Broadband oscillation parameter identification method and system based on Bayesian reasoning

Abstract

The invention discloses a broadband oscillation parameter identification method based on Bayesian reasoning. The method comprises the following steps: acquiring PMU oscillation recording data by connecting a WAMS database interface; setting a decomposition window length, initializing VMD parameters, and carrying out modal decomposition to obtain each oscillation mode; bayesian reasoning is carried out on the oscillation modes, and posterior parameter distribution and a mean value of broadband oscillation mode parameters (including amplitude, frequency and damping) are obtained through MCMC solving. The invention provides a broadband oscillation parameter identification method based on Bayesian reasoning, and the method system can judge and identify the modal components of complex multimode aliasing broadband oscillation signals, and accurately infer the modal information (including amplitude, frequency and damping) of the broadband oscillation data of a power grid.

Description

Technical Field

[0001] The present invention relates to the technical field of wind and solar energy power generation, and in particular to a method and system for identifying broadband oscillation parameters based on Bayesian reasoning. Background Art

[0002] Oscillation is one of the key factors threatening the safe and stable operation of interconnected power grids. In the past few decades, low-frequency oscillations have been widely studied, and the generation mechanism of low-frequency oscillations has been clarified. With the development of power systems, the operation mode of the system has become increasingly complex under the background of "double highs", and new oscillations have become increasingly frequent. In order to improve the safety level of power system operation, it is imperative to study sub- and super-synchronous oscillations of large power grids based on PMU recording data. In the early days, there were few PMU configurations in power systems, and single-channel measurement data (such as active power of connecting lines, generator speed or active power) were mostly used to extract frequency and damping ratio. However, generators are coupled to each other through the network, and the actual electromechanical oscillation process, especially the inter-regional oscillation mode, shows strong global characteristics, which are difficult to detect and analyze by simply relying on a single measurement channel. In recent years, with the increase in the number of PMU configurations, experts and scholars at home and abroad have introduced dynamic feature extraction methods based on multi-channel data sources to analyze dynamic behavior. WAMS captures real-time dynamic information of each point in the large power grid under the same reference time frame. The information includes key quantities such as active power, reactive power, voltage, phase angle, frequency, etc., which accurately reflects the operating status of the power grid. It is very important to reasonably mine the WAMS measurement information and develop a method for identifying the characteristics of sub- and super-synchronous oscillations in large power grids. The modal characteristics are mainly modal frequency, modal damping and modal vibration shape.

[0003] The data-driven oscillation mode analysis methods are mainly divided into four categories: various improved Prony algorithms, the least squares rotational invariant technique (TLS-ESPRIT) algorithm, the Hilbert-Huang transform (HHT) algorithm, and the random subspace method (SSI) algorithm. From the control theory, we know that the final output of the system contains the information of all states involved in the dynamic process of the system at that moment. The system state identification based on the terminal output is essentially a mathematical inversion problem. By monitoring and analyzing the oscillation state of the measurement signal, the above limitations of the mathematical model method can be overcome. Summary of the invention

[0004] In view of the above existing problems, the present invention is proposed.

[0005] Therefore, the present invention provides a method and system for identifying wide-band oscillation parameters based on Bayesian reasoning, which defines the frequency and damping prior distribution of the oscillation signal to construct a parameter prior for implementing the Bayesian regression algorithm; extracts the modal information of the wide-band domain measurement data of the power grid through Markov Monte Carlo, and gives an uncertainty estimate.

[0006] In order to solve the above technical problems, the present invention provides the following technical solution, a method for identifying broadband oscillation parameters based on Bayesian reasoning, comprising:

[0007] Obtain PMU oscillation recording data by connecting to the WAMS database interface;

[0008] Set the decomposition window length, initialize VMD parameters and perform modal decomposition to obtain each oscillation mode;

[0009] Bayesian inference is performed on the oscillation modes respectively, and the posterior parameter distribution and mean of the broadband oscillation mode parameters are obtained through MCMC solution.

