A wind turbine low-voltage ride-through parameter setting method, device and medium

Abstract

The application discloses a wind turbine low-voltage ride-through parameter setting method, equipment and medium, which is applied to the technical field of new energy power generation grid connection control and power system safety and stability. The method relies on a power system transient simulation model, and the influence law of active and reactive current parameters on system frequency, power angle stability, transient overvoltage and mechanical load characteristics of the wind turbine in the low-voltage ride-through process is clear. The reactive current parameter is set for the critical working condition of large-scale access of the wind farm to the weak power grid, near-end three-phase short-circuit fault and maximum equivalent impedance of the power grid after removal, and the active current parameter is set for the composite working condition of maximum frequency drop, minimum power angle stability margin and strongest transmission chain load impact, so that the problem that multiple targets of the existing control strategy are difficult to consider is effectively solved, system stability and equipment safety are considered, various operation risks are avoided, multiple index collaborative optimization is realized, the low-voltage ride-through capability of the wind turbine and the adaptability of the power grid are improved.

Description

Technical Field

[0001] This application relates to the field of grid connection control for new energy power generation and the safety and stability of power systems, and in particular to a method, equipment and medium for setting low-voltage ride-through parameters for wind turbine generators. Background Technology

[0002] With the rapid development of new energy power generation technologies, the installed capacity of wind power in the power system continues to increase. Under scenarios with a high proportion of new energy access, small-scale power grids, and weak power grids, the equivalent inertia of the system decreases, and its sensitivity to power disturbances increases significantly.

[0003] When a short circuit or other fault occurs in the power grid, wind turbines need to maintain grid-connected operation through a low-voltage ride-through (LDV) control strategy. Existing doubly-fed induction generator (DFIG) wind turbine LDV ride-through strategies generally adopt a reactive power priority control principle. This means that when the converter's current capacity is limited, reactive current is prioritized to support the grid voltage, while active current is distributed according to the converter's remaining output capacity. After the fault is cleared, the wind turbine gradually restores its active power output to the pre-fault level according to a preset active power recovery rate.

[0004] When wind power penetration is low, the above control strategies can meet the system operation requirements. However, in scenarios with small-scale power grids or a large number of doubly-fed wind turbines connected to the grid, existing low-voltage ride-through control strategies reveal a series of intertwined and difficult-to-coordinate contradictions: on the one hand, in order to suppress the system frequency drop caused by the synchronous and slow recovery of active power by a large number of wind turbines after a fault, simply increasing the active power recovery rate will cause severe mechanical shocks and significantly deteriorate the load safety of key components such as the wind turbine drive train; on the other hand, the reactive current injected to support the voltage during the fault and the transient process after the fault is cleared can easily induce the risk of transient overvoltage and power angle instability in weak power grids.

[0005] In view of the above-mentioned technologies, it is an urgent problem for those skilled in the art to find a method for setting low-voltage ride-through parameters of wind turbines that can systematically and synergistically optimize power angle stability, frequency safety, transient voltage and equipment mechanical load. Summary of the Invention

[0006] The purpose of this application is to provide a method, device, and medium for setting low-voltage ride-through parameters for wind turbines. This can solve the problem that existing low-voltage ride-through control strategies cannot simultaneously ensure system frequency stability, power angle stability, transient voltage safety, and the load safety of key wind turbine components in high-penetration scenarios such as small-scale power grids or concentrated doubly-fed induction generator (DFIG) wind turbines. This easily leads to the problem of mutual constraints between frequency support, voltage support, and equipment mechanical protection.

[0007] To address the aforementioned technical problems, this application provides a method for tuning low-voltage ride-through parameters of wind turbine generators, comprising: Based on the transient simulation model of the power system, the influence of the active current parameters and reactive current parameters of the wind turbine on the system frequency characteristics, power angle stability characteristics, transient overvoltage characteristics and mechanical load characteristics during the low-voltage ride-through process is determined. Under the worst operating conditions corresponding to transient overvoltage, the reactive current parameters are set based on the influence law and reactive power regulation law; among them, the worst operating conditions corresponding to transient overvoltage are: under weak grid conditions, the wind farm is connected on a large scale and a near-end three-phase short-circuit fault occurs, and the grid equivalent impedance is the largest after the fault is cleared. Based on the influence laws, a multi-objective parameter tuning model is constructed under the worst operating conditions corresponding to system frequency, power angle stability margin, and mechanical load, and the active current parameters are tuned based on the multi-objective parameter tuning model. Among them, the worst operating condition corresponding to system frequency is the operating condition with the largest system frequency drop, the worst operating condition corresponding to power angle stability margin is the operating condition with the lowest power angle stability margin, and the worst operating condition corresponding to mechanical load is the operating condition with the largest impact amplitude of the wind turbine drive chain load.

