Method and device for subsynchronous oscillation suppression based on multi-virtual synchronous generator system

Abstract

The application relates to a method and device for suppressing subsynchronous oscillation based on a multi-virtual synchronous generator system. The method comprises the following steps: establishing a parameter collaborative optimization framework of impedance remodeling link parameters; based on the parameter collaborative optimization framework, taking the phase difference between the grid impedance and the output impedance of the virtual synchronous generator in the target frequency range as the optimization target, collaboratively optimizing the virtual inertia parameters, the virtual damping parameters and the impedance remodeling link parameters of each virtual synchronous generator to obtain an optimized parameter set; based on the optimized parameter set, calculating the minimum compensation capacity of the superconducting magnetic energy storage system required for suppressing the subsynchronous oscillation by analyzing the transient power shortage during the subsynchronous oscillation; and determining the design parameters of the superconducting magnetic energy storage magnet according to the minimum compensation capacity. The method can closely couple the system oscillation characteristic analysis and the energy storage device design to overcome the problem that the SMES design is disconnected with the SSO suppression demand of the multi-VSG system in the prior art.

Description

Technical Field

[0001] This application relates to the field of generator technology, and in particular to a method, apparatus, computer equipment, computer-readable storage medium, and computer program product for suppressing subsynchronous oscillations based on a multi-virtual synchronous generator system. Background Technology

[0002] With the high proportion of renewable energy being integrated and the widespread deployment of power electronic equipment, sub-synchronous oscillation (SSO) has become an increasingly prominent stability threat. If left unchecked, it may lead to damage to critical equipment or even trigger cascading power outages.

[0003] To enhance the grid's capacity to absorb new energy sources and improve operational stability, Virtual Synchronous Generator (VSG) technology has been widely adopted. This technology uses control algorithms to enable inverters to simulate the inertia and damping characteristics of traditional synchronous generators, thereby enhancing the system's frequency and voltage support capabilities. However, in parallel-operating systems containing multiple VSGs, the complex dynamic coupling between the control loops of each VSG and between the VSG and the grid impedance makes the system more prone to oscillations in the subsynchronous frequency band, i.e., the SSO problem in multi-VSG systems. These oscillation characteristics differ significantly from SSO in traditional single-unit or conventional power systems, making them more difficult to suppress.

[0004] Superconducting magnetic energy storage (SMES) devices are considered an effective means of providing dynamic support and suppressing power oscillations due to their advantages such as high power density, fast response speed, and flexible four-quadrant power adjustment. However, existing SMES magnet design methods and capacity configuration strategies generally do not fully consider the frequency coupling oscillation mechanism and dynamic interaction effects unique to multi-VSG systems. This may lead to insufficient adaptability, limited suppression effect, or failure to fully realize their technical advantages due to capacity and response characteristic mismatch when existing SMES are applied in multi-VSG systems, or even the introduction of additional stability risks.

[0005] Therefore, there is an urgent need for a method, device, computer equipment, computer-readable storage medium, and computer program product for suppressing subsynchronous oscillations based on a multi-virtual synchronous generator system, which can closely couple the analysis of system oscillation characteristics with the design of energy storage devices, in order to overcome the problem of the disconnect between SMES design and the SSO suppression requirements of multi-VSG systems in the existing technology. Summary of the Invention

[0006] Based on this, it is necessary to provide a method, device, computer equipment, computer-readable storage medium, and computer program product for subsynchronous oscillation suppression based on a multi-virtual synchronous generator system, which can closely couple system oscillation characteristic analysis and energy storage device design to overcome the problem of the disconnect between SMES design and SSO suppression requirements of multi-VSG systems in the prior art.

[0007] In a first aspect, this application provides a method for suppressing subsynchronous oscillations based on a multi-virtual synchronous generator system, including:

[0008] A parameter co-optimization framework for impedance reshaping link parameters is established, which is used to correlate the control parameters of a multi-virtual synchronous generator system with the design parameters of a superconducting magnetic energy storage system.

[0009] Based on the aforementioned parameter collaborative optimization framework, with the goal of minimizing the phase difference between the grid impedance and the output impedance of the virtual synchronous generator in the target frequency band, the virtual inertia parameters, virtual damping parameters, and impedance reshaping link parameters of each virtual synchronous generator in the multi-virtual synchronous generator system are collaboratively optimized to obtain the optimized parameter set.

[0010] Based on the optimized parameter set, the minimum compensation capacity of the superconducting magnetic energy storage system required to suppress subsynchronous oscillations is calculated by analyzing the transient power deficit during subsynchronous oscillations.

[0011] Based on the minimum compensation capacity, the design parameters of the superconducting magnetic energy storage magnet are determined.

[0012] In one embodiment, the parameter co-optimization framework for establishing impedance reshaping link parameters includes:

[0013] A small-signal impedance model of the multi-virtual synchronous generator system is constructed. The small-signal impedance model is used to characterize the dynamic coupling relationship between each virtual synchronous generator and its interaction with the power grid.

[0014] Based on the small-signal impedance model, the expressions for the grid-side impedance and the output impedance of each virtual synchronous generator at the point of common coupling are determined.

[0015] The objective is to minimize the sum of the phase differences between the grid-side impedance and the corresponding virtual synchronous generator output impedance at the crossover frequency within the subsynchronous oscillation band of each virtual synchronous generator. The objective function and optimization problem model of the parameter collaborative optimization framework are constructed using the virtual inertia parameter, virtual damping parameter, and impedance reshaping link parameter as optimization variables.

[0016] In one embodiment, the collaborative optimization of the virtual inertia parameters, virtual damping parameters, and impedance reshaping link parameters of each virtual synchronous generator in the multi-virtual synchronous generator system includes:

[0017] An improved snow melting optimization algorithm is used to collaboratively optimize the virtual inertia parameters, virtual damping parameters, and impedance reshaping link parameters of each virtual synchronous generator in the multi-virtual synchronous generator system.

[0018] The improved snow melting optimization algorithm is suitable for efficiently searching the multidimensional, coupled parameter space composed of the virtual inertia parameter, virtual damping parameter, and impedance reshaping link parameter to obtain the global optimal or near-optimal solution of the objective function.

[0019] In one embodiment, the step of calculating the minimum compensation capacity of the superconducting magnetic energy storage system required to suppress subsynchronous oscillations by analyzing the transient power deficit during subsynchronous oscillations, based on the optimized parameter set, includes:

[0020] Based on the optimized parameter set, the power fluctuation amplitude and oscillation frequency of the subsynchronous oscillation that the multi-virtual synchronous generator system will induce under the preset disturbance scenario are determined.

[0021] Based on the power fluctuation amplitude and oscillation frequency, calculate the transient compensation energy that needs to be supplemented externally from the occurrence of the disturbance to the first power overshoot.

[0022] The value of the transient compensation energy is determined as the minimum compensation capacity of the superconducting magnetic energy storage system.

[0023] In one embodiment, determining the design parameters of the superconducting magnetic energy storage magnet based on the minimum compensation capacity includes:

[0024] Based on the minimum compensation capacity and the preset safety margin, the target energy storage value, rated operating current and inductance value of the superconducting magnetic energy storage magnet are determined.

[0025] Using the target energy storage value and rated operating current as constraints, a genetic algorithm is used to optimize the structure of the high-temperature superconducting solenoid magnet to obtain the geometric dimensions of the magnet and the arrangement parameters of the high-temperature superconducting tape. The optimization design process takes at least one of minimizing the magnet volume, optimizing the magnetic field uniformity, or reducing the amount of tape as the optimization objective.

[0026] In one embodiment, the method further includes:

[0027] Based on the design parameters, a dynamic electrical model of the superconducting magnetic energy storage magnet is constructed, and the dynamic electrical model is integrated into the electromagnetic transient simulation platform of the multi-virtual synchronous generator system.

