Self-adaptive network construction method and device for wind turbine generator

Abstract

The invention provides a wind turbine generator self-adaptive network construction method and device, and the method comprises the steps: collecting the related operation state data of a wind turbine generator based on the active output power, the reactive output power, the rotor speed and the frequency deviation in real time, and comprehensively and accurately mastering the operation conditions of the wind turbine generator at different moments. A small signal stability analysis model and a mapping relation are determined based on state-space equations of a machine-side converter, a grid-side converter and an energy storage converter of the wind turbine generator, so that the model can more accurately describe dynamic characteristics of the wind turbine generator in different converter working states, and the stability of the wind turbine generator in different operation conditions is evaluated. The virtual inertia correction, the damping coefficient correction and the voltage droop coefficient correction required by network construction are obtained through analysis, then adaptive control parameters and converter modulation signals are determined, the running state and power output of the converter can be adjusted in real time according to the change of the power grid frequency, and virtual inertia, damping and voltage support are provided.

Description

A method and device for adaptive grid construction of wind turbines Technical Field

[0001] This application relates to the field of power electronics technology, and in particular to an adaptive grid construction method and device for wind turbine generators. Background Technology

[0002] As wind power penetration continues to increase, the power grid places higher demands on the active support capabilities of wind farms. To improve the stability of wind farms, grid-based control technology is widely used. However, the grid-based control strategies currently used in wind turbines have significant shortcomings. Their control parameters (including virtual inertia, damping coefficient, and droop coefficient) are mostly fixed values, making it difficult to adapt to the wide range of changes in the operating conditions of wind turbines, thus affecting the stable operation of wind farms under different operating conditions.

[0003] It should be noted that the above introduction to the technical background is only for the purpose of providing a clear and complete explanation of the technical solutions of this application and facilitating understanding by those skilled in the art. It should not be assumed that these technical solutions are known to those skilled in the art simply because they have been described in the background section of this application. Summary of the Invention

[0004] The purpose of this application is to at least partially solve one of the technical problems in the related art.

[0005] Therefore, the first objective of this application is to propose an adaptive grid construction method for wind turbines.

[0006] The second objective of this application is to propose an adaptive grid construction device for wind turbine units.

[0007] The third objective of this application is to propose an electronic device.

[0008] The fourth objective of this application is to provide a computer-readable storage medium.

[0009] The fifth objective of this application is to provide a computer program product.

[0010] To achieve the above objectives, the first aspect of this application proposes an adaptive grid-building method for wind turbines, comprising: real-time acquisition of operating status data of wind turbines, wherein the operating status data is based on active power output, reactive power output, rotor speed, and frequency deviation; inputting the operating status data into a small-signal stability analysis model to obtain virtual inertia correction, damping coefficient correction, and voltage droop coefficient correction required for grid-building, wherein the small-signal stability analysis model is determined based on the state-space equations of the wind turbine's turbine-side converter, grid-side converter, and energy storage converter, and the state-space equations are determined based on frequency domain modal analysis to determine the mapping relationship of the small-signal stability analysis model; determining adaptive control parameters based on the wind turbine's basic control parameters, the virtual inertia correction, the damping coefficient correction, and the voltage droop coefficient correction, and generating a converter modulation signal for adjusting the wind turbine based on the adaptive control parameters to support grid frequency, wherein the converter modulation signal includes a turbine-side converter modulation sub-signal, a grid-side converter modulation sub-signal, and an energy storage converter modulation sub-signal.

[0011] To achieve the above objectives, a second aspect of this application provides a wind turbine adaptive grid construction device, which is configured to implement the steps of the wind turbine adaptive grid construction method proposed in the first aspect of this application.

[0012] To achieve the above objectives, a third aspect of this application provides an electronic device, comprising: a processor; and a memory for storing processor-executable instructions; wherein the processor is configured to execute the instructions to implement the steps of the wind turbine adaptive grid construction method proposed in the first aspect of this application.

[0013] To achieve the above objectives, a fourth aspect of this application provides a computer-readable storage medium that, when the instructions in the storage medium are executed by a processor of an electronic device, enables the electronic device to perform the steps of the wind turbine adaptive grid construction method proposed in the first aspect of this application.

[0014] To achieve the above objectives, a fifth aspect of this application provides a computer program product, including a computer program that, when executed by a processor in a communication device, implements the steps of the wind turbine adaptive grid construction method proposed in the first aspect of this application.