[0010] As a preferred solution of the broadband oscillation parameter identification method based on Bayesian reasoning described in the present invention, wherein: the initialization of VMD parameters for modal decomposition to obtain each oscillation mode includes, the oscillation behavior of WBO in the power system is described by the following formula:

[0011]

[0012] In the formula, S n (t) represents the broadband oscillation component in the measured signal, S dc (t) represents the DC component in the measured signal, S noise (t) represents the noise term, N represents the number of broadband oscillation component modes, A represents the amplitude, ξ represents the damping factor, and w represents the angular frequency.

[0013] As a preferred solution of the broadband oscillation parameter identification method based on Bayesian reasoning described in the present invention, wherein: the initialization of VMD parameters for modal decomposition to obtain each oscillation mode includes: assuming that the original signal S WBO (t) is decomposed into k components, ensuring that the decomposition sequence is a modal component with a limited bandwidth and a center frequency. At the same time, the sum of the estimated bandwidths of each mode is minimized. The constraint condition is that the sum of all modes is equal to the original signal. The VMD constrained variational model is as shown in formula (5), which decomposes the target signal into k intrinsic mode functions IMF:

[0014]

[0015] Where: {s k}={s1,…,s K} are the k modal components obtained by decomposition; {ω k}={ω1,…,ω K} is the frequency center of each component.

[0016] As a preferred solution of the broadband oscillation parameter identification method based on Bayesian reasoning described in the present invention, wherein: the initialization of VMD parameters for modal decomposition to obtain each oscillation mode includes introducing a quadratic penalty factor α and a Lagrangian multiplication operator λ(t) to solve the constrained variational problem, and obtaining an augmented Lagrangian formula:

[0017]

[0018] The multiplication operator alternating direction method is used to solve the variational problem of formula (3), by alternatingly updating s n+1k ,ω n+1k and λ n+1 Seek the saddle point of the augmented Lagrangian expression; Formula (2) is the optimal solution of the constrained variational model, so S WBO (t) is decomposed into k narrowband IMF components; the k IMF components include trend terms, noise terms and actual broadband oscillation components.

[0019] As a preferred solution of the broadband oscillation parameter identification method based on Bayesian reasoning described in the present invention, wherein: the initialization of VMD parameters for modal decomposition to obtain each oscillation mode includes defining SMR and R 2 Find the number of oscillation modes Kop. The Kop acquisition criterion is based on minimizing SMR and maximizing R 2 :

[0020]

[0021] Kop←min SMR (Σ sk )∩max R2 (Σ sk )(6)

[0022] Independent Bayesian inference is performed on the Kop residual IMFs after removing the DC component and noise term. First, s i Assign to y(t), that is, y(t)←s i ,i=1,...,Kop∈k; specify the prior distribution of the inferred parameters, A and f adopt strong prior distribution, and take values ​​in the uniform distribution according to the FFT result; assume that D obeys the normal distribution Normal(0,v); add a noise term σ~HalfNormal(τ) to enhance the robustness of the inference model; the likelihood function is specified as the attenuation model A of the oscillating signal i e -Dit cos(2πf i t), the broadband oscillation mode identification problem is transformed into a data regression problem, that is, to infer A in the time window t. i , D i and f i The combination of The error between the true y(t) and the true y(t) is minimized.

[0023] As a preferred solution of the broadband oscillation parameter identification method based on Bayesian reasoning described in the present invention, wherein: the broadband oscillation mode parameters are obtained by MCMC solution, the inference process adopts MCMC sampling,

[0024]

[0025] In the formula, A min , A max is FFT(s i ) The neighborhood value of the amplitude in the result, f min , f max is FFT(s i ) is the neighborhood value of the frequency in the result; v and τ are both constants.

[0026]

[0027] Among them, A i is the amplitude; D i =ξ i ω i is the damping factor; f i is the frequency;

[0028]

[0029] In the formula, θ m ~prior(θ), θ={A,D,f} is the modal parameter dependent variable; y m is the sample point; is the objective function of model parameter estimation, that is, the parameters are solved by the maximum likelihood estimation method. Φ (θ m |y m ) represents the observed data y under parameter θ m The conditional probability distribution of .

