A wind power converter broadband oscillation online risk assessment method and related device

Abstract

This invention discloses a method and related apparatus for online risk assessment of broadband oscillations in wind power converters, comprising: acquiring real-time operating data of the wind power converter and grid parameters; calculating multi-dimensional stability characteristics based on the real-time operating data of the wind power converter and grid parameters; inputting the multi-dimensional stability characteristics into a weighted geometric mean fusion model to obtain an oscillation risk coefficient R; and assessing the broadband oscillation risk of the wind power converter based on the oscillation risk coefficient R. This method and related apparatus can provide early warning and quantitative forecasting of oscillation risks.

Description

Technical Field

[0001] This invention belongs to the field of wind power generation technology, and relates to a method and related device for online risk assessment of broadband oscillation of wind power converters. Background Technology

[0002] As wind power penetration in the power grid continues to increase, the problem of broadband oscillations caused by power electronic equipment is becoming increasingly prominent, seriously threatening the safe and stable operation of the power grid. Wind power converters, as the core interface equipment of wind turbine generators, are a key component in the interaction between wind turbines and the power grid that generates broadband oscillations.

[0003] Existing methods for assessing oscillation risks often have limitations. For example, some methods rely on only a single type of characteristic, such as analyzing only impedance characteristics or observing only time-domain waveforms, resulting in an incomplete assessment dimension and difficulty in capturing complex oscillation risks early and comprehensively. Other methods, while employing multiple characteristics, lack an effective fusion mechanism and cannot provide a unified, quantitative risk indicator. Furthermore, traditional threshold settings often rely on fixed empirical values, making it difficult to adapt to changes in different wind fields and operating conditions, leading to insufficient accuracy in early warning.

[0004] Therefore, there is an urgent need in this field for a method that can be executed online and automatically, and can comprehensively and quantitatively assess oscillation risks from multiple physical dimensions, in order to achieve the transformation from "post-event processing" to "pre-event warning". Summary of the Invention

[0005] The purpose of this invention is to overcome the shortcomings of the prior art and provide a method and related device for online risk assessment of broadband oscillations in wind power converters. This method and related device can provide early warning and quantitative analysis of oscillation risks.

[0006] To achieve the above objectives, this invention discloses an online risk assessment method for broadband oscillations in wind power converters, comprising: The system acquires real-time operating data of the wind power converter and grid parameters, and calculates multi-dimensional stability characteristics based on the real-time operating data of the wind power converter and grid parameters. The multidimensional stability features are input into the weighted geometric mean fusion model to obtain the oscillation risk coefficient R; The broadband oscillation risk of the wind power converter is assessed based on the aforementioned oscillation risk coefficient R.

[0007] Furthermore, the multidimensional stability features include at least impedance stability features. Small signal stability characteristics Dynamic response characteristics and system strength characteristics .

[0008] Furthermore, the weighted geometric mean fusion model is expressed as:

[0009] where the weight coefficients α, β, γ, δ satisfy α + β + γ + δ = 1, and according to the impedance-dominant principle, α>β, α>γ, α>δ, is the comprehensive risk coefficient.

[0010] Furthermore, the process of evaluating the broadband oscillation risk of the wind power converter according to the oscillation risk coefficient R is as follows: When R < R_low, it is determined as low broadband oscillation risk; when R_low ≤ R < R_high, it is determined as medium broadband oscillation risk; when R ≥ R_high, it is determined as high broadband oscillation risk, where R_low is the medium risk threshold and R_high is the high risk threshold.

[0011] Furthermore, the medium risk threshold R_low = Q_{85}({R_historical}) × k_safety; The high risk threshold R_high = Q_{95}({R_historical}) × k_safety; where Q_{85} and Q_{95} are the 85% and 95% quantiles respectively, k_safety is the safety factor, and R_historical is the statistical quantile of the historical comprehensive risk coefficient.

