Wind turbine generator multi-stage self-adaptive droop control method considering rotor kinetic energy constraint and frequency change rate

Abstract

The invention discloses a wind turbine generator multi-stage self-adaptive droop control method considering rotor kinetic energy constraint and frequency change rate, which comprises the following steps: 1, constructing a rotation speed influence factor based on a Logistic function, mapping a rotor rotation speed into a frequency modulation contribution coefficient, and quantitatively evaluating a rotor kinetic energy releasable range of a fan under different operation conditions; 2, designing a self-adaptive droop control strategy based on a segmented frequency change rate, adjusting a droop coefficient in real time by identifying a frequency change rate interval, forming a hierarchical response mechanism, providing strong active support in a sharp frequency drop stage to quickly suppress frequency drop, and converting into moderate support in a frequency recovery stage to maintain stability and prevent secondary drop; and 3, building a wind-fire combined system simulation model, and verifying the effectiveness of the provided strategy in the aspects of improving the frequency response characteristic of the system and improving the frequency stability through a multi-scene contrast experiment. According to the method, the frequency regulation capability of the power system under the high wind power permeability can be remarkably enhanced.

Description

Technical Field

[0001] This invention relates to wind power frequency control technology in power systems, specifically a multi-stage adaptive droop control method for wind turbine generators that takes into account rotor kinetic energy constraints and frequency change rate, applicable to frequency control in power systems with high wind power penetration. Background Technology

[0002] Wind energy development and utilization is a crucial component of renewable energy development and a key technological pathway for promoting energy structure transformation and achieving sustainable development. In the current context of energy transition, wind turbines are required not only to generate electricity efficiently but also to support stable grid operation. This necessitates systematic technological breakthroughs tailored to the actual needs of the power grid. By integrating existing technological achievements and engaging in independent innovation, wind power control technology can evolve in tandem with the demands of modern power system development. Due to the immaturity of the technology in this field and the limited availability of directly applicable engineering experience, the development of relevant control strategies faces numerous challenges. However, through continuous technological exploration and systematic innovation, mastering the core technology of wind turbine frequency control and constructing a technological system based on the principles of "safety, reliability, flexible adjustment, and wide adaptability" will drive the transformation and upgrading of wind power from "grid connection" to "grid construction," ultimately forming a wind power frequency control technology system with independent intellectual property rights.

[0003] With the continuous increase in wind power penetration and the ever-increasing demands for frequency stability in power systems, the frequency support capability of doubly-fed induction generators (DFIGs), as the mainstream model, has become a focus of industry attention. Currently, wind turbines typically participate in system regulation through additional frequency control mechanisms, with control methods mainly falling into two categories: virtual droop control and virtual inertial control. Virtual droop control, in particular, simulates the primary frequency regulation mechanism of a synchronous generator, using the system frequency deviation as an input signal to adjust the output power of the wind turbine.

[0004] Current technologies often employ a fixed droop coefficient for frequency regulation. However, the rotor kinetic energy reserves of doubly-fed induction generators (DFIGs) vary significantly under different wind speeds, making it difficult to balance the frequency regulation potential at high wind speeds with operational safety at low wind speeds. Specifically, at high wind speeds, the rotor kinetic energy reserves are sufficient, and a larger gain can enhance the frequency support effect. However, under low wind speed conditions, excessive gain can easily cause the rotor speed to drop below the critical value, posing a risk of grid disconnection. To address this issue, existing research proposes designing the droop coefficient as a quadratic function of the rotor speed, dynamically adjusting the gain based on the speed level to suppress excessive response when the turbine lacks kinetic energy. However, this method still has limitations in frequency regulation capability when the turbine is operating near its maximum power point.

[0005] To further improve the dynamic performance of frequency regulation, existing methods need to consider both rotor speed constraints and the actual state of the system frequency. The magnitude of the frequency deviation directly determines the strength of the frequency regulation demand. While adaptive droop control strategies based on frequency deviation can effectively suppress frequency fluctuations, the small frequency deviation in the initial stage of disturbances leads to slow turbine response, affecting the timeliness of frequency support. Another method introduces the rate of change of frequency as an auxiliary control signal to improve the initial response speed; however, this type of strategy fails to fully consider the differentiated requirements for the droop coefficient at different stages of frequency regulation and exhibits poor robustness under different disturbance scenarios. Summary of the Invention

[0006] To address the shortcomings of existing wind turbine droop control strategies in adapting to different wind speed conditions, coordinating rotational speed safety with frequency regulation requirements, and improving frequency response performance across all stages, this invention proposes a multi-stage adaptive droop control method for wind turbines that considers rotor kinetic energy constraints and frequency change rate. This aims to fully exploit the frequency regulation potential of wind turbines, enabling them to participate safely and efficiently in system frequency regulation under different operating conditions, thereby improving frequency response performance across all stages and effectively enhancing the frequency stability of power systems with high wind power penetration.