[0015] In this embodiment, real-time acquisition of operating status data of the wind turbine based on active power output, reactive power output, rotor speed, and frequency deviation enables a comprehensive and accurate understanding of the unit's operating status at different times. A small-signal stability analysis model is determined based on the state-space equations of the wind turbine's turbine-side converter, grid-side converter, and energy storage converter. The mapping relationship of the state-space equations is determined through frequency domain modal analysis. This construction method allows the model to more accurately describe the dynamic characteristics of the wind turbine under different converter operating states, fully considering the interactions and influences between the converters, improving the model's fit to the actual system, and thus providing a more reliable model foundation for stability analysis. Analyzing the operating status data using the small-signal stability analysis model allows for a thorough evaluation of the wind turbine's stability under different operating conditions. The analysis yields the virtual inertia correction, damping coefficient correction, and voltage droop coefficient correction required for grid construction. These corrections reflect the unit's instability and the directions for adjustment. Adaptive control parameters are determined based on the basic control parameters of the wind turbine and the aforementioned virtual inertia correction, damping coefficient correction, and voltage droop coefficient correction. This adaptive control strategy dynamically adjusts the control parameters according to the real-time operating status and stability requirements of the wind turbine, making the control strategy more closely aligned with actual operating conditions and improving the accuracy and effectiveness of control. Compared with traditional fixed parameter control, adaptive control can better cope with various uncertainties and changes during wind turbine operation, improving the performance and stability of the unit. The adaptive control parameters generate a converter modulation signal to regulate the wind turbine. This converter modulation signal can adjust its operating status and power output in real time according to changes in the grid frequency, providing virtual inertia, damping, and voltage support, effectively mitigating grid frequency fluctuations and enhancing grid stability and reliability.

[0016] Additional aspects and advantages of this application will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of this application. Attached Figure Description

[0017] The above and / or additional aspects and advantages of this application will become apparent and readily understood from the following description of the embodiments in conjunction with the accompanying drawings, in which: Figure 1 is a flowchart illustrating an adaptive grid construction method for wind turbines provided in an embodiment of this application; Figure 2 is a flowchart illustrating another adaptive grid construction method for wind turbines provided in an embodiment of this application; and Figure 3 is a structural schematic diagram of an electronic device provided in an embodiment of this application. Detailed Implementation

[0018] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with those of this application. Rather, they are merely examples of apparatuses and methods consistent with some aspects of the embodiments of this application as detailed in the appended claims.

[0019] The terminology used in the embodiments of this application is for the purpose of describing particular embodiments only and is not intended to limit the embodiments of this application. The singular forms “a” and “the” as used in the embodiments of this application and the appended claims are also intended to include the plural forms, unless the context clearly indicates otherwise. It should also be understood that the term “and / or” as used herein refers to and includes any or all possible combinations of one or more of the associated listed items.

[0020] It should be understood that although the terms first, second, third, etc., may be used to describe various information in the embodiments of this application, such information should not be limited to these terms. These terms are only used to distinguish information of the same type from each other. For example, without departing from the scope of the embodiments of this application, first information may also be referred to as second information, and similarly, second information may also be referred to as first information. Depending on the context, the words "if" and "suppose" as used herein can be interpreted as "when," "when," or "in response to a determination."

[0021] The embodiments of this application are described in detail below. Examples of these embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and intended to explain this application, and should not be construed as limiting this application.

[0022] With the transformation of the energy structure, wind power, as a clean and renewable energy form, is increasingly penetrating the power system. This change brings more challenges to the operation and management of the power grid, and the power grid places more stringent requirements on the active support capabilities of wind farms.

[0023] In traditional power grid operation, wind power is primarily viewed as a passive power source, with its power output fluctuating with natural wind speed, contributing limited to grid stability. However, with increasing wind power penetration, its role in the power grid is becoming increasingly important. It is no longer merely an energy supplement but needs to actively support the grid to address complex issues such as grid frequency fluctuations and voltage stability. In some scenarios, when grid faults or load surges occur, wind farms should be able to rapidly adjust their power output to provide necessary inertia and damping support to the grid, preventing significant fluctuations in grid frequency and voltage, and ensuring the safe and stable operation of the grid.

[0024] Grid-based control strategies simulate the operating characteristics of synchronous generators, enabling wind turbines to provide frequency and voltage support to the power grid like traditional generators, thereby enhancing the interaction and compatibility between wind farms and the grid. These strategies also allow wind farms to actively participate in grid frequency and voltage regulation during grid-connected operation, improving the grid's anti-interference capabilities and stability.

