A four-dimensional index method for evaluating the support capability of grid-connected wind farms under weak grid conditions

Abstract

The present disclosure relates to a four-dimensional index method for evaluating the support capability of a grid-connected wind farm under weak grid conditions. In view of the problem that the existing evaluation method has a single index and is difficult to adapt to mixed penetration rates and dynamic operating conditions, the present disclosure is based on the equivalent impedance obtained in advance at the grid-connected point of the wind farm, and constructs a four-dimensional index evaluation system including the equivalent short-circuit ratio, impedance stability margin, virtual inertia support coefficient and voltage support coefficient. By calculating, jointly comparing, risk grading and feasible region determining the four indexes, the grid strength enhancement capability, wideband oscillation risk, frequency support capability and voltage regulation capability of the wind farm under different penetration rates, wind speeds and grid strengths are evaluated, and the operating boundary of the system from the unstable region, critical region to the stable support region is determined, realizing the fine evaluation of the operating state and safety boundary of the wind farm.

Description

Technical Field

[0001] This disclosure relates to the field of new energy power generation technology, and more specifically, to a four-dimensional index method and apparatus for evaluating the support capacity of grid-connected wind farms under weak grid conditions. Background Technology

[0002] With breakthroughs in grid-based control technology, wind farms are shifting from a traditional passive following mode to an active support mode. Grid-based wind turbines, through virtual synchronous machine control, virtual inertia, and reactive power regulation technology, can provide critical voltage and frequency support for weak power grids. However, in weak power grid environments with small short-circuit capacity and low short-circuit ratios, the grid's equivalent impedance is large and the phase coupling is tight, meaning even small power disturbances can induce severe voltage and frequency fluctuations. Simultaneously, the overall system inertia is insufficient, placing extremely high demands on the wind farm's active support response speed and multi-dimensional adjustment capabilities.

[0003] Regarding the assessment of the support capacity of grid-type wind farms under weak power grids, existing technologies and research mainly have the following three limitations: First, the evaluation indicators are too simplistic to comprehensively depict the multi-dimensional support and stability interaction mechanisms. Existing evaluation methods are mostly limited to isolated dimensions such as low-voltage ride-through, reactive power regulation, frequency response, or a single short-circuit ratio. However, a single indicator can only reflect a local operating state and cannot simultaneously reveal the wind farm's enhancement effect on grid strength, the impedance matching state between the wind turbine and the weak grid, the dynamic frequency response of virtual inertia, and the reactive power support capability under voltage disturbances. This makes it difficult for current methods to accurately determine whether the system is in a stable support zone, a critical transition zone, or an unstable and destructive zone.

[0004] Second, the mechanisms affecting wind farms with mixed penetration rates are intertwined, lacking a unified and comprehensive quantitative assessment framework. As hybrid wind farms, consisting of grid-connected and grid-linked units, become mainstream, changes in grid penetration rates significantly reconstruct the equivalent impedance and support characteristics of wind farms. Although increasing the proportion of grid-connected units can enhance system inertia and voltage support, the changes in grid strength improvement, impedance matching stability, and frequency-voltage response under different penetration rates are not simply linear superpositions. Existing research is mostly limited to adjusting single control parameters or extrapolating capacity ratios, and a unified quantitative assessment system that can take into account different grid penetration rates has not yet been established.

[0005] Third, static assessments dominate and are ill-suited to the dynamic evolution under wind speed fluctuations and operating point migrations. Random wind speed fluctuations, power output shear, and transient fault disturbances drive frequent migrations of wind farm operating points, causing time-varying evolutions in converter port equivalent impedance, reactive power margin, and frequency response characteristics. Existing assessment methods are mostly based on rated operating conditions or a single static operating point, lacking a systematic decoupling analysis of the evolution of support capabilities under multiple wind speed conditions and time-varying disturbances, and thus failing to delineate the feasible boundary of the system's stable operation.

[0006] Therefore, one or more methods are needed to solve the above problems.

[0007] It should be noted that the information disclosed in the background section above is only used to enhance the understanding of the background of this disclosure, and therefore may include information that does not constitute prior art known to those skilled in the art. Summary of the Invention

[0008] The purpose of this disclosure is to provide a four-dimensional index method and apparatus for evaluating the support capacity of grid-type wind farms under weak grid conditions, thereby overcoming, to at least to some extent, one or more problems caused by the limitations and defects of related technologies.

[0009] According to one aspect of this disclosure, a four-dimensional index method for evaluating the support capacity of grid-connected wind farms under weak grid conditions is provided, comprising: Obtain the equivalent impedance of the wind farm's grid connection point, the equivalent impedance of the power grid, the rated capacity of the wind farm, the capacity ratio of grid-connected units, virtual inertia parameters, and reactive power regulation and voltage change information. The acquired information is subjected to a consistency check, which includes consistency checks on frequency range, per-unit reference, rated capacity, and sampling time. Based on the verified information, the equivalent short-circuit ratio, impedance stability margin, virtual inertia support coefficient, and voltage support coefficient are calculated respectively to construct a four-dimensional index evaluation system. Based on the comparison results between the four-dimensional indicators and the preset thresholds, a joint judgment and risk classification are performed to determine whether the operating point is in a stable support zone, a critical support zone, or a zone with insufficient support. The above calculations and judgments are repeated under different combinations of grid penetration, wind speed and grid short-circuit ratio to output the feasible domain and the grid capacity configuration boundary that meets the target support level.

[0010] In one exemplary embodiment of this disclosure, the acquired information is organized into an input vector according to the evaluation time:

[0011] in, For a moment ,frequency The equivalent impedance Z obtained in advance at the grid connection point of the wind farm g (jω,t) represents the corresponding equivalent impedance of the power grid. For the proportion of network-type capacity, J eq (t) represents the equivalent virtual inertia.

[0012] In one exemplary embodiment of this disclosure, the power-voltage state matching error is constructed before performing the four-dimensional index calculation:

[0013] in, , , This is the recording state corresponding to the equivalent impedance data. , , As the rated reference, , , The weights are 1, and their sum is 1. When the state matching error is not greater than a preset threshold, the corresponding impedance data is used for the calculation of the four-dimensional index at the current moment.

[0014] In one exemplary embodiment of this disclosure, the equivalent short-circuit ratio is calculated based on the rated capacity of the wind farm, the rated voltage of the power grid, and the series impedance magnitude of the equivalent impedance of the wind farm at the grid connection point and the equivalent impedance of the power grid. The calculation formula is as follows:

[0015] Among them, U N S is the rated voltage of the power grid. wfN R is the rated capacity of the wind farm. mix X mix R represents the real and imaginary parts of the equivalent impedance at the grid connection point of the wind farm, respectively. g X g These are the real and imaginary parts of the equivalent impedance of the power grid, respectively.

[0016] In one exemplary embodiment of this disclosure, the impedance stability margin is calculated based on the ratio of the equivalent impedance modulus at the wind farm grid connection point to the equivalent impedance modulus of the power grid, and the critical impedance ratio. The calculation formula is as follows:

[0017] Among them, K Z,c Let |Z_mix| be the critical impedance ratio, and |Z_mix| be the equivalent impedance modulus at the grid connection point of the wind farm. g | represents the equivalent impedance magnitude of the power grid.

[0018] In an exemplary embodiment of this disclosure, the virtual inertia support coefficient is calculated based on the rated capacity of the grid-type wind turbine, the virtual inertia of a single unit, the rated capacity of the wind farm, and the rated inertia of a synchronous generator of the same capacity. The calculation formula is as follows:

[0019] Among them, J i Let J be the virtual inertia of the i-th grid-connected wind turbine unit. synJ is the rated inertia of a synchronous generator of the same capacity. eq This represents the equivalent virtual inertia of a wind farm.

