by the fuzzy logic reasoning in step S310 is a qualitative, linguistic fuzzy set, while the subsequent PID controller requires a precise, quantitative value when performing mathematical operations, the technical solution of this invention further defuzzifies the fuzzy logic to obtain the proportional gain, integral gain, and derivative gain. This transforms the qualitative conclusion obtained from fuzzy reasoning into a deterministic value that the controller can execute. In this way, the intelligent reasoning results of fuzzy logic can be accurately applied to actual physical control, completing the conversion from human experience to machine instructions, and providing specific parameters for subsequent calculation of power correction.

[0032] More specifically, in a specific example of the invention, the defuzzification step in step S320 is performed immediately after the fuzzy logic reasoning in step S310. (In terms of proportional gain) Taking the calculation as an example, the controller uses the centroid method for defuzzification. First, the outputs of multiple rules activated in fuzzy inference are merged to form a comprehensive, irregularly shaped fuzzy output membership function graph. Then, the centroid, i.e., the position of the centroid, of the region covered by this fuzzy output membership function graph is calculated. The projection value of this centroid onto the horizontal axis is then determined as the final, precise proportional gain. The value, for example, calculated The value is 10.0. Similarly, for the integral gain... and differential gain The fuzzy logic outputs were also defuzzified in parallel using the same centroid method to obtain their precise values. The values ​​of proportional gain, integral gain, and differential gain were then used to calculate the power correction.

[0033] Specifically, in step S400, the power correction is calculated based on the speed deviation signal, proportional gain, integral gain, and derivative gain. It can be understood that since the preceding steps have already obtained the speed deviation signal as input to the PID controller, and the proportional, integral, and derivative gains as controller parameters adapted to the real-time operating conditions, the technical solution of this invention further calculates the power correction based on the speed deviation signal, proportional gain, integral gain, and derivative gain. This is used to execute the core operation of the PID control law, transforming the dynamic speed deviation and adaptive gain parameters into a specific, quantified control output signal. This generates a comprehensive adjustment command that can respond to the current deviation, eliminate accumulated steady-state error, and predict deviation trends. This command, the power correction, will serve as a key basis for subsequent power command synthesis, ensuring the accuracy and stability of the recovery control.

[0034] More specifically, in a particular example of the invention, the calculation of this power correction strictly follows a preset PID control law. This calculation incorporates three key inputs obtained in the preceding steps: the current speed deviation signal. and the proportional gain output from the adaptive tuning step. Integral gain and differential gain .

[0035] The power correction is calculated based on the speed deviation signal, proportional gain, integral gain, and derivative gain using the following formula: ; in, Indicates the current moment. The time-series distribution of the speed deviation signal is shown. Represents a time variable. This is the power correction amount at the current moment. First, the speed deviation signal at the current moment is... With proportional gain Multiplying yields a proportional term, which responds to the magnitude of the current deviation; secondly, the time-series distribution of the speed deviation signal is analyzed. Since the start of recovery ( Perform time integration, then multiply by the integration gain. The integral term is obtained, which is used to eliminate the static deviation that accumulates over time; finally, the speed deviation signal at the current moment is calculated. The rate of change over time, multiplied by the differential gain This yields the differential term, which is used to predict the trend of deviation changes and suppress oscillations. Adding these three parts—the proportional term, the integral term, and the differential term—gives the final power correction for the current moment. This power correction is a dynamically changing value; it quantifies the power required to maintain power under the current operating conditions. Approaching zero requires adjusting the output power.

