Frequency modulation method and device of doubly-fed fan, equipment and medium
By introducing a Smith predictor controller into the doubly fed wind turbine to compensate for the pitch system lag, and by introducing fuzzy control rules into the integrated inertial controller, the problem of poor frequency regulation effect of the doubly fed wind turbine was solved, and a faster and more stable frequency regulation effect was achieved.
Patent Information
- Application Number
- CN202511498305.4
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-10-20
- Publication Date
- 2025-12-19
AI Technical Summary
Doubly fed wind turbines exhibit time-delay inertial characteristics in their pitch control at high wind speeds, leading to a delay in frequency regulation energy. Traditional integrated inertia control with fixed control coefficients cannot fully utilize the frequency regulation capability, resulting in poor existing frequency regulation performance.
A Smith predictor controller is used to compensate for hysteresis in the pitch system, thereby improving the pitch system. Fuzzy control rules are introduced into the integrated inertial controller to optimize the integrated inertial controller and improve the frequency modulation effect through synergistic effect.
It significantly improved the frequency regulation response speed and stability of wind turbine units, optimized the adaptive adjustment capability of the integrated inertial controller, and enhanced the frequency regulation effect.
Smart Images
Figure CN121162452A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of power technology, and in particular to frequency regulation methods, devices, equipment and media for doubly fed wind turbines. Background Technology
[0002] Currently, doubly fed wind turbines often employ both pitch control and integrated inertial control in high wind speeds to participate in system frequency regulation.
[0003] However, when used for pitch control in wind turbines, due to factors such as the nonlinearity of the wind turbine, its large moment of inertia, and the thruster delay of the pitch converter, pitch control exhibits time-delay inertial characteristics. This results in a delay in the energy participating in system frequency regulation, affecting the frequency regulation effect. Furthermore, traditional integrated inertia control uses fixed control coefficients, leading to poor frequency regulation performance. Therefore, existing frequency regulation methods for doubly-fed inertial wind turbines suffer from poor frequency regulation performance. Summary of the Invention
[0004] Based on this, it is necessary to propose frequency regulation methods, devices, equipment, and media for doubly fed wind turbines to address the above problems. By optimizing and synergistically utilizing the two control methods, the frequency regulation effect of wind turbine units can be effectively improved, thereby enhancing the stability of the wind turbine unit's operating system.
[0005] To achieve the above objectives, the first aspect of this application provides a frequency regulation method for a doubly-fed wind turbine, wherein the doubly-fed wind turbine is a wind turbine unit that uses a pitch system and integrated inertial control for coordinated frequency regulation, and the method includes: The Smith predictive controller was used to perform hysteresis compensation on the pitch system of the wind turbine, resulting in an improved pitch system. Fuzzy control rules are introduced into the integrated inertial controller of the wind turbine to obtain an optimized integrated inertial controller; The wind turbine is frequency-regulated based on the improved pitch system and the optimized integrated inertial controller.
[0006] Furthermore, the improvement of the pitch system by using the Smith predictor controller to perform hysteresis compensation on the wind turbine's pitch system specifically includes: The controlled object of the wind turbine is equivalent to a first-order inertial delay element containing pure delay, and the dynamic response model of the wind turbine is obtained. Based on the transfer function of the dynamic response model and the transfer function of the preset PID controller, the transfer function of the pitch system of the wind turbine is generated. By introducing a prediction compensation term from the Smith predictor controller into the transfer function of the pitch system for hysteresis compensation, an improved pitch system is obtained.
[0007] Furthermore, the transfer function of the dynamic response model of the wind turbine is expressed by the following equation:
[0008] In the formula, Let be the transfer function of the controlled object of the wind turbine, and s be the Laplace variable. This refers to the pure delay element in the operating system of the wind turbine.
[0009] Furthermore, the introduction of a prediction compensation term from a Smith predictor controller into the transfer function of the pitch system for hysteresis compensation, resulting in an improved pitch system, specifically includes: The transfer function of the Smith predictor control is added to the transfer function of the pitch system to obtain the initial transfer function of the pitch system after compensation. The initial transfer function is subjected to time-domain differential transformation to obtain a discretized differential equation, and the improved pitch system contains the differential equation.
[0010] Furthermore, the initial transfer function is expressed by the following equation:
[0011] In the formula, Let $\frac{ ... The input signal of the pitch system is passed through the PID controller and output. The input signal of the pitch system is the output of the Smith predictor compensator after hysteresis compensation, where s is a Laplace variable. This refers to the pure delay element in the operating system of the wind turbine.