[0028] Multiple typical disturbance scenarios, including power command step and grid short-circuit fault, are set up, and simulations are performed on the electromagnetic transient simulation platform based on these scenarios.

[0029] Based on simulation results, the suppression effect of the superconducting magnetic energy storage system on subsynchronous oscillations is verified, and whether the design parameters ensure that the system meets the stable operation standards is verified.

[0030] Secondly, this application also provides a subsynchronous oscillation suppression device based on a multi-virtual synchronous generator system, comprising:

[0031] The framework construction module is used to establish a parameter co-optimization framework for impedance reshaping link parameters. The parameter co-optimization framework is used to associate the control parameters of a multi-virtual synchronous generator system with the design parameters of a superconducting magnetic energy storage system.

[0032] The parameter optimization module is used to perform collaborative optimization on the virtual inertia parameters, virtual damping parameters, and impedance reshaping link parameters of each virtual synchronous generator in the multi-virtual synchronous generator system based on the parameter collaborative optimization framework, with the optimization objective of minimizing the phase difference between the grid impedance and the output impedance of the virtual synchronous generator in the target frequency band, to obtain the optimized parameter set.

[0033] The capacity calculation module is used to calculate the minimum compensation capacity of the superconducting magnetic energy storage system required to suppress subsynchronous oscillations by analyzing the transient power deficit during subsynchronous oscillations based on the optimized parameter set.

[0034] The magnet design module is used to determine the design parameters of the superconducting magnetic energy storage magnet based on the minimum compensation capacity.

[0035] Thirdly, this application also provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to perform the following steps:

[0036] A parameter co-optimization framework for impedance reshaping link parameters is established, which is used to correlate the control parameters of a multi-virtual synchronous generator system with the design parameters of a superconducting magnetic energy storage system.

[0037] Based on the aforementioned parameter collaborative optimization framework, with the goal of minimizing the phase difference between the grid impedance and the output impedance of the virtual synchronous generator in the target frequency band, the virtual inertia parameters, virtual damping parameters, and impedance reshaping link parameters of each virtual synchronous generator in the multi-virtual synchronous generator system are collaboratively optimized to obtain the optimized parameter set.

[0038] Based on the optimized parameter set, the minimum compensation capacity of the superconducting magnetic energy storage system required to suppress subsynchronous oscillations is calculated by analyzing the transient power deficit during subsynchronous oscillations.

[0039] Based on the minimum compensation capacity, the design parameters of the superconducting magnetic energy storage magnet are determined.

[0040] Fourthly, this application also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, performs the following steps:

[0041] A parameter co-optimization framework for impedance reshaping link parameters is established, which is used to correlate the control parameters of a multi-virtual synchronous generator system with the design parameters of a superconducting magnetic energy storage system.

[0042] Based on the aforementioned parameter collaborative optimization framework, with the goal of minimizing the phase difference between the grid impedance and the output impedance of the virtual synchronous generator in the target frequency band, the virtual inertia parameters, virtual damping parameters, and impedance reshaping link parameters of each virtual synchronous generator in the multi-virtual synchronous generator system are collaboratively optimized to obtain the optimized parameter set.

[0043] Based on the optimized parameter set, the minimum compensation capacity of the superconducting magnetic energy storage system required to suppress subsynchronous oscillations is calculated by analyzing the transient power deficit during subsynchronous oscillations.

[0044] Based on the minimum compensation capacity, the design parameters of the superconducting magnetic energy storage magnet are determined.

[0045] Fifthly, this application also provides a computer program product, including a computer program that, when executed by a processor, performs the following steps:

[0046] A parameter co-optimization framework for impedance reshaping link parameters is established, which is used to correlate the control parameters of a multi-virtual synchronous generator system with the design parameters of a superconducting magnetic energy storage system.

[0047] Based on the aforementioned parameter collaborative optimization framework, with the goal of minimizing the phase difference between the grid impedance and the output impedance of the virtual synchronous generator in the target frequency band, the virtual inertia parameters, virtual damping parameters, and impedance reshaping link parameters of each virtual synchronous generator in the multi-virtual synchronous generator system are collaboratively optimized to obtain the optimized parameter set.

[0048] Based on the optimized parameter set, the minimum compensation capacity of the superconducting magnetic energy storage system required to suppress subsynchronous oscillations is calculated by analyzing the transient power deficit during subsynchronous oscillations.

[0049] Based on the minimum compensation capacity, the design parameters of the superconducting magnetic energy storage magnet are determined.

[0050] The aforementioned method, device, computer equipment, computer-readable storage medium, and computer program product for suppressing subsynchronous oscillations based on a multi-virtual synchronous generator system (SMES) establishes a collaborative optimization framework between the control parameters of the SMES system and the design parameters of the superconducting magnetic energy storage system. With phase matching between the grid impedance and the output impedance of the virtual synchronous generators as the objective, collaborative parameter optimization is performed. This allows for precise reshaping of impedance characteristics and pre-configuration of oscillation suppression capabilities during the system design phase. Starting from the system-level objective of suppressing subsynchronous oscillations, the collaborative optimization framework determines control parameters, analyzes transient demands, calculates the minimum compensation capacity, and ultimately derives a precise set of superconducting magnetic energy storage magnet design parameters that strictly match system requirements. This ensures that the designed SMES equipment fundamentally possesses the required power response speed and energy support capacity, avoiding the problem of overly conservative or insufficient capacity estimation in traditional designs, and achieving an optimal balance between equipment performance and economy. Attached Figure Description

[0051] To more clearly illustrate the technical solutions in the embodiments of this application or related technologies, the drawings used in the description of the embodiments of this application or related technologies will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0052] Figure 1 This is an application environment diagram of a subsynchronous oscillation suppression method based on a multi-virtual synchronous generator system in one embodiment.

[0053] Figure 2 This is a flowchart illustrating a subsynchronous oscillation suppression method based on a multi-virtual synchronous generator system in one embodiment.

[0054] Figure 3 This is a circuit topology diagram of a parameter co-optimization framework in one embodiment;

[0055] Figure 4 This is a schematic diagram of the structure of a multi-virtual synchronous generator system in one embodiment;

[0056] Figure 5 This is a block diagram of the collaborative control system structure of the SMES-battery hybrid energy storage support unit in one embodiment;

[0057] Figure 6 This is a flowchart illustrating a subsynchronous oscillation suppression method based on a multi-virtual synchronous generator system in another embodiment.

[0058] Figure 7This is a control flow diagram of a multi-virtual synchronous generator system with an impedance renormalization link in one embodiment.

[0059] Figure 8 This is a power response curve of a multi-virtual synchronous generator system in one embodiment;

[0060] Figure 9 This is a structural block diagram of a subsynchronous oscillation suppression device based on a multi-virtual synchronous generator system in one embodiment;

[0061] Figure 10 This is an internal structural diagram of a computer device in one embodiment. Detailed Implementation

[0062] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0063] It should be noted that the terms "first," "second," etc., used in this application can be used to describe various elements, but these elements are not limited by these terms. These terms are only used to distinguish the first element from the second element. The terms "comprising" and "having," and any variations thereof, used in this application, are intended to cover non-exclusive inclusion. The term "multiple" used in this application refers to two or more. The term "and / or" used in this application refers to one of the embodiments, or any combination of multiple embodiments.

[0064] The subsynchronous oscillation suppression method based on a multi-virtual synchronous generator system provided in this application can be applied to, for example... Figure 1 In the application environment shown, terminal 102 communicates with server 104 via a network. A data storage system can store the data that server 104 needs to process. The data storage system can be integrated onto server 104, or it can be located in the cloud or on another network server.