[0036] In step S500, the power correction amount, the current optimal power, and the real-time rotor angular velocity are combined and executed to obtain the electromagnetic torque reference command. Specifically, this step includes: The final power command is synthesized and executed using the following formula to obtain the electromagnetic torque reference command: ; ; in, For the current optimal power, This represents the power correction amount at the current moment. The power reference command at the current moment. This refers to the real-time angular velocity of the rotor. This is the electromagnetic torque reference command. It is understandable that this is due to the calculation obtained in the previous step. This is merely a regulation for smoothly restoring the rotational speed, not the final converter execution command. To simultaneously achieve smooth rotational speed restoration and efficient wind energy capture, this regulation must be aligned with the wind turbine's baseline power generation target, i.e., the current optimal power output. To achieve coordinated superposition, and considering that the direct setpoint for the inner loop control of the wind power converter is electromagnetic torque rather than power, the synthesized power command must be converted into an equivalent torque command. Therefore, in the technical solution of this invention, the power correction amount, the current optimal power, and the real-time rotor angular velocity are further synthesized and executed into a final power command to obtain an electromagnetic torque reference command. Specifically, the current optimal power is first... Subtract the power correction amount at the current moment This yields a smooth, coordinated power reference command for the current moment. Then, Divide by the real-time angular velocity of the rotor obtained through real-time acquisition. This is converted into an electromagnetic torque reference command that can be directly executed by the converter inner loop. This ensures that the wind power converter no longer executes a sudden command that would cause a secondary impact, but rather a smooth command under dynamic, closed-loop feedback regulation. This command drives the rotor angular velocity to recover smoothly to the optimal value, while also causing the grid-connected power of the wind turbine to converge smoothly to the optimal power point, thereby eliminating power oscillations during the speed recovery phase.

[0037] In summary, the wind power output smoothing control method for wind turbine converters based on rotor kinetic energy according to embodiments of the present invention is explained. It is activated when a speed recovery is detected, and calculates in real time the deviation between the current speed (rotor angular velocity) and the optimal target speed. This deviation signal is used as the core input and fed into an adaptive PID controller. This controller dynamically adjusts its proportional, integral, and derivative gain parameters based on real-time operating conditions such as the magnitude of the current speed deviation, grid frequency, and wind speed turbulence intensity. Through this adaptive mechanism, the controller can intelligently generate a power correction amount that matches the current system state. This power correction amount is not a direct command but is superimposed with a conventional maximum power point tracking command to form a smoothed final power reference command, which is then converted into an electromagnetic torque command executed by the wind turbine converter. In this way, the speed recovery process is transformed from a passive, abrupt hard switching into an active, controlled, and condition-adaptive closed-loop feedback regulation process, thereby ensuring power generation efficiency while eliminating the impact on the grid during the recovery phase.

[0038] The present invention also provides a wind power converter output power smoothing control system based on rotor kinetic energy.

[0039] Figure 5 This is a block diagram of a wind power converter output power smoothing control system based on rotor kinetic energy according to an embodiment of the present invention. Figure 5 As shown, the wind power output smoothing control system 100 based on rotor kinetic energy of the present invention includes: a data acquisition and flag generation module 110, used to acquire the real-time rotor angular velocity of the wind turbine generator in response to the power smoothing control activation flag being inactive, and compare the real-time rotor angular velocity with the optimal angular velocity under the current wind speed to determine whether to set the speed recovery mode trigger flag to 1; a target angular velocity and speed deviation signal acquisition module 120, used to acquire the real-time wind speed and real-time rotor angular velocity at the current time point in response to the speed recovery mode trigger flag being 1, and calculate the PID input to obtain the target angular velocity and speed deviation signal; a PID parameter adaptive tuning module 130, used to perform PID parameter adaptive tuning on the speed deviation signal, the real-time grid frequency, and the wind speed turbulence intensity to obtain the proportional gain, integral gain, and derivative gain; a power correction calculation module 140, used to calculate the power correction amount based on the speed deviation signal, the proportional gain, the integral gain, and the derivative gain; and an instruction synthesis and execution module 150, used to synthesize and execute the final power instruction based on the power correction amount, the current optimal power, and the real-time rotor angular velocity to obtain the electromagnetic torque reference instruction.

[0040] For example, the data acquisition and tag generation module 110 includes: The angular velocity deviation calculation unit is used to calculate the absolute deviation between the rotor's real-time angular velocity and the optimal angular velocity under the current wind speed to obtain the angular velocity deviation. The flag trigger unit is used to compare the angular velocity deviation with the preset speed dead zone threshold. When the angular velocity deviation is greater than the preset speed dead zone threshold, the speed recovery mode trigger flag is set to 1.