[0007] The present invention adopts the following technical solution to solve the technical problem: The present invention provides a multi-stage adaptive droop control method for wind turbine generators that takes into account rotor kinetic energy constraints and frequency change rate, characterized by the following steps: Step 1: Establish a mathematical model of the frequency response of the wind turbine; Step 2: Based on the frequency response mathematical model of the wind turbine, a multi-stage adaptive droop control strategy that takes into account both rotor kinetic energy constraints and frequency dynamic characteristics is constructed to obtain the dynamic adaptive droop power increment, which is used to suppress frequency fluctuations in order to achieve multi-stage adaptive droop control of the wind turbine.

[0008] The multi-stage adaptive droop control method for wind turbines that takes into account rotor kinetic energy constraints and frequency change rate, as described in this invention, is characterized in that step 1 is performed as follows: Step 1.1: Construct an aerodynamic model of the wind turbine using equation (1) to describe the relationship between wind energy capture power and wind speed, tip speed ratio, and blade pitch angle: (1) In equation (1), air density, For the area swept by the wind turbine, For wind speed, It is the tip speed ratio. It is the pitch angle. It is the wind energy utilization coefficient; Step 1.2: Construct the rotor motion equation of the single-mass block model of the wind turbine transmission system using equation (2): (2) In equation (2), It is the moment of inertia. , These are mechanical torque and electromagnetic torque, respectively. This represents the current rotor angular velocity of the wind turbine generator. For time, To represent the differential; Step 1.3: Construct the active power reference value of the wind turbine in maximum power point tracking mode using equation (3). : (3) In equation (3), This is the active power reference value in maximum power point tracking mode. It is the radius of the wind turbine rotor of the wind turbine generator set. It is the optimal tip speed ratio. Indicates when the pitch angle At that time, the wind energy utilization coefficient is at the optimal tip speed ratio The maximum value reached at that location; Step 1.4: Calculate the additional power generated by the wind turbine participating in primary frequency regulation using equation (4). : (4) In equation (4), It is an additional active power command used for frequency support. This represents the base sag control coefficient. This indicates the system frequency deviation value; Step 1.5: Obtain the active power command value of the wind turbine using equation (4). : (5) In equation (5), This indicates the active power command value of the wind turbine generator.

[0009] Furthermore, step 2 is performed as follows: Step 2.1: Construct the rotational speed influence factor based on the Logistic function using equation (6). : (6) In equation (6), The factor affecting rotational speed. Rotational speed influence factor growth rate This is the lower limit of the wind turbine's rotational speed. This refers to the upper limit of the wind turbine's rotational speed. This indicates the rotor speed of the wind turbine generator when it operates at its maximum power point under the current wind speed. Step 2.2: Construct a piecewise adjustment coefficient based on the frequency change rate using equation (7). : (7) In equation (7), This represents the frequency change rate adjustment coefficient. This indicates the gain during the frequency degradation phase. It is the gain during the frequency recovery phase. Indicates the real-time frequency of the power grid. Indicates the rate of change of the power grid frequency; Step 2.3: Obtain the adaptive droop power increment using equation (8) : (8).

[0010] The present invention provides an electronic device, including a memory and a processor, characterized in that the memory is used to store a program supporting the processor in performing the method described therein, and the processor is configured to execute the program stored in the memory.

[0011] The present invention discloses a computer-readable storage medium storing a computer program, characterized in that the computer program is executed by a processor to perform the steps of the method described thereon.