[0008] Preferably, it includes: constructing a multi-objective parameter tuning model, including: The corresponding control parameter vector is determined based on the active current parameters; A multi-objective function is constructed based on the control parameter vector, the frequency deviation corresponding to the system frequency characteristics in the influence law, the power angle oscillation amplitude corresponding to the power angle stability characteristics in the influence law, the transient overvoltage level corresponding to the transient overvoltage characteristics in the influence law, and the response index corresponding to the mechanical load characteristics in the influence law. The corresponding multi-objective parameter tuning model is determined based on the multi-objective function.

[0009] Preferably, the expression for the control parameter vector is: ; in, For the control parameter vector; This refers to the voltage correlation coefficient during the low-voltage ride-through process of wind turbine generators. This refers to the active power inheritance coefficient during the low-voltage ride-through process of the wind turbine. This represents the minimum active current support required during the low-voltage ride-through of the wind turbine. The active power recovery rate parameter is used during the fault recovery phase; among which, the voltage correlation coefficient, active power inheritance coefficient, minimum active power current support, and active power recovery rate parameter constitute the active power current parameter.

[0010] Preferably, the expression for the multi-objective function is: ; in, It is a multi-objective function; For the control parameter vector; The frequency coefficients corresponding to the system's frequency characteristics; This is for frequency deviation; This refers to the angle coefficient corresponding to the angle stability characteristic; This represents the amplitude of the oscillation angle. The overvoltage coefficient corresponding to the transient overvoltage characteristics; Transient overvoltage level; This is the load factor corresponding to the mechanical load characteristics; For response indicators.

[0011] Preferably, the active current parameters are tuned based on a multi-objective parameter tuning model, including: A multi-objective optimization algorithm is used to solve the multi-objective parameter tuning model to obtain the optimal solution set; The appropriate active current parameters are selected from the optimal solution set according to the current operating conditions, thereby setting the active current parameters.

[0012] Preferably, after setting the reactive current parameters and active current parameters, the process includes: Verification results were obtained based on the power system simulation model to determine the set reactive current parameters and active current parameters. Determine whether the verification result meets the constraints; If not, then reset the reactive current parameters and active current parameters.

[0013] Preferably, it further includes: Various fault scenarios are simulated based on power system simulation software models, and the influence of set reactive current parameters and active current parameters on system frequency characteristics, power angle stability characteristics, transient overvoltage characteristics and mechanical load characteristics are determined based on each fault scenario. A dataset of parameter influence characteristics is generated based on the influence patterns, so as to optimize the power system simulation software model according to the dataset of parameter influence characteristics.

[0014] Preferably, the constraints include: the frequency safety threshold corresponding to the system frequency characteristics, the power angle stability limit corresponding to the power angle stability characteristics, the voltage allowable range corresponding to the transient overvoltage characteristics, and the load limit corresponding to the mechanical load characteristics.

[0015] On the other hand, this application also provides an electronic device, including a memory for storing computer programs; The processor is used to execute computer programs to implement the steps of the above-described method for setting low-voltage ride-through parameters of wind turbine generators.

[0016] On the other hand, this application also provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the steps of the above-described method for setting low-voltage ride-through parameters of wind turbine generators.

[0017] The low-voltage ride-through parameter setting method for wind turbines provided in this application determines the influence of the active and reactive current parameters of the wind turbine on the system frequency characteristics, power angle stability characteristics, transient overvoltage characteristics, and mechanical load characteristics during the low-voltage ride-through process based on a power system transient simulation model. Under the worst-case operating condition corresponding to the transient overvoltage, the reactive current parameters are set based on the influence law and reactive power regulation law. The worst-case operating condition corresponding to the transient overvoltage is: under weak grid conditions, large-scale wind farm integration and near-voltage... The system is designed to handle three-phase short-circuit faults and the conditions under which the equivalent impedance of the power grid is maximized after the fault is cleared. A multi-objective parameter tuning model is constructed based on the influence laws under the worst operating conditions corresponding to system frequency, power angle stability margin, and mechanical load, and the active current parameters are tuned based on this model. The worst operating condition corresponding to system frequency is the one with the largest system frequency drop, the worst operating condition corresponding to power angle stability margin is the one with the lowest power angle stability margin, and the worst operating condition corresponding to mechanical load is the one with the largest impact amplitude of the wind turbine drivetrain load. Therefore, this method accurately depicts the influence of active and reactive current parameters on system frequency characteristics, power angle stability characteristics, transient overvoltage characteristics, and wind turbine mechanical load characteristics through transient simulation models. It also addresses the severe transient overvoltage conditions—such as large-scale wind farm integration under weak grids, near-end three-phase short-circuit faults with the largest equivalent grid impedance after fault clearing, and the complex severe conditions with the largest system frequency drop, lowest power angle stability margin, and largest wind turbine drivetrain load impact amplitude—by differentiating reactive current parameters and constructing a multi-objective optimization model to adjust active current parameters, effectively overcoming [the challenges of]... This solution resolves the technical contradiction of multiple objectives being mutually constrained and difficult to balance in existing low-voltage ride-through control strategies. It avoids the severe mechanical shock and critical component load degradation caused by blindly increasing the active power recovery rate to quickly mitigate frequency drops. It also suppresses the risks of transient overvoltage and power angle instability caused by fault transient reactive power support and transient processes after fault clearing under weak grid conditions. It achieves coordinated optimization and global balance of system frequency safety, power angle stability, transient voltage level and wind turbine mechanical load, significantly improving the low-voltage ride-through capability and grid adaptability of wind turbines under complex and harsh operating conditions. Attached Figure Description