[0012] Furthermore, the integrated inertial controller includes at least one fuzzy controller, each of which takes at least one operating parameter of the wind turbine as a variable input, and the output of the fuzzy controller is a control coefficient; The process of introducing fuzzy control rules into the integrated inertial controller of the wind turbine to obtain an optimized integrated inertial controller specifically includes: Based on the preset universe of discourse of the variables and the universe of discourse of the control coefficients of the fuzzy controller, a fuzzy subset of the variables and control coefficients of the fuzzy controller is determined. The control rules of the fuzzy controller are established based on the fuzzy subset of the variables and control coefficients of the fuzzy controller, and the preset membership functions of the variables and control coefficients. Based on the control rules of the fuzzy controller, a fuzzy control rule table for the control coefficients corresponding to the fuzzy controller is determined. The optimized integrated inertial controller includes a fuzzy control rule table for the control coefficients corresponding to each fuzzy controller. The fuzzy control rule table contains the correspondence between the fuzzy subsets of the control coefficients and the fuzzy subsets of each variable.
[0013] Furthermore, the frequency regulation of the wind turbine based on the improved pitch system and the optimized integrated inertial controller specifically includes: Based on the transfer function of the improved pitch system, Smith prediction control of the pitch system is performed in the preset module code to realize frequency regulation operation of the wind turbine based on the improved pitch system. Based on the Mamdani algorithm, adaptive fuzzy inference is performed according to the fuzzy control rule table of the control coefficients corresponding to each fuzzy controller and the obtained operating parameters of the wind turbine to obtain the fuzzy values of the control coefficients corresponding to each fuzzy controller. The fuzzy values of the control coefficients corresponding to each fuzzy controller are defuzzified using the centroid method to obtain the control coefficients corresponding to each fuzzy controller. The control coefficients corresponding to each of the adaptively adjusted fuzzy controllers are applied to the integrated inertial controller to achieve frequency regulation of the wind turbine based on the optimized integrated inertial controller.
[0014] To achieve the above objectives, a second aspect of this application provides a frequency regulation device for a doubly-fed wind turbine, wherein the doubly-fed wind turbine is a wind turbine unit that uses a pitch system and integrated inertial control for coordinated frequency regulation, and the device includes: The pitch control unit is used to perform hysteresis compensation on the pitch system of the wind turbine using the Smith predictor controller to obtain an improved pitch system. An integrated inertial control unit is used to introduce fuzzy control rules into the integrated inertial controller of the wind turbine to obtain an optimized integrated inertial controller. An optimized frequency modulation unit is used to regulate the frequency of the wind turbine based on the improved pitch system and the optimized integrated inertial controller.
[0015] To achieve the above objectives, a third aspect of this application provides a computer-readable storage medium storing a computer program that, when executed by a processor, causes the processor to perform the steps of the method described in the first aspect.
[0016] To achieve the above objectives, a fourth aspect of this application provides a computer device including a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, the processor performs the steps of the method described in the first aspect.
[0017] The embodiments of the present invention have the following beneficial effects: This invention proposes a frequency regulation method for doubly-fed induction generator (DFIG) wind turbines. The DFIG is a wind turbine unit employing a pitch control system and integrated inertial control for coordinated frequency regulation. The method includes: using a Smith predictive controller to compensate for hysteresis in the pitch control system of the wind turbine unit, resulting in an improved pitch control system; introducing fuzzy control rules into the integrated inertial controller of the wind turbine unit, resulting in an optimized integrated inertial controller; and performing frequency regulation on the wind turbine unit based on the improved pitch control system and the optimized integrated inertial controller. This invention, on the one hand, introduces Smith predictive control to improve the pitch control system of the wind turbine unit. Smith predictive control can effectively compensate for the hysteresis characteristics of the pitch control system and improve the response speed of frequency regulation energy. On the other hand, it introduces fuzzy control rules into the integrated inertial controller to optimize the integrated inertial controller and achieve adaptive adjustment of the control parameters in the integrated inertial controller. Through the optimization and synergistic effect of the two control methods, the frequency regulation effect of the wind turbine unit is effectively improved, and the stability of the wind turbine unit's operating system is enhanced. Attached Figure Description
[0018] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.
[0019] in: Figure 1 This is a flowchart illustrating the frequency regulation method for a doubly fed fan in an embodiment of the present invention. Figure 2 Pitch control diagram of wind turbine in this embodiment of the invention; Figure 3 This is a diagram of the optimized frequency regulation strategy for doubly-fed wind turbines under high wind speeds in an embodiment of the present invention. Figure 4 A system block diagram for adding a Smith predictor compensation controller to the pitch system in this embodiment of the invention; Figure 5(a) is a curve of the virtual inertia control input membership function in an embodiment of the present invention; Figure 5(b) is a graph of the membership function of the droop control input in an embodiment of the present invention; Figure 5(c) is a graph of the virtual inertia coefficient and droop coefficient output membership function in an embodiment of the present invention; Figure 6 The diagram shows the fuzzy inference results for a wind speed of 15 m / s and a frequency change rate of -0.25 Hz / s in this embodiment of the invention. Figure 7 This is a block diagram of the adaptive integrated inertial control of the doubly fed wind turbine in this embodiment of the invention; Figure 8 This is a structural block diagram of the frequency modulation device for a doubly fed fan in an embodiment of the present invention; Figure 9 This is an internal structural diagram of a computer device in an embodiment of the present invention. Detailed Implementation
[0020] 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. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.