[0065] The terminal 102 can be, but is not limited to, various personal computers, laptops, smartphones, tablets, IoT devices, and portable wearable devices. IoT devices can include smart speakers, smart TVs, smart air conditioners, smart in-vehicle systems, and projection devices. Portable wearable devices can include smartwatches, smart bracelets, and head-mounted displays. Head-mounted displays can be virtual reality (VR) devices, augmented reality (AR) devices, and smart glasses. The server 104 can be a standalone physical server, a server cluster or distributed system composed of multiple physical servers, or a cloud server providing cloud computing services.

[0066] In one exemplary embodiment, such as Figure 2 As shown, a method for suppressing subsynchronous oscillations based on a multi-virtual synchronous generator system is provided, and this method is applied to... Figure 1 Taking the server in the example, the explanation includes the following steps S202 to S206. Wherein:

[0067] Step S202: Establish a parameter co-optimization framework for impedance reshaping link parameters. The parameter co-optimization framework is used to correlate the control parameters of the multi-virtual synchronous generator system with the design parameters of the superconducting magnetic energy storage system.

[0068] Specifically, a parameter co-optimization framework for impedance reshaping link parameters is constructed. This framework aims to establish the intrinsic correlation and co-design relationship between the control parameters of the multi-VSG system (virtual synchronous generator system) and the design parameters of the SMES system (superconducting magnetic energy storage system).

[0069] Specifically, the parameter co-optimization framework defines the virtual inertia parameters, virtual damping parameters, and impedance reshaping link parameters (e.g., filter parameters and phase compensation parameters in the additional damping controller) of each VSG unit in a multi-VSG system as the set of control parameters to be optimized. Simultaneously, it defines the key design parameters of the magnet in the SMES system (such as rated energy storage capacity, rated power, and inductance value) as the associated set of equipment parameters to be determined. This framework, through a unified mathematical model and optimization objective, incorporates these two types of parameters belonging to different subsystems (control domain and equipment domain) into the same optimization problem for co-solution. This breaks the limitation of independent control parameter tuning and energy storage device selection in traditional design, achieving a comprehensive and integrated design from system-level impedance characteristic objectives to specific control and equipment parameters.

[0070] like Figure 3 As shown, Figure 3 The circuit topology of the parameter collaborative optimization framework is shown below: Multiple virtual synchronous generators (VSGs) are connected in parallel to the main circuit. Each VSG interface is equipped with an LC filter, and the inverter is connected to a shared connection point via independent transmission lines. To achieve collaborative support between the SMES and the battery, this invention integrates an H-bridge DC chopper and a bidirectional DC chopper to connect the SMES magnet and the battery cell, forming an SMES-battery support unit.

[0071] The DC voltage at the inverter terminals is denoted as u. dcVoltage stabilization control is required to ensure stability, thereby guaranteeing the effective grid connection of the VSG control scheme with inertial and damping characteristics. The specific configuration is as follows: a SMES magnet and a lithium-ion battery are connected in parallel to each VSG control inverter, and this connection path is equipped with two DC choppers; to reduce conduction losses, MOSFETs are used instead of traditional power diodes in the DC choppers.

[0072] Figure 4 This is a schematic diagram of a multi-virtual synchronous generator system. The grid impedance is represented by Zg, and the currents contributed by VSG1, VSG2, and VSGn are represented by i1, i2, and in, respectively.

[0073] Figure 5 This is a block diagram of the collaborative control system for the SMES-battery hybrid energy storage support unit. The system employs a hierarchical control strategy to achieve efficient and coordinated management of short-term power transients in the power system.

[0074] Specifically, the control system mainly includes the following components:

[0075] 1. Voltage outer loop (DC bus voltage stabilization loop):

[0076] Input: Actual value of DC bus voltage u dc Its reference value u dc_ref The deviation.

[0077] Controller: The deviation signal is processed by a proportional-integral (PI) controller. The transfer function of the PI controller is k... smp +k smi / S, where k smp k is the proportionality coefficient. smi Let S be the integral coefficient, and S be the Laplace operator.

[0078] Output: The output of this PI controller, superimposed with the DC bus capacitor energy storage term Cu. 2 dc The voltage inertia effect, represented by / 2, together generate the reference value P of the active power required for the output of the superconducting magnetic energy storage (SMES) unit. SMES The fundamental goal of this loop is to maintain the stability of the DC bus voltage.

[0079] 2. Power Inner Loop (SMES Current / Power Tracking Loop):

[0080] Input: mainly consists of two parts:

[0081] Control law and battery coordination: First, the current output power P from the battery cell. bat After passing through a feedforward channel (gain of 1 / U) b U b(Based on the reference voltage) is processed. Meanwhile, P SMES The deviation from its feedback value (not explicitly shown in the block diagram, but implicit within the system) is controlled by another PI controller (with parameter k). bp k bi The feedforward is added to the output of the PI controller.

[0082] Output: The final output of the power inner loop is the current reference value i of the SMES inductor. L This design enables SMES to respond quickly to power commands, while ensuring tracking accuracy through PI regulation and incorporating battery power information for initial power allocation coordination.

[0083] In summary, this control structure clearly reveals the mechanism by which the SMES assumes the role of short-term, rapid power support. The voltage loop senses voltage disturbances at the system level and generates overall power compensation commands; the power loop coordinates the power distribution between the SMES and the battery, and ensures that the SMES accurately tracks the commands; finally, execution is achieved through a high-frequency chopper, enabling the SMES to effectively manage power transients at the second or even millisecond level, thereby supporting the DC bus voltage and laying the foundation for stable interaction between the entire hybrid energy storage system and the AC grid.

[0084] Step S204: Based on the parameter collaborative optimization framework, with the optimization objective of minimizing the phase difference between the grid impedance and the output impedance of the virtual synchronous generator in the target frequency band, the virtual inertia parameters, virtual damping parameters, and impedance reshaping link parameters of each virtual synchronous generator in the multi-virtual synchronous generator system are collaboratively optimized to obtain the optimized parameter set.

[0085] Specifically, under the constraints of the parameter co-optimization framework, the virtual inertia parameter (J), virtual damping parameter (D), and impedance reshaping link parameters (such as the resistance value R of the additional virtual impedance link) of each virtual synchronous generator in the multi-virtual synchronous generator system are optimized. vir Inductance value L vir The optimization process involves iteratively optimizing the control parameters (including the control compensator parameters Kc(s)). The goal is to adjust these control parameters so that the phase characteristics of the equivalent output impedance ZVSG(s) of the multi-VSG cluster are as consistent as possible with the phase characteristics of the grid's equivalent impedance Zgrid(s) within a specified subsynchronous frequency band, thus minimizing the global phase difference. Solving this optimization problem yields an optimized set of parameters, including coordinated virtual inertia, virtual damping, and impedance reshaping link parameters for all VSG units. This allows for the pre-shaping of equivalent impedance characteristics at the control level that are beneficial for suppressing oscillations in specific frequency bands.

[0086] Step S206: Based on the optimized parameter set, the minimum compensation capacity of the superconducting magnetic energy storage system required to suppress the subsynchronous oscillation is calculated by analyzing the transient power deficit during the subsynchronous oscillation.

[0087] Specifically, after obtaining the optimized set of virtual inertia, virtual damping, and impedance reshaping link parameters, these are substituted into the complete mathematical model of the multi-virtual synchronous generator system for time-domain or frequency-domain simulation analysis. The focus is on simulating the transient processes exhibited by the system under typical subsynchronous oscillation disturbance scenarios (such as torsional vibration excitation of a specific mode or subsynchronous voltage disturbance on the grid side). By analyzing the instantaneous power balance relationship required for the system to maintain stability during this process, the transient power deficit ΔP caused by oscillations and not completely offset by the conventional generator and the optimized VSG control system is precisely quantified. SSO (t). This power deficit characterizes the real-time compensation power required from external equipment to completely suppress or rapidly dampen this synchronous oscillation.