[0020] In one exemplary embodiment of this disclosure, the voltage support coefficient is calculated based on the voltage fluctuation amplitude, the grid rated voltage, the imaginary part of the equivalent impedance at the wind farm grid connection point, and the reactive power. The calculation formula is as follows:

[0021] Where ΔU is the voltage fluctuation amplitude of the power grid, X mix R is the imaginary part of the equivalent impedance at the grid connection point of the wind farm. mix Q is the real part of the equivalent impedance at the grid connection point of the wind farm. mix Reactive power of wind farm In one exemplary embodiment of this disclosure, the joint determination and risk classification includes: reorganizing the four-dimensional indicators into a four-dimensional evaluation indicator vector at the same runtime:

[0022] Define a standardized margin for each indicator:

[0023] The minimum margin of the power grid strength and impedance stability indicators is used as the stability margin, and the minimum margin of the frequency support and voltage support indicators is used as the support margin.

[0024] The operating points that satisfy the four margin constraints constitute the supporting feasible region:

[0025] For a given wind speed v and grid short-circuit ratio SCR, the minimum grid penetration rate required to meet the target support level is determined by the following formula: .

[0026] In one exemplary embodiment of this disclosure, the method further includes: For each wind speed operating window or each sampling moment, the equivalent impedance of the wind farm, the equivalent impedance of the power grid, power, reactive power, voltage, grid capacity ratio and virtual inertia parameters that match the state are read. The consistency of the data for each state is checked in chronological order and the four-dimensional indicators are calculated. The four-dimensional indicators and their levels at each moment are used to form a time-series evaluation result to identify high-value windows, critical transition windows and insufficient support windows of support capacity.

[0027] In one aspect of this disclosure, a four-dimensional index device for evaluating the support capacity of grid-connected wind farms under weak grid conditions is provided, comprising: The data acquisition module is used to acquire the equivalent impedance of the wind farm grid connection point, the equivalent impedance of the grid, the rated capacity of the wind farm, the capacity ratio of grid-connected units, virtual inertia parameters, and reactive power regulation and voltage change information. The consistency verification module is used to perform consistency verification on the acquired information. The consistency verification includes consistency verification of frequency range, per-unit reference, rated capacity and sampling time. The four-dimensional index calculation module is used to calculate the equivalent short-circuit ratio, impedance stability margin, virtual inertia support coefficient and voltage support coefficient based on the verified information, and to construct a four-dimensional index evaluation system. The joint judgment module is used to perform joint judgment and risk classification based on the comparison results of the four-dimensional indicators and preset thresholds, and to determine whether the operating point is in the stable support zone, critical support zone or insufficient support zone. The feasible domain output module is used to repeatedly perform the above calculations and judgments under different combinations of grid penetration, wind speed and grid short-circuit ratio, and output the supporting feasible domain and the grid capacity configuration boundary that meets the target support level.

[0028] This disclosure presents an exemplary embodiment of a four-dimensional index method for evaluating the support capability of grid-connected wind farms under weak grid conditions. Addressing the limitations of existing evaluation methods, which rely on single indicators and struggle to adapt to varying penetration rates and dynamic operating conditions, this invention constructs a four-dimensional index evaluation system based on the pre-obtained equivalent impedance at the wind farm's grid connection point. This system includes the equivalent short-circuit ratio, impedance stability margin, virtual inertia support coefficient, and voltage support coefficient. By calculating, jointly comparing, risk-classifying, and determining the feasible region for these four indicators, the method assesses the wind farm's grid strength enhancement capability, broadband oscillation risk, frequency support capability, and voltage regulation capability under different penetration rates, wind speeds, and grid strengths. Furthermore, it determines the operational boundaries of the system transitioning from the instability zone and critical zone to the stable support zone, achieving a refined assessment of the wind farm's operating status and safety boundaries.

[0029] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and are not intended to limit this disclosure. Attached Figure Description

[0030] The above and other features and advantages of this disclosure will become more apparent from the detailed description of exemplary embodiments thereof with reference to the accompanying drawings.

[0031] Figure 1 A flowchart is shown for a four-dimensional index method for evaluating the support capacity of a grid-connected wind farm under weak grid conditions, according to an exemplary embodiment of the present disclosure. Figure 2The diagram illustrates the joint determination of four-dimensional support capability indices and the feasible domain output structure of a four-dimensional index method for evaluating the support capability of a grid-type wind farm under weak grid conditions, according to an exemplary embodiment of the present disclosure. Figure 3 The diagram illustrates the theoretical variation trend of the support capacity index under different GFM ratios in a four-dimensional index method for evaluating the support capacity of a grid-connected wind farm under weak grid conditions, according to an exemplary embodiment of the present disclosure. Figure 4 A flowchart of multi-wind speed state data input and four-dimensional support capability time-series evaluation of a four-dimensional index method for evaluating the support capability of a grid-type wind farm under weak grid conditions, according to an exemplary embodiment of the present disclosure, is shown. Figure 5 The diagram illustrates the time-varying evolution trend of the support capacity index as a function of wind speed in a four-dimensional index method for evaluating the support capacity of a grid-type wind farm under weak grid conditions, according to an exemplary embodiment of this disclosure. Figure 6 This invention illustrates a four-dimensional index method for evaluating the support capacity of grid-connected wind farms under weak grid conditions, according to an exemplary embodiment of the present disclosure, using the equivalent short-circuit ratio (ESCR) and impedance stability margin at different GFM penetration rates. K sm A diagram illustrating the evolution of the Pareto front; Figure 7 This invention illustrates a four-dimensional index method for evaluating the voltage support capacity of grid-connected wind farms under weak grid conditions, according to an exemplary embodiment of the present disclosure, using voltage support coefficients at different GFM penetration rates. K U With virtual inertia support coefficient K J A diagram illustrating the evolution of the Pareto front; Figure 8 This invention illustrates a four-dimensional index method for evaluating the support capacity of grid-connected wind farms under weak grid conditions, according to an exemplary embodiment of the present disclosure, with ESCR and [missing information - likely related to grid strength] under different GFM penetration rates and grid strengths. K sm Joint distribution characteristic diagram of stability indices; Figure 9 This invention illustrates a four-dimensional index method for evaluating the support capacity of grid-connected wind farms under weak grid conditions, according to an exemplary embodiment of the present disclosure, under different GFM penetration rates and grid strengths. K J and K U Joint distribution characteristics of supporting indicators; Figure 10The diagram illustrates the dynamic response of electrical quantities of a wind farm under multi-platform wind speed conditions (SCR=2.5) for a four-dimensional index method for evaluating the support capacity of a grid-type wind farm under weak grid conditions, according to an exemplary embodiment of this disclosure. Figure 11 The time series characteristic diagram of the four-dimensional support capability index of a wind farm under multi-platform wind speed conditions (SCR=2.5) is shown in an exemplary embodiment of the present disclosure, which is a four-dimensional index method for evaluating the support capability of a grid-type wind farm under weak grid conditions. Figure 12 The diagram shows a comparison of the transient responses of wind farms with different GFM penetration rates under a three-phase short-circuit fault using a four-dimensional index method for evaluating the support capacity of grid-connected wind farms under weak grid conditions, according to an exemplary embodiment of the present disclosure. Figure 13 The diagram shows a comparison of frequency and power support characteristics of different GFM penetration rates under power step disturbances in a four-dimensional index method for evaluating the support capacity of grid-connected wind farms under weak grid conditions, according to an exemplary embodiment of the present disclosure. Figure 14 A schematic block diagram of a four-dimensional index device for evaluating the support capacity of a grid-type wind farm under weak grid conditions is shown according to an exemplary embodiment of the present disclosure. Detailed Implementation

[0032] Exemplary embodiments will now be described more fully with reference to the accompanying drawings. However, these exemplary embodiments can be implemented in many forms and should not be construed as limited to the embodiments set forth herein; rather, they are provided so that this disclosure will be thorough and complete, and will fully convey the concept of the exemplary embodiments to those skilled in the art. The same reference numerals in the drawings denote the same or similar parts, and therefore repeated descriptions of them will be omitted.

[0033] Furthermore, the described features, structures, or characteristics can be combined in any suitable manner in one or more embodiments. Numerous specific details are provided in the following description to give a thorough understanding of embodiments of this disclosure. However, those skilled in the art will recognize that the technical solutions of this disclosure can be practiced without one or more of the specific details described, or other methods, components, materials, apparatuses, steps, etc., can be employed. In other instances, well-known structures, methods, apparatuses, implementations, materials, or operations are not shown or described in detail to avoid obscuring various aspects of this disclosure.