[0018] To more clearly illustrate the embodiments of this application, the accompanying drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0019] Figure 1 A flowchart of a method for setting low-voltage ride-through parameters for wind turbines provided in this application; Figure 2 A schematic diagram of the power system transient simulation model provided in this application; Figure 3 The simulation structure diagram for dynamic modeling and load evaluation of key components of the wind turbine provided in this application; Figure 4 A complete flowchart of the wind turbine low-voltage ride-through parameter tuning method provided in this application; Figure 5 A structural diagram of an electronic device provided in another embodiment of this application. Detailed Implementation

[0020] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the protection scope of this application.

[0021] The core of this application is to provide a method, equipment, and medium for setting low-voltage ride-through parameters for wind turbine generators.

[0022] To enable those skilled in the art to better understand the present application, the present application will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0023] Figure 1 A flowchart of a method for setting low-voltage ride-through parameters for wind turbines provided in this application is shown below. Figure 1 As shown, it includes the following steps: S10: Based on the transient simulation model of the power system, determine the influence of the active current parameters and reactive current parameters of the wind turbine on the system frequency characteristics, power angle stability characteristics, transient overvoltage characteristics and mechanical load characteristics during the low-voltage ride-through process.

[0024] In a specific embodiment, the power system transient simulation model is based on synchronous generators and wind turbines, and its schematic diagram is shown below. Figure 2 As shown. Among them. Figure 2It includes two wind turbine units, two thermal power units, three reactors X1-X3, and one main grid, where U1-U3 are bus voltages, and P G1 and P G2 The active power delivered by the two thermal power units, P w1 and P w2 These represent the active power delivered by the two wind turbine units, respectively. and These are the power angles of the thermal power generators corresponding to the two thermal power units; P G3 The active power supplied to the main grid; The power angle of the main grid. The corresponding simulation structure diagram for the dynamic modeling and load assessment of key components of the wind turbine is shown below. Figure 3 As shown, it includes: blade model, hub model, transmission chain model, basic parameters, wind model, airfoil, result output, post-processing tools, power transient simulation model, FAST (Fatigue, Aerodynamics, Structures, and Turbulence simulation platform) and AeroDyn (Aerodynamic Dynamics module).

[0025] It is easy to understand that this application relies on a power system transient simulation model to simulate the actual low-voltage ride-through dynamic process of wind turbines. By quantitatively analyzing the changes in active current parameters and reactive current parameters, it examines the effects of these changes on the system's frequency characteristics, power angle stability characteristics, transient overvoltage characteristics, and wind turbine mechanical load characteristics. This clarifies the intrinsic relationship between each control parameter and the system stability indicators and equipment operating characteristics, providing accurate theoretical basis and data support for subsequent parameter tuning under different operating conditions.

[0026] This step, by establishing the influence of control parameters (active current parameters and reactive current parameters) and multi-dimensional operating characteristics (system frequency characteristics, power angle stability characteristics, transient overvoltage characteristics, and mechanical load characteristics) in advance, can avoid the blindness and isolation of parameter tuning, and achieve accurate grasp of the changing trends of frequency, power angle, voltage, and mechanical load during low-voltage ride-through. This lays the foundation for subsequent collaborative optimization and overcomes the shortcomings of traditional methods that rely solely on experience for tuning and are difficult to consider multiple objective constraints, thereby improving the scientificity and reliability of parameter tuning.

[0027] S11: Under the worst operating conditions corresponding to transient overvoltage, adjust the reactive current parameters based on the influence law and reactive power regulation law; among them, the worst operating conditions corresponding to transient overvoltage are: under weak grid conditions, the wind farm is connected on a large scale and a near-end three-phase short-circuit fault occurs, and the grid equivalent impedance is the largest after the fault is cleared.