[0088] Subsequently, with the constraint of effectively suppressing oscillations, the minimum compensation capacity of the superconducting magnetic energy storage system required to meet this power compensation demand is calculated. This calculation typically involves two aspects: first, based on the maximum amplitude of the transient power deficit max(|ΔP) SSO The minimum instantaneous power capacity (peak power) required by the SMES unit is determined by analyzing the power deficit curve over a certain time window or by analyzing the maximum energy throughput demand within the oscillation period. This determination of the minimum compensation capacity ensures that the SMES system has the ability to quell the target subsynchronous oscillation from both power and energy perspectives, while avoiding over-configuration of equipment.

[0089] Step S208: Determine the design parameters of the superconducting magnetic energy storage magnet based on the minimum compensation capacity.

[0090] Specifically, the minimum compensation capacity (including instantaneous peak power capacity P) necessary to effectively suppress the target subsynchronous oscillation. SMES With total energy storage capacity E SMES This allows for the reverse derivation and determination of the core design parameters of the SMES magnet, thereby completing the final mapping from system stability requirements to physical equipment specifications.

[0091] Therefore, E SMES and P SMES This can be deduced from the following formula:

[0092] ;

[0093] In the formula, L SC and I SMES This represents the inductance and current of a magnet. Pref-SMES This indicates the reference power of SMES.

[0094] The specific determination process is as follows:

[0095] 1. Determine magnet current parameters based on power capacity requirements:

[0096] Instantaneous peak power capacity P in minimum compensation capacity SMES The rated voltage U on the DC side of the SMES converter dc Together, they determine the maximum operating current I that the magnet needs to provide during transient power compensation. max Their relationship satisfies P SMES ≈U dc ×I max .

[0097] To ensure the safety and reliability of the superconducting magnet under repeated charge-discharge conditions, a reasonable safety margin needs to be set. Based on this, the rated operating current I of the magnet is determined. rated This serves as the benchmark for the steady-state design of the magnet. rated and the corresponding critical current I c Requirements (must meet I) rated <I c This directly constrains the selection of conductors, cross-sectional area, and critical current density specifications of superconducting coils.

[0098] 2. Determine the magnet inductance parameters based on energy storage capacity requirements:

[0099] Total energy storage capacity E in minimum compensation capacity SMES This reflects the maximum magnetic energy stored or released for damped oscillations. For a SMES magnet with an inductance of L, the stored magnetic energy is E. SMES =LI 2 / 2.

[0100] With the determined rated operating current I rated Based on this, in order to meet the energy demand E SMES The required minimum inductance value L of the magnet min It can be achieved through formula L min =2E SMES / I 2 rated The inductance value L is calculated to be one of the key parameters in magnet design, fundamentally affecting the number of turns, geometry, and magnetic field distribution of the coil.

[0101] 3. Comprehensive derivation of the overall design parameters of the magnet:

[0102] Based on the rated current I determined above ratedWith the inductance value L, a series of specific design parameters for the SMES magnet can be further calculated and determined. These parameters mainly include:

[0103] Coil structural parameters, such as the total number of turns N, the average radius R of the coil, and the axial height h, have a definite physical relationship with the inductance value L (for example, for a solenoid coil, L and N are related). 2 (Related to geometric dimensions such as R).

[0104] Electromagnetic performance parameters, such as the central magnetic flux density B0 and the maximum magnetic field strength, can be evaluated using electromagnetic field calculation formulas such as the Biot-Savart law.

[0105] Superconducting material parameters: Based on the operating current and magnetic field environment, the total length of the required superconducting tape, interlayer insulation requirements, and preliminary thermal load estimates of the cryogenic cooling system are finally determined.

[0106] The aforementioned subsynchronous oscillation suppression method based on a multi-virtual synchronous generator system establishes a collaborative optimization framework for the control parameters of the multi-virtual synchronous generator system and the design parameters of the superconducting magnetic energy storage system. With phase matching between the grid impedance and the output impedance of the virtual synchronous generators as the objective, parameter collaborative optimization is performed. This allows for precise reshaping of impedance characteristics and pre-configuration of oscillation suppression capabilities during the system design phase. Starting from the system-level objective of suppressing subsynchronous oscillations, the collaborative optimization framework determines control parameters, analyzes transient demands, calculates the minimum compensation capacity, and ultimately derives a precise set of superconducting magnetic energy storage magnet design parameters that strictly match system requirements. This ensures that the designed SMES equipment fundamentally possesses the required power response speed and energy support capacity, avoiding the problem of overly conservative or insufficient capacity estimation in traditional designs, and achieving an optimal balance between equipment performance and economy.

[0107] In one embodiment, such as Figure 6 As shown, a parameter co-optimization framework for impedance reshaping link parameters is established, including:

[0108] Step S602: Construct a small-signal impedance model for the multi-virtual synchronous generator system. The small-signal impedance model is used to characterize the dynamic coupling relationship between each virtual synchronous generator and its interaction with the power grid.

[0109] Step S604: Based on the small-signal impedance model, determine the expressions for the grid-side impedance and the output impedance of each virtual synchronous generator at the point of common coupling.

[0110] Step S606: The objective is to minimize the sum of the phase differences between the grid-side impedance and the corresponding virtual synchronous generator-side output impedance at the crossover frequency within the subsynchronous oscillation frequency band for each virtual synchronous generator. The objective function and optimization problem model of the parameter collaborative optimization framework are constructed using virtual inertia parameters, virtual damping parameters, and impedance reshaping link parameters as optimization variables.

[0111] Specifically, the small-signal impedance model refers to a mathematical modeling method used to analyze the dynamic stability of a system, especially its frequency domain stability (such as subsynchronous oscillations and harmonic resonances). Through the small-signal impedance model, it is possible to clearly see how the control loops of each VSG interact (dynamic coupling) in a complex multi-VSG parallel system, and how the entire VSG cluster interacts with the grid impedance as a whole.

[0112] The optimization objective is to reshape the impedance perception of the system at the point of common coupling (PCC), thus requiring the separate quantification of the grid-side impedance and the impedance from the VSG cluster. Based on the established small-signal impedance model, and using circuit or control theory (such as output impedance calculation methods), mathematical expressions for the grid-side equivalent impedance Zgrid(s) and the output impedance ZVSG_i(s) of the i-th VSG at the PCC are derived. These two expressions are the direct inputs for subsequent calculations of the phase difference. They condense the complex system dynamics into two transfer functions that can be analyzed in the frequency domain (let s = jω), enabling the optimization objective to be calculated accurately.

[0113] Next, the optimization is explicitly limited to the subsynchronous oscillation frequency band (e.g., 5Hz-45Hz) to ensure its targeted nature. The crossover frequencies of each VSG impedance and the mains impedance (i.e., the frequency points where the absolute values ​​of the impedance amplitudes are equal) are selected as the key evaluation points for optimization. This is because the system stability is most sensitive to phase relationships at these frequency points.

[0114] By minimizing the sum of the phase differences between the output impedance and the grid impedance of all VSGs at their respective cross frequencies, the equivalent output impedance of the entire VSG cluster is made to be as close as possible to the phase of the grid impedance at key frequency points, thereby maximizing the phase margin of the system and fundamentally weakening the phase conditions for the formation of subsynchronous oscillations.

[0115] The virtual inertia parameters (J) of each VSG i Virtual damping parameters (D) i ) and impedance reshaping link parameters added to achieve impedance reshaping (such as the additional virtual impedance R) i L i (etc.) are used as decision variables to be optimized.