[0034] The block diagrams shown in the accompanying drawings are merely functional entities and do not necessarily correspond to physically independent entities. That is, these functional entities can be implemented in software, or in one or more software-hardened modules, or in different network and / or processor devices and / or microcontroller devices.

[0035] In this example embodiment, a four-dimensional index method for evaluating the support capacity of grid-connected wind farms under weak grid conditions is first provided; (Refer to...) Figure 1 As shown, this four-dimensional index method for evaluating the support capacity of grid-connected wind farms under weak grid conditions may include the following steps: Step S110: Obtain the equivalent impedance of the wind farm grid connection point, the equivalent impedance of the grid, the rated capacity of the wind farm, the capacity ratio of grid-connected units, the virtual inertia parameters, and the reactive power regulation and voltage change information. Step S120: Perform a consistency check on the acquired information. The consistency check includes consistency checks on frequency range, per-unit reference, rated capacity, and sampling time. Step S130: Based on the verified information, calculate the equivalent short-circuit ratio, impedance stability margin, virtual inertia support coefficient, and voltage support coefficient respectively, and construct a four-dimensional index evaluation system. Step S140: Based on the comparison results between the four-dimensional indicators and the preset threshold, a joint judgment and risk classification are performed to determine whether the operating point is in a stable support zone, a critical support zone, or a zone with insufficient support. Step S150: Repeat the above calculation and judgment under different combinations of grid penetration rate, wind speed and grid short-circuit ratio, and output the feasible support domain and the grid capacity configuration boundary that meets the target support level.

[0036] This disclosure presents an exemplary embodiment of a four-dimensional index method for evaluating the support capability of grid-connected wind farms under weak grid conditions. Addressing the limitations of existing evaluation methods, which rely on single indicators and struggle to adapt to varying penetration rates and dynamic operating conditions, this invention constructs a four-dimensional index evaluation system based on the pre-obtained equivalent impedance at the wind farm's grid connection point. This system includes the equivalent short-circuit ratio, impedance stability margin, virtual inertia support coefficient, and voltage support coefficient. By calculating, jointly comparing, risk-classifying, and determining the feasible region for these four indicators, the method assesses the wind farm's grid strength enhancement capability, broadband oscillation risk, frequency support capability, and voltage regulation capability under different penetration rates, wind speeds, and grid strengths. Furthermore, it determines the operational boundaries of the system transitioning from the instability zone and critical zone to the stable support zone, achieving a refined assessment of the wind farm's operating status and safety boundaries.

[0037] The following section will further explain a four-dimensional index method for evaluating the support capacity of a grid-type wind farm under weak grid conditions in this example embodiment.

[0038] Example 1: In step S110, the equivalent impedance of the wind farm grid connection point, the equivalent impedance of the grid, the rated capacity of the wind farm, the capacity ratio of grid-connected units, the virtual inertia parameters, and the reactive power regulation and voltage change information can be obtained in advance.

[0039] In this example embodiment, the acquired information is organized into an input vector according to the evaluation time:

[0040] in, For a moment ,frequency The equivalent impedance Z obtained in advance at the grid connection point of the wind farm g (jω,t) represents the corresponding equivalent impedance of the power grid. For the proportion of network-type capacity, J eq (t) represents the equivalent virtual inertia.

[0041] In step S120, the acquired information can be checked for consistency, including consistency checks on frequency range, per-unit reference, rated capacity and sampling time. In this example embodiment, the method constructs the power-voltage state matching error before performing the four-dimensional index calculation:

[0042] in, , , This is the recording state corresponding to the equivalent impedance data. , , As the rated reference, , , The weights are 1, and their sum is 1. When the state matching error is not greater than a preset threshold, the corresponding impedance data is used for the calculation of the four-dimensional index at the current moment.

[0043] In step S130, based on the verified information, the equivalent short-circuit ratio, impedance stability margin, virtual inertia support coefficient, and voltage support coefficient can be calculated respectively to construct a four-dimensional index evaluation system.

[0044] In this example embodiment, the equivalent short-circuit ratio is calculated based on the wind farm's rated capacity, the grid's rated voltage, and the series impedance modulus of the wind farm's grid connection point's equivalent impedance and the grid's equivalent impedance. The calculation formula is as follows:

[0045] Among them, U N S is the rated voltage of the power grid. wfN R is the rated capacity of the wind farm. mix X mix R represents the real and imaginary parts of the equivalent impedance at the grid connection point of the wind farm, respectively. gX g These are the real and imaginary parts of the equivalent impedance of the power grid, respectively.

[0046] In this example embodiment, the impedance stability margin is calculated based on the ratio of the equivalent impedance modulus at the wind farm grid connection point to the equivalent impedance modulus of the power grid, and the critical impedance ratio. The calculation formula is as follows:

[0047] Among them, K Z,c Let |Z_mix| be the critical impedance ratio, and |Z_mix| be the equivalent impedance modulus at the grid connection point of the wind farm. g | represents the equivalent impedance magnitude of the power grid.

[0048] In this example embodiment, the virtual inertia support coefficient is calculated based on the rated capacity of the grid-type wind turbine, the virtual inertia of a single unit, the rated capacity of the wind farm, and the rated inertia of a synchronous generator of the same capacity. The calculation formula is as follows:

[0049] Among them, J i Let J be the virtual inertia of the i-th grid-connected wind turbine unit. syn J is the rated inertia of a synchronous generator of the same capacity. eq This represents the equivalent virtual inertia of a wind farm.

[0050] In this example embodiment, the voltage support coefficient is calculated based on the voltage fluctuation amplitude, the grid rated voltage, the imaginary part of the equivalent impedance at the wind farm grid connection point, and the reactive power. The calculation formula is as follows:

[0051] Where ΔU is the voltage fluctuation amplitude of the power grid, X mix R is the imaginary part of the equivalent impedance at the grid connection point of the wind farm. mix Q is the real part of the equivalent impedance at the grid connection point of the wind farm. mix This refers to the reactive power of the wind farm.

[0052] In step S140, a joint judgment and risk classification can be performed based on the comparison results of the four-dimensional indicators and the preset thresholds to determine whether the operating point is in a stable support zone, a critical support zone, or a zone with insufficient support.

[0053] In this example embodiment, the joint determination and risk classification includes: reorganizing the four-dimensional indicators into a four-dimensional evaluation indicator vector at the same runtime:

[0054] Define a standardized margin for each indicator:

[0055] The minimum margin of the power grid strength and impedance stability indicators is used as the stability margin, and the minimum margin of the frequency support and voltage support indicators is used as the support margin.

[0056] The operating points that satisfy the four margin constraints constitute the supporting feasible region:

[0057] For a given wind speed v and grid short-circuit ratio SCR, the minimum grid penetration rate required to meet the target support level is determined by the following formula: .

[0058] In step S150, the above calculations and judgments can be repeated under different combinations of grid penetration, wind speed and grid short-circuit ratio to output the feasible support domain and the grid capacity configuration boundary that meets the target support level.

[0059] In this example embodiment, the method further includes: For each wind speed operating window or each sampling moment, the equivalent impedance of the wind farm, the equivalent impedance of the power grid, power, reactive power, voltage, grid capacity ratio and virtual inertia parameters that match the state are read. The consistency of the data for each state is checked in chronological order and the four-dimensional indicators are calculated. The four-dimensional indicators and their levels at each moment are used to form a time-series evaluation result to identify high-value windows, critical transition windows and insufficient support windows of support capacity.

[0060] Example 2: This invention presents a four-dimensional index method for evaluating the support capacity of grid-connected wind farms under weak grid conditions. This addresses the problem that existing evaluation methods, which rely solely on short-circuit ratio, voltage deviation, frequency deviation, or a single impedance index, are insufficient to comprehensively characterize the support capacity of grid-connected wind farms under varying grid-connected turbine penetration rates, wind speeds, and grid strength. This invention uses pre-obtained equivalent impedance at the wind farm's grid connection point, grid equivalent impedance, wind farm rated capacity, grid-connected turbine capacity ratio, virtual inertia parameters, and reactive power regulation and voltage variation information as inputs to construct a four-dimensional index evaluation system including equivalent short-circuit ratio, impedance stability margin, virtual inertia support coefficient, and voltage support coefficient. Through calculation, joint comparison, risk classification, and feasible region determination of the four indices, the method assesses the wind farm's grid strength enhancement capability, broadband oscillation risk, frequency support capability, and voltage regulation capability under different operating conditions, and determines the operating boundary for the system's transition from the instability zone and critical zone to the stable support zone. This invention does not limit the wind farm's equivalent impedance to be obtained through modeling, identification, or aggregation.