[0021] One embodiment of this invention proposes a frequency regulation method for doubly-fed induction generator (DFIG) wind turbines, which improves the pitch reserve and integrated inertial control of DFIG wind turbines under high wind speeds, thereby enhancing the frequency regulation effect. In this embodiment, the DFIG wind turbine is a wind turbine unit that uses a pitch system and integrated inertial control for coordinated frequency regulation; for details, please refer to [reference needed]. Figure 1 , Figure 1 This is a flowchart illustrating the frequency regulation method for a doubly-fed wind turbine according to an embodiment of the present invention. The method includes: Step 100: Use the Smith predictor controller to perform hysteresis compensation on the pitch system of the wind turbine to obtain the improved pitch system.
[0022] In this embodiment, the pitch control of the doubly-fed induction generator (DFIG) wind turbine is mainly achieved by adjusting the blade pitch angle β to control the wind energy capture efficiency, thereby adjusting the angular velocity of the wind turbine. w And the output power of the synchronous generator. The pitch system is used to adjust the blade pitch angle of the wind turbine, which is designed to control the wind energy capture efficiency and affect the angular velocity and output power of the wind turbine.
[0023] For reference Figure 2 , Figure 2 This is a pitch control diagram for a wind turbine in an embodiment of the present invention. This pitch control diagram illustrates a closed-loop control system for regulating the turbine's rotational speed. First, the target rotational speed is set. ω ref , and actual speed ωThe difference is used as the input to the PID controller, which calculates the pitch angle. β The adjustment amount is transmitted to the pitch controller, but due to mechanical inertia and delay, the actual pitch angle adjustment lags behind. The adjusted pitch angle affects the operating state of the wind turbine, thereby changing the input speed of the synchronous generator. ω ,and ω The signal is then fed back to the PID controller, forming a closed-loop regulation. However, in the pitch controller of a doubly-fed induction generator (DFIG) wind turbine, due to factors such as the nonlinearity of the turbine, large moment of inertia, and thruster delay of the pitch controller, the pitch control exhibits time-delay inertial characteristics, resulting in a delay in its participation in system frequency regulation and energy supply. Smith predictive control can be used to compensate for this delay, thereby reducing or eliminating the hysteresis phenomenon in the pitch system and achieving a pitch system with better performance.
[0024] The Smith predictor controller anticipates system behavior during lag time and adjusts system inputs in advance to compensate for the impact of pure time delay on system stability. Introducing the Smith predictor controller into the pitch system significantly improves lag issues, greatly enhances frequency response speed and accuracy, and results in superior overall performance.
[0025] Step 200: Introduce fuzzy control rules into the integrated inertial controller of the wind turbine to obtain the optimized integrated inertial controller.
[0026] In this embodiment, the integrated inertial controller for the wind turbine is designed to simulate the inertial characteristics of a traditional synchronous generator to improve the frequency stability of the power grid. However, traditional integrated inertial control uses fixed control coefficients and cannot adaptively change the control coefficients according to the system's operating state, preventing the integrated inertial control of the doubly-fed inertial generator from fully utilizing its frequency regulation capability. Therefore, fuzzy control rules are introduced to enable the controller to adaptively adjust control parameters according to different operating conditions, thereby optimizing the controller's performance. Fuzzy control rules are a control strategy based on fuzzy logic, which uses fuzzy reasoning to handle uncertainty and complexity, and is suitable for systems that are difficult to describe with precise mathematical models.
[0027] The integrated inertial controller, after being adjusted by fuzzy control rules, can more effectively cope with wind speed changes and grid frequency fluctuations, thereby improving the frequency regulation performance of wind turbine units.
[0028] Step 300: Adjust the frequency of the wind turbine based on the improved pitch system and the optimized integrated inertial controller.
[0029] For details, please refer to [link / reference]. Figure 3 , Figure 3This is a diagram illustrating the optimized high-wind-speed down-frequency regulation strategy for doubly-fed induction generator (DFIG) wind turbines in this embodiment of the invention. The optimized high-wind-speed down-frequency regulation strategy mainly includes: the pitch controller of the wind turbine adjusts the frequency according to the target rotational speed. ω ref and actual speed ω The difference is used to calculate the pitch angle using a PID controller. β The adjustment amount, after being compensated for by the Smith predictor controller to compensate for the mechanical delay of the pitch system, is transmitted to the wind turbine to adjust the pitch angle. β This, in turn, changes the rotational speed of the wind turbine. ω Wind turbine rotation speed ω The frequency deviation Δ of the power system affects the output power of the synchronous generator, which is then fed into the power system after being controlled and regulated by the converter. f and the rate of change of frequency dΔ f / dt is monitored in real time and used as an input signal for the integrated inertial control. Simultaneously, wind speed v is also used as one of the input signals. In the integrated inertial controller, the fuzzy control module adjusts the input based on wind speed... v Frequency deviation Δ f and the rate of change of frequency dΔ f / dt, dynamically adjusts control coefficients such as virtual inertia coefficient and droop coefficient, thereby optimizing the power output of wind turbine units and achieving regulation of power system frequency.