[0028] In a specific embodiment, this application selects the most severe transient overvoltage condition: large-scale wind farm integration under a weak grid, a near-end three-phase short-circuit fault, and the maximum equivalent grid impedance after fault clearing. Combining the parameter influence laws obtained through previous transient simulations with the reactive power regulation laws of wind turbine low-voltage ride-through, targeted reactive current parameter tuning is carried out. This process uses extreme operating and fault scenarios most likely to induce severe overvoltage as constraints, combining parameter influence laws with reactive power voltage regulation mechanisms to achieve precise matching and setting of reactive current output amplitude, response speed, and dynamic change process.

[0029] This step employs the most stringent transient overvoltage conditions for reactive current parameter tuning, fundamentally avoiding the insufficient safety margin issues associated with conventional tuning. It effectively suppresses the risk of transient overvoltage under weak grid conditions and severe faults, ensuring that wind turbines do not experience overvoltage disconnection under extreme voltage fluctuations. Simultaneously, tuning based on established influence and regulation laws ensures that the reactive power support response meets grid guidelines while avoiding voltage distortion and excessive equipment insulation stress caused by improper reactive power injection, thus improving voltage stability and operational safety during low-voltage ride-through.

[0030] S12: Construct a multi-objective parameter tuning model based on the influence law under the worst operating conditions corresponding to system frequency, power angle stability margin, and mechanical load, and tune the active current parameters based on the multi-objective parameter tuning model; among them, the worst operating condition corresponding to system frequency is the operating condition with the largest system frequency drop, the worst operating condition corresponding to power angle stability margin is the operating condition with the lowest power angle stability margin, and the worst operating condition corresponding to mechanical load is the operating condition with the largest impact amplitude of the wind turbine drive chain load.

[0031] In a specific embodiment, multiple extreme operating conditions—the largest system frequency drop, the lowest power angle stability margin, and the largest wind turbine drivetrain load impact amplitude—are used as tuning boundary conditions. This fully incorporates the influence of active current parameters on system frequency characteristics, power angle stability, and wind turbine mechanical loads obtained from previous power system transient simulation models. The model establishes a multi-objective parameter tuning model with system frequency support performance, power angle stability, and wind turbine drivetrain load safety as core optimization objectives. The model explicitly incorporates stability thresholds and load safety limits corresponding to each extreme operating condition as constraints. Through a multi-objective optimization algorithm, key parameters such as active current are collaboratively optimized to ensure that the tuned active current parameters not only meet the system's stable requirements for rapid active power response and avoid the risk of abnormal frequency fluctuations, but also control the wind turbine drivetrain load within a safe range to prevent damage to mechanical components. Ultimately, this achieves the optimal match between system stability and equipment safety, providing scientific and rigorous technical support for subsequent parameter application and verification.

[0032] In this step, the construction of a multi-objective parameter tuning model includes the following steps: The corresponding control parameter vector is determined based on the active current parameters, and its expression is as follows: ; A multi-objective function is constructed based on the control parameter vector, the frequency deviation corresponding to the system frequency characteristics in the influence law, the power angle oscillation amplitude corresponding to the power angle stability characteristics in the influence law, the transient overvoltage level corresponding to the transient overvoltage characteristics in the influence law, and the response index corresponding to the mechanical load characteristics in the influence law. The expression of the multi-objective function is as follows: ; The corresponding multi-objective parameter tuning model is determined based on the multi-objective function.

[0033] In addition, the active current value during low-voltage pausing is as follows: ; During the fault recovery phase, the active power recovery process is as follows: ; in, For the control parameter vector; This refers to the voltage correlation coefficient during the low-voltage ride-through process of wind turbine generators. This refers to the active power inheritance coefficient during the low-voltage ride-through process of the wind turbine. This represents the minimum active current support required during the low-voltage ride-through of the wind turbine. This refers to the active power recovery rate parameter during the fault recovery phase. It is a multi-objective function; The frequency coefficients corresponding to the system's frequency characteristics; This is for frequency deviation; This refers to the angle coefficient corresponding to the angle stability characteristic; This represents the amplitude of the oscillation angle. The overvoltage coefficient corresponding to the transient overvoltage characteristics; Transient overvoltage level; This is the load factor corresponding to the mechanical load characteristics; For response indicators; This refers to the amplitude of the terminal voltage. This is the initial active current.

[0034] It is not difficult to understand that the voltage correlation coefficient Active succession coefficient Minimum active current support and active recovery rate parameters These constitute the active current parameters.

[0035] Furthermore, it can be seen that its frequency deviation The expression is: ; Power angle oscillation amplitude The expression is: ; Transient overvoltage level The expression is: ; Response indicators The expression is: ; That, This is the minimum frequency value; This is a frequency reference value; and These are the power angles of the thermal power generators corresponding to the two thermal power units; This is a function to find the maximum value. This is the maximum voltage value; This is a standard indicator.