[0030] As a preferred solution of the broadband oscillation parameter identification method based on Bayesian reasoning described in the present invention, the posterior parameter distribution and mean include: the Bayesian reasoning posterior parameter distribution can obtain the modal parameters of the oscillation as shown in formula (10), that is, the frequency f, damping ratio ξ and amplitude A of the modal component contained in the broadband oscillation measurement signal are obtained:

[0031]

[0032] Where f is the frequency estimate obtained by Bayesian reasoning, ξ is the damping ratio estimate obtained by Bayesian reasoning, and A is the amplitude estimate obtained by Bayesian reasoning.

[0033] As a preferred solution of the broadband oscillation parameter identification system based on Bayesian reasoning described in the present invention, it includes: a data acquisition module, a data processing unit, and an algorithm module; the data acquisition module is used to collect parameter data; the data processing unit is used to process the collected data information; and the algorithm module processes the required algorithm.

[0034] A computer device comprises a memory and a processor, wherein the memory stores a computer program, and is characterized in that when the processor executes the computer program, the steps of a method for identifying wide-band oscillation parameters based on Bayesian reasoning are implemented.

[0035] A computer-readable storage medium stores a computer program, wherein when the computer program is executed by a processor, the computer program implements the steps of a method for identifying wide-band oscillation parameters based on Bayesian reasoning.

[0036] Beneficial effects of the invention: This patent proposes a method and system for extracting broadband signal modal data from a power grid based on a Bayesian fitting algorithm. The Bayesian method is a probabilistic framework that explicitly incorporates uncertainty into the model. In the power system, there are many uncertainties from different sources, such as load changes, noise, etc. The Bayesian method can represent these uncertainties through probability distribution, which can more accurately estimate model parameters and consider the impact of uncertainty on the results. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0038] Figure 1 A schematic flow chart of a method for identifying broadband oscillation parameters based on Bayesian reasoning is provided for one embodiment of the present invention.

[0039] Figure 2 A schematic diagram of a broadband oscillation signal with modal aliasing according to a broadband oscillation parameter identification method based on Bayesian reasoning provided by an embodiment of the present invention.

[0040] Figure 3 A schematic diagram of VMD-FFT mode extraction of a broadband oscillation parameter identification method based on Bayesian reasoning is provided as an embodiment of the present invention.

[0041] Figure 4A schematic diagram of the posterior distribution of subsynchronous oscillation (SSO) modal parameters of a wide-band oscillation parameter identification method based on Bayesian reasoning provided by an embodiment of the present invention.

[0042] Figure 5 A schematic diagram of the posterior distribution of supersynchronous oscillation (SURSO) modal parameters of a wide-band oscillation parameter identification method based on Bayesian reasoning provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0043] In order to make the above-mentioned purposes, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the drawings of the specification. Obviously, the described embodiments are part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary persons in the art without creative work should fall within the scope of protection of the present invention.

[0044] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein, and those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0045] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The term "in one embodiment" that appears in different places in this specification does not necessarily refer to the same embodiment, nor does it refer to a separate or selective embodiment that is mutually exclusive with other embodiments.

[0046] The present invention is described in detail with reference to schematic diagrams. When describing the embodiments of the present invention, for the sake of convenience, the cross-sectional diagrams showing the device structure will not be partially enlarged according to the general scale, and the schematic diagrams are only examples, which should not limit the scope of protection of the present invention. In addition, in actual production, the three-dimensional dimensions of length, width and depth should be included.

[0047] At the same time, in the description of the present invention, it should be noted that the directions or positional relationships indicated by the terms "upper, lower, inner and outer" are based on the directions or positional relationships shown in the drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific direction, be constructed and operated in a specific direction, and therefore cannot be understood as limiting the present invention. In addition, the terms "first, second or third" are only used for descriptive purposes and cannot be understood as indicating or implying relative importance.