[0030] In this embodiment of the invention, a Smith predictor controller is introduced into the pitch system to mitigate the mechanical delay performance of the pitch system. Fuzzy control rules are used to adjust the integrated inertial control coefficients, enabling the control coefficients of the integrated inertial control to be adaptively adjusted. Through the coordinated frequency modulation of the two, the extreme value of frequency drop and the frequency stability value can be greatly improved.
[0031] In one embodiment of the present invention, step 100, using a Smith predictor controller to perform hysteresis compensation on the pitch system of the wind turbine to obtain an improved pitch system, specifically includes: Step 110: Equivalently transform the controlled object of the wind turbine into a first-order inertial delay element containing pure delay to obtain the dynamic response model of the wind turbine.
[0032] In this embodiment, the controlled objects of the wind turbine are the pitch controller and the wind turbine. Therefore, the pitch controller and wind turbine of the wind turbine are equivalent to a first-order inertial delay element.
[0033] The transfer function of the dynamic response model of a wind turbine is expressed by the following equation:
[0034] In the formula, Let be the transfer function of the controlled object of the wind turbine generator, and s be the Laplace variable. It is a pure delay element in the operating system of a wind turbine.
[0035] The transfer function of the dynamic response model of a wind turbine is a first-order inertial plus pure delay model, which is used to mathematically simulate and simplify the dynamic response characteristics of the wind turbine pitch control system.
[0036] Step 120: Generate the transfer function of the pitch system of the wind turbine based on the transfer function of the dynamic response model and the preset transfer function of the PID controller.
[0037] In this embodiment, the transfer function of the pitch system without Smith prediction compensation is expressed by the following equation:
[0038] In the formula, The actual output signal of the pitch system (after Laplace transform) represents the blade pitch angle. The reference input for the pitch system (after Laplace transform) represents the rated angular velocity of the wind turbine. ω ref , This is the transfer function of the PID controller. Let be the transfer function of the controlled object of the wind turbine generator, and s be the Laplace variable. It is a pure delay element in the operating system of a wind turbine.
[0039] Step 130: Introduce the prediction compensation term of the Smith predictor controller into the transfer function of the pitch system to perform hysteresis compensation, and obtain the improved pitch system.
[0040] When the controller issues a command to change the blade angle, the actual action will lag for a period of time due to mechanical inertia and other reasons. This delay will seriously affect the speed and accuracy of frequency regulation. To solve the inherent mechanical delay problem of the pitch system, a Smith predictive controller is introduced into the pitch control system to eliminate the mechanical delay problem.
[0041] In one embodiment, Step 130 involves introducing a prediction compensation term from the Smith predictor controller into the transfer function of the pitch system to perform hysteresis compensation, thereby obtaining an improved pitch system. Specifically, this includes: Step 131: Add the transfer function of the Smith predictor control to the transfer function of the pitch system to obtain the initial transfer function of the compensated pitch system.
[0042] In this embodiment, the system block diagram of the pitch system incorporating the Smith predictor compensation controller is as follows: Figure 4 As shown, firstly, the reference input signal... With feedback signal The error signal is obtained by subtraction. E(s) Secondly, E(s) After passing through the PID controller Generate controller output . The controlled object input to the pitch system The transfer function of the pitch system after Smith prediction compensation. By predicting the time characteristics of the lagging components and compensating for the control signals in advance, the effects of the lagging components are eliminated. Ultimately, the system output... The effects of hysteresis are almost eliminated, response speed is accelerated, and stability is improved. Through the compensation effect of the Smith predictor, the entire process enables the pitch system to achieve faster and more stable control when dealing with controlled objects with significant time delays.
[0043] In this embodiment, after introducing Smith predictive compensation control, the transfer function of the pitch system is expressed by the following equation:
[0044] in, It is the transfer function of the Smith predictor controller with no time delay. It is a pure time-delay term.
[0045] From the above formula, it can be seen that when When the lag time t equals the sampling time T, the effect of the lag element in the transfer function on the system will be eliminated.
[0046] Based on the above, the initial transfer function of the pitch system with the Smith predictor controller can be expressed by the following equation:
[0047] In the formula, Let be the initial transfer function of the pitch system after Smith prediction compensation. The input signal of the pitch control system is passed through the PID controller for output. The input signal of the pitch system is the output signal (feedback signal) after lag compensation by the Smith predictor compensator, where s is the Laplace variable. It is a pure delay element in the operating system of a wind turbine.