[0036] Therefore, the core of constructing its multi-objective parameter tuning model lies in transforming the multiple requirements of system stability and wind turbine equipment safety into a quantifiable and optimizable mathematical model based on the influence law of active current parameters obtained from previous power system transient simulations. Its complete principle aligns with the entire model construction process, as follows: First, clarify the core components of the active current parameters, including the voltage correlation coefficient... Active succession coefficient Minimum active current support and active power recovery rate parameters during the fault recovery phase The parameters are integrated into a control parameter vector, which enables centralized characterization and unified management of active current parameters. This provides clear parameter objects for subsequent optimization, ensuring that the tuning process can accurately locate each key active-related parameter. Secondly, based on the influence patterns obtained through transient simulation, the frequency deviation corresponding to the system's frequency characteristics is... The amplitude of the angular oscillation corresponding to the angular stability characteristic Transient overvoltage characteristics and corresponding transient overvoltage levels Response indexes corresponding to mechanical load characteristics As the core optimization objective of the multi-objective function, a frequency coefficient is also introduced. , power angle coefficient Overvoltage coefficient and load factor This model quantifies the weight of each optimization objective, achieving a balanced fit among different objectives. It ensures the priority of system stability-related objectives (frequency, power angle) while also considering the safety constraints of the wind turbine's mechanical load, avoiding the unintended consequences of optimizing a single objective. Finally, using the control parameter vector as the optimization variable and a multi-objective function as the core optimization direction, and combining the stability thresholds and load safety limits corresponding to each worst-case operating condition (maximum system frequency drop, lowest power angle stability margin, and maximum wind turbine drivetrain load impact amplitude) as constraints, a multi-objective parameter tuning model is ultimately constructed. Essentially, this model transforms the logical closed loop of "parameter influence law - multi-objective quantification - constraint control" into a mathematical model solvable by a multi-objective optimization algorithm. This provides scientific and accurate model support for the subsequent coordinated tuning of active current parameters (including fault recovery rate parameters), ensuring that the tuning process is systematic and evidence-based.

[0037] The construction of this multi-objective parameter tuning model effectively solves the technical pain points of poor model specificity, difficulty in coordinating multiple objectives, and lack of quantitative basis for parameter tuning in existing wind turbine low-voltage ride-through parameter tuning. By decomposing the active current parameter into quantifiable control parameter vectors such as voltage correlation coefficient and active power inheritance coefficient, the core role of each parameter is clarified, avoiding the fuzzy control of traditional tuning and improving the accuracy and operability of parameter tuning. At the same time, system frequency deviation, power angle oscillation amplitude, and mechanical load response indicators are incorporated into a unified optimization framework, and multi-objective balanced optimization is achieved by combining various weight coefficients, thus overcoming the problem of focusing on system... The inherent contradiction between prioritizing system stability and neglecting equipment load safety, and prioritizing equipment protection and sacrificing system stability, is addressed by using a model that closely aligns with the influence patterns of previous parameters. By using stability and safety limits corresponding to multiple worst-case operating conditions as constraint boundaries, the model ensures strong adaptability to operating conditions and a high safety margin. It also comprehensively optimizes the active power recovery rate during fault recovery and the active current parameters during low-voltage ride-through, achieving coordinated tuning of active power parameters across all operating conditions. Furthermore, it provides a scientific and standardized parameter basis for subsequent semi-physical platform verification, comprehensively improving the operational reliability, grid adaptability, and equipment operational safety of wind turbines throughout the entire process of low-voltage ride-through and fault recovery.

[0038] In addition, the specific implementation method for tuning active current parameters based on the multi-objective parameter tuning model is as follows: A multi-objective optimization algorithm is used to solve the multi-objective parameter tuning model to obtain the optimal solution set; The appropriate active current parameters are selected from the optimal solution set according to the current operating conditions, thereby setting the active current parameters.

[0039] In a specific embodiment, this application constructs a multi-objective parameter tuning model under the most severe operating conditions, including the largest system frequency drop, the lowest power angle stability margin, and the largest impact amplitude of the wind turbine drivetrain load. This model is based on previously discovered influence patterns of active current parameters on various characteristics. First, based on the voltage correlation coefficient, active power inheritance coefficient, minimum active current support, and active power recovery rate parameters during the fault recovery phase, which constitute the active current parameters, the corresponding control parameter vector is determined. Then, combining this control parameter vector, the frequency deviation corresponding to the system frequency characteristics, the power angle oscillation amplitude corresponding to the power angle stability characteristics, and the response index corresponding to the mechanical load characteristics, a multi-objective function is constructed, thus determining the complete multi-objective parameter tuning model. Subsequently, an appropriate multi-objective optimization algorithm is used to iteratively solve the multi-objective parameter tuning model, obtaining a Pareto optimal solution set that satisfies multiple constraints. The system uses a set (Pareto optimal solution set / non-dominated solution set) and combines it with the actual operating requirements, stability threshold, and load safety limits of the current operating conditions. It then selects suitable parameters from the optimal solution set that fit the operating conditions and take into account various optimization objectives. Finally, it completes the precise tuning of active current parameters during low-voltage ride-through. This method can not only achieve coordinated quantitative optimization of system stability and wind turbine load, and ensure sufficient parameter safety margin by adhering to stringent operating conditions, but also eliminate the blindness and subjectivity of traditional experience-based tuning. It takes into account frequency stability, power angle stability, and equipment load safety, solves the problem of mutual constraints among multiple objectives, improves the flexibility of parameter adaptation to different operating conditions, and ensures the operational reliability and grid compatibility of wind turbine units throughout the low-voltage ride-through and fault recovery process.