[0041] For example, the target angular velocity and rotational speed deviation signal acquisition module 120 includes: The target angular velocity acquisition unit is used to obtain the target angular velocity by passing the real-time wind speed through the maximum power point tracking module; The speed deviation signal acquisition unit is used to calculate the difference between the target angular velocity and the real-time angular velocity of the rotor to obtain the speed deviation signal.

[0042] The specific implementation method of the wind power converter output power smoothing control system 100 based on rotor kinetic energy provided in this embodiment of the invention can be found in the wind power converter output power smoothing control method based on rotor kinetic energy provided in this embodiment of the invention, and will not be repeated here.

[0043] The rotor kinetic energy-based wind power converter output power smoothing control system 100 according to embodiments of the present invention can be implemented in various computing devices, such as data servers deployed in wind farm monitoring centers, main controllers of wind turbine generators, wind power converter controllers, or industrial computers. In one possible implementation, the rotor kinetic energy-based wind power converter output power smoothing control system 100 according to embodiments of the present invention can be integrated into the computing device as a software module and / or a hardware module. For example, the rotor kinetic energy-based wind power converter output power smoothing control system 100 can be a software module in the operating system of the computing device, or it can be a dedicated application developed for the computing device; of course, the rotor kinetic energy-based wind power converter output power smoothing control system 100 can also be one of many hardware modules of the computing device.

[0044] Those skilled in the art will understand that the above embodiments are specific implementations of the present invention, and in practical applications, various changes can be made in form and detail without departing from the spirit and scope of the present invention.

Claims

1. A method for smoothing the output power control of a wind power converter based on rotor kinetic energy, characterized in that, include: In response to the power smoothing control activation flag being inactive, the real-time rotor angular velocity of the wind turbine generator is obtained, and the real-time rotor angular velocity is compared with the optimal angular velocity under the current wind speed to determine whether to set the speed recovery mode trigger flag to 1. In response to the speed recovery mode trigger flag being set to 1, the real-time wind speed and rotor real-time angular velocity at the current time point are collected, and the PID input is calculated to obtain the target angular velocity and speed deviation signals; The speed deviation signal, real-time grid frequency, and wind speed turbulence intensity are adaptively tuned using PID parameters to obtain the proportional gain, integral gain, and derivative gain. The power correction is calculated based on the speed deviation signal, proportional gain, integral gain, and derivative gain. The power correction amount, the current optimal power, and the real-time rotor angular velocity are combined and executed to obtain the electromagnetic torque reference command.

2. The wind power converter output power smoothing control method based on rotor kinetic energy according to claim 1, characterized in that, The real-time rotor angular velocity is compared with the optimal angular velocity under the current wind speed to determine whether to set the speed recovery mode trigger flag to 1, including: Calculate the absolute deviation between the real-time rotor angular velocity and the optimal angular velocity under the current wind speed to obtain the angular velocity deviation; The angular velocity deviation is compared with the preset speed dead zone threshold. When the angular velocity deviation is greater than the preset speed dead zone threshold, the speed recovery mode trigger flag is set to 1.

3. The wind power converter output power smoothing control method based on rotor kinetic energy according to claim 1, characterized in that, The system collects real-time wind speed and rotor angular velocity at the current time point, and calculates the PID input to obtain the target angular velocity and rotational speed deviation signals, including: The real-time wind speed is used to obtain the target angular velocity through the maximum power point tracking module; The difference between the target angular velocity and the real-time angular velocity of the rotor is calculated to obtain the speed deviation signal.

4. The wind power converter output power smoothing control method based on rotor kinetic energy according to claim 1, characterized in that, The speed deviation signal, real-time grid frequency, and wind speed turbulence intensity are adaptively tuned using PID parameters to obtain the proportional gain, integral gain, and derivative gain, including: Using the speed deviation signal, real-time grid frequency, and wind speed turbulence intensity as address indexes, the corresponding proportional gain, integral gain, and derivative gain are queried from a multidimensional lookup table and output.