[0048] Step 132: Perform time-domain differential transformation on the initial transfer function to obtain the discretized differential equation. The improved pitch system contains differential equations.
[0049] In this embodiment, to achieve more precise control, the initial transfer function is digitized and transformed from an S-domain equation into a time-domain differential equation, which can be expressed by the following formula:
[0050] In the formula, This represents the actual pitch angle output by the pitch control system at any given time t. This represents the feedback value at time t after Smith's prediction and compensation.
[0051] The discretized differential equation is:
[0052] In the formula, k It is a discrete time sequence number or sampling time (e.g.) k =0, 1, 2, 3...), T represents the sampling period, in seconds. Representative at the k At each sampling time, the system measures the propeller pitch angle. This represents the predicted pitch angle at the next sampling time (i.e., the (k+1)th time). Representative at n The feedback value after Smith's prediction and compensation before each sampling step Representative at the k The feedback value after Smith prediction compensation at each sampling time.
[0053] in, n=t / T If we take T = 0.5s, then n =2. Let k = k -1, finally yielding the prediction expression for Smith control:
[0054] In the formula: This represents the propeller pitch angle output by the system at the previous moment. represents the feedback value after Smith's prediction and compensation at the previous time step, and u(k-3) represents the feedback value after Smith's prediction and compensation three time steps ago.
[0055] In one embodiment of the present invention, the integrated inertial controller includes at least one fuzzy controller, each fuzzy controller taking at least one wind turbine's operating parameters as variable inputs, and the output of the fuzzy controller being control coefficients; then step 200, introducing fuzzy control rules into the integrated inertial controller of the wind turbine to obtain an optimized integrated inertial controller, specifically includes: Step 210: Based on the preset universe of discourse of the variables and the universe of discourse of the fuzzy controller, determine the fuzzy subset of the variables and the control coefficients of the fuzzy controller.
[0056] In this embodiment, the controller structure is designed using a two-dimensional fuzzy controller with single-variable output. The integrated inertial controller includes a virtual inertia controller (FIS1) and a droop controller (FIS2). The input signals of the virtual inertia controller are wind speed and the system frequency change rate, and the virtual inertia coefficient is the output signal; the input signals of the droop controller are wind speed and frequency deviation, and the droop coefficient is the output signal.
[0057] In one embodiment, frequency regulation of the wind turbine is performed under high wind speeds. High wind speeds refer to wind speeds exceeding the rated value. The rated wind speed of a wind turbine is typically 12 m / s, while 17 m / s is considered a high wind speed range. Therefore, the domain of discussion for wind speed can be set to [12-17]. This range represents the typical operating area of the wind turbine after reaching the rated wind speed, which can fully utilize the frequency regulation capability of the wind turbine.
[0058] The domain of discourse for the system frequency change rate is set to [-0.4, 0.4], which covers most normal disturbance conditions and is suitable for the design and verification of frequency response control strategies for wind turbine units.
[0059] The domain of discourse for the system frequency deviation is set to [-0.5, 0]. The [-0.5, 0] Hz range covers all accident scenarios in which the wind turbine needs to participate in frequency regulation, such as frequency drops caused by sudden load increases or generator disconnection. When the frequency exceeds this range, the power grid has already triggered low-frequency load shedding protection, and the wind turbine frequency regulation becomes meaningless.
[0060] The domain of the virtual inertia coefficient is set to [1, 1.5]. This range can provide sufficient inertial support at high wind speeds, avoiding excessive frequency fluctuations due to insufficient inertia. At the same time, this range is not too large, avoiding excessive release of rotor kinetic energy that would cause excessive reduction in the output power of the wind turbine, thereby affecting the stability and operating efficiency of the unit.
[0061] The domain of the droop coefficient is set to [3.5, 5.5]. This range can provide sufficient inertial support at high wind speeds to avoid excessive frequency fluctuations due to insufficient inertia. At the same time, this range is not too large to avoid excessive release of rotor kinetic energy, which would cause an excessive drop in the output power of the wind turbine and thus affect the stability and operating efficiency of the unit.
[0062] The purpose of setting different domains is to establish fuzzy rules for virtual inertia coefficient and droop coefficient under different scenarios, so as to achieve adaptive adjustment of virtual inertia coefficient and droop coefficient under different wind speeds, frequency change rates and frequency deviations.
[0063] In one embodiment, the fuzzy subset of wind speed is {L, M, H}, representing {small, medium, large}, the fuzzy subset of system frequency change rate is {NL, NS, ZO, PS, PL}, representing {negative large, negative small, zero, positive small, positive large}, and the fuzzy subsets of frequency deviation, virtual inertia coefficient, and droop coefficient are all set to {VS, S, M, B, VB}, representing {very small, relatively small, medium, relatively large, very large}.
[0064] Step 220: Establish the control rules of the fuzzy controller based on the fuzzy subsets of the variables and control coefficients of the fuzzy controller, as well as the preset membership functions of the variables and control coefficients.