[0012] Compared with existing technologies, the beneficial effects of this invention are reflected in: 1. This invention proposes an adaptive model of the rotational speed influence factor based on the Logistic function, which can smoothly and continuously adjust the droop control coefficient according to the real-time rotor speed of the wind turbine. It effectively balances the frequency regulation potential under high wind speed and the operational safety constraints under low wind speed, avoids the limitations of fixed coefficient or simple quadratic function adjustment methods, and achieves the optimal frequency regulation contribution within the rotor kinetic energy safety boundary.

[0013] 2. This invention designs an adaptive droop control strategy that combines segmented frequency change rate, achieving phased and refined control of the frequency modulation process through frequency change rate interval identification. It provides strong active power support during the rapid frequency change phase to quickly suppress frequency degradation, and switches to moderate support during the frequency recovery phase to maintain system stability and prevent secondary drooping, significantly improving the dynamic response performance and support effect of the wind turbine throughout the entire frequency change process.

[0014] 3. This invention integrates speed adaptation and frequency change rate segmented response mechanisms to form a complete multi-stage adaptive droop control strategy applicable to different wind speed conditions and disturbance scenarios. This strategy not only improves the frequency response speed and support capability of wind turbine units, but also ensures the stability of the unit's operation, providing an effective technical means for the frequency safety and stability control of high-proportion new energy power systems. Attached Figure Description

[0015] Figure 1 This is a flowchart of the multi-stage adaptive droop control method for wind turbine generators according to the present invention. Detailed Implementation

[0016] In this embodiment, refer to Figure 1 A multi-stage adaptive droop control method for wind turbines, taking into account rotor kinetic energy constraints and frequency change rate, is implemented according to the following steps: Step 1: Establish a mathematical model of the frequency response of the wind turbine; like Figure 1 As shown in the “Mathematical Model Establishment” box, in order to accurately simulate the dynamic behavior of wind turbines under grid frequency disturbances, it is first necessary to build a physical model in the simulation environment to link the mechanical characteristics (aerodynamics) and electrical characteristics (rotor motion, power output) of the wind turbine. Specifically, an aerodynamic model is constructed using Equation (1), and the nonlinear relationship between wind speed, tip speed ratio, and blade pitch angle is used to accurately calculate the mechanical power captured by the wind turbine. At the same time, the single mass block model of Equation (2) describes the dynamic balance relationship between rotor kinetic energy, electromagnetic torque, and mechanical torque, clarifying the energy source when the unit participates in frequency regulation. On this basis, Equation (3) is used to establish the maximum power tracking benchmark of the unit under standard operating conditions, and Equations (4) and (5) are used to set the foundation droop control framework, providing accurate mathematical boundaries for the subsequent introduction of an adaptive adjustment mechanism.

[0017] Step 1.1: Construct an aerodynamic model of the wind turbine using equation (1) to describe the relationship between wind energy capture power and wind speed, tip speed ratio, and blade pitch angle: (1) In equation (1), air density, For the area swept by the wind turbine, For wind speed, It is the tip speed ratio. It is the pitch angle. It is the wind energy utilization coefficient.

[0018] Step 1.2: Construct the rotor motion equation of the single-mass block model of the wind turbine transmission system using equation (2): (2) In equation (2), It is the moment of inertia. , These are mechanical torque and electromagnetic torque, respectively. This represents the current rotor angular velocity of the wind turbine generator. For time, It is the differential of the rotor angular velocity with respect to time.

[0019] Step 1.3: Construct the active power reference value of the wind turbine in maximum power point tracking mode using equation (3). : (3) In equation (3), This is the active power reference value in maximum power point tracking mode. It is the radius of the wind turbine rotor of the wind turbine generator set. It is the optimal tip speed ratio. Indicates when the pitch angle At that time, the wind energy utilization coefficient is at the optimal tip speed ratio The maximum value reached at that point.

[0020] Step 1.4: Calculate the additional power generated by the wind turbine participating in primary frequency regulation using equation (4). : (4) In equation (4), It is an additional active power command used for frequency support. This represents the base sag control coefficient. This represents the system frequency deviation value. Equation (4) defines the basic droop adjustment element, reflecting the fundamental principle that active power is proportional to frequency deviation. However, in this invention, here... This is only a basic parameter; the actual control gain that will take effect will be determined by the speed influence factor discussed later. and segmented adjustment coefficient Make corrections.

[0021] Step 1.5: Obtain the active power command value of the wind turbine using equation (4). : (5) In equation (5), This indicates the active power command value of the wind turbine generator.