[0048] In the present invention, unless otherwise clearly specified and limited, the terms "install, connect, connect" should be understood in a broad sense, for example: it can be a fixed connection, a detachable connection or an integral connection; it can also be a mechanical connection, an electrical connection or a direct connection, or it can be indirectly connected through an intermediate medium, or it can be the internal communication of two components. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0049] Example 1, reference Figure 1-Figure 5 , which is the first embodiment of the present invention, provides a method for identifying broadband oscillation parameters based on Bayesian reasoning, comprising:

[0050] This patent proposes a method and system for broadband oscillation parameter identification based on Bayesian reasoning. The method and system mainly consist of modal separation of broadband oscillation measurement data and Bayesian oscillation parameter reasoning. The flow chart is as follows: Figure 1 As shown in the figure. First, the PMU oscillation recording data is obtained by connecting to the WAMS database interface. Secondly, the decomposition window length is set, and the VMD parameters are initialized to perform modal decomposition to obtain each oscillation mode. Finally, Bayesian inference is performed on the oscillation modes, and the posterior parameter distribution and mean of the broadband oscillation mode parameters (including amplitude, frequency and damping) are obtained through MCMC solution.

[0051] In new power systems, local periodic disturbances or even significant oscillations caused by load power fluctuations at a single node can be described by nonlinear equations. The general solution of the equation describes the changes in broadband system characteristics, while the special solution is related to the disturbance. The oscillation behavior of WBO in power systems can be described by the following formula:

[0052]

[0053] In the formula, S n (t) represents the broadband oscillation component in the measured signal, S dc (t) represents the DC component in the measured signal, S noise (t) represents the noise term, N represents the number of broadband oscillation component modes, A represents the amplitude, ξ represents the damping factor, and w represents the angular frequency.

[0054] VMD has a good processing effect on non-stationary and nonlinear signals. Assuming that the original signal S WBO (t) is decomposed into k components, ensuring that the decomposition sequence is a modal component with a limited bandwidth and a center frequency, and the sum of the estimated bandwidths of each mode is minimized. The constraint condition is that the sum of all modes is equal to the original signal. The VMD constrained variational model is as shown in formula (5), which decomposes the target signal into k intrinsic mode functions (IMFs)

[0055]

[0056] Where: {s k}={s1,…,s K} are the k modal components obtained by decomposition; {ω k}={ω1,…,ω K}

[0057] is the frequency center of each component. The quadratic penalty factor α and the Lagrangian multiplication operator λ(t) are introduced to solve the constrained variational problem, and the augmented Lagrangian formula is obtained:

[0058]

[0059] The multiplication operator alternating direction method is used to solve the variational problem of formula (3), by alternatingly updating s n+1k ,ω n+1k and λ n+1 Seek the saddle point of the augmented Lagrangian expression. Formula (2) is the optimal solution of the constrained variational model, so S WBO (t) is decomposed into k narrowband IMF components. The k IMF components contain trend terms, noise terms and actual broadband oscillation components. SMR and R are defined as follows: 2 To find the number of oscillation modes Kop. The Kop acquisition criterion is based on minimizing SMR and maximizing R 2 , as shown in formula (6).

[0060]

[0061] Kop←minSMR(Σsk)∩maxR2(Σsk)(6)

[0062] For K to remove DC components and noise terms op The remaining IMFs perform independent Bayesian inference, first converting s i Assign to y(t), that is, y(t)←s i ,i=1,...,K op ∈k. Specify the prior distribution of the inferred parameters, as in equation (7), A and f adopt strong prior distribution, and take values ​​in the uniform distribution according to the FFT result; assume that D obeys the normal distribution Normal(0,v); and add a noise term σ~HalfNormal(τ) to enhance the robustness of the inference model. The likelihood function is specified as the attenuation model A of the oscillating signal i e -Dit cos(2πf i t), the broadband oscillation mode identification problem is transformed into a data regression problem, that is, to infer A in the time window t. i , D i and f i The combination of The error between the true y(t) and the true y(t) is minimized. The inference process adopts MCMC sampling, as shown in formula (9).

[0063] In the formula, A min ,A max is FFT(s i ) The neighborhood value of the amplitude in the result, f min , f max is FFT(s i ) is the neighborhood value of the frequency in the result; v and τ are both constants.