[0040] It is not difficult to understand that the multi-objective optimization algorithm can specifically be a multi-objective particle swarm optimization algorithm or an NSGA-II genetic algorithm (Non-dominated Sorting Genetic Algorithm II) for parameter search. Therefore, its process includes: 1. Parameter initialization; 2. Transient simulation calculation; 3. Calculation of the objective function; 4. Parameter update; 5. Convergence determination, finally obtaining the Pareto optimal solution set, and selecting the final parameters according to engineering preferences.

[0041] Based on the above embodiments, as a preferred embodiment, after setting the reactive current parameters and active current parameters, the method further includes: Verification results were obtained based on the power system simulation model to determine the set reactive current parameters and active current parameters. Determine whether the verification results meet the constraints, which include: the frequency safety threshold corresponding to the system frequency characteristics, the power angle stability limit corresponding to the power angle stability characteristics, the voltage allowable range corresponding to the transient overvoltage characteristics, and the load limit corresponding to the mechanical load characteristics. If not, then reset the reactive current parameters and active current parameters.

[0042] In addition, it includes simulating various fault scenarios based on power system simulation software models, and determining the influence of set reactive current parameters and active current parameters on system frequency characteristics, power angle stability characteristics, transient overvoltage characteristics, and mechanical load characteristics based on each fault scenario; generating parameter influence characteristic datasets based on the influence characteristics datasets in order to optimize the power system simulation software models.

[0043] In a specific embodiment, this application first uses a power system simulation software model to simulate various fault scenarios, accurately determining the influence of active and reactive current parameters before and after tuning on the system frequency characteristics, power angle stability characteristics, transient overvoltage characteristics, and wind turbine mechanical load characteristics. Based on this law, a parameter influence characteristic dataset is generated to optimize and iterate the power system simulation software model. After tuning the reactive and active current parameters, the tuned parameters are verified based on the power system simulation model. The verification results are then used to determine whether they meet the constraints of frequency safety threshold, power angle stability limit, voltage allowable range, and load limit. If not, the parameters are readjusted. By forming a closed-loop optimization process, this design not only solidifies the accuracy of parameter influence patterns through multi-scenario simulations and continuously optimizes the simulation model accuracy using parameter influence characteristic datasets, improving the simulation's fit to actual operating conditions, but also eliminates issues such as substandard tuning parameters and insufficient safety margins through parameter verification and closed-loop iterative correction. This ensures that the final parameters fully comply with multiple constraints on system stability and equipment safety, further improving the accuracy and reliability of parameter tuning. Simultaneously, it achieves bidirectional optimization of the simulation model and parameter tuning, making the entire low-voltage ride-through parameter tuning method more closely aligned with actual engineering scenarios and comprehensively enhancing the ride-through stability and operational safety of wind turbines under various fault conditions.

[0044] In addition, during the parameter tuning process, the active power recovery rate can be set in segments based on the initial active power value of the wind turbine and the degree of voltage drop at the turbine terminals. Furthermore, in wind-solar-thermal power bundled transmission systems or high-proportion wind power integration systems, faults such as DC blocking and AC short circuits can be configured. After applying the tuning parameters, the frequency response curve, power angle curve, overvoltage value, and wind turbine load data can be compared to ensure that both grid safety and equipment economic requirements are met simultaneously.

[0045] In summary, the complete process of the low-voltage ride-through parameter tuning method for wind turbine units is as follows: Figure 4 As shown, it includes the following steps: S20: Establish a transient simulation model of the power system (including synchronous generators, wind turbines, power grid and load models).

[0046] S21: Determine the most unfavorable operating mode and typical fault scenarios (AC short circuit fault, DC blocking fault or heavy load condition).

[0047] S22: Setting reactive current parameters during low-voltage ride-through.

[0048] S23: Initialize the range of active current parameters during low-voltage ride-through.

[0049] S24: Perform transient simulation calculations to determine the system's frequency response, synchronous motor power angle, voltage changes, and loads on key components of the wind turbine.

[0050] S25: Construct a multi-objective parameter tuning model.

[0051] S26: Multi-objective optimization algorithm to search for parameters, using particle swarm optimization or NSGA-II genetic algorithm to iteratively optimize the parameters.