[0022] Step 2: Based on the frequency response mathematical model of the wind turbine, construct a multi-stage adaptive droop control strategy that takes into account both rotor kinetic energy constraints and frequency dynamic characteristics, and obtain the dynamic adaptive droop power increment. It is used to suppress frequency fluctuations in order to achieve multi-stage adaptive droop control of wind turbine units.

[0023] Step 2.1: To address the issue of speed instability that may result from forced frequency adjustment at low speeds, a speed influence factor based on the Logistic function is constructed using equation (6). : (6) In equation (6), The factor affecting rotational speed. Rotational speed influence factor growth rate This is the lower limit of the wind turbine's rotational speed. This refers to the upper limit of the wind turbine's rotational speed. This represents the rotor speed of the wind turbine generator when it operates at its maximum power point under the current wind speed. Rotor speed influence factor. The difference between the real-time speed and the upper and lower speed limits exhibits an S-shaped variation characteristic. When the real-time speed... Approaching the lower limit hour, Approaching 0. This means that no matter how severe the frequency drop, the wind turbine will "lock" its frequency regulation function, prioritizing its own safe operation. When the speed approaches the upper limit... hour, Approaching zero, to avoid excessive participation of the wind turbine in frequency regulation. When kinetic energy is sufficient, The value approaches 1, allowing the unit to fully participate in grid support, thereby imposing kinetic safety constraints on the control strategy at the physical level.

[0024] Step 2.2: Construct a piecewise adjustment coefficient based on the frequency change rate using equation (7). : (7) In equation (7), This represents the frequency change rate adjustment coefficient. This indicates the gain during the frequency degradation phase. It is the gain during the frequency recovery phase. Indicates the real-time frequency of the power grid. This represents the rate of change of the power grid frequency.

[0025] This step uses the frequency change rate to determine the urgency and stage of the disturbance, and designs segmented adjustment coefficients accordingly to optimize the control effect. Especially in the frequency recovery stage, when the frequency deviation is negative but the frequency change rate is positive, traditional control logic will generate a command to reduce output due to the positive frequency change rate, thus hindering the frequency recovery. To this end, this method adopts a sign flipping strategy in equation (7), setting the adjustment coefficient at this time to a negative value. This mathematical processing eliminates the weakening effect of the positive frequency change rate on the droop gain, and transforms the control signal that would originally hinder recovery into a signal that maintains or smoothly exits, thereby preventing the wind turbine from prematurely reducing power support when the frequency just begins to recover, and effectively accelerating the smooth return of the system frequency.

[0026] Step 2.3: Obtain the adaptive droop power increment using equation (8) This is used to suppress grid frequency fluctuations in order to achieve multi-stage adaptive droop control of wind turbine units. (8) In equation (8), This represents the power increment generated by adaptive droop control. The term in equation (8) Essentially, this constitutes a dynamic droop coefficient, where the droop coefficient is no longer a fixed value, but rather determined by the base coefficient. With the correction term weighted by the rate of change of frequency Jointly determined. In the actual execution of the control logic, when the system is in a frequency deterioration phase, the adjustment coefficient... Taking a positive value increases the composite droop coefficient, thereby enhancing the fan output's response to frequency deviation. The sensitivity enables strong support; and during the frequency recovery phase, thanks to step 2.2... Switch to Correction item The sign and value were recalibrated to prevent power drops from hindering recovery and to avoid problems with speed recovery caused by over-support. The entire variable coefficient droop command is ultimately controlled by the speed factor. The multiplicative effect ensures that any coefficient correction based on the rate of frequency change will not cause the wind turbine to exceed its own kinetic energy constraint boundary.

[0027] Combination Figure 1 As shown in the "Adaptive Droop Control Structure Construction" module, this step is the core of the control strategy. Unlike traditional fixed-coefficient control, this invention designs an adaptive structure that includes "Logistic speed influence factor modeling" and "segmented frequency change rate adjustment coefficient design." This strategy calculates a time-varying power increment in real time based on the grid frequency state (deterioration / recovery) and the wind turbine speed state (high / low kinetic energy). .

[0028] Step 3: Simulation verification of the combined wind and fire system: Step 3.1: Based on the frequency response mathematical model established in Step 1 and the adaptive droop control structure constructed in Step 2, build a simulation model of the combined wind and fire system.