[0012] The present invention discloses an on-line risk assessment system for broadband oscillation of a wind power converter, including: Obtain the real-time operation data of the wind power converter and grid parameters, and calculate multi-dimensional stability characteristic quantities according to the real-time operation data of the wind power converter and grid parameters; Input the multi-dimensional stability characteristic quantities into the weighted geometric mean fusion model to obtain the oscillation risk coefficient R; Evaluate the broadband oscillation risk of the wind power converter according to the oscillation risk coefficient R.

[0013] Furthermore, the weighted geometric mean fusion model is expressed as:

[0014] where the weight coefficients α, β, γ, δ satisfy α + β + γ + δ = 1, and according to the impedance-dominant principle, α>β, α>γ, α>δ, is the comprehensive risk coefficient, and the multi-dimensional stability characteristic quantities at least include impedance stability characteristic quantities and small-signal stability characteristic quantities Dynamic response feature quantity and system strength feature quantity .

[0015] Furthermore, the process of evaluating the broadband oscillation risk of the wind power converter according to the oscillation risk coefficient R is as follows: When R < R_low, it is determined as low broadband oscillation risk; when R_low ≤ R < R_high, it is determined as medium broadband oscillation risk; when R ≥ R_high, it is determined as high broadband oscillation risk, where R_low is the medium risk threshold and R_high is the high risk threshold; The medium risk threshold R_low = Q_{85}({R_historical}) × k_safety; The high risk threshold R_high = Q_{95}({R_historical}) × k_safety; Among them, Q_{85} and Q_{95} are the 85% and 95% quantiles respectively, k_safety is the safety factor, and R_historical is the statistical quantile of the historical comprehensive risk coefficient.

[0016] The present invention discloses a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the steps of the online risk assessment method for the broadband oscillation of the wind power converter are implemented.

[0017] The present invention discloses a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, the steps of the online risk assessment method for the broadband oscillation of the wind power converter are implemented.

[0018] The present invention has the following beneficial effects: When the online risk assessment method for the broadband oscillation of the wind power converter and related devices of the present invention are specifically operated, the multi-dimensional stability feature quantity is input into the weighted geometric mean fusion model to obtain the oscillation risk coefficient R; the broadband oscillation risk of the wind power converter is evaluated according to the oscillation risk coefficient R. Among them, the features of four different physical dimensions of impedance, eigenvalue, time-frequency domain, and system strength are fused to achieve an all-round perception of the oscillation risk. In addition, the weighted geometric mean model is used for fusion, which is very sensitive to the deterioration of any feature, and by assigning the highest weight to the impedance feature, it ensures a solid physical basis for the evaluation and more accurate results.

[0019] Furthermore, the risk threshold in this invention is dynamically updated based on historical data, which can adapt to changes in different sites and operating conditions, avoiding the drawbacks of a fixed threshold and improving the accuracy and reliability of early warning. Attached Figure Description

[0020] To more clearly illustrate the technical solutions of the embodiments of this application, the drawings used in the description of the embodiments of this application will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0021] Figure 1 This is a flowchart of the method of the present invention. Detailed Implementation

[0022] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0023] In the description of this invention, it should be understood that the terms "comprising" and "including" indicate the presence of the described features, integrals, steps, operations, elements and / or components, but do not exclude the presence or addition of one or more other features, integrals, steps, operations, elements, components and / or collections thereof.

[0024] It should also be understood that the terminology used in this specification is for the purpose of describing particular embodiments only and is not intended to limit the invention. As used in this specification and the appended claims, the singular forms “a,” “an,” and “the” are intended to include the plural forms unless the context clearly indicates otherwise.

[0025] It should also be further understood that the term "and / or" as used in this specification and the appended claims refers to any combination and all possible combinations of one or more of the associated listed items, and includes such combinations. For example, A and / or B can represent three cases: A alone, A and B simultaneously, and B alone. Additionally, the character " / " in this invention generally indicates that the preceding and following objects have an "or" relationship.