[0065] In one embodiment, triangular membership functions are used for both fuzzy controllers FIS1 and FIS2. The input membership function curves of fuzzy controllers FIS1 and FIS2 are shown in Figure 5(a) and Figure 5(b), respectively, and the membership function curves of the output control coefficients are shown in Figure 5(c).
[0066] In one embodiment, the control rules for the designed fuzzy controllers FIS1 and FIS2 are as follows: (1) In the high wind speed area, when the wind speed is low or moderate, the rotation speed of the wind turbine rotor is relatively high, which contains a certain amount of rotor kinetic energy and can provide more rotor energy for frequency regulation. In order to fully utilize the frequency regulation potential of the wind turbine, the virtual inertia coefficient and droop coefficient can be increased with the increase of wind speed to provide more active power support for the system.
[0067] (2) When the wind speed is high in the high wind speed area, the rotation speed of the wind turbine rotor will reach the upper limit, and its rotational kinetic energy content is very high. However, since the active power output of the wind turbine is limited due to being close to the rated value, in order to avoid the active power output of the wind turbine exceeding the limit, the values of the virtual inertia coefficient and droop coefficient can be appropriately reduced.
[0068] (3) In the initial stage of frequency drop, the system frequency deviation is small and the frequency change rate is large. In order to make full use of the limited rotor stored kinetic energy to slow down the frequency drop, the virtual inertia coefficient is set to be large and the droop coefficient is set to be small. As the frequency drops to the lowest point, the frequency deviation increases and the frequency change rate decreases. In order to avoid excessive release of rotor kinetic energy, the virtual inertia coefficient is decreased and the droop coefficient is increased to raise the frequency to the lowest point. During the frequency recovery process, the frequency deviation decreases, the virtual inertia coefficient is increased and the droop coefficient is appropriately decreased.
[0069] Step 230: Based on the control rules of the fuzzy controller, determine the fuzzy control rule table of the control coefficients corresponding to the fuzzy controller. The optimized integrated inertial controller includes the fuzzy control rule table of the control coefficients corresponding to each fuzzy controller. The fuzzy control rule table contains the correspondence between the fuzzy subsets of the control coefficients and the fuzzy subsets of each variable.
[0070] In one embodiment, fuzzy rule tables for virtual inertia coefficients and droop coefficients are designed based on the control rules of the fuzzy controller in Step 220. Refer to Tables 1 and 2, where Table 1 is the fuzzy control rule table for virtual inertia coefficients and Table 2 is the fuzzy control rule table for droop coefficients.
[0071] Table 1. Fuzzy Control Rules for Virtual Inertia Coefficient
[0072] Table 2. Fuzzy Control Rules for Droop Coefficient
[0073] In one embodiment of the present invention, step 300, frequency regulation of the wind turbine based on the improved pitch system and the optimized integrated inertial controller, specifically includes: Step 310: Based on the transfer function of the improved pitch system, Smith prediction control of the pitch system is performed in the preset module code to realize frequency regulation operation of the wind turbine based on the improved pitch system.
[0074] In this embodiment, Smith predictive control of the doubly fed wind turbine pitch system is implemented in the MATLAB Function module based on the improved transfer function of the pitch system.
[0075] Step 320: Based on the Mamdani algorithm, adaptive fuzzy inference is performed according to the fuzzy control rule table of the control coefficients corresponding to each fuzzy controller and the obtained operating parameters of the wind turbine to obtain the fuzzy values of the control coefficients corresponding to each fuzzy controller.
[0076] In this embodiment, the Mamdani algorithm is used for fuzzy inference.
[0077] Taking a wind speed of 15 m / s and a frequency change rate of -0.25 Hz / s as an example, fuzzy inference of the virtual inertia coefficient is performed. From Figure 5(a), we know that the wind speed is 15 m / s, M = 1; the frequency change rate is -0.25 Hz / s, NL = 0.25, and NS = 0.75. The triggering rule is R1 = min(M(v), NL(dΔ)). f / dt))=min(1,0.25)=0.25, corresponding to VB in the virtual inertia coefficient fuzzy control rule table in Table 1; R2=min(M(v), N(dΔ f / dt))=min(1,0.75)=0.75, corresponding to B in the virtual inertia coefficient fuzzy control rule table in Table 1. The final fuzzy logic inference output is obtained, which can be referenced. Figure 6 , Figure 6 The fuzzy inference result is based on a wind speed of 15 m / s and a frequency change rate of -0.25 Hz / s in this embodiment of the invention.
[0078] Step 330: Use the centroid method to defuzzify the fuzzy values of the control coefficients corresponding to each fuzzy controller, and obtain the control coefficients corresponding to each fuzzy controller.
[0079] In this embodiment, the centroid method is used to complete the deblurring process, and the calculation formula can be expressed as follows:
[0080] In the formula, This is the output value after deblurring. For the domain, For the input variables, For elements The membership degree of a fuzzy set A.