[0061] In this example embodiment, the support capacity and physical characteristics of a grid-type wind farm in the assessment basis include: In this invention, the equivalent impedance of a wind farm refers to the equivalent impedance data presented by the wind farm at the grid connection point, within a given frequency range, and under a given operating condition. This data may include positive and negative sequence impedances, real and imaginary parts of the impedance, or impedance magnitudes used to calculate corresponding indicators; and a correspondence is established with wind speed, power, reactive power, grid penetration rate, and voltage status at the time of data collection. Before performing a four-dimensional evaluation of the input data, the consistency between the wind farm's equivalent impedance and the grid's equivalent impedance is checked for frequency range, per-unit reference, rated capacity, and sampling time. If the data does not meet the consistency conditions, frequency alignment, reference conversion, and status label matching are performed before indicator calculation. This invention uses equivalent impedances obtained through other methods as input and does not involve single-machine control models, current limiting models, cluster partitioning algorithms, parameter aggregation algorithms, or the equivalent methods for collector networks themselves.

[0062] To ensure that the equivalent impedance used in subsequent calculations corresponds one-to-one with the current operating conditions, the input at any evaluation moment is organized as follows: (1) In the formula, For a moment ,frequency The equivalent impedance Z obtained in advance at the grid connection point of the wind farm g (jω,t) represents the corresponding equivalent impedance of the power grid. For the proportion of network-type capacity, J eq (t) represents the equivalent virtual inertia. This input vector only organizes existing data and does not include single-machine control modeling, current limiting calculation, cluster partitioning, parameter aggregation, or collector network equivalence.

[0063] To avoid inconsistencies between impedance data acquisition and evaluation states, a power-voltage state matching error is constructed: (2) In the formula, , , This is the recording state corresponding to the equivalent impedance data. , , As the rated reference, , , The weights are denoted as , and their sum is 1. When... Not greater than the preset threshold If the impedance data is used, it is used to calculate the four-dimensional index at the current moment; otherwise, state label matching or re-acquiring the corresponding data is performed first.

[0064] With a high proportion of new energy sources being integrated into the grid, the proportion of traditional synchronous turbines is gradually decreasing, leading to a reduction in system inertia and short-circuit capacity, and a weakening trend in the power grid. Grid-connected wind farms can provide certain voltage, frequency, and oscillation suppression support under weak grid conditions, but their support effect varies with grid strength, the proportion of grid-connected capacity, wind speed, and operating status.

[0065] From the perspective of station-level operation results, the support capability of grid-connected wind farms can be manifested in the ability to maintain voltage and frequency at the grid connection point, the ability to recover electrical quantities after faults or disturbances, and the ability to suppress oscillation risks. These performances need to be jointly quantified through four dimensions: grid strength, impedance matching, frequency support, and voltage regulation, and cannot be judged based on a single control mechanism or a single operational quantity.

[0066] The strength of these capabilities depends not only on the control parameters of a single GFM unit but also on the equivalent impedance characteristics exhibited by the wind farm's grid connection point under its current operating condition. For wind farms with multiple GFM and GFL units operating in combination, this invention directly uses the pre-obtained equivalent impedance of the grid connection point as the evaluation input to characterize the interaction behavior between the wind farm and the external power grid. This invention does not repeatedly derive the single-unit modeling, limiting correction, cluster partitioning, or impedance aggregation process for this equivalent impedance.

[0067] In this example embodiment, the assessment process for the support capacity of a grid-type wind farm under a weak power grid includes: A weak power grid refers to a grid-connected environment with a small short-circuit capacity, typically accompanied by insufficient grid strength, voltage sensitivity, and limited frequency regulation. The short-circuit ratio (SCR) is the ratio of the grid's short-circuit capacity to the wind farm's rated capacity; when SCR < 3, the grid's equivalent impedance is relatively large, and power disturbances easily cause voltage fluctuations. Lower system inertia also places higher demands on the frequency support for new energy sources.

[0068] The assessment process includes input data consistency verification, four-dimensional index calculation, joint classification, and result output. Inputs include at least the grid connection point equivalent impedance, grid equivalent impedance, rated capacity, GFM capacity ratio, virtual inertia, reactive power regulation, and voltage variation information. Equivalent impedance can be provided by existing models, offline frequency sweeps, online identification, manufacturer data, or other engineering data. Given GFM penetration rate, wind speed, and grid strength, the support capability level is output based on the four-dimensional indexes and joint criteria.

[0069] In this example embodiment, the four-dimensional index system for evaluating the support capacity of grid-type wind farms includes: This system is based on the equivalent impedance pre-obtained at the wind farm's grid connection point. It forms a calculable, comparable, and quantifiable evaluation framework from four key dimensions: grid strength enhancement, impedance matching, frequency support, and voltage regulation. The equivalent impedance is only used as an evaluation input and does not constitute the modeling or aggregation steps of this invention.

[0070] This system combines theoretical modeling with engineering evaluation, providing systematic support for wind farms with weak grids. Based on this system, the following four core evaluation indicators are defined: (1) Equivalent Short-Circuit Ratio (ESCR) The Equivalent Short-Circuit Ratio (ESCR) measures the actual grid strength of a system containing a wind farm. It is the ratio of the equivalent short-circuit capacity (resulting from the combined effect of the pre-obtained equivalent impedance at the wind farm's grid connection point and the grid's equivalent impedance) to the wind farm's rated capacity. It reflects the impact of the wind farm's grid connection on grid strength and voltage stability margin. For weak grid topologies, the ESCR is calculated using the following formula: (3) In the formula, the rated capacity of the wind farm and the rated voltage of the grid are used to represent the rated reference; the series impedance of the equivalent impedance of the wind farm at the grid connection point and the equivalent impedance of the grid is used to reflect the interaction strength between the wind farm and the grid. Among them, the equivalent impedance of the wind farm at the grid connection point is the equivalent impedance input obtained in advance.

[0071] The magnitude of ESCR is determined by the equivalent series impedance of the wind farm and the power grid. Let the equivalent impedance of the power grid be... Then the equivalent series impedance The modulus is: (4) Combining equation (3), its quantification model can be obtained as follows: (5) The smaller the series impedance magnitude, the higher the ESCR, and the greater the grid strength margin. In weak grids, when the ESCR reaches a preset threshold (e.g., ESCR≥3), it indicates that the grid strength support capability is good; otherwise, there is a stability risk.

[0072] (2) Impedance stability margin K sm Impedance stability margin K sm Impedance ratio characterizes the impedance matching between the wind farm and the power grid, reflecting the system's ability to suppress broadband oscillations. To quantify this indicator, an impedance ratio is defined. K Z The ratio of the equivalent impedance modulus obtained in advance at the wind farm's grid connection point to the equivalent impedance modulus of the power grid: (6) Based on this, impedance stability marginK sm Defined as the relative deviation between the actual impedance ratio and the critical impedance ratio: (7) In the formula, The critical impedance ratio is typically taken as [value missing] for voltage source grid-connected systems in weak power grids. .when At that time, the system was stable; It is a critical state; Resonance may then occur.

[0073] Substituting the above impedance ratio into the formula for calculating the impedance stability margin, we can obtain... K sm The explicit relationship between the wind farm's grid connection point equivalent impedance and the grid's equivalent impedance: (8) The further the ratio of the two types of impedances is from the critical impedance ratio and the more stable it is, the better. K sm The larger the impedance, the lower the risk of resonance. In practical assessment, the two types of impedances under the current operating conditions can be directly used for calculation. K sm .

[0074] (3) Virtual inertia support coefficient K J Virtual inertia support coefficient K J Used to measure the frequency support capability of a wind farm, reflecting the level of its simulated synchronous machine inertia characteristics. The calculation formula is: (9) In the formula, n c This refers to the number of GFM wind turbine units. and These are their rated capacity and single-machine virtual inertia, respectively. This is the rated inertia of a synchronous generator of the same capacity. GFL wind turbines have no virtual inertia support capability. K J No contribution.