[0026] It should be understood that although terms such as first, second, third, etc., may be used in the embodiments of the present invention to describe the preset range, these preset ranges should not be limited to these terms. These terms are only used to distinguish the preset ranges from one another. For example, without departing from the scope of the embodiments of the present invention, the first preset range may also be referred to as the second preset range, and similarly, the second preset range may also be referred to as the first preset range.

[0027] Depending on the context, the word "if" as used here can be interpreted as "when," "when," "in response to determination," or "in response to detection." Similarly, depending on the context, the phrase "if determination" or "if detection (of the stated condition or event)" can be interpreted as "when determination," "in response to determination," "when detection (of the stated condition or event)," or "in response to detection (of the stated condition or event)."

[0028] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. The components of the embodiments of the present invention described and shown in the accompanying drawings can generally be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present invention provided in the accompanying drawings is not intended to limit the scope of the claimed invention, but merely to illustrate selected embodiments of the invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without inventive effort are within the scope of protection of the present invention.

[0029] The accompanying drawings illustrate various structural schematic diagrams according to embodiments disclosed in this invention. These drawings are not to scale, and some details have been enlarged for clarity, and some details may have been omitted. The shapes of the various regions and layers shown in the drawings, as well as their relative sizes and positional relationships, are merely exemplary and may deviate from reality due to manufacturing tolerances or technical limitations. Furthermore, those skilled in the art can design regions / layers with different shapes, sizes, and relative positions as needed.

[0030] Example 1 refer to Figure 1 The online risk assessment method for broadband oscillation of wind power converters described in this invention includes: 1) Obtain real-time operating data of the wind power converter and grid parameters, and calculate multi-dimensional stability characteristics based on the real-time operating data of the wind power converter and grid parameters; 2) Input the multi-dimensional stability features into the weighted geometric mean fusion model to obtain the oscillation risk coefficient R; 3) Compare the oscillation risk coefficient R with the risk level threshold dynamically set based on historical data, assess the current risk level based on the comparison result, and output the assessment result.

[0031] The multidimensional stability characteristics include at least impedance stability characteristics. Small signal stability characteristics Dynamic response characteristics and system strength characteristics Specifically: 11) Impedance stability characteristic quantity Impedance stability characteristic quantity The minimum distance d_min of the converter impedance to grid impedance ratio L(f) relative to the critical point (-1, 0) on the Nyquist plot is calculated using the formula... The mapping yields a value where d_0 is the safety distance constant.

[0032] 12) Small-signal stability characteristics Small-signal stability characteristics The damping ratio ξ is calculated based on the real part σ and imaginary part ω of the eigenvalues ​​of the dominant oscillation mode of the system, and then the formula is used to calculate the damping ratio ξ. We obtain, where ξ_critical is the critical damping ratio; 13) Dynamic response characteristics Dynamic response characteristics By performing time-frequency analysis on the output current signal, the Shannon entropy H of the energy distribution is calculated, and then expressed using the formula... Normalization yields the value, where H_max is the maximum possible entropy value; 14) System strength characteristics System strength characteristics Based on the generalized short-circuit ratio (GSCR) at the grid connection point, using the formula Calculated.

[0033] In this embodiment, the weighted geometric mean fusion model is:

[0034] The weighting coefficients α, β, γ, δ satisfy α + β + γ + δ = 1, and according to the impedance dominance principle, α>β, α>γ, α>δ. For example, α = 0.45, β = 0.25, γ = 0.20, δ = 0.10.

[0035] In this embodiment, the risk level threshold is set by calculating the statistical quantile R_historical of the historical comprehensive risk coefficient and multiplying it by a safety factor, specifically: Medium-risk threshold R_low = Q_{85}({R_historical}) × k_safety High-risk threshold R_high = Q_{95}({R_historical}) × k_safety Where Q_{85} and Q_{95} are the 85th and 95th percentiles, respectively, and k_safety is the safety factor.