[0116] By combining the above objectives with variables and considering the physical constraints of the parameters (such as J>0, D>0), a complete mathematical model of parameter co-optimization with clear engineering significance is constructed.

[0117] After small-signal linearization of the system, its dynamic behavior in the subsynchronous frequency band can be considered as a linear time-invariant system. According to the superposition principle, the system's total oscillatory response (such as power or voltage fluctuations at the point of common coupling) can be considered as a linear superposition of the responses generated by each independent excitation source (here, this can be understood as the equivalent oscillatory power source generated by each VSG due to its control dynamics). The power matrix according to the superposition principle is:

[0118] ;

[0119] In the formula, n represents the number of VSGs. N i (s) = ΔP ref，i / (2J i s+D i J i and D i The inertial and damping parameters of the i-th VSG are shown, ω g Let ΔPi represent the gate angular frequency, ΔPi represent the small-signal disturbance (i.e., a small change relative to the steady-state value) of the active power output of the i-th VSG, and Ni(s) represent the equivalent transfer function of the virtual synchronous rotor motion equation of the i-th VSG in the frequency domain (s-domain). ref，i The disturbance to the active power reference value of the VSG can be considered as a control input or external disturbance, and s represents the Laplace operator, which is a complex frequency domain variable.

[0120] Small-signal impedance model A ii (s),A ij (s),B i (s) will be:

[0121] ;

[0122] ;

[0123] ;

[0124] In the formula, K g K represents the rigidity of the power grid. g =U g U pcc cosδ g0 / X g δ g0 Labeled as voltage U g and U pcc The phase angle difference, G piY represents the transfer function associated with the power control loop of the VSG. i (s),Y j (s),Y k (s) represent the equivalent output admittances of the i-th, j-th, and k-th VSGs as seen from their own output ports towards the power grid, respectively. A ii (s) represents the self-coupling coefficient or self-interaction coefficient of the i-th VSG, A ij (s) represents the mutual coupling coefficient or interaction coefficient between the j-th VSG and the i-th VSG, B i (s) describes how frequency fluctuations in the grid background affect the power output of each VSG, and ω0 represents the system's rated angular frequency (e.g., 2π × 50 rad / s).

[0125] Based on the impedance analysis in the above equation, when a change in the power reference acts as a disturbance, the i-th VSG will cause voltage fluctuations at the point of common coupling (PCC). This voltage disturbance will then trigger other VSGs, leading to dynamic coupling oscillations within the system. Therefore, reducing the impedance phase difference is crucial. To achieve SSO suppression, an optimal objective function is created for multi-VSG systems, aiming to obtain the minimum impedance phase difference:

[0126] ;

[0127] Where ω c J represents the crossover frequency when the positive sequence impedance of the i-th VSG equals the gate impedance. min J max D min D max ,T 1min ,T 1max ,T 2min ,T 2max T represents the lower and upper limits of the inertial parameter, damping parameter, and impedance renormalization link parameter, respectively. i1 and T i2 This represents the parameters of the impedance renormalization link associated with the i-th VSG.

[0128] Please explain the formula for Z. gi The crossover frequency ω corresponding to i VSGs c The calculated grid-side impedance, Z vpi This represents the equivalent positive-sequence output impedance of the unit as viewed from the i-th virtual synchronous generator at the point of common coupling (PCC), at the crossover frequency ω. c Complex values ​​at the location.

[0129] Figure 7The VSG control flow diagram for the impedance renormalization link shows that the VSG control simulates the external characteristics of a synchronous generator, including active power / frequency regulation driven by the speed governor and reactive voltage regulation driven by the excitation. To mitigate subsynchronous oscillations caused by grid-VSG impedance mismatch, the impedance renormalization link (consisting of time constants T1 and T2) is incorporated into the speed governor regulation loop. In this respect, T1 and T2 form a first-order lead-lag compensator (T... 1s +1) / T 2s +1), used to adjust the phase characteristics of the output impedance of the VSG at the subsynchronous frequency.

[0130] In this embodiment, a small-signal impedance model is constructed to accurately characterize the dynamic coupling and grid-source interaction characteristics within the system. Based on this model, quantitatively analyzable output impedance expressions for the grid side and each virtual synchronous generator side are derived. Finally, a mathematical model is constructed with impedance phase matching within the key frequency band as the core objective and core control parameters as optimization variables. Thus, in the system design phase, the stability requirement for suppressing subsynchronous oscillations is transformed into a computable and solvable parameter co-optimization problem.

[0131] In one embodiment, the virtual inertia parameters, virtual damping parameters, and impedance reshaping link parameters of each virtual synchronous generator in the multi-virtual synchronous generator system are collaboratively optimized, including:

[0132] An improved snow melting optimization algorithm is used to collaboratively optimize the virtual inertia parameters, virtual damping parameters, and impedance reshaping link parameters of each virtual synchronous generator in a multi-virtual synchronous generator system.

[0133] Among them, the improved snow melting optimization algorithm is suitable for efficiently searching the multidimensional, coupled parameter space composed of virtual inertia parameters, virtual damping parameters, and impedance reshaping link parameters to obtain the global optimal or near-optimal solution of the objective function.

[0134] Specifically, the improved Snow Melting Optimization (ISAO) algorithm is used to collaboratively optimize the virtual inertia parameters, virtual damping parameters, and impedance reshaping link parameters of each virtual synchronous generator in the multi-virtual synchronous generator system. ISAO is derived from its simplified ergonomic model and calculation process. The dual-population balancing strategy can further enhance the robustness and convergence speed of the algorithm, making it exhibit stable and efficient performance in index weight optimization.

[0135] By optimizing key VSG variables (J, D) and impedance reshaping link parameters (T1, T2), and then deriving the minimum SMES capacity based on these optimized parameters, a quantitative correlation between SSO suppression and SMES design was established. The virtual inertia J and damping D of the VSG directly determine the frequency / amplitude and attenuation rate of SSO, respectively. Time constants T1 and T2 reshape the VSG impedance phase at critical frequencies (e.g., crossover frequency ωc) to mitigate SSO caused by impedance mismatch. These variables are closely coupled with SMES design: the larger J is, the greater the peak power deviation, requiring SMES compensation, while the higher D is, the faster the SMES response. Using ISAO, the VSG parameters (J, D, T1, T2) are optimized to minimize the sum of the grid-VSG impedance phase difference at ωc. Then, the minimum SMES capacity is derived through transient power imbalance integration to balance the SSO suppression effect and the practicality of SMES.

[0136] Figure 8 The diagram shows the power response of a multi-virtual synchronous generator (VSG) system, where a 10kW power increase is applied to VSG1 at t=2s. The improved Snow Melting Optimization Algorithm (ISAO) simultaneously optimizes four parameters (virtual inertia J, damping coefficient D, impedance reconfiguration time constants T1 and T2) for each VSG, aiming to minimize the sum of the phase differences between the grid and VSG impedances at the subsynchronous frequency. Therefore, the virtual inertia J is not solely determined by the power reference value but must work in conjunction with the damping coefficient D and the impedance reconfiguration time constants T1 and T2 to achieve system-level subsynchronous oscillation (SSO) suppression. For example, VSG1 (power reference value P...) ref1 =70kW) with a large virtual inertia J=0.045kg / m 2 This is combined with a high damping coefficient D=32.95 and a short impedance reconfiguration time T1=16.4s. This parameter combination, through enhanced damping and targeted impedance reconfiguration, suppresses SSO while avoiding excessive frequency deviation. VSG2(P ref2 =60kW) and VSG3 (P ref3 =55kW) has a similar power reference value, but its virtual inertia J value (0.03kg / m) 2 vs 0.031kg / m 2 There are slight differences. This is because VSG2 is connected to a power grid section with slightly higher line impedance, so the virtual inertia J needs to work in conjunction with a longer impedance reconfiguration time T1=19.5s to alleviate impedance mismatch; in contrast, VSG3 uses a shorter reconfiguration time T2=3.4s and a slightly larger virtual inertia J to balance damping and impedance matching.