[0075] and K J The core of quantification lies in the calculation of equivalent virtual inertia. Let the total rated capacity of the wind farm be... The proportion of wind turbine capacity of each grid type They are respectively: (10) In the formula, n g For the number of GFL wind turbine units, Its rated capacity.

[0076] The equivalent virtual inertia of the wind farm That is, the equivalent inertia referred to in the total fixed capacity is: (11) Substituting into formula (9), we can obtain its quantization model: (12) The model shows that, The value is positively correlated with the proportion of grid-connected wind turbine capacity. When the proportion of GFM wind turbines increases from 0 to 100%, The value increases linearly, and the frequency support capability of wind farms is significantly enhanced.

[0077] (4) Voltage support coefficient K U Voltage support coefficient K U The reactive power regulation capability of a wind farm under voltage disturbances is characterized by the following expression: (13) In the formula, X mix This represents the imaginary part of the equivalent impedance pre-obtained at the wind farm's grid connection point. The reactive power regulation of the GFM wind turbine will alter the wind farm's port response, thus affecting... K U This invention calculates based on the equivalent impedance input and reactive voltage response data under the current operating state. K U However, it does not deduce its internal control or aggregation process.

[0078] The voltage support coefficient is quantified by combining reactive power regulation data of the wind farm under current operating conditions with the imaginary part of the equivalent impedance: (14) Grid voltage fluctuation range and They are negatively correlated. Substituting this relationship into formula (13), we can obtain its quantitative model: (15) This model reflects the combined impact of reactive power voltage response and the imaginary part of the grid connection point equivalent impedance on voltage support capability. During evaluation, reactive power, voltage, and equivalent impedance data obtained under the same operating conditions are read to avoid substituting actual port responses with data from rated or mismatched conditions.

[0079] In the embodiments of this example, as Figure 2 As shown, the joint determination of four-dimensional indicators and the output of the feasible region include: In obtaining ESCR, K sm , K J and K U Subsequently, a support capability assessment vector consisting of four indicators is constructed. Critical thresholds for each indicator are pre-set for the target wind farm, and the grid strength, impedance stability, frequency support, and voltage support are determined to meet the requirements.

[0080] When ESCR and K sm All meet the stability threshold, and K J and K U When all indicators reach their corresponding support thresholds, the operating point is determined to be in a stable support zone; when any stability indicator is in the critical range, or when any support indicator fails to reach its target value, it is determined to be in a critical support zone; when ESCR or K sm If the stability conditions are not met, the area is identified as having insufficient support. The threshold can be pre-configured according to grid connection specifications, equipment capacity, or engineering operation requirements.

[0081] Furthermore, the above calculations and judgments are repeated under different combinations of GFM penetration rate, wind speed and grid short-circuit ratio, and the feasible domain results including operating point, four-dimensional indicators, support level and boundary conditions are output to determine the GFM configuration range and operating range that meet the target support level.

[0082] In this example embodiment, the calculation of the joint margin and configuration boundary of the four-dimensional indicators includes: Based on the above threshold determination for each indicator, in order to unify the four indicators into comparable margin results and further output the grid-type capacity configuration boundary under different wind speeds and grid strengths, normalized margin calculation and joint determination are performed on each indicator. This calculation only uses the results of the aforementioned four indicators and does not change the original calculation formulas and physical meanings of each indicator.

[0083] To reorganize the above four original indicators according to the same running time, the four-dimensional evaluation indicator vector is defined as follows: (16) To eliminate the influence of different dimensions, orders of magnitude, and critical thresholds of various indicators on the joint judgment, the following measures are taken for the first... i The standardized margin of each indicator is defined as follows: (17) In the formula, For the first i The preset threshold of the indicator It is a normalized scale greater than zero, which can be configured according to grid connection specifications, equipment capacity, or engineering operation requirements.

[0084] The minimum margins of the grid strength and impedance stability indices are used as the stability margins, and the minimum margins of the frequency support and voltage support indices are used as the support margins. (18) in, This represents the stability margin determined by both grid strength and impedance stability. This indicates the support margin determined by both frequency support and voltage support. These correspond to the aforementioned rules for determining the stable support zone, critical support zone, and insufficient support zone.

[0085] Within the parameter space defined by GFM penetration, wind speed, and grid short-circuit ratio, the operating points that satisfy the four margin constraints constitute the supporting feasible region: (19) For a given wind speed v The minimum GFM penetration rate required to meet the target support level, based on the grid short-circuit ratio (SCR), can be determined by the following formula: (20) The above formula transforms the existing four-dimensional index calculation results into comparable joint margins, feasible region boundaries, and configuration lower limits.

[0086] In this example embodiment, the support capacity mechanism under GFM permeability and wind speed conditions, and the variation law of support capacity under different GFM permeability, include: Under different GFM penetration scenarios, the equivalent impedance of the wind farm grid connection point, the capacity ratio of the grid configuration, the virtual inertia parameter, and the reactive voltage response data are read to match the penetration rate, wind speed, and grid strength. Subsequently, the support level and feasible region location of each scenario are obtained by calculating and judging according to the four-dimensional indicators and joint criteria.

[0087] When GFM penetration is low, if ESCR or K sm If the system fails to reach a stable threshold, it is considered to be in a region of insufficient support; as penetration increases, if the input data indicates that ESCR, K sm , K J and K UIf the system crosses a set threshold sequentially or simultaneously, its operating point transitions to a critical support zone or a stable support zone. This determination is based on the combined results of four-dimensional indicators, rather than on remodeling or aggregating the units within the wind farm.

[0088] Figure 3 The typical trends of the four-dimensional indicators and operational levels under different GFM penetration scenarios are shown. The evaluation results can be used to identify the minimum GFM penetration rate required to achieve the target support level, and to determine the configuration margin in combination with different short-circuit ratio conditions.

[0089] In this example embodiment, the time-varying support capability assessment under multiple wind speed conditions includes: Wind speed variations alter the active power, reactive power, voltage, and available capacity of grid-connected turbines in a wind farm, consequently causing changes in the equivalent impedance and four-dimensional support capability of the wind farm's grid connection point over time. This invention, for each wind speed operating window or each sampling moment, reads the wind farm's equivalent impedance, grid equivalent impedance, power, reactive power, voltage, grid-connected capacity ratio, and virtual inertia parameters matching that state.

[0090] For a given wind speed sequence, perform consistency checks on each state data in chronological order, and calculate ESCR. K sm , K J and K U The four-dimensional indicators and their levels at each time point are used to form a time-series evaluation result. This identifies the high-value window, critical transition window, and insufficient support window, and provides the corresponding wind speed range and operational constraints.

[0091] This invention does not establish a wind speed-impedance mapping model for a single GFM or GFL unit, nor does it aggregate the impedances of different unit groups; the required wind speed state equivalent impedance can be obtained from existing models, experimental tests, online identification, or other engineering data. Figure 4 It can serve as a schematic diagram of the change in equivalent impedance input under different wind speed conditions. Figure 5 This is used to display the time-varying results of the four-dimensional index obtained based on this input.

[0092] Example 3: In this example embodiment, the network support capability simulation platform and parameter settings include: To verify the effectiveness of the method of this invention, an onshore wind farm with a rated capacity of 200MW was selected as a case study. Five GFM penetration rates were set at 0%, 15%, 30%, 45%, and 60%, and the equivalent impedance of the grid connection point, the capacity ratio of the grid configuration, the virtual inertia parameter, and the reactive voltage response data were obtained under the corresponding operating conditions, while the total installed capacity and grid connection voltage level remained consistent. Five typical wind speeds of 4m / s, 8m / s, 10m / s, 12m / s, and 16m / s were used to form a multi-condition evaluation input sequence.