[0036] Example 2 This embodiment includes the following steps: 1) System initialization and parameter setting; Set the evaluation period T_interval to 1 hour. Initialize the weighting coefficients: α=0.45, β=0.25, γ=0.20, δ=0.10. Set the initial risk thresholds: R_low=0.3, R_high=0.6. Set the safety factor k_safety=1.2.

[0037] 2) Periodic evaluation process, refer to Figure 1 ; 21) The system automatically initiates an evaluation every hour. First, it obtains real-time data on the three-phase current, voltage, power, and grid parameters of the converter from the wind farm monitoring system.

[0038] 22) Calculate multidimensional feature quantities; 221) Calculate the impedance stability characteristic quantity Based on the acquired data, the pre-stored converter and grid impedance models Z_wind(f) and Z_grid(f) are identified online or invoked. The system scans within the desired frequency band (e.g., 5Hz to 1000Hz), calculates L(f) = Z_wind(f) / Z_grid(f), and finds its minimum distance d_min to the Nyquist critical point (-1, 0), substituting this distance into the formula. middle, The value is 0.5, thus yielding the impedance stability characteristic. .

[0039] 222) Calculate the small-signal stability characteristics. Linearize the system based on the current operating point to obtain the state-space matrix and calculate its eigenvalues. Identify the eigenvalues ​​(σ, ω) of the dominant oscillation mode and calculate the damping ratio. Setting ξ_critical=0.05, the small-signal stability characteristic is calculated by substituting it into the formula. .

[0040] 223) Calculate the dynamic response characteristic quantities : Perform 4-layer 'db4' wavelet packet decomposition on the output current signal over a past period (e.g., 10 seconds) to obtain 16 sub-band energies. Calculate the energy probability and Shannon entropy H, and then divide by ln(16) to obtain .

[0041] 224) Calculate the system strength characteristic quantity : Calculate the generalized short-circuit ratio GSCR of the current grid connection point according to the grid parameters, and set GSCR_critical = 2.0, and substitute it into the formula for calculation .

[0042] 23) Risk fusion and assessment; The calculated , , , are substituted into the weighted geometric mean fusion model to obtain the comprehensive risk coefficient R as:

[0043] 24) Risk determination and warning; Compare the comprehensive risk coefficient R with the currently effective threshold. If R < R_low, it is determined as a low broadband oscillation risk, and only log is recorded; when R_low ≤ R < R_high, it is determined as a medium broadband oscillation risk, and a yellow warning is issued on the monitoring interface to prompt the operator to pay attention. When R ≥ R_high, it is determined as a high broadband oscillation risk, and an immediate red alarm is issued, and the preset safety strategy can be automatically executed, such as limiting the active power of the converter to 80% of the rated value.

[0044] 3) Threshold adaptive update; Automatically recalculate the risk threshold once a month, collect all historical R values in the past month, calculate their 85% and 95% quantiles, and multiply by the safety factor 1.2 to obtain the new R_low and R_high for the next month's assessment.

[0045] Example 3 The on-line risk assessment system for broadband oscillation of the wind power converter described in the present invention includes: Obtain the real-time operation data and grid parameters of the wind power converter, and calculate the multi-dimensional stability characteristic quantity according to the real-time operation data and grid parameters of the wind power converter; Input the multi-dimensional stability characteristic quantity into the weighted geometric mean fusion model to obtain the oscillation risk coefficient R; Evaluate the broadband oscillation risk of the wind power converter according to the oscillation risk coefficient R.

[0046] In this embodiment, the weighted geometric mean fusion model is expressed as:

[0047] Among them, the weight coefficients α, β, γ, δ satisfy α + β + γ + δ = 1, and according to the impedance dominance principle, α > β, α > γ, α > δ. is the comprehensive risk coefficient, and the multi-dimensional stability characteristic quantity at least includes the impedance stability characteristic quantity , the small-signal stability characteristic quantity , the dynamic response characteristic quantity and the system strength characteristic quantity .