[0064]

[0065] Among them, A i is the amplitude; D i =ξ i ω i is the damping factor; f i is the frequency.

[0066]

[0067] In the formula, θ m ~prior(θ),θ={A,D,f} is the modal parameter dependent variable; y m is the sample point.

[0068] Based on this Bayesian reasoning posterior parameter distribution, the modal parameters of the oscillation can be obtained as shown in formula (10), that is, the frequency f, damping ratio ξ and amplitude A of the modal components contained in the broadband oscillation measurement signal are obtained.

[0069]

[0070] A multi-modal aliasing broadband oscillation signal is constructed using subsynchronous oscillation 0.49e-0.06×tsin(2π×26.58×t), supersynchronous oscillation 0.32e0.17×tsin(2π×93.42×t) and a nonlinear trend term 0.01t2-0.03t+5 to demonstrate the effectiveness of this method. Figure 2 To change the time domain curve of the broadband signal within 10s, set a larger decomposition value K = 9, and perform VMD-FFT processing on the broadband signal. The result is as follows Figure 3 As shown in the figure, we can see that the trend term of 0Hz, SSO around 26Hz, SURSO around 93Hz and other higher frequency noise components are successfully separated. By performing Bayesian inference on IMF2 and IMF3, we can get the probability posterior distribution of their oscillation mode parameters as follows: Figure 4 , 5 shown.

[0071] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

[0072] Example 3

[0073] The third embodiment of the present invention is different from the first two embodiments in that:

[0074] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the methods described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, etc., which can store program codes.

[0075] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0076] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0077] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0078] Although the preferred embodiments of the present application have been described, those skilled in the art may make other changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications falling within the scope of the present application.

[0079] Obviously, those skilled in the art can make various changes and modifications to the present application without departing from the spirit and scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the claims of the present application and their equivalents, the present application is also intended to include these modifications and variations.

Claims

1. A method for identifying broadband oscillation parameters based on Bayesian reasoning, characterized in that: include, Obtain PMU oscillation recording data by connecting to the WAMS database interface; Set the decomposition window length, initialize VMD parameters and perform modal decomposition to obtain each oscillation mode; Bayesian inference is performed on the oscillation modes respectively, and the posterior parameter distribution and mean of the broadband oscillation mode parameters are obtained through MCMC solution.

2. A method for identifying broadband oscillation parameters based on Bayesian reasoning as claimed in claim 1, characterized in that: The initialization of VMD parameters for modal decomposition to obtain various oscillation modes includes: the oscillation behavior of WBO in the power system is described by the following formula: In the formula, S n (t) represents the broadband oscillation component in the measured signal, S dc (t) represents the DC component in the measured signal, S noise (t) represents the noise term, N represents the number of broadband oscillation modes, A represents the amplitude, ξ represents the damping factor, w represents the angular frequency, S WBO (t) is a broadband oscillation signal.

3. A method for identifying broadband oscillation parameters based on Bayesian reasoning as claimed in claim 2, characterized in that: The initialization of VMD parameters for modal decomposition to obtain each oscillation mode includes: assuming that the original signal S WBO (t) is decomposed into k components, ensuring that the decomposition sequence is a modal component with a limited bandwidth and a center frequency. At the same time, the sum of the estimated bandwidths of each mode is minimized. The constraint condition is that the sum of all modes is equal to the original signal. The VMD constrained variational model is as shown in formula (5), which decomposes the target signal into k intrinsic mode functions IMF: Where: {s k }={s1,…,s K } are the k modal components obtained by decomposition; {ω k }={ω1,…,ω K } is the frequency center of each component.