[0029] Step 3.2: Set up a multi-scenario comparison experiment, set different wind speed conditions (including constant wind speed and random wind speed) and different load disturbance levels, and conduct simulation tests on the traditional droop control and the proposed multi-stage adaptive droop control strategy respectively. Step 3.3: Analyze the simulation results and compare and evaluate the system frequency response indicators, including the maximum frequency deviation, quasi-steady-state frequency deviation and the change in fan rotor speed, to verify the effectiveness of the proposed control strategy in improving the system frequency stability.

[0030] In this embodiment, an electronic device includes a memory and a processor. The memory stores a program that supports the processor in executing the methods described above, and the processor is configured to execute the program stored in the memory.

[0031] In this embodiment, a computer-readable storage medium stores a computer program, which is executed by a processor to perform the steps of the above method.

Claims

1. A multi-stage adaptive droop control method for wind turbine generators that considers rotor kinetic energy constraints and frequency change rate, characterized in that, The procedure is as follows: Step 1: Establish a mathematical model of the frequency response of the wind turbine; Step 2: Based on the frequency response mathematical model of the wind turbine, a multi-stage adaptive droop control strategy that takes into account both rotor kinetic energy constraints and frequency dynamic characteristics is constructed to obtain the dynamic adaptive droop power increment, which is used to suppress frequency fluctuations in order to achieve multi-stage adaptive droop control of the wind turbine.

2. The multi-stage adaptive droop control method for wind turbines considering rotor kinetic energy constraints and frequency change rate as described in claim 1, characterized in that, Step 1 is performed as follows: Step 1.1: Construct an aerodynamic model of the wind turbine using equation (1) to describe the relationship between wind energy capture power and wind speed, tip speed ratio, and blade pitch angle: (1) In equation (1), air density, For the area swept by the wind turbine, For wind speed, It is the tip speed ratio. It is the pitch angle. It is the wind energy utilization coefficient; Step 1.2: Construct the rotor motion equation of the single-mass block model of the wind turbine transmission system using equation (2): (2) In equation (2), It is the moment of inertia. , These are mechanical torque and electromagnetic torque, respectively. This represents the current rotor angular velocity of the wind turbine generator. For time, To represent the differential; Step 1.3: Construct the active power reference value of the wind turbine in maximum power point tracking mode using equation (3). : (3) In equation (3), This is the active power reference value in maximum power point tracking mode. It is the radius of the wind turbine rotor of the wind turbine generator set. It is the optimal tip speed ratio. Indicates when the pitch angle At that time, the wind energy utilization coefficient is at the optimal tip speed ratio The maximum value reached at that location; Step 1.4: Calculate the additional power generated by the wind turbine participating in primary frequency regulation using equation (4). : (4) In equation (4), It is an additional active power command used for frequency support. This represents the base sag control coefficient. This indicates the system frequency deviation value; Step 1.5: Obtain the active power command value of the wind turbine using equation (4). : (5) In equation (5), This indicates the active power command value of the wind turbine generator.

3. The multi-stage adaptive droop control method for wind turbines considering rotor kinetic energy constraints and frequency change rate according to claim 2, characterized in that, Step 2 is performed as follows: Step 2.1: Construct the rotational speed influence factor based on the Logistic function using equation (6). : (6) In equation (6), The factor affecting rotational speed. Rotational speed influence factor growth rate This is the lower limit of the wind turbine's rotational speed. This refers to the upper limit of the wind turbine's rotational speed. This indicates the rotor speed of the wind turbine generator when it operates at its maximum power point under the current wind speed. Step 2.2: Construct a piecewise adjustment coefficient based on the frequency change rate using equation (7). : (7) In equation (7), This represents the frequency change rate adjustment coefficient. This indicates the gain during the frequency degradation phase. It is the gain during the frequency recovery phase. Indicates the real-time frequency of the power grid. Indicates the rate of change of the power grid frequency; Step 2.3: Obtain the adaptive droop power increment using equation (8) : (8)。 4. An electronic device, comprising a memory and a processor, characterized in that, The memory is used to store a program that supports a processor in executing the method of any one of claims 1-3, the processor being configured to execute the program stored in the memory.

5. A computer-readable storage medium storing a computer program thereon, characterized in that, The computer program is executed by the processor to perform the steps of the method according to any one of claims 1-3.

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