[0137] Data shows that the impedance reconstruction method can effectively suppress subsynchronous oscillations (SSO) in multi-virtual synchronous generator (multi-VSG) systems during power fluctuations. Specifically, without suppression, VSG1 exhibits an 8Hz subsynchronous oscillation with a power oscillation amplitude of 92kW.

[0138] In this embodiment, the improved snow melting optimization algorithm is designed for efficient global search of a multidimensional, nonlinear, and strongly coupled parameter space composed of virtual inertia parameters, virtual damping parameters, and impedance reshaping link parameters. By simulating the sublimation and melting process of snow in nature, the algorithm constructs an iterative search mechanism with exploration and development capabilities, effectively addressing the problem that traditional optimization methods are prone to getting trapped in local optima when solving such high-dimensional, non-convex optimization problems. During the optimization process, each candidate solution represents a specific set of parameter combinations (i.e., the values ​​of Ji, Di, Ti1, and Ti2 for each set). The algorithm evaluates the corresponding objective function value (i.e., the sum of impedance phase differences FF) and iterates continuously within the parameter constraints according to the improved update rule, ultimately obtaining the set of optimized parameters that makes the objective function globally optimal or nearly optimal. This improved algorithm enhances the convergence speed and optimization accuracy in complex solution spaces, thereby ensuring that the collaborative optimization framework can reliably and efficiently obtain the desired optimal parameter configuration.

[0139] In one embodiment, based on the optimized parameter set, the minimum compensation capacity of the superconducting magnetic energy storage system required to suppress subsynchronous oscillations is calculated by analyzing the transient power deficit during subsynchronous oscillations, including:

[0140] Based on the optimized parameter set, the power fluctuation amplitude and oscillation frequency of the subsynchronous oscillations that will be caused by the multi-virtual synchronous generator system under the preset disturbance scenario are determined.

[0141] Based on the power fluctuation amplitude and oscillation frequency, calculate the transient compensation energy that needs to be supplemented externally from the occurrence of the disturbance to the first power overshoot.

[0142] The value of the transient compensation energy is determined as the minimum compensation capacity of the superconducting magnetic energy storage system.

[0143] Specifically, firstly, the optimized parameter set is substituted into the complete simulation model of the multi-virtual synchronous generator system, and typical subsynchronous oscillation disturbance scenarios (such as torsional vibration excitation of a specific mode) are set. Through time-domain simulation or model-based eigenvalue analysis, the steady-state power fluctuation amplitude and the dominant oscillation frequency f of the subsynchronous oscillations that the system will induce are accurately determined. sso This step quantifies the abstract oscillation risk into a concrete, computable time-domain power fluctuation signal.

[0144] Subsequently, based on the aforementioned power fluctuation characteristics, the total energy instantaneously provided by external equipment to smooth out the oscillation and maintain system power balance within the time window from the moment the disturbance occurs to the first occurrence of the system power response's maximum value (i.e., the first power overshoot) is calculated. This energy is the transient compensation energy. The calculation is based on the following: at the initial stage of the oscillation, the system cannot immediately provide sufficient damping power due to inertia and control lag, at which point the power deficit is most significant; by integrating the power deficit curve within this time period, the critical energy value that must be rapidly injected or absorbed by the energy storage device can be obtained.

[0145] Ultimately, the calculated transient compensation energy value is directly determined as the minimum compensation capacity (i.e., its rated energy storage capacity) required by the superconducting magnetic energy storage system. This method starts from the actual transient process of suppressing oscillations, extracts the most demanding instantaneous energy requirement as the benchmark for capacity design, thus theoretically ensuring that the SMES has the minimum energy reserve to quell this synchronous oscillation, while avoiding capacity redundancy or insufficiency caused by experience or rough estimation.

[0146] The power fluctuation amplitude ΔP caused by SSO SSO =15 kW (15% of 100kW, S n The transient compensation energy required by the SMES (i.e., the rated apparent power of the VSG) is derived from the following formula:

[0147] ;

[0148] In the formula, , K is the power coefficient; ω g ω is the gate-side rated angular frequency; n It is the natural frequency that controls the oscillation frequency. From the above formula, the correlation between the transient energy compensated by SMES and the core characteristics (J, D) of VSG during underdamped oscillation can be quantified.

[0149] In this embodiment, by accurately simulating and quantifying the transient power fluctuation characteristics of subsynchronous oscillations based on the co-optimized control parameters, and then calculating the minimum compensation capacity based on the actual energy deficit during the first power overshoot, the precise and minimized design of the superconducting magnetic energy storage system capacity is achieved. Starting from the physical essence of oscillation suppression, this ensures that the determined capacity not only meets the energy requirements for quickly calming specific oscillation modes but also avoids capacity redundancy caused by conservative empirical estimations. Thus, while ensuring the transient stability of the system, the economic efficiency of energy storage device configuration is significantly improved.

[0150] In one embodiment, the design parameters of the superconducting magnetic energy storage magnet are determined based on the minimum compensation capacity, including:

[0151] Based on the minimum compensation capacity and the preset safety margin, the target energy storage value, rated operating current and inductance value of the superconducting magnetic energy storage magnet are determined.

[0152] Using the target energy storage value and rated operating current as constraints, a genetic algorithm is used to optimize the structure of the high-temperature superconducting solenoid magnet in order to obtain the geometric dimension parameters of the magnet and the arrangement parameters of the high-temperature superconducting tape. The optimization design process takes at least one of minimizing the magnet volume, optimizing the magnetic field uniformity, or reducing the amount of tape as the optimization objective.

[0153] Specifically, firstly, a mapping calculation is performed from system requirements to magnet electrical parameters. This is based on the minimum compensation capacity E. min The target energy storage value E of the SMES magnet is determined by incorporating the pre-set safety margin of the project. target Subsequently, based on the DC-side voltage level of the SMES converter, the critical current characteristics of the superconducting tape, and the system dynamic response requirements, the rated operating current I of the magnet was comprehensively determined. rated Based on the magnetic energy formula E target =LI 2 rated / 2, and by reverse calculation, the required inductance value L of the magnet can be derived, thus completing the process from energy demand to the core electrical parameter I. rated The precise quantification of L).

[0154] Secondly, a magnet physical structure design based on multi-objective optimization was carried out. This was based on the aforementioned electrical parameters (E... target I rated Using rigid constraints (L, ...), a genetic algorithm is employed to automatically optimize the physical structure of a high-temperature superconducting solenoid magnet. The optimization process uses the magnet's geometric dimensions (such as coil inner diameter, outer diameter, and height) and the arrangement parameters of the high-temperature superconducting tape (such as number of turns, number of layers, and winding method) as optimization variables, with at least one of the following optimization objectives: minimizing magnet volume, optimizing magnetic field uniformity within the working area, or reducing the total amount of high-temperature superconducting tape used. Through iterative search using the genetic algorithm, a magnet design scheme satisfying one or more physical / economic optimal objectives is found, while meeting all electrical and electromagnetic performance constraints. This ultimately determines a complete and optimized set of magnet design parameters.