[0093] A 200MW wind farm represents a typical installed capacity for current onshore wind power development. It consists of 32 wind turbine generators, each with a capacity of 6.25MW, a hub height of 160m to 180m, and a rotor diameter of 202m to 220m. It is equipped with a 220kV substation and two 100MVA main transformers, connected to the grid via a single 220kV line. After full grid connection, the average annual power generation is approximately 500 million to 560 million kWh, with an equivalent full-load operating hours of 2500 to 3150 hours per year. This translates to annual savings of approximately 160,000 to 190,000 tons of standard coal and a reduction in carbon dioxide emissions of approximately 400,000 to 560,000 tons, demonstrating significant ecological benefits. Regarding operational characteristics, the equivalent impedance, power output, and reactive power regulation capability of a wind farm exhibit significant time-varying characteristics with wind speed. These characteristics are also influenced by key factors such as grid-factory (GFM) penetration and grid short-circuit ratio (SCR). These characteristics are the core focus of the four-dimensional support capability assessment method of this invention.

[0094] The grid-side rated voltage is 220kV, and the rated frequency is 50Hz. Under typical weak grid conditions with SCR=2.5, the grid's equivalent impedance is determined by the resistance... R g =0.15Ω and inductance L g =0.032H characterization. ESCR, K sm , K J and K U The calculations are based on the equivalent impedance at the grid connection point, the equivalent impedance of the grid, the ratio of actual impedance to critical impedance, the inertia parameters and capacity ratio of grid-connected units, and the reactive voltage response and imaginary part of the equivalent impedance. For protected or controlled saturated states, the equivalent impedance under these conditions is directly used. Based on the above parameters, steady-state, multi-wind-speed, and transient fault verifications are conducted, and the feasible domain boundary is defined using a four-dimensional index joint criterion.

[0095] In this example embodiment, the verification of the network's steady-state support capability and the feasible domain analysis include: (1) Quantitative verification of support capability under typical weak power grid conditions To verify the applicability of the quantitative assessment model for support capacity in a weak grid environment, a weak grid operating condition with a short-circuit ratio (SCR) of 2.5 was set, and the steady-state support performance under different GFM penetration configurations was systematically evaluated. Furthermore, using GFM penetration as a configuration variable, the ESCR and... K sm , K U and K J We conducted Pareto frontier analysis for the dual-objective evaluation metrics to characterize the synergistic improvement and trade-off relationship between stability and support indicators as penetration rate changes.

[0096] Figure 6 This demonstrates the equivalent short-circuit ratio (ESCR) and impedance stability margin. The Pareto front evolution pattern. Under this weak power grid condition, as the GFM penetration rate increases, both exhibit a coordinated growth trend, with the operating point migrating from the lower left to the upper right along the Pareto front. In the figure, ESCR=3 and... Two critical lines divide the coordinate plane into stable and unstable regions: in the low permeability range, the system operating point is located in the lower left region. A significantly negative value indicates the presence of subsynchronous oscillation risk, and the system strength is insufficient to support the stable operation of a weak power grid. As the penetration rate increases, the operating point gradually crosses the double critical constraints and enters the feasible region in the upper right corner, marking the transformation of the system from a passive follower dependent on the strength of the external power grid to an active supporter with autonomous voltage construction capabilities.

[0097] Figure 7 The voltage support coefficient was further presented. With virtual inertia support coefficient The Pareto front relationship. Similar to the stability indices above, and The supporting indicators for both of these indicators increase in tandem with the increase in GFM wind power penetration, but their growth characteristics differ significantly: The near-linear growth trend reflects the direct and continuous contribution of grid-connected wind turbines to the system's inertia support capability; while It climbs rapidly when penetration is low, and then approaches the ideal critical value. The voltage then tends to saturate, indicating that the voltage support capacity exhibits diminishing marginal returns.

[0098] Table 1 quantitatively presents the specific values ​​and operational status assessments of the four support capability indicators under various penetration rate configurations, further verifying the aforementioned evolutionary trends. In the 0% to 30% penetration rate range, although some indicators have improved, Still negative and Below the ideal threshold, the system is in an unstable state; when the penetration rate reaches 45%, all four indicators simultaneously meet the requirement of ESCR≥3. , Under the conditions, the operating status improved to excellent; it continued to improve to 60%, and various indicators were further optimized, but the marginal benefits decreased significantly.

[0099] Table 1. Simulation results of support capacity indices corresponding to different GFM penetration rates under weak grid conditions with SCR=2.5.

[0100] (2) Distribution characteristics of support capacity based on GFM penetration rate and power grid strength To further reveal the combined impact of GFM penetration and grid strength on support capabilities and to define the technically feasible domain boundaries under different SCR conditions, this section uses a four-dimensional index joint analysis method to systematically evaluate the evolution characteristics of support capabilities with GFM penetration of 0%-60% and SCR ranging from 1.5 to 4.0, and defines the technically feasible domain boundaries under different grid strengths accordingly. Figure 8 This demonstrates the equivalent short-circuit ratio (ESCR) and impedance stability margin. The joint distribution law of stability indices.

[0101] Figure 8 In (a), with the increase in GFM penetration and the enhancement of grid strength, ESCR exhibits a synergistic growth trend due to both factors. The double-line shaded area centered on ESCR=3 marks the critical transition zone, dividing the parameter plane into a red area of ​​insufficient compensation for weak grids and a green area of ​​sufficient strength. In the typical weak grid dimension with SCR=2.5, ESCR gradually increases from 1.45 with the increase in GFM penetration, reaching 3.25 at a penetration rate of 45%, and crossing the critical shaded zone for the first time to enter the area of ​​sufficient strength; while in the deep weak grid region with SCR<2.5, even a 60% GFM penetration rate is still insufficient to make ESCR exceed the threshold of 3.0.

[0102] Figure 8 (b) Impedance stability margin The distribution exhibits an evolutionary pattern similar to ESCR, but with... The stable critical shadow zone is narrower and shifts to the left towards lower permeability as grid strength increases. In the lower left corner of the low-permeability zone, The predominantly negative values ​​within the red zone indicate a risk of subsynchronous oscillations in the system. As the GFM ratio increases, the stability of the upper right green region gradually improves, and the stability margin gradually increases. Under the SCR=2.5 condition, When the GFM penetration rate reached 45%, it turned positive to 2.15, entering the green stable region, marking the transition of the system from the unstable region to the stable operating region.

[0103] Figure 9 The virtual inertia support coefficient was further demonstrated. With voltage support coefficient The distribution of their supporting capacity indicators shows distinct evolutionary characteristics. Figure 9 In (a), The penetration rate of GFM and the strength of the power grid show an approximately linear growth trend. As the center value, its sensitivity to SCR changes is relatively low. Under the condition of SCR=2.5, The monotonically increasing value from 0.30 at 0% GFM penetration to 6.50 at 60% indicates that the inertia support capability of grid-connected units can be effectively utilized in weak grid environments, and the value increases positively correlated with the GFM penetration rate.

[0104] and Figure 9 (b) Voltage support coefficient It exhibits significant nonlinear saturation characteristics. It climbs rapidly when GFM penetration is low, and then approaches a critical value. Growth slowed significantly after the shaded area. Under the condition of SCR=2.5, At a GFM penetration rate of 45%, it reaches 1.012, just crossing the boundary of the ideal support zone; however, in areas with deep weak power grids where SCR < 2.5, even a GFM penetration rate of 60% is still insufficient. Exceeding the threshold indicates a significant grid strength threshold effect in voltage support capability.

[0105] Table 1 quantitatively presents the specific values ​​of the four-dimensional index corresponding to different GFM penetration rates under typical weak power grid conditions with SCR=2.5. The data in this table is consistent with... Figure 8 , Figure 9 The distribution patterns corroborate each other: when the GFM penetration rate reaches the configuration range that satisfies the four-dimensional threshold, the system enters the stable support zone; when the penetration rate is low and the key indicators have not reached the threshold, the system is in the insufficient support zone. The above steady-state assessment results are for subsequent transient... (3) Sensitivity analysis of the evaluation of consistent input of operating status To verify the impact of the consistency between the evaluation input and the actual operating state on the four-dimensional support capability evaluation results, the evaluation results using the current operating state equivalent impedance input and the rated state equivalent impedance input are compared. This comparison is used to illustrate that inconsistent inputs may lead to index deviations; this invention does not involve internal modeling or correction methods for state-related impedances.