[0025] However, the control parameters (including virtual inertia, damping coefficient, and droop coefficient) of existing grid-type control strategies are usually set to fixed values. This fixed-parameter control method can meet basic control requirements when the wind turbine's operating state is relatively stable and the operating conditions change little. But in actual operation, the operating state of the wind turbine is affected by a variety of factors, exhibiting a wide range of variation characteristics.

[0026] In some scenarios, wind speed is random and fluctuates, and can change significantly in different seasons, at different times of day, and even at different times within the same period. Changes in wind speed directly affect the output power and rotational speed of wind turbines, thus altering their operating status. For example, in strong winds, wind turbines may operate at full capacity, reaching their rated output power; while in weak winds or calm weather, the output power may decrease significantly, or even shut down.

[0027] In some scenarios, the load demand of the power grid fluctuates constantly with changes in time, season, and user behavior. During peak electricity consumption periods, the power grid demands higher power output from wind farms; while during off-peak periods, the grid may not require as much wind power, and wind farms may even need to reduce their output or store energy. Furthermore, grid faults and abnormal operating conditions can also affect the operating status of wind turbines. For example, when a short-circuit fault occurs in the grid, the grid voltage drops significantly, and wind turbines need to quickly adjust their operating parameters to avoid damage due to low voltage while providing necessary voltage support to the grid.

[0028] Because current grid-based control strategies use fixed control parameters, they cannot be adjusted in real time according to the wide range of changes in wind turbine operating conditions. This severely impacts the stable operation of wind farms under different operating conditions. Under conditions of rapid wind speed changes or significant grid load fluctuations, fixed-parameter control methods cannot respond to these changes promptly and accurately, potentially leading to problems such as mismatch between wind farm output power and grid demand, and excessive frequency and voltage fluctuations, thus threatening the safe and stable operation of the power grid. Therefore, improving grid-based control strategies to enable adaptive adjustment of control parameters based on the actual operating conditions of wind turbines has become a critical issue urgently needing to be addressed in the wind power sector.

[0029] The adaptive grid construction method and apparatus for wind turbines according to embodiments of this application are described below with reference to the accompanying drawings.

[0030] Figure 1 is a flowchart illustrating an adaptive grid construction method for wind turbines provided in an embodiment of this application.

[0031] As shown in Figure 1, the adaptive grid construction method for wind turbines includes, but is not limited to, the following steps: S101, real-time acquisition of the operating status data of the wind turbines, wherein the operating status data is based on active power output, reactive power output, rotor speed and frequency deviation.

[0032] In one feasible implementation, real-time acquisition of wind turbine operating status data is a crucial step in ensuring the stable and efficient operation of the power system during the operation and management of wind farms. Active power output, reactive power output, rotor speed, and frequency deviation constitute an index system for evaluating the performance of wind turbines and their interaction with the power grid.

[0033] In some embodiments, active power output, as the actual electrical energy delivered by the wind turbine to the grid, is a direct indicator of its power generation efficiency and capacity. By monitoring changes in active power in real time using high-precision sensors, the real-time power generation capacity of the wind turbine can be accurately determined.

[0034] In some embodiments, reactive power output reflects the wind turbine's ability to maintain grid voltage stability. The proper allocation and regulation of reactive power is crucial for improving the grid's power factor and reducing line losses. Therefore, real-time acquisition of reactive power output data helps assess the voltage support capability of wind turbines and guides the development of reactive power compensation strategies.

[0035] In some embodiments, rotor speed is a core parameter of wind turbine operation, related to the unit's mechanical and electrical energy conversion efficiency. The stability of rotor speed not only affects the unit's power generation performance but also its safe operation. Real-time monitoring of rotor speed allows for the timely detection of speed anomalies, prevention of mechanical failures, and optimization of speed control strategies to improve power generation efficiency.

[0036] In some embodiments, frequency deviation serves as a crucial indicator of grid stability, reflecting the degree of deviation between the grid frequency and the rated frequency. As part of the power grid, the frequency response capability of wind turbines is vital for maintaining grid frequency stability. Real-time acquisition of frequency deviation data allows for the assessment of wind turbines' frequency regulation capabilities and guides adjustments to frequency control strategies, ensuring stable grid operation during load changes.

[0037] S102, input the operating status data into the small-signal stability analysis model to obtain the virtual inertia correction, damping coefficient correction and voltage droop coefficient correction required for grid construction. The small-signal stability analysis model is determined based on the state space equations of the wind turbine generator-side converter, grid-side converter and energy storage converter. The mapping relationship of the small-signal stability analysis model is determined based on frequency domain modal analysis.