[0052] S27: Determine whether the constraints are met.

[0053] S28: If so, output the optimal low-voltage ride-through control parameter combination.

[0054] S29. If not, update the active current parameters and return to step S26.

[0055] S30: Perform parameter verification on a semi-physical real-time simulation platform to evaluate the system's frequency stability, power angle stability, voltage safety, and wind turbine load response level.

[0056] Since S20-S30 are all summaries of the above, they will not be repeated here.

[0057] Therefore, the low-voltage ride-through parameter tuning method for wind turbines provided in this application has the following advantages: 1. By reasonably increasing the active power output of wind turbines during low voltage ride-through, the short-term active power loss after a fault is reduced, and the frequency security level of small-scale power grids is significantly improved. 2. By collaboratively optimizing the active power recovery rate after a fault, the mechanical shock caused by blindly adopting a high recovery rate in pursuit of frequency safety is avoided, which significantly reduces the ultimate load of the transmission chain and improves the reliability and service life of the wind turbine. 3. The power angle stability and transient overvoltage constraints were clearly considered during the tuning process, ensuring that the optimized parameters improve frequency safety and reduce load without compromising other safe and stable operating boundaries of the system; 4. Applicable to weak power grids, small-scale power grids and new energy-dominated power systems with a high proportion of double-fed wind power.

[0058] Figure 5 A structural diagram of an electronic device provided in another embodiment of this application, such as... Figure 5 As shown, the electronic device includes: a memory 20 for storing computer programs; The processor 21 is used to execute a computer program to implement the steps of a wind turbine low-voltage ride-through parameter setting method as mentioned in the above embodiments.

[0059] The electronic devices provided in this embodiment may include, but are not limited to, smartphones, tablets, laptops, or desktop computers.

[0060] The processor 21 may include one or more processing cores, such as a quad-core processor or an octa-core processor. The processor 21 may be implemented using at least one of the following hardware forms: Digital Signal Processor (DSP), Field-Programmable Gate Array (FPGA), or Programmable Logic Array (PLA). The processor 21 may also include a main processor and a coprocessor. The main processor, also known as the Central Processing Unit (CPU), is used to process data in the wake-up state; the coprocessor is a low-power processor used to process data in the standby state. In some embodiments, the processor 21 may integrate a Graphics Processing Unit (GPU), which is responsible for rendering and drawing the content to be displayed on the screen. In some embodiments, the processor 21 may also include an Artificial Intelligence (AI) processor, which is used to handle computational operations related to machine learning.

[0061] The memory 20 may include one or more computer-readable storage media, which may be non-transitory. The memory 20 may also include high-speed random access memory and non-volatile memory, such as one or more disk storage devices or flash memory devices. In this embodiment, the memory 20 is used to store at least the following computer program 201, which, after being loaded and executed by the processor 21, is capable of implementing the relevant steps of the wind turbine low-voltage ride-through parameter tuning method disclosed in any of the foregoing embodiments. In addition, the resources stored in the memory 20 may also include an operating system 202 and data 203, and the storage method may be temporary storage or permanent storage. The operating system 202 may include Windows, Unix, Linux, etc.

[0062] In some embodiments, the electronic device may further include a display screen 22, an input / output interface 23, a communication interface 24, a power supply 25, and a communication bus 26.

[0063] Those skilled in the art will understand that Figure 5 The structures shown do not constitute a limitation on electronic devices and may include more or fewer components than those shown.

[0064] The electronic device provided in this application includes a memory and a processor. When the processor executes the program stored in the memory, it can implement the above-mentioned method for setting low-voltage ride-through parameters of wind turbine generators and has the same beneficial effects.

[0065] Finally, this application also provides an embodiment corresponding to a computer-readable storage medium. The computer-readable storage medium stores a computer program, which, when executed by a processor, implements the steps described in the above method embodiments.

[0066] It is understood that if the methods in the above embodiments are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and executes all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0067] The foregoing provides a detailed description of a method, equipment, and medium for setting low-voltage ride-through parameters for wind turbine generators. The various embodiments in the specification are described in a progressive manner, with each embodiment focusing on its differences from other embodiments. Similar or identical parts between embodiments can be referred to interchangeably. For the apparatus disclosed in the embodiments, since it corresponds to the method disclosed in the embodiments, the description is relatively simple; relevant parts can be referred to in the method section. It should be noted that those skilled in the art can make various improvements and modifications to this application without departing from the principles of this application, and these improvements and modifications also fall within the protection scope of the claims of this application.