5. The wind power converter output power smoothing control method based on rotor kinetic energy according to claim 1, characterized in that, The speed deviation signal, real-time grid frequency, and wind speed turbulence intensity are adaptively tuned using PID parameters to obtain the proportional gain, integral gain, and derivative gain, including: After fuzzification of the speed deviation signal, the real-time frequency of the power grid and the wind speed turbulence intensity, fuzzy logic reasoning is performed on them based on fuzzy rules to obtain fuzzy logic. The fuzzy logic is defuzzified to obtain the proportional gain, integral gain, and differential gain.

6. The wind power converter output power smoothing control method based on rotor kinetic energy according to claim 1, characterized in that, The power correction is calculated based on the speed deviation signal, proportional gain, integral gain, and derivative gain, including: The power correction is calculated based on the speed deviation signal, proportional gain, integral gain, and derivative gain using the following formula: ； in, , , These are proportional gain, integral gain, and derivative gain, respectively. Indicates the current moment. This is the speed deviation signal at the current moment. The time-series distribution of the speed deviation signal is shown. Represents a time variable. This represents the power correction amount at the current moment.

7. The wind power converter output power smoothing control method based on rotor kinetic energy according to claim 1, characterized in that, The power correction amount, the current optimal power, and the real-time rotor angular velocity are combined and executed to obtain the electromagnetic torque reference command, including: The final power command is synthesized and executed using the following formula to obtain the electromagnetic torque reference command: ； ； in, For the current optimal power, Indicates the current moment. This represents the power correction amount at the current moment. The power reference command at the current moment. This refers to the real-time angular velocity of the rotor. This is the electromagnetic torque reference command.

8. A wind power converter output power smoothing control system based on rotor kinetic energy, characterized in that, include: The data acquisition and flag generation module is used to acquire the real-time angular velocity of the wind turbine rotor in response to the power smoothing control activation flag being inactive, and compare the real-time angular velocity of the rotor with the optimal angular velocity under the current wind speed to determine whether to set the speed recovery mode trigger flag to 1. The target angular velocity and rotational speed deviation signal acquisition module is used to acquire the real-time wind speed and rotor real-time angular velocity at the current time point in response to the rotational speed recovery mode trigger flag being 1, and to calculate the PID input to obtain the target angular velocity and rotational speed deviation signals; The PID parameter adaptive tuning module is used to adaptively tune the PID parameters of the speed deviation signal, the real-time frequency of the power grid and the wind speed turbulence intensity to obtain the proportional gain, integral gain and derivative gain. The power correction calculation module is used to calculate the power correction based on the speed deviation signal, proportional gain, integral gain, and derivative gain. The instruction synthesis and execution module is used to synthesize and execute the final power instruction from the power correction amount, the current optimal power, and the real-time rotor angular velocity to obtain the electromagnetic torque reference instruction.

9. The wind power converter output power smoothing control system based on rotor kinetic energy according to claim 8, characterized in that, The data acquisition and label generation module includes: The angular velocity deviation calculation unit is used to calculate the absolute deviation between the rotor's real-time angular velocity and the optimal angular velocity under the current wind speed to obtain the angular velocity deviation. The flag trigger unit is used to compare the angular velocity deviation with the preset speed dead zone threshold. When the angular velocity deviation is greater than the preset speed dead zone threshold, the speed recovery mode trigger flag is set to 1.

10. The wind power converter output power smoothing control system based on rotor kinetic energy according to claim 8, characterized in that, The target angular velocity and rotational speed deviation signal acquisition module includes: The target angular velocity acquisition unit is used to obtain the target angular velocity by passing the real-time wind speed through the maximum power point tracking module; The speed deviation signal acquisition unit is used to calculate the difference between the target angular velocity and the real-time angular velocity of the rotor to obtain the speed deviation signal.

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