[0038] In one feasible implementation, the construction of the small-signal stability analysis model relies on the state-space equations of the turbine-side converter, grid-side converter, and energy storage converter in the wind turbine. State-space equations are mathematical models used to describe the dynamic characteristics of the system, representing the relationship between the system's inputs, outputs, and internal states in the form of a set of first-order differential equations or difference equations. In the wind turbine, the turbine-side converter converts the frequency-converted AC power generated by the wind turbine into DC power, the grid-side converter converts the DC power into AC power that meets grid requirements, and the energy storage converter is used to store and release electrical energy.

[0039] In one feasible implementation, frequency domain modal analysis (FDMA) is used to determine the mapping relationship between the state-space equations and the small-signal stability analysis model. FDMA is a system analysis method based on the frequency domain. By applying excitation signals of different frequencies to the system and measuring the system's response, modal parameters such as natural frequencies, damping ratios, and mode shapes are obtained. In wind turbine generators, FDMA can perform comprehensive dynamic characteristic analysis on complex systems composed of turbine-side converters, grid-side converters, and energy storage converters. By analyzing the system's response characteristics at different frequencies, the various modes of the system can be accurately identified, and the impact of each mode on power system stability can be determined.

[0040] In one feasible implementation, when real-time collected operating status data is input into the small-signal stability analysis model, the model will perform in-depth analysis and processing of the data based on pre-determined state-space equations and mapping relationships. The small-signal stability analysis model assesses the stability of the power system under its current operating state by analyzing the operating status data. It analyzes whether the power system oscillates or diverges when subjected to minor disturbances. If the power system exhibits an unstable trend, the small-signal stability analysis model will determine the virtual inertia correction, damping coefficient correction, and voltage droop coefficient correction required for grid construction based on the power system's dynamic characteristics and stability requirements.

[0041] In one feasible implementation, the virtual inertia correction is used to adjust the response characteristics of wind turbines to grid frequency changes during grid connection. By increasing or decreasing the virtual inertia, wind turbines can better simulate the inertial behavior of synchronous generators, providing more effective frequency support when grid frequency changes and enhancing grid frequency stability.

[0042] In one feasible implementation, the damping coefficient correction is used to optimize the system's ability to suppress oscillations. When power oscillations occur in the system, adjusting the damping coefficient can increase the system's damping, reduce the amplitude and duration of the oscillations, and improve the system's dynamic stability.

[0043] In one feasible implementation, the voltage droop factor correction is used to adjust the wind turbine's output voltage in real time according to changes in the grid voltage. When the grid voltage changes, adjusting the voltage droop factor can keep the wind turbine's output voltage matched to the grid voltage, ensuring grid voltage stability.

[0044] S103 determines the adaptive control parameters based on the basic control parameters of the wind turbine, the virtual inertia correction, the damping coefficient correction, and the voltage droop coefficient correction, and generates the converter modulation signal for adjusting the wind turbine based on the adaptive control parameters to support the grid frequency. The converter modulation signal includes the turbine-side converter modulation sub-signal, the grid-side converter modulation sub-signal, and the energy storage converter modulation sub-signal.

[0045] In one feasible implementation, the basic control parameters of the wind turbine, including the rated power (P_rated), speed-power characteristic curve (P-ω curve), cut-in wind speed, and grid dispatch instructions, are used as initial conditions. Adaptive control parameters are determined by combining virtual inertia correction, damping coefficient correction, and voltage droop coefficient correction. The adaptive control parameters are determined by a weighted recursive least squares algorithm. The stability constraints of the basic parameters include phase margin > 45° and gain margin > 6dB.

[0046] In some embodiments, the adaptive control parameters are determined using the following expression:

[0047] in, The initial proportional gain is obtained based on the initial conditions. This is a virtual inertia correction factor. This is the correction amount for the damping coefficient. This is the correction amount for the voltage droop factor. For frequency deviation, This refers to the voltage deviation.

[0048] It should be noted that the adaptive control parameters are based on adaptive control theory and are obtained by online identification of parameters such as inductance and grid impedance, combined with real-time wind speed and grid frequency and voltage deviations.

[0049] In one feasible implementation, a converter modulation signal for regulating the wind turbine is generated based on adaptive control parameters. The converter modulation signal includes a machine-side converter modulation sub-signal, a grid-side converter modulation sub-signal, and an energy storage converter modulation sub-signal.

[0050] In some embodiments, for the generator-side converter, the generator-side converter modulator sub-signal is based on the maximum power point tracking algorithm and combined with virtual inertia correction to adjust the generator torque command, thereby achieving dynamic matching between the mechanical power and electromagnetic power of the wind turbine.