[0048] In this embodiment, the process of evaluating the wide-frequency oscillation risk of the wind power converter according to the oscillation risk coefficient R is as follows: When R < R_low, it is determined as a low wide-frequency oscillation risk; when R_low ≤ R < R_high, it is determined as a medium wide-frequency oscillation risk; when R ≥ R_high, it is determined as a high wide-frequency oscillation risk, where R_low is the medium risk threshold and R_high is the high risk threshold; The medium risk threshold R_low = Q_{85}({R_historical}) × k_safety; The high risk threshold R_high = Q_{95}({R_historical}) × k_safety; Among them, Q_{85} and Q_{95} are the 85% and 95% quantiles respectively, k_safety is the safety factor, and R_historical is the statistical quantile of the historical comprehensive risk coefficient.

[0049] The division of modules in the embodiments of the present application is illustrative, only a logical function division. In actual implementation, there may be other division methods. In addition, in each embodiment of the present application, each functional module can be integrated in one processor, or can exist physically alone, or two or more modules can be integrated in one module. The above integrated modules can be implemented in the form of hardware or in the form of software functional modules.

[0050] Embodiment 4 A computer device includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the steps of a method for online risk assessment of broadband oscillations in a wind power converter. For example, the method includes: acquiring real-time operating data of the wind power converter and grid parameters; calculating multi-dimensional stability characteristics based on the real-time operating data and grid parameters; inputting the multi-dimensional stability characteristics into a weighted geometric mean fusion model to obtain an oscillation risk coefficient R; and assessing the broadband oscillation risk of the wind power converter based on the oscillation risk coefficient R. The memory may include main memory, such as high-speed random access memory, and may also include non-volatile memory, such as at least one disk storage device. The processor, network interface, and memory are interconnected via an internal bus, which may be an industry standard architecture bus, a peripheral component interconnection standard bus, an extended industry standard architecture bus, etc. The bus may be classified as an address bus, data bus, control bus, etc. The memory stores the program; specifically, the program may include program code, which includes computer operation instructions. The memory may include main memory and non-volatile memory, and provides instructions and data to the processor.

[0051] Example 5 A computer-readable storage medium stores a computer program that, when executed by a processor, implements the steps of the online risk assessment method for broadband oscillations of a wind power converter. For example, the method includes: acquiring real-time operating data of the wind power converter and grid parameters; calculating multi-dimensional stability characteristics based on the real-time operating data and grid parameters; inputting the multi-dimensional stability characteristics into a weighted geometric mean fusion model to obtain an oscillation risk coefficient R; and assessing the broadband oscillation risk of the wind power converter based on the oscillation risk coefficient R. Specifically, the computer-readable storage medium includes, but is not limited to, volatile memory and / or non-volatile memory. The volatile memory may include random access memory (RAM) and / or cache memory, etc. The non-volatile memory may include read-only memory (ROM), hard disk, flash memory, optical disk, magnetic disk, etc.

[0052] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0053] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0054] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0055] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0056] Other embodiments of the invention will readily occur to those skilled in the art upon consideration of the specification and disclosure of the invention. This application is intended to cover any variations, uses, or adaptations of the invention that follow the general principles of the invention and include common knowledge or customary techniques in the art not disclosed herein. The specification and examples are to be considered exemplary only, and the true scope and spirit of the invention are indicated by the following claims.

[0057] It should be understood that the present invention is not limited to the precise structure described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope. The scope of the invention is limited only by the appended claims.

[0058] The above description is merely a preferred embodiment of the present invention and does not constitute any limitation on the present invention. Any simple modifications, alterations, or equivalent structural changes made to the above embodiments based on the technical essence of the present invention shall still fall within the protection scope of the present invention.