4. A method for identifying broadband oscillation parameters based on Bayesian reasoning as claimed in claim 3, characterized in that: The initialization of VMD parameters for modal decomposition to obtain each oscillation mode includes introducing a quadratic penalty factor α and a Lagrangian multiplication operator λ(t) to solve the constrained variational problem, and obtaining an augmented Lagrangian formula: The variational problem of (3) is solved by using the alternating direction method of multiplication operators, by alternatingly updating the signal component s of the kth mode in the n+1th iteration n+1k , the angular frequency ω of the kth mode in the n+1th iteration n+1k and the Lagrange multiplier λ in the n+1th iteration n+1 , find the saddle point of the augmented Lagrangian expression, j is an imaginary unit, is the angular frequency, λ is the Lagrange multiplier, S k (t) is the time domain signal component of the kth mode, σ(t) is the damping factor of the kth mode, describing the exponential decay rate of each oscillation component, and δ(t) is the time pulse function, which is used to highlight specific moments or transitions. Formula (2) is the optimal solution of the constrained variational model, which converts S WBO (t) is decomposed into k narrowband IMF components; the k IMF components include trend terms, noise terms and actual broadband oscillation components.

5. A method for identifying broadband oscillation parameters based on Bayesian reasoning as claimed in claim 4, characterized in that: The initialization of VMD parameters for modal decomposition to obtain each oscillation mode includes defining SMR and R 2 Find the number of oscillation modes Kop. The Kop acquisition criterion is based on minimizing SMR and maximizing R 2 : Kop←min SMR (S sk )∩max R2 (S sk )(6) Where M is the number of decomposed IMFs, Σ sk is the sum of the first k decomposition signals, y is the true value of the broadband oscillation, IMF m is the mth component decomposed by VMD, is the estimated value of broadband oscillation; for the Kop residual IMFs after removing the DC component and noise term, independent Bayesian inference is performed. First, the s i Assign to y(t), that is, y(t)←s i ,i=1,...,Kop∈k; specify the prior distribution of the inferred parameters, A and f adopt strong prior distribution, and take values ​​in the uniform distribution according to the FFT result; assume that D obeys the normal distribution Normal(0,v); add a noise term σ~HalfNormal(τ) to enhance the robustness of the inference model; the likelihood function is specified as the attenuation model A of the oscillating signal i e -Dit cos(2πf i t), at this time, the broadband oscillation mode identification problem is transformed into a data regression problem, that is, inferring A in the time window t i , D i and f i The combination of The error between the true y(t) and the true y(t) is minimized.

6. A method for identifying broadband oscillation parameters based on Bayesian reasoning as claimed in claim 5, characterized in that: The method of obtaining the broadband oscillation mode parameters by MCMC solution includes: the inference process adopts MCMC sampling as follows: In the formula, A min , A max is FFT(s i ) The neighborhood value of the amplitude in the result, f min , f max is FFT(s i ) is the neighborhood value of the frequency in the result; v and τ are both constants. Among them, A i is the amplitude; D i =ξ i ω i is the damping factor; f i is the frequency; In the formula, θ m ~prior(θ), θ={A,D,f} is the modal parameter dependent variable; y m is the sample point, is the objective function of model parameter estimation, that is, solving the parameters by the maximum likelihood estimation method. Φ (θ m |y m ) represents the observed data y under parameter θ m The conditional probability distribution of q Φ is the parameterized model of the probability distribution, θ m A set of samples for model parameters includes amplitude A, damping factor D and frequency f.

7. A method for identifying broadband oscillation parameters based on Bayesian reasoning as claimed in claim 6, characterized in that: The posterior parameter distribution and mean include: Bayesian inference of the posterior parameter distribution can obtain the modal parameters of the oscillation as shown in formula (10), that is, the frequency f, damping ratio ξ and amplitude A of the modal component contained in the broadband oscillation measurement signal are obtained: Where f is the frequency estimate obtained by Bayesian reasoning, ξ is the damping ratio estimate obtained by Bayesian reasoning, and A is the amplitude estimate obtained by Bayesian reasoning.

8. A system using a method for identifying broadband oscillation parameters based on Bayesian reasoning as claimed in any one of claims 1 to 7, characterized in that: Including data acquisition module, data processing unit, algorithm module; The data acquisition module is used to collect parameter data; The data processing unit is used to process the collected data information; The algorithm module processes the required algorithm.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

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