[0051] In some embodiments, for grid-side converters, the modulator signal of the grid-side converter adjusts the reactive power output through the voltage droop coefficient correction amount, while using the damping coefficient correction amount to suppress grid frequency oscillations and improve grid connection stability.

[0052] In some embodiments, for an energy storage converter, the modulator signal dynamically adjusts the charging and discharging power of the energy storage unit according to the frequency change rate, providing rapid frequency support.

[0053] In summary, the adaptive grid-connection method for wind turbines provided in this application comprehensively and accurately grasps the operating status of the turbines at different times by collecting real-time operating status data related to active power output, reactive power output, rotor speed, and frequency deviation. A small-signal stability analysis model is determined based on the state-space equations of the turbine-side converter, grid-side converter, and energy storage converter. Furthermore, the mapping relationship of the state-space equations is determined through frequency domain modal analysis. This construction method allows the model to more accurately describe the dynamic characteristics of the wind turbines under different converter operating states, fully considering the interactions and influences between the converters, improving the model's fit to the actual system, and thus providing a more reliable model foundation for stability analysis. Analyzing the operating status data using the small-signal stability analysis model allows for a thorough evaluation of the stability of the wind turbines under different operating conditions. The analysis yields the virtual inertia correction, damping coefficient correction, and voltage droop coefficient correction required for grid connection. These corrections reflect the unit's instability and the directions for adjustment. Adaptive control parameters are determined based on the basic control parameters of the wind turbine and the aforementioned virtual inertia correction, damping coefficient correction, and voltage droop coefficient correction. This adaptive control strategy dynamically adjusts the control parameters according to the real-time operating status and stability requirements of the wind turbine, making the control strategy more closely aligned with actual operating conditions and improving the accuracy and effectiveness of control. Compared with traditional fixed parameter control, adaptive control can better cope with various uncertainties and changes during wind turbine operation, improving the performance and stability of the unit. The adaptive control parameters generate a converter modulation signal to regulate the wind turbine. This converter modulation signal can adjust its operating status and power output in real time according to changes in the grid frequency, providing virtual inertia, damping, and voltage support, effectively mitigating grid frequency fluctuations and enhancing grid stability and reliability.

[0054] Figure 2 is a flowchart illustrating another adaptive grid construction method for wind turbines provided in an embodiment of this application.

[0055] As shown in Figure 2, the adaptive grid construction method for wind turbines includes, but is not limited to, the following steps: S201, real-time acquisition of the operating status data of the wind turbines, wherein the operating status data is based on active power output, reactive power output, rotor speed and frequency deviation.

[0056] For a further detailed description of step S201, please refer to the relevant content in the above embodiments, which will not be repeated here.

[0057] S202 uses the rotor flux linkage and stator current of the wind turbine generator as state variables to establish the state-space equation of the generator-side converter.

[0058] In one feasible implementation, based on automatic control theory, state analysis is performed on the rotor flux linkage and stator current to obtain the state-space equation of the machine-side converter.

[0059] In some embodiments, the state-space equations of the machine-side converter are determined using the following expression:

[0060] in, For rotor flux linkage, For stator current, Stator voltage, For rotor voltage, These are the switching signals input to the generator-side converter, A1, A2, B1, B2, , and The coefficient matrix used to determine the design parameters of wind turbine generators.

[0061] It should be noted that the design parameters of wind turbine generators include the rotational inertia and translational mass of the wind turbine rotor, transmission chain, and generator rotor. A coefficient matrix related to the design parameters of the wind turbine generator is obtained through numerical analysis.

[0062] S203 uses the DC-side capacitor voltage and grid-side current of the grid-side converter as state variables to establish the state-space equation of the grid-side converter.

[0063] In one feasible implementation, based on automatic control theory, state analysis is performed on the DC-side capacitor voltage and grid-side current to obtain the state-space equation of the grid-side converter.

[0064] In some embodiments, the state-space equations of the grid-side converter are determined using the following expression:

[0065] in, This is the DC-side capacitor voltage. For grid-side current, For input power, This is the grid voltage. The switching signal input to the grid-side converter. , , , , , and This is a coefficient matrix relating the capacitors, inductors, and resistors in the grid-side converter to the grid parameters.

[0066] It should be noted that the voltage equations related to capacitance, inductance, resistance and power grid are obtained based on the three-phase stationary coordinate system, and the voltage equations are converted into matrix form to obtain the coefficient matrix.

[0067] S204 uses the state of charge and output current of the energy storage unit as state variables to establish the state-space equation of the energy storage converter.