[0068] It should also be noted that, in this specification, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

Claims

1. A method for tuning low-voltage ride-through parameters of a wind turbine generator set, characterized in that, include: Based on the transient simulation model of the power system, the influence of the active current parameters and reactive current parameters of the wind turbine on the system frequency characteristics, power angle stability characteristics, transient overvoltage characteristics and mechanical load characteristics during the low-voltage ride-through process is determined. Under the worst operating conditions corresponding to transient overvoltage, the reactive current parameters are adjusted based on the aforementioned influence law and reactive power regulation law; wherein, the worst operating conditions corresponding to transient overvoltage are: under weak grid conditions, the wind farm is connected on a large scale and a near-end three-phase short-circuit fault occurs, and the grid equivalent impedance is the largest after the fault is cleared. Based on the aforementioned influence laws, a multi-objective parameter tuning model is constructed under the worst operating conditions corresponding to system frequency, power angle stability margin, and mechanical load, respectively, and the active current parameters are tuned based on the multi-objective parameter tuning model; wherein, the worst operating condition corresponding to system frequency is the operating condition with the largest system frequency drop, the worst operating condition corresponding to power angle stability margin is the operating condition with the lowest power angle stability margin, and the worst operating condition corresponding to mechanical load is the operating condition with the largest impact amplitude of the wind turbine drive chain load.

2. The method for setting low-voltage ride-through parameters of wind turbine generators according to claim 1, characterized in that, include: Constructing the multi-objective parameter tuning model includes: The corresponding control parameter vector is determined based on the active current parameters; A multi-objective function is constructed based on the control parameter vector, the frequency deviation corresponding to the system frequency characteristics in the influence law, the power angle oscillation amplitude corresponding to the power angle stability characteristics in the influence law, the transient overvoltage level corresponding to the transient overvoltage characteristics in the influence law, and the response index corresponding to the mechanical load characteristics in the influence law. The corresponding multi-objective parameter tuning model is determined based on the multi-objective function.

3. The method for setting low-voltage ride-through parameters of wind turbine generators according to claim 2, characterized in that, The expression for the control parameter vector is: ； in, The control parameter vector; This refers to the voltage correlation coefficient during the low-voltage ride-through process of the wind turbine. The active power inheritance coefficient during the low-voltage ride-through process of the wind turbine generator; This refers to the minimum active current support during the low-voltage ride-through process of the wind turbine. The active power recovery rate parameter is defined as follows: the voltage correlation coefficient, the active power inheritance coefficient, the minimum active power current support, and the active power recovery rate parameter constitute the active power current parameter.

4. The method for setting low-voltage ride-through parameters of wind turbine generators according to claim 2, characterized in that, The expression for the multi-objective function is: ; in, For the multi-objective function; The control parameter vector; The frequency coefficients corresponding to the frequency characteristics of the system; The frequency deviation; This refers to the power angle coefficient corresponding to the power angle stability characteristic. The amplitude of the power angle oscillation; The overvoltage coefficient corresponding to the transient overvoltage characteristic; The transient overvoltage level; This refers to the load coefficient corresponding to the mechanical load characteristics; This refers to the response metric.

5. The method for setting low-voltage ride-through parameters of wind turbine generators according to claim 1, characterized in that, The active current parameters are tuned based on the multi-objective parameter tuning model, including: The multi-objective parameter tuning model is solved using a multi-objective optimization algorithm to obtain the optimal solution set; The active current parameters are selected from the optimal solution set according to the current operating conditions, thereby setting the active current parameters.

6. The method for setting low-voltage ride-through parameters of a wind turbine according to any one of claims 1-5, characterized in that, After setting the reactive current parameters and the active current parameters, the process includes: The verification results corresponding to the adjusted reactive current parameters and active current parameters are determined based on the power system simulation model. Determine whether the verification result meets the constraints; If not, then the reactive current parameters and the active current parameters are readjusted.

7. The method for setting low-voltage ride-through parameters of a wind turbine generator according to claim 6, characterized in that, Also includes: Various fault scenarios are simulated based on power system simulation software models, and the influence of setting the reactive current parameters and the active current parameters on the system frequency characteristics, power angle stability characteristics, transient overvoltage characteristics, and mechanical load characteristics is determined based on each fault scenario. A parameter influence characteristic dataset is generated based on the aforementioned influence patterns, so as to optimize the power system simulation software model according to the parameter influence characteristic dataset.

8. The method for setting low-voltage ride-through parameters of a wind turbine generator according to claim 6, characterized in that, The constraints include: the frequency safety threshold corresponding to the system frequency characteristics, the power angle stability limit corresponding to the power angle stability characteristics, the voltage allowable range corresponding to the transient overvoltage characteristics, and the load limit corresponding to the mechanical load characteristics.

9. An electronic device, characterized in that, Includes memory used to store computer programs; A processor, configured to execute the computer program to implement the steps of the wind turbine low-voltage ride-through parameter setting method as described in any one of claims 1 to 8.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, implements the steps of the wind turbine low-voltage ride-through parameter tuning method as described in any one of claims 1 to 8.

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