[0081] Taking a wind speed of 15 m / s and a frequency change rate of -0.25 Hz / s as an example, the fuzzy inference result with a wind speed of 15 m / s and a frequency change rate of -0.25 Hz / s is defuzzified, and the virtual inertia coefficient is calculated to be 1.43.
[0082] Step 340: Apply the control coefficients corresponding to each fuzzy controller after adaptive adjustment to the integrated inertial controller to achieve frequency regulation of the wind turbine based on the optimized integrated inertial controller.
[0083] The embodiments of the present invention employ a comprehensive inertial control coefficient based on fuzzy control, which adaptively adjusts the virtual inertial control coefficient and droop control coefficient according to the wind speed, system frequency change rate, and deviation, so as to fully utilize the frequency regulation capability of the wind turbine and effectively suppress frequency fluctuations.
[0084] For details, please refer to [link / reference]. Figure 7 , Figure 7 This is a block diagram of the adaptive integrated inertial control of the doubly-fed wind turbine in this embodiment of the invention. First, the grid frequency deviation Δ is calculated. f And by taking the derivative, the rate of change of frequency dΔ is obtained. f / dt; Then, the frequency deviation is passed through a high-pass filter to extract the high-frequency components, which are used to calculate the droop coefficient K. dThe frequency change rate is filtered through a low-pass filter to extract the low-frequency components, which are then used to calculate the virtual inertia coefficient K. i Meanwhile, according to wind speed v Fuzzy controllers FIS1 and FIS2 adjust K respectively i and K d The value of K. Next, using K... i and Δ f Calculate the power adjustment ΔP1 using K d and dΔ f / The power adjustment ΔP2 is calculated using dt. Finally, ΔP1 and ΔP2 are compared with the maximum power point tracking power P. MPPT The total power output is obtained by summing the values and then limited to the wind turbine's capacity by a capacity limiter. The entire process dynamically adjusts control parameters based on wind speed and frequency changes, optimizing the frequency regulation performance of the wind turbine.
[0085] One embodiment of the present invention also proposes a frequency modulation device for a doubly-fed fan, which can be referred to. Figure 8 , Figure 8 This is a structural block diagram of the frequency regulation device for a doubly-fed induction generator (DFIG) wind turbine according to an embodiment of the present invention. The DFIG wind turbine is a wind turbine generator that uses a pitch control system and integrated inertial control for coordinated frequency regulation. The device includes: The pitch control unit 801 is used to perform hysteresis compensation on the pitch system of the wind turbine using the Smith predictor controller to obtain an improved pitch system.
[0086] The integrated inertial control unit 802 is used to introduce fuzzy control rules into the integrated inertial controller of the wind turbine to obtain an optimized integrated inertial controller.
[0087] The frequency modulation unit 803 is optimized for frequency modulation of the wind turbine based on the improved pitch system and the optimized integrated inertial controller.
[0088] In this embodiment of the invention, the frequency regulation device for the doubly-fed induction generator (DFIG) wind turbine incorporates Smith predictive control to improve the pitch system of the wind turbine. Smith predictive control effectively compensates for the hysteresis characteristics of the pitch system, enhancing the response speed of the frequency regulation energy. Simultaneously, fuzzy control rules are introduced into the integrated inertial controller to optimize it and achieve adaptive adjustment of control parameters. Through the optimization and synergistic effect of these two control methods, the frequency regulation performance of the wind turbine is effectively improved, enhancing the stability of the wind turbine's operating system.
[0089] Figure 9 An internal structural diagram of a computer device according to one embodiment of the present invention is shown. This computer device can specifically be a terminal or a system. Figure 9As shown, the computer device includes a processor, memory, and network interface connected via a system bus. The memory includes a non-volatile storage medium and internal memory. The non-volatile storage medium stores an operating system and may also store a computer program. When executed by the processor, this computer program causes the processor to perform the steps in the above-described method embodiments. The internal memory may also store a computer program, which, when executed by the processor, causes the processor to perform the steps in the above-described method embodiments. Those skilled in the art will understand that... Figure 9 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.
[0090] In one embodiment, a computer device is provided, including a memory and a processor, the memory storing a computer program that, when executed by the processor, causes the processor to perform the steps in the above method embodiments.
[0091] In one embodiment, a computer-readable storage medium is provided storing a computer program that, when executed by a processor, causes the processor to perform the steps in the above method embodiments.
[0092] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments described above. Any references to memory, storage, databases, or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), RAMbus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and RAMbus dynamic RAM (RDRAM), etc.
[0093] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.
[0094] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of this patent application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this patent application should be determined by the appended claims.