[0068] In one feasible implementation, based on automatic control theory, the state of charge and output current of the energy storage unit are analyzed to obtain the state-space equation of the energy storage converter.

[0069] In some embodiments, the state-space equations of the energy storage converter are determined using the following expression:

[0070] Where SOC represents the state of charge of the energy storage unit. The output current of the energy storage unit. This is the terminal voltage of the energy storage unit. The switching signal input to the energy storage converter. , , , and This is a coefficient matrix relating the capacity and internal resistance of the energy storage unit to the parameters of the energy storage converter.

[0071] It should be noted that the coefficient matrix related to the capacity, internal resistance, and parameters of the energy storage unit is determined based on the admittance characteristics of the energy storage converter's output port to the power grid.

[0072] S205. Input the operating status data into the small-signal stability analysis model to obtain the virtual inertia correction, damping coefficient correction and voltage droop coefficient correction required for grid construction. The small-signal stability analysis model is determined based on the state-space equations of the wind turbine generator-side converter, grid-side converter and energy storage converter. The mapping relationship of the small-signal stability analysis model is determined based on frequency domain modal analysis.

[0073] In one feasible implementation, the virtual inertia correction is determined using the following expression:

[0074] in, This is a virtual inertia correction factor. The moment of inertia of the wind turbine. This refers to the rated power of the wind turbine. This refers to the output power of the wind turbine. and These are the weighting coefficients determined based on root locus analysis.

[0075] In one feasible implementation, the damping coefficient correction is determined using the following expression:

[0076] in, This is the correction amount for the damping coefficient. For frequency deviation, and The weighting coefficients are obtained by optimizing the damping ratio based on the particle swarm optimization algorithm.

[0077] The voltage droop correction factor is determined using the following expression:

[0078] in, This is the correction amount for the voltage droop factor. This refers to the voltage deviation at the wind turbine terminals. Let f be the maximum reactive power capacity of the wind turbine, and f be the mapping relationship determined based on the Nyquist curve analysis.

[0079] For a more detailed description of the small-signal stability analysis model and mapping relationship, please refer to the relevant content in the above embodiments, which will not be repeated here.

[0080] S206. Based on the basic control parameters of the wind turbine, the virtual inertia correction, the damping coefficient correction, and the voltage droop coefficient correction, the adaptive control parameters are determined, and the converter modulation signal of the wind turbine is generated according to the adaptive control parameters to support the grid frequency. The converter modulation signal includes the turbine-side converter modulation sub-signal, the grid-side converter modulation sub-signal, and the energy storage converter modulation sub-signal.

[0081] In one feasible implementation, the basic control parameters include the virtual inertia of the foundation, the foundation damping coefficient, and the foundation sag coefficient. The virtual inertia of the foundation is determined based on the moment of inertia formula of the synchronous generator, combined with the power rating and rated power of the virtual synchronous generator. The foundation damping coefficient is determined based on the damping torque coefficient of the synchronous generator, combined with the power rating and rated power of the virtual synchronous generator. The foundation sag coefficient is determined based on the governor and excitation regulator characteristics of the synchronous generator, combined with the power rating and rated power of the virtual synchronous generator.

[0082] In one feasible implementation, a first intermediate variable is obtained by weighted summation of the basic virtual inertia and the virtual inertia correction; a second intermediate variable is obtained by weighted summation of the basic damping coefficient and the damping coefficient correction; a third intermediate variable is obtained by weighted summation of the basic droop coefficient and the voltage droop coefficient correction; and the adaptive control parameters are obtained by summing the first, second, and third intermediate variables.

[0083] For a more detailed explanation of how adaptive control parameters generate and adjust the converter modulation signal of the wind turbine for grid frequency support, please refer to the relevant descriptions in the above embodiments, which will not be repeated here.