[0155] In this embodiment, the minimum compensation capacity requirement at the system level is precisely mapped to the core electrical parameters of the magnet (target energy storage value, rated current, and inductance value). These parameters serve as constraints to drive multi-objective structural optimization based on a genetic algorithm, achieving quantification, automation, and optimization of the superconducting magnetic energy storage magnet design from performance requirements to physical structure. This ensures that the magnet possesses the energy storage and rapid throughput capabilities to precisely match oscillation suppression requirements. Through multi-objective optimization, while meeting electrical performance requirements, the magnet volume is minimized, the magnetic field distribution is optimized, and the use of expensive high-temperature superconducting materials is reduced simultaneously. This significantly improves the power density, economy, and engineering feasibility of the magnet itself while ensuring the system's transient stability and reliability.

[0156] In one embodiment, the method further includes:

[0157] Based on the design parameters, a dynamic electrical model of the superconducting magnetic energy storage magnet is constructed, and the dynamic electrical model is integrated into the electromagnetic transient simulation platform of a multi-virtual synchronous generator system.

[0158] Various typical disturbance scenarios, including power command step and grid short-circuit fault, are set up, and simulations are performed on the electromagnetic transient simulation platform based on these scenarios.

[0159] Based on simulation results, the suppression effect of the superconducting magnetic energy storage system on subsynchronous oscillations is verified, and the design parameters are checked to ensure that the system meets the stable operation standards.

[0160] Specifically, firstly, a simulation model is constructed and integrated. Based on the design parameters of the superconducting magnetic energy storage magnet determined in the aforementioned steps (such as inductance, rated current, and geometric dimensions), a dynamic electrical model of the superconducting magnetic energy storage magnet that accurately reflects its electromagnetic dynamics and thermodynamic constraints is established. Subsequently, this high-precision magnet model is integrated with the converter control model to form a complete SMES system model, which is then integrated into an electromagnetic transient simulation platform that includes multiple VSG systems, power grids, and loads, constituting a closed-loop digital simulation system that can be used for large-signal transient studies.

[0161] Secondly, simulation tests were set up and executed. A series of typical disturbance scenarios covering varying degrees of severity were set up in the simulation platform. These scenarios included at least: power command step disturbances simulating load switching or command changes, and grid short-circuit faults simulating grid asymmetric faults. By sequentially running these scenarios in electromagnetic transient simulation, the dynamic response of the multi-VSG-SMES hybrid system was comprehensively examined after the control parameters were co-optimized and the SMES devices were connected according to the design parameters.

[0162] Finally, the system performance is analyzed and verified. Based on the simulation results, the actual suppression effect of the superconducting magnetic energy storage system on subsynchronous oscillations is quantitatively verified, for example, by analyzing key indicators such as the attenuation rate of oscillation power and the amplitude suppression ratio. At the same time, it is verified whether the dynamic behavior of the entire system (such as frequency deviation, voltage recovery, and transient overshoot) under this configuration meets the preset stable operation standards (e.g., conforming to the stability requirements specified in relevant IEC or IEEE standards).

[0163] In this embodiment, by constructing a high-precision dynamic model of the magnet and integrating it into a system-level electromagnetic transient simulation platform, comprehensive closed-loop simulation tests are conducted, covering multiple typical disturbance scenarios such as power step disturbances and grid short circuits. This not only quantitatively verifies the actual suppression effect of the superconducting magnetic energy storage system on subsynchronous oscillations, but also rigorously verifies whether the overall system, after collaborative optimization and precise design, meets the preset stable operation standards. This process achieves a closed-loop process from parameter design and capacity determination to performance verification, providing sufficient digital simulation evidence for the effectiveness, reliability, and engineering applicability of the design scheme. This minimizes technical risks before system commissioning and ensures the stable expected outcome of the final engineering implementation.

[0164] It should be understood that although the steps in the flowcharts of the embodiments described above are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the embodiments described above may include multiple steps or multiple stages. These steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the steps or stages of other steps.

[0165] Based on the same inventive concept, this application also provides a subsynchronous oscillation suppression device for implementing the above-described subsynchronous oscillation suppression method for a multi-virtual synchronous generator system. The solution provided by this device is similar to the implementation described in the above-described method. Therefore, the specific limitations of one or more embodiments of the subsynchronous oscillation suppression device for a multi-virtual synchronous generator system provided below can be found in the limitations of the subsynchronous oscillation suppression method for a multi-virtual synchronous generator system described above, and will not be repeated here.

[0166] In one exemplary embodiment, such as Figure 9 As shown, a subsynchronous oscillation suppression device based on a multi-virtual synchronous generator system is provided, comprising:

[0167] Framework construction module 902 is used to establish a parameter co-optimization framework for impedance reshaping link parameters. The parameter co-optimization framework is used to correlate the control parameters of the multi-virtual synchronous generator system with the design parameters of the superconducting magnetic energy storage system.

[0168] The parameter optimization module 904 is used to perform collaborative optimization of the virtual inertia parameters, virtual damping parameters, and impedance reshaping link parameters of each virtual synchronous generator in a multi-virtual synchronous generator system based on a parameter collaborative optimization framework, with the optimization objective of minimizing the phase difference between the grid impedance and the output impedance of the virtual synchronous generator in the target frequency band, to obtain the optimized parameter set.

[0169] The capacity calculation module 906 is used to calculate the minimum compensation capacity of the superconducting magnetic energy storage system required to suppress subsynchronous oscillations by analyzing the transient power deficit during subsynchronous oscillations based on the optimized parameter set.

[0170] The magnet design module 908 is used to determine the design parameters of the superconducting magnetic energy storage magnet based on the minimum compensation capacity.

[0171] In an exemplary embodiment, the framework construction module 902 is specifically used to construct a small-signal impedance model of a multi-virtual synchronous generator system. The small-signal impedance model is used to characterize the dynamic coupling relationship between each virtual synchronous generator and its interaction with the power grid. Based on the small-signal impedance model, the expressions for the grid-side impedance and the output impedance of each virtual synchronous generator at the point of common coupling are determined. The optimization objective is to minimize the sum of the phase differences between the grid-side impedance and the corresponding virtual synchronous generator output impedance at the crossover frequency within the subsynchronous oscillation frequency band of each virtual synchronous generator. The objective function and optimization problem model of the parameter collaborative optimization framework are constructed using virtual inertia parameters, virtual damping parameters, and impedance reshaping link parameters as optimization variables.

[0172] In an exemplary embodiment, the parameter optimization module 904 is specifically used to employ an improved snow ablation optimization algorithm to collaboratively optimize the virtual inertia parameters, virtual damping parameters, and impedance reshaping link parameters of each virtual synchronous generator in a multi-virtual synchronous generator system. The improved snow ablation optimization algorithm is suitable for efficiently searching the multi-dimensional, coupled parameter space composed of virtual inertia parameters, virtual damping parameters, and impedance reshaping link parameters to obtain the globally optimal or near-optimal solution of the objective function.

[0173] In an exemplary embodiment, the capacity calculation module 906 is specifically used to determine the power fluctuation amplitude and oscillation frequency of the subsynchronous oscillation that will be caused by the multi-virtual synchronous generator system under a preset disturbance scenario based on the optimized parameter set; calculate the transient compensation energy that needs to be supplemented externally from the occurrence of the disturbance to the first power overshoot based on the power fluctuation amplitude and oscillation frequency; and determine the value of the transient compensation energy as the minimum compensation capacity of the superconducting magnetic energy storage system.

[0174] In an exemplary embodiment, the magnet design module 908 is specifically used to determine the target energy storage value, rated operating current, and inductance value of the superconducting magnetic energy storage magnet based on the minimum compensation capacity and a preset safety margin; using the target energy storage value and rated operating current as constraints, a genetic algorithm is used to optimize the structure of the high-temperature superconducting solenoid magnet to obtain the geometric dimension parameters of the magnet and the arrangement parameters of the high-temperature superconducting tape; wherein, the optimization design process takes minimizing the magnet volume, optimizing the magnetic field uniformity, or reducing the amount of tape as the optimization objective.