Claims

1. A method for online risk assessment of broadband oscillation in wind power converters, characterized in that, include: The system acquires real-time operating data of the wind power converter and grid parameters, and calculates multi-dimensional stability characteristics based on the real-time operating data of the wind power converter and grid parameters. The multidimensional stability features are input into the weighted geometric mean fusion model to obtain the oscillation risk coefficient R; The broadband oscillation risk of the wind power converter is assessed based on the aforementioned oscillation risk coefficient R.

2. The online risk assessment method for broadband oscillation of wind power converters according to claim 1, characterized in that, The multidimensional stability characteristics include at least impedance stability characteristics. Small signal stability characteristics Dynamic response characteristics and system strength characteristics .

3. The online risk assessment method for broadband oscillation of wind power converters according to claim 2, characterized in that, The weighted geometric mean fusion model is expressed as follows: The weighting coefficients α, β, γ, δ satisfy α + β + γ + δ = 1, and according to the impedance dominance principle, α > β, α > γ, α > δ. This is the comprehensive risk coefficient.

4. The online risk assessment method for broadband oscillation of wind power converters according to claim 2, characterized in that, The process of assessing the broadband oscillation risk of the wind power converter based on the oscillation risk coefficient R is as follows: When R < R_low, it is judged as low broadband oscillation risk; when R_low ≤ R < R_high, it is judged as medium broadband oscillation risk; when R ≥ R_high, it is judged as high broadband oscillation risk. Here, R_low is the medium risk threshold and R_high is the high risk threshold.

5. The online risk assessment method for broadband oscillation of wind power converters according to claim 4, characterized in that, Medium-risk threshold R_low = Q_{85}({R_historical}) × k_safety; High-risk threshold R_high = Q_{95}({R_historical}) × k_safety; Where Q_{85} and Q_{95} are the 85th and 95th percentiles, respectively, k_safety is the safety factor, and R_historical is the statistical quantile of the historical comprehensive risk factor.

6. A broadband oscillation online risk assessment system for wind power converters, characterized in that, include: The system acquires real-time operating data of the wind power converter and grid parameters, and calculates multi-dimensional stability characteristics based on the real-time operating data of the wind power converter and grid parameters. The multidimensional stability features are input into the weighted geometric mean fusion model to obtain the oscillation risk coefficient R; The broadband oscillation risk of the wind power converter is assessed based on the aforementioned oscillation risk coefficient R.

7. The online risk assessment system for wideband oscillation of wind power converters according to claim 6, characterized in that, The weighted geometric mean fusion model is expressed as follows: The weighting coefficients α, β, γ, δ satisfy α + β + γ + δ = 1, and according to the impedance dominance principle, α > β, α > γ, α > δ. To comprehensively assess the risk coefficient, the multi-dimensional stability characteristics should include at least the impedance stability characteristics. Small signal stability characteristics Dynamic response characteristics and system strength characteristics .

8. The online risk assessment system for wideband oscillation of wind power converters according to claim 7, characterized in that, The process of assessing the broadband oscillation risk of the wind power converter based on the oscillation risk coefficient R is as follows: When R < R_low, it is judged as low broadband oscillation risk; when R_low ≤ R < R_high, it is judged as medium broadband oscillation risk; when R ≥ R_high, it is judged as high broadband oscillation risk, where R_low is the medium risk threshold and R_high is the high risk threshold. Medium-risk threshold R_low = Q_{85}({R_historical}) × k_safety; High-risk threshold R_high = Q_{95}({R_historical}) × k_safety; Where Q_{85} and Q_{95} are the 85th and 95th percentiles, respectively, k_safety is the safety factor, and R_historical is the statistical quantile of the historical comprehensive risk factor.

9. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the online risk assessment method for broadband oscillation of wind power converters as described in any one of claims 1-5.

10. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements the steps of the online risk assessment method for broadband oscillation of wind power converters as described in any one of claims 1-5.

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