[0084] In summary, the adaptive grid-connection method for wind turbines provided in this application comprehensively and accurately grasps the operating status of the turbines at different times by collecting real-time operating status data related to active power output, reactive power output, rotor speed, and frequency deviation. A small-signal stability analysis model is determined based on the state-space equations of the turbine-side converter, grid-side converter, and energy storage converter. Furthermore, the mapping relationship of the state-space equations is determined through frequency domain modal analysis. This construction method allows the model to more accurately describe the dynamic characteristics of the wind turbines under different converter operating states, fully considering the interactions and influences between the converters, improving the model's fit to the actual system, and thus providing a more reliable model foundation for stability analysis. Analyzing the operating status data using the small-signal stability analysis model allows for a thorough evaluation of the stability of the wind turbines under different operating conditions. The analysis yields the virtual inertia correction, damping coefficient correction, and voltage droop coefficient correction required for grid connection. These corrections reflect the unit's instability and the directions for adjustment. Adaptive control parameters are determined based on the basic control parameters of the wind turbine and the aforementioned virtual inertia correction, damping coefficient correction, and voltage droop coefficient correction. This adaptive control strategy dynamically adjusts the control parameters according to the real-time operating status and stability requirements of the wind turbine, making the control strategy more closely aligned with actual operating conditions and improving the accuracy and effectiveness of control. Compared with traditional fixed parameter control, adaptive control can better cope with various uncertainties and changes during wind turbine operation, improving the performance and stability of the unit. The adaptive control parameters generate a converter modulation signal to regulate the wind turbine. This converter modulation signal can adjust its operating status and power output in real time according to changes in the grid frequency, providing virtual inertia, damping, and voltage support, effectively mitigating grid frequency fluctuations and enhancing grid stability and reliability.

[0085] Corresponding to the aforementioned adaptive grid configuration method for wind turbines, this application also provides an adaptive grid configuration device for wind turbines. Since the embodiments of the adaptive grid configuration device for wind turbines in this application correspond to the embodiments of the aforementioned adaptive grid configuration method for wind turbines, details not disclosed in the embodiments of the adaptive grid configuration device for wind turbines can be found in the embodiments of the adaptive grid configuration method for wind turbines, and will not be repeated here.

[0086] In one feasible implementation, the wind turbine adaptive grid-building device is configured to execute the steps of the wind turbine adaptive grid-building method provided in the embodiments of this application. In some embodiments, the wind turbine adaptive grid-building device has specific functional modules, algorithms, or logic, and can generate and adjust the converter modulation signal of the wind turbine to support the grid frequency, according to a series of steps, rules, and strategies of the wind turbine adaptive grid-building method described in the embodiments of this application. In some embodiments, by writing specific program code, the wind turbine adaptive grid-building method is transformed into instructions that the wind turbine adaptive grid-building device can understand and execute. This code may include logic such as condition judgment, loop control, and data processing to generate and adjust the converter modulation signal of the wind turbine to support the grid frequency.

[0087] The methods and apparatus provided in the embodiments of this application have been described above. To achieve the functions of the methods provided in the embodiments of this application, the methods and apparatus can be further refined using electronic devices.

[0088] Figure 3 is a schematic diagram of an electronic device provided according to an embodiment of this application. The electronic device shown in Figure 3 is merely an example and should not impose any limitation on the function and scope of use of the embodiments of this application.

[0089] As shown in Figure 3, the electronic device 300 includes a processor 301, which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 302 or a program loaded from a memory 306 into a random access memory (RAM) 303. The RAM 303 also stores various programs and data required for the operation of the electronic device 300. The processor 301, ROM 302, and RAM 303 are interconnected via a bus 304. An input / output (I / O) interface 305 is also connected to the bus 304.

[0090] The following components are connected to I / O interface 305: memory 306 including hard disks, etc.; and communication section 307 including network interface cards such as LAN (Local Area Network) cards, modems, etc., which performs communication processing via a network such as the Internet; and driver 308 is also connected to I / O interface 305 as needed.

[0091] Specifically, according to embodiments of this application, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments of this application include a computer program carried on a computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via communication section 307. When the computer program is executed by processor 301, it performs the functions defined in the methods of this application.

[0092] In an exemplary embodiment, a storage medium including instructions is also provided, such as a memory including instructions, which can be executed by a processor 301 of an electronic device 300 to perform the above-described method. Optionally, the storage medium may be a non-transitory computer-readable storage medium, such as a ROM, random access memory (RAM), CD-ROM, magnetic tape, floppy disk, and optical data storage device.

[0093] In this application, a computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in connection with an instruction execution system, apparatus, or device. In this application, a computer-readable signal medium can include a data signal propagated in baseband or as part of a carrier wave, carrying computer-readable program code. Such propagated data signals can take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. A computer-readable signal medium can also be any computer-readable medium other than a computer-readable storage medium, which can transmit, propagate, or transmit a program for use by or in connection with an instruction execution system, apparatus, or device. The program code contained on the computer-readable medium can be transmitted using any suitable medium, including but not limited to: wireless, wireline, optical fiber, RF, etc., or any suitable combination thereof.

[0094] In an exemplary embodiment, a computer program product is also provided, including a computer program that, when executed by a processor in a communication device, implements the above-described method.