Claims
1. A frequency regulation method for a doubly-fed fan, characterized in that, The doubly-fed wind turbine is a wind turbine unit that uses a pitch control system and integrated inertial control for coordinated frequency regulation. The method includes: The Smith predictive controller was used to perform hysteresis compensation on the pitch system of the wind turbine, resulting in an improved pitch system. Fuzzy control rules are introduced into the integrated inertial controller of the wind turbine to obtain an optimized integrated inertial controller; The wind turbine is frequency-regulated based on the improved pitch system and the optimized integrated inertial controller.
2. The method as described in claim 1, characterized in that, The improved pitch system is obtained by using a Smith predictor controller to perform hysteresis compensation on the wind turbine pitch system, specifically including: The controlled object of the wind turbine is equivalent to a first-order inertial delay element containing pure delay, and the dynamic response model of the wind turbine is obtained. Based on the transfer function of the dynamic response model and the transfer function of the preset PID controller, the transfer function of the pitch system of the wind turbine is generated. By introducing a prediction compensation term from the Smith predictor controller into the transfer function of the pitch system for hysteresis compensation, an improved pitch system is obtained.
3. The method as described in claim 2, characterized in that, The transfer function of the dynamic response model of the wind turbine is expressed by the following formula: In the formula, Let be the transfer function of the controlled object of the wind turbine, and s be the Laplace variable. This refers to the pure delay element in the operating system of the wind turbine.
4. The method as described in claim 2, characterized in that, The improved pitch system is obtained by introducing a prediction compensation term from a Smith predictor controller into the transfer function of the pitch system for hysteresis compensation, specifically including: The transfer function of the Smith predictor control is added to the transfer function of the pitch system to obtain the initial transfer function of the pitch system after compensation. The initial transfer function is subjected to time-domain differential transformation to obtain a discretized differential equation, and the improved pitch system contains the differential equation.
5. The method as described in claim 4, characterized in that, The initial transfer function is expressed by the following equation: In the formula, This is the initial transfer function of the pitch system after Smith prediction compensation. The input signal of the pitch system is passed through the PID controller and output. The input signal of the pitch system is the output of the Smith predictor compensator after hysteresis compensation, where s is a Laplace variable. This refers to the pure delay element in the operating system of the wind turbine.
6. The method as described in claim 1, characterized in that, The integrated inertial controller includes at least one fuzzy controller, each of which takes at least one operating parameter of the wind turbine as a variable input, and the output of the fuzzy controller is a control coefficient. The process of introducing fuzzy control rules into the integrated inertial controller of the wind turbine to obtain an optimized integrated inertial controller specifically includes: Based on the preset universe of discourse of the variables and the universe of discourse of the control coefficients of the fuzzy controller, a fuzzy subset of the variables and control coefficients of the fuzzy controller is determined. The control rules of the fuzzy controller are established based on the fuzzy subset of the variables and control coefficients of the fuzzy controller, and the preset membership functions of the variables and control coefficients. Based on the control rules of the fuzzy controller, a fuzzy control rule table for the control coefficients corresponding to the fuzzy controller is determined. The optimized integrated inertial controller includes a fuzzy control rule table for the control coefficients corresponding to each fuzzy controller. The fuzzy control rule table contains the correspondence between the fuzzy subsets of the control coefficients and the fuzzy subsets of each variable.
7. The method as described in claim 6, characterized in that, The frequency regulation of the wind turbine based on the improved pitch system and the optimized integrated inertial controller specifically includes: Based on the transfer function of the improved pitch system, Smith prediction control of the pitch system is performed in the preset module code to realize frequency regulation operation of the wind turbine based on the improved pitch system. Based on the Mamdani algorithm, adaptive fuzzy inference is performed according to the fuzzy control rule table of the control coefficients corresponding to each fuzzy controller and the obtained operating parameters of the wind turbine to obtain the fuzzy values of the control coefficients corresponding to each fuzzy controller. The fuzzy values of the control coefficients corresponding to each fuzzy controller are defuzzified using the centroid method to obtain the control coefficients corresponding to each fuzzy controller. The control coefficients corresponding to each of the adaptively adjusted fuzzy controllers are applied to the integrated inertial controller to achieve frequency regulation of the wind turbine based on the optimized integrated inertial controller.
8. A frequency modulation device for a doubly-fed fan, characterized in that, The doubly-fed wind turbine is a wind turbine unit that uses a pitch control system and integrated inertial control for coordinated frequency regulation. The device includes: The pitch control unit is used to perform hysteresis compensation on the pitch system of the wind turbine using the Smith predictor controller to obtain an improved pitch system. An integrated inertial control unit is used to introduce fuzzy control rules into the integrated inertial controller of the wind turbine to obtain an optimized integrated inertial controller. An optimized frequency modulation unit is used to regulate the frequency of the wind turbine based on the improved pitch system and the optimized integrated inertial controller.
9. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by a processor, it causes the processor to perform the steps of the method as described in any one of claims 1 to 7.
10. A computer device, comprising a memory and a processor, characterized in that, The memory stores a computer program that, when executed by the processor, causes the processor to perform the steps of the method as described in any one of claims 1 to 7.