[0175] In an exemplary embodiment, the verification module is used to construct a dynamic electrical model of the superconducting magnetic energy storage magnet based on design parameters, and integrate the dynamic electrical model into an electromagnetic transient simulation platform for a multi-virtual synchronous generator system; set up a variety of typical disturbance scenarios, including power command step and grid short-circuit faults, and perform simulations based on the various typical disturbance scenarios in the electromagnetic transient simulation platform; based on the simulation results, verify the suppression effect of the superconducting magnetic energy storage system on subsynchronous oscillations, and verify whether the design parameters ensure that the system meets the stable operation standards.

[0176] Each module in the aforementioned subsynchronous oscillation suppression device based on a multi-virtual synchronous generator system can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in a computer device, or stored in the memory of a computer device as software, so that the processor can call and execute the corresponding operations of each module.

[0177] In one exemplary embodiment, a computer device is provided, which may be a server, and its internal structure diagram may be as follows: Figure 10As shown, this computer device includes a processor, memory, input / output (I / O) interfaces, and a communication interface. The processor, memory, and I / O interfaces are connected via a system bus, and the communication interface is also connected to the system bus via the I / O interfaces. The processor provides computational and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system, computer programs, and a database. The internal memory provides the environment for the operating system and computer programs stored in the non-volatile storage media. The database stores data. The I / O interfaces are used for exchanging information between the processor and external devices. The communication interface is used for communicating with external terminals via a network. When executed by the processor, the computer program implements a subsynchronous oscillation suppression method based on a multi-virtual synchronous generator system.

[0178] Those skilled in the art will understand that Figure 10 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0179] In one exemplary embodiment, a computer device is provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps of the method described above.

[0180] In one embodiment, a computer-readable storage medium is provided having a computer program stored thereon, which, when executed by a processor, implements the steps of the above-described method.

[0181] In one embodiment, a computer program product is provided, including a computer program that, when executed by a processor, implements the steps of the method described above.

[0182] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of the relevant data must comply with relevant regulations.

[0183] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile memory and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM). The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, artificial intelligence (AI) processors, etc., and are not limited to these.

[0184] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this application.

[0185] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of this patent application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this application should be determined by the appended claims.

Claims

1. A method for suppressing subsynchronous oscillations based on a multi-virtual synchronous generator system, characterized in that, The method includes: A parameter co-optimization framework for impedance reshaping link parameters is established, which is used to correlate the control parameters of a multi-virtual synchronous generator system with the design parameters of a superconducting magnetic energy storage system. Based on the aforementioned parameter collaborative optimization framework, with the goal of minimizing the phase difference between the grid impedance and the output impedance of the virtual synchronous generator in the target frequency band, the virtual inertia parameters, virtual damping parameters, and impedance reshaping link parameters of each virtual synchronous generator in the multi-virtual synchronous generator system are collaboratively optimized to obtain the optimized parameter set. Based on the optimized parameter set, the minimum compensation capacity of the superconducting magnetic energy storage system required to suppress subsynchronous oscillations is calculated by analyzing the transient power deficit during subsynchronous oscillations. Based on the minimum compensation capacity, the design parameters of the superconducting magnetic energy storage magnet are determined.

2. The method according to claim 1, characterized in that, The parameter collaborative optimization framework for establishing impedance reshaping link parameters includes: A small-signal impedance model of the multi-virtual synchronous generator system is constructed. The small-signal impedance model is used to characterize the dynamic coupling relationship between each virtual synchronous generator and its interaction with the power grid. Based on the small-signal impedance model, the expressions for the grid-side impedance and the output impedance of each virtual synchronous generator at the point of common coupling are determined. The objective is to minimize the sum of the phase differences between the grid-side impedance and the corresponding virtual synchronous generator output impedance at the crossover frequency within the subsynchronous oscillation band of each virtual synchronous generator. The objective function and optimization problem model of the parameter collaborative optimization framework are constructed using the virtual inertia parameter, virtual damping parameter, and impedance reshaping link parameter as optimization variables.

3. The method according to claim 2, characterized in that, The coordinated optimization of the virtual inertia parameters, virtual damping parameters, and impedance reshaping link parameters of each virtual synchronous generator in the multi-virtual synchronous generator system includes: An improved snow melting optimization algorithm is used to collaboratively optimize the virtual inertia parameters, virtual damping parameters, and impedance reshaping link parameters of each virtual synchronous generator in the multi-virtual synchronous generator system. The improved snow melting optimization algorithm is suitable for efficiently searching the multidimensional, coupled parameter space composed of the virtual inertia parameter, virtual damping parameter, and impedance reshaping link parameter to obtain the global optimal or near-optimal solution of the objective function.

4. The method according to claim 1, characterized in that, Based on the optimized parameter set, the minimum compensation capacity of the superconducting magnetic energy storage system required to suppress subsynchronous oscillations is calculated by analyzing the transient power deficit during subsynchronous oscillations, including: Based on the optimized parameter set, the power fluctuation amplitude and oscillation frequency of the subsynchronous oscillation that the multi-virtual synchronous generator system will induce under the preset disturbance scenario are determined. Based on the power fluctuation amplitude and oscillation frequency, calculate the transient compensation energy that needs to be supplemented externally from the occurrence of the disturbance to the first power overshoot. The value of the transient compensation energy is determined as the minimum compensation capacity of the superconducting magnetic energy storage system.

5. The method according to claim 1, characterized in that, The step of determining the design parameters of the superconducting magnetic energy storage magnet based on the minimum compensation capacity includes: Based on the minimum compensation capacity and the preset safety margin, the target energy storage value, rated operating current and inductance value of the superconducting magnetic energy storage magnet are determined. Using the target energy storage value and rated operating current as constraints, a genetic algorithm is used to optimize the structure of the high-temperature superconducting solenoid magnet to obtain the geometric dimensions of the magnet and the arrangement parameters of the high-temperature superconducting tape. The optimization design process takes at least one of minimizing the magnet volume, optimizing the magnetic field uniformity, or reducing the amount of tape as the optimization objective.

6. The method according to claim 1, characterized in that, The method further includes: Based on the design parameters, a dynamic electrical model of the superconducting magnetic energy storage magnet is constructed, and the dynamic electrical model is integrated into the electromagnetic transient simulation platform of the multi-virtual synchronous generator system. Multiple typical disturbance scenarios, including power command step and grid short-circuit fault, are set up, and simulations are performed on the electromagnetic transient simulation platform based on these scenarios. Based on simulation results, the suppression effect of the superconducting magnetic energy storage system on subsynchronous oscillations is verified, and whether the design parameters ensure that the system meets the stable operation standards is verified.

7. A subsynchronous oscillation suppression device based on a multi-virtual synchronous generator system, characterized in that, The device includes: The framework construction module is used to establish a parameter co-optimization framework for impedance reshaping link parameters. The parameter co-optimization framework is used to associate the control parameters of a multi-virtual synchronous generator system with the design parameters of a superconducting magnetic energy storage system. The parameter optimization module is used to perform collaborative optimization on the virtual inertia parameters, virtual damping parameters, and impedance reshaping link parameters of each virtual synchronous generator in the multi-virtual synchronous generator system based on the parameter collaborative optimization framework, with the optimization objective of minimizing the phase difference between the grid impedance and the output impedance of the virtual synchronous generator in the target frequency band, to obtain the optimized parameter set. The capacity calculation module is used to calculate the minimum compensation capacity of the superconducting magnetic energy storage system required to suppress subsynchronous oscillations by analyzing the transient power deficit during subsynchronous oscillations based on the optimized parameter set. The magnet design module is used to determine the design parameters of the superconducting magnetic energy storage magnet based on the minimum compensation capacity.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 6.

10. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 6.

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