[0095] Other embodiments of the invention will readily occur to those skilled in the art upon consideration of the specification and practice of the invention disclosed herein. This application is intended to cover any variations, uses, or adaptations of the invention that follow the general principles of the invention and include common knowledge or customary techniques in the art not disclosed herein. The specification and examples are to be considered exemplary only, and the true scope and spirit of this application are indicated by the following claims.

[0096] It should be understood that this application is not limited to the precise structure described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope. The scope of this application is limited only by the appended claims.

Claims

1. A wind turbine adaptive grid construction method, characterized in that... [Wuse1] includes: real-time acquisition of wind turbine operating status data, wherein the operating status data is based on active power output, reactive power output, rotor speed and frequency deviation [Wuse2]; inputting the operating status data into a small-signal stability analysis model to obtain the virtual inertia correction, damping coefficient correction and voltage droop coefficient correction required for grid construction, wherein the small-signal stability analysis model is determined based on the state space equations of the wind turbine's machine-side converter, grid-side converter and energy storage converter, and the state space equations are determined based on frequency domain modal analysis to determine the mapping relationship of the small-signal stability analysis model; determining adaptive control parameters according to the wind turbine's basic control parameters, the virtual inertia correction, the damping coefficient correction and the voltage droop coefficient correction, and generating a converter modulation signal to adjust the wind turbine for grid frequency support according to the adaptive control parameters, wherein the converter modulation signal includes a machine-side converter modulation sub-signal, a grid-side converter modulation sub-signal and an energy storage converter modulation sub-signal.

2. The method according to claim 1, characterized in that, include: The state-space equations of the generator-side converter are established by using the rotor flux linkage and stator current of the wind turbine generator as state variables; the state-space equations of the grid-side converter are established by using the DC-side capacitor voltage and grid-side current of the grid-side converter as state variables; and the state of charge and output current of the energy storage unit are established by using the energy storage unit as state variables.

3. The method according to claim 2, characterized in that, The state-space equations of the machine-side converter are determined using the following expressions: in, For rotor flux linkage, For stator current, Stator voltage, For rotor voltage, These are the switching signals input to the generator-side converter, A1, A2, B1, B2, 、 and The coefficient matrix used to determine the design parameters of wind turbine generators.

4. The method according to claim 2, characterized in that, The state-space equations of the grid-side converter are determined using the following expressions: in, This is the DC-side capacitor voltage. For grid-side current, For input power, This is the grid voltage. The switching signal input to the grid-side converter. 、 、 、 、 、 and This is a coefficient matrix relating the capacitors, inductors, and resistors in the grid-side converter to the grid parameters.

5. The method according to claim 2, characterized in that, The state-space equations of the energy storage converter are determined using the following expression: Where SOC represents the state of charge of the energy storage unit. The output current of the energy storage unit. This is the terminal voltage of the energy storage unit. The switching signal input to the energy storage converter. 、 、 、 and This is a coefficient matrix relating the capacity and internal resistance of the energy storage unit to the parameters of the energy storage converter.

6. The method according to claim 1, characterized in that, The virtual inertia correction is determined using the following expression: in, This is a virtual inertia correction factor. The moment of inertia of the wind turbine. This refers to the rated power of the wind turbine. This refers to the output power of the wind turbine. and These are the weighting coefficients determined based on root locus analysis.

7. The method according to claim 1, characterized in that, The damping coefficient correction is determined using the following expression: in, This is the correction amount for the damping coefficient. For frequency deviation, and The weighting coefficients are obtained by optimizing the damping ratio based on the particle swarm optimization algorithm.

8. The method according to claim 1, characterized in that, The voltage droop correction factor is determined using the following expression: in, This is the correction amount for the voltage droop factor. This refers to the voltage deviation at the wind turbine terminals. Let f be the maximum reactive power capacity of the wind turbine, and f be the mapping relationship determined based on the Nyquist curve analysis.

9. The method according to claim 1, characterized in that, The basic control parameters include the basic virtual inertia, the basic damping coefficient, and the basic droop coefficient. Adaptive control parameters are determined based on the wind turbine's basic control parameters, the virtual inertia correction, the damping coefficient correction, and the voltage droop coefficient correction. This process includes: weighted summing of the basic virtual inertia and the virtual inertia correction to obtain a first intermediate variable; weighted summing of the basic damping coefficient and the damping coefficient correction to obtain a second intermediate variable; weighted summing of the basic droop coefficient and the voltage droop coefficient correction to obtain a third intermediate variable; and summing the first, second, and third intermediate variables to obtain the adaptive control parameters.

10. A wind turbine adaptive grid configuration device, characterized in that, The steps are configured to implement the method of any one of claims 1 to 9.

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