[0106] The comparative results show that if the rated state impedance is used instead of the equivalent impedance under the current operating condition, the four-dimensional indicators may exhibit systematic deviations, leading to misjudgments of the feasible domain boundary and operating level. Especially under conditions of high output, control saturation, or activated protection constraints, the rated state data is insufficient to reflect the true interactive characteristics of the wind farm ports. Therefore, this invention correlates and verifies the impedance input with the corresponding power, reactive power, and voltage states during evaluation, and uses data obtained under the same state to complete the calculation of the four-dimensional indicators.

[0107] In this example embodiment, the time-varying support characteristics under dynamic wind speed fluctuations include: To verify the established time-varying support capability assessment method and evaluate the dynamic support performance of wind farms under actual wind speed fluctuation conditions, a dynamic assessment was conducted based on the pre-obtained equivalent impedance and operating status data of the grid connection point at each time point.

[0108] (1) Simulation working condition design based on Figure 10 The dynamic response characteristics of the electrical quantities of the wind farm are shown, and the operational behavior and grid interaction mechanism of the wind farm under time-varying wind speed conditions are analyzed in depth. The simulation is set with grid strength SCR=2.5 and GFM penetration rate of 30%, and a stepped multi-platform wind speed sequence is designed to successively traverse typical operating conditions of 4m / s, 8m / s, 10m / s, 16m / s, and 12m / s to cover the low wind speed, maximum power point tracking, rated power, and high wind speed operating ranges. Smooth transitions are adopted between each platform, and the equivalent impedance, power, reactive power, and grid connection point voltage data of the wind farm corresponding to each operating window are obtained.

[0109] from Figure 10 The active power response curves show a significant nonlinear mapping relationship between wind farm output power and wind speed. During the low wind speed period of 4 m / s, the total active power drops to approximately 7 MW, about 3.5% of the system's rated capacity. As the wind speed increases to 8 m / s and 10 m / s, the power recovers to 100 MW and 160 MW, respectively. When the wind speed reaches 16 m / s and triggers pitch control, the active power output remains constant at the rated value of 200 MW. Furthermore, under the symmetrical 8.56 m / s condition, the active power is 118.9 MW and 117.9 MW, respectively, with a deviation of less than 1%, indicating good steady-state convergence of the system. Reactive power output exhibits a strongly coupled time-varying characteristic with active power output. Reactive power demand decreases synchronously during low wind speed periods, while reactive power output reaches the rated limit during high output periods. This coupling relationship affects voltage regulation capability.

[0110] Any additional reactive power demand could cause the system to enter a control-constrained or capacity-limited state, thereby affecting the port voltage build-up capability. This simulation condition provides a dynamic benchmark for subsequent time-varying analysis of four-dimensional support capability indicators, ensuring that the support capability assessment results can reflect changes in operating status caused by wind speed fluctuations.

[0111] (2) Verification of the time-varying characteristics of the supporting capability indicators based on Figure 11 The time-series characteristics of the four-dimensional support capability indicators shown can quantitatively evaluate the evolution of the active support performance of wind farms under different time-varying conditions. The four indicators generally show a trend of first increasing and then decreasing, with better performance in the 8m / s to 10m / s range, while significant attenuation occurs when extending to both low and high wind speeds; this result is consistent with the overall variation pattern of the aforementioned multi-wind-speed condition analysis.

[0112] exist Figure 11 During the initial 0-2s period, all four indicators under the 8.56m / s operating condition were at a good level, with ESCR consistently above the critical threshold. K sm It is in the positive stability margin range. K J and K U All indicators remained at a high level. As the wind speed suddenly dropped to 4 m / s, all four indicators deteriorated simultaneously. After the wind speed recovered to 8 m / s and 10 m / s, the indicators gradually rebounded and formed a relatively optimal support window. When the wind speed further increased to 16 m / s and the system entered pitch control mode, the indicators declined again. This time-series result indicates that the four-dimensional indicators can identify high, critical, and insufficient support capabilities caused by wind speed changes.

[0113] In this example embodiment, the support performance verification under transient faults includes: To comprehensively evaluate the support capability of grid-connected wind farms under extreme conditions, this section further conducts transient fault simulation verification based on the aforementioned steady-state assessment and dynamic analysis. Considering that the main challenges faced by renewable energy power plants under weak grid conditions come from short-term severe faults and high-power disturbances, two typical operating conditions are set up: three-phase short-circuit fault and power step disturbance. The grid short-circuit ratio is maintained at SCR=2.5. The system voltage withstand, frequency support, and power recovery characteristics are compared and analyzed under three different GFM penetration rates of 15%, 45%, and 60%.

[0114] (1) Three-phase short circuit fault condition Simulation results under three-phase short-circuit fault are as follows: Figure 12 As shown, a three-phase metallic short-circuit fault occurs at point PCC at time t=1.0s. The fault lasts for 0.15s and is cleared at t=1.15s, simulating a severe voltage drop condition.

[0115] Figure 12 In the case of a 15% GFM penetration rate, the voltage at the PCC point drops to around 0.65 pu during a fault, and the voltage recovery process after fault clearance exhibits continuous low-frequency oscillations. With a 45% GFM configuration, the voltage drops to approximately 0.82 pu during a fault, and recovery after fault clearance is faster. The voltage response of the 60% GFM configuration is similar to that of the 45% configuration, indicating that when... K U Once a certain level is reached, the marginal improvement from further increasing GFM penetration tends to diminish. From the perspective of reactive power support, the reactive power output recovery process under low penetration conditions is more prone to oscillations, while higher penetration conditions can provide smoother reactive power support, which is consistent with the aforementioned four-dimensional evaluation results for different GFM penetration rates.

[0116] (2) Power step disturbance condition Simulation results of power step disturbance conditions reveal the significant impact of different GFM penetration rates on frequency support capability, such as... Figure 13 As shown, a step decrease in the active power output of the wind farm is set at t=1.0s to simulate a power deficit scenario caused by a sudden drop in wind speed or grid dispatch.

[0117] Figure 13 In the 15% GFM configuration, the power deficit caused a continuous drop in system frequency, indicating insufficient virtual inertia support at low penetration rates. With a 45% GFM configuration, the frequency remained within a safe operating range and the recovery process was relatively smooth; with a 60% GFM configuration, the frequency deviation further decreased and power recovery was faster. These results demonstrate... K J Equal frequency support indicators can reflect the power disturbance tolerance under different GFM penetration rates.

[0118] The above transient simulation results correspond to the four-dimensional steady-state evaluation index: when ESCR is below the threshold and K sm When on the unstable side, continuous oscillation is likely to occur after a fault; when ESCR and K sm Satisfy stability conditions, and at the same time K U and K J When the support requirements are met, both voltage sag and frequency deviation are improved. This verifies the effective discrimination capability of the four-dimensional steady-state evaluation method based on the equivalent impedance input of the grid connection point for transient stability.

[0119] It should be noted that although the steps of the method in this disclosure are described in a specific order in the accompanying drawings, this does not require or imply that the steps must be performed in that specific order, or that all the steps shown must be performed to achieve the desired result. Additional or alternative steps may be omitted, multiple steps may be combined into one step, and / or a step may be broken down into multiple steps.

[0120] Furthermore, this example embodiment also provides a four-dimensional index device for evaluating the support capacity of grid-connected wind farms under weak grid conditions. (Refer to...) Figure 14 As shown, the four-dimensional index device 200 for evaluating the support capacity of grid-connected wind farms under weak grid conditions may include: a data acquisition module 210, a consistency verification module 220, a four-dimensional index calculation module 230, a joint judgment module 240, and a feasible domain output module 250. Wherein: Data acquisition module 210 is used to acquire the equivalent impedance of the wind farm grid connection point, the equivalent impedance of the grid, the rated capacity of the wind farm, the capacity ratio of grid-connected units, virtual inertia parameters, and reactive power regulation and voltage change information. The consistency verification module 220 is used to perform consistency verification on the acquired information. The consistency verification includes consistency verification of frequency range, per-unit reference, rated capacity and sampling time. The four-dimensional index calculation module 230 is used to calculate the equivalent short-circuit ratio, impedance stability margin, virtual inertia support coefficient and voltage support coefficient based on the verified information, and to construct a four-dimensional index evaluation system. The joint judgment module 240 is used to perform joint judgment and risk classification based on the comparison results of the four-dimensional indicators and the preset threshold, and to determine whether the operating point is in the stable support zone, the critical support zone or the insufficient support zone. The feasible domain output module 250 is used to repeatedly perform the above calculations and judgments under different combinations of grid penetration, wind speed and grid short-circuit ratio, and output the supporting feasible domain and the grid capacity configuration boundary that meets the target support level.

[0121] The specific details of the four-dimensional index device module for evaluating the support capacity of grid-connected wind farms under each of the above-mentioned weak grid conditions have been described in detail in the corresponding four-dimensional index method for evaluating the support capacity of grid-connected wind farms under weak grid conditions, so they will not be repeated here.

[0122] It should be noted that although several modules or units of a four-dimensional index device 200 for evaluating the support capacity of grid-connected wind farms under weak grid conditions are mentioned in the detailed description above, this division is not mandatory. In fact, according to the embodiments of this disclosure, the features and functions of two or more modules or units described above can be embodied in one module or unit. Conversely, the features and functions of one module or unit described above can be further divided and embodied by multiple modules or units.

[0123] Furthermore, the above figures are merely illustrative of the processes included in the method according to exemplary embodiments of the present invention, and are not intended to be limiting. It is readily understood that the processes shown in the above figures do not indicate or limit the temporal order of these processes. Additionally, it is readily understood that these processes may be executed synchronously or asynchronously, for example, in multiple modules.

[0124] Other embodiments of this disclosure will readily occur to those skilled in the art upon consideration of the specification and practice of the invention disclosed herein. This application is intended to cover any variations, uses, or adaptations of this disclosure that follow the general principles of this disclosure and include common knowledge or customary techniques in the art not disclosed herein. The specification and embodiments are to be considered exemplary only, and the true scope and spirit of this disclosure are indicated by the claims.

[0125] It should be understood that this disclosure is not limited to the precise structures described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope. The scope of this disclosure is limited only by the appended claims.

Claims

1. A four-dimensional index method for evaluating the support capacity of grid-connected wind farms under weak grid conditions, characterized in that, The method includes: Obtain the equivalent impedance of the wind farm's grid connection point, the equivalent impedance of the power grid, the rated capacity of the wind farm, the capacity ratio of grid-connected units, virtual inertia parameters, and reactive power regulation and voltage change information. The acquired information is subjected to a consistency check, which includes consistency checks on frequency range, per-unit reference, rated capacity, and sampling time. Based on the verified information, the equivalent short-circuit ratio, impedance stability margin, virtual inertia support coefficient, and voltage support coefficient are calculated respectively to construct a four-dimensional index evaluation system. Based on the comparison results between the four-dimensional indicators and the preset thresholds, a joint judgment and risk classification are performed to determine whether the operating point is in a stable support zone, a critical support zone, or a zone with insufficient support. The above calculations and judgments are repeated under different combinations of grid penetration, wind speed and grid short-circuit ratio to output the feasible domain and the grid capacity configuration boundary that meets the target support level.

2. The method as described in claim 1, characterized in that, In the method described above, the acquired information is organized into an input vector according to the evaluation time: in, For a moment ,frequency The equivalent impedance Z obtained in advance at the grid connection point of the wind farm g (jω,t) represents the corresponding equivalent impedance of the power grid. For the proportion of network-type capacity, J eq (t) represents the equivalent virtual inertia.

3. The method as described in claim 2, characterized in that, In the method described above, a power-voltage state matching error is constructed before calculating the four-dimensional index: in, , , This is the recording state corresponding to the equivalent impedance data. , , As the rated reference, , , The weights are 1, and their sum is 1. When the state matching error is not greater than a preset threshold, the corresponding impedance data is used for the calculation of the four-dimensional index at the current moment.

4. The method as described in claim 3, characterized in that, In the method described above, the equivalent short-circuit ratio is calculated based on the rated capacity of the wind farm, the rated voltage of the power grid, and the series impedance modulus of the equivalent impedance at the wind farm's grid connection point and the equivalent impedance of the power grid. The calculation formula is as follows: Among them, U N S is the rated voltage of the power grid. wfN R is the rated capacity of the wind farm. mix X mix R represents the real and imaginary parts of the equivalent impedance at the grid connection point of the wind farm, respectively. g X g These are the real and imaginary parts of the equivalent impedance of the power grid, respectively.

5. The method as described in claim 4, characterized in that, In the method described above, the impedance stability margin is calculated based on the ratio of the equivalent impedance modulus at the wind farm's grid connection point to the equivalent impedance modulus of the power grid, as well as the critical impedance ratio. The calculation formula is as follows: Among them, K Z,c Let |Z_mix| be the critical impedance ratio, and |Z_mix| be the equivalent impedance modulus at the grid connection point of the wind farm. g | represents the equivalent impedance magnitude of the power grid.

6. The method as described in claim 5, characterized in that, In the method described above, the virtual inertia support coefficient is calculated based on the rated capacity of the grid-type wind turbine, the virtual inertia of a single unit, the rated capacity of the wind farm, and the rated inertia of a synchronous generator of the same capacity. The calculation formula is as follows: Among them, J i Let J be the virtual inertia of the i-th grid-connected wind turbine unit. syn J is the rated inertia of a synchronous generator of the same capacity. eq This represents the equivalent virtual inertia of a wind farm.

7. The method as described in claim 6, characterized in that, In the method described above, the voltage support coefficient is calculated based on the voltage fluctuation amplitude, the grid rated voltage, the imaginary part of the equivalent impedance at the wind farm grid connection point, and the reactive power. The calculation formula is as follows: Where ΔU is the voltage fluctuation amplitude of the power grid, X mix R is the imaginary part of the equivalent impedance at the grid connection point of the wind farm. mix Q is the real part of the equivalent impedance at the grid connection point of the wind farm. mix This refers to the reactive power of the wind farm.

8. The method as described in claim 7, characterized in that, The joint determination and risk classification includes: reorganizing the four-dimensional indicators into a four-dimensional evaluation indicator vector according to the same runtime: Define a standardized margin for each indicator: The minimum margin of the power grid strength and impedance stability indicators is used as the stability margin, and the minimum margin of the frequency support and voltage support indicators is used as the support margin. The operating points that satisfy the four margin constraints constitute the supporting feasible region: For a given wind speed v and grid short-circuit ratio SCR, the minimum grid penetration rate required to meet the target support level is determined by the following formula: 。 9. The method as described in claim 1, characterized in that, The method further includes: For each wind speed operating window or each sampling moment, the equivalent impedance of the wind farm, the equivalent impedance of the power grid, power, reactive power, voltage, grid capacity ratio and virtual inertia parameters that match the state are read. The consistency of the data for each state is checked in chronological order and the four-dimensional indicators are calculated. The four-dimensional indicators and their levels at each moment are combined to form a time-series evaluation result to identify high-value windows, critical transition windows and insufficient support windows of support capacity.

10. A four-dimensional index device for evaluating the support capacity of grid-connected wind farms under weak grid conditions, characterized in that, The device includes: The data acquisition module is used to acquire the equivalent impedance of the wind farm grid connection point, the equivalent impedance of the grid, the rated capacity of the wind farm, the capacity ratio of grid-connected units, virtual inertia parameters, and reactive power regulation and voltage change information. The consistency verification module is used to perform consistency verification on the acquired information. The consistency verification includes consistency verification of frequency range, per-unit reference, rated capacity and sampling time. The four-dimensional index calculation module is used to calculate the equivalent short-circuit ratio, impedance stability margin, virtual inertia support coefficient and voltage support coefficient based on the verified information, and to construct a four-dimensional index evaluation system. The joint judgment module is used to perform joint judgment and risk classification based on the comparison results of the four-dimensional indicators and preset thresholds, and to determine whether the operating point is in the stable support zone, critical support zone or insufficient support zone. The feasible domain output module is used to repeatedly perform the above calculations and judgments under different combinations of grid penetration, wind speed and grid short-circuit ratio, and output the supporting feasible domain and the grid capacity configuration boundary that meets the target support level.

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