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 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 of the broadband oscillation data of a power grid.

Description

Technical Field

[0001] This invention relates to the field of wind and solar new energy power generation technology, and in particular to a broadband oscillation parameter identification method and system based on Bayesian inference. Background Technology

[0002] In the early days, power system PMUs were less common, and single-channel measurement data (such as active power of connecting lines, generator speed, or active power) were often 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, exhibits 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 PMUs, 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 various points in the large power grid within the same reference time frame. This information includes key quantities such as active power, reactive power, voltage, phase angle, and frequency, accurately reflecting the operating status of the power grid. It is crucial to rationally mine WAMS measurement information and develop oscillation feature identification methods suitable for subsynchronous and supersynchronous oscillations in large power grids. Modal features mainly include three types: modal frequency, modal damping, and mode shape.

[0003] Data-driven oscillation mode analysis methods are mainly divided into four categories: various improved Prony algorithms, least squares rotation-invariant techniques (TLS-ESPRIT) algorithms, Hilbert-Huang transform (HHT) algorithms, and stochastic subspace method (SSI) algorithms. As control theory shows, the final output of a system contains information about all states involved in the system's dynamic process at that moment. System state identification based on terminal output is essentially a mathematical inversion problem. By monitoring and analyzing the oscillation state of the measured signal, the aforementioned limitations of mathematical model methods can be overcome. Summary of the Invention

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

[0005] Therefore, this invention provides a broadband oscillation parameter identification method and system based on Bayesian inference. By defining the frequency and damping prior distribution of the oscillation signal, a parameter prior for implementing the Bayesian regression algorithm is constructed. Modal information of broadband measurement data of the power grid is extracted through Markov Monte Carlo, and uncertainty estimation is given.

[0006] To solve the above-mentioned technical problems, the present invention provides the following technical solution: a broadband oscillation parameter identification method based on Bayesian inference, comprising: 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 was performed on each oscillation mode, and the posterior parameter distribution and mean of the broadband oscillation mode parameters were obtained by solving the MCMC.

[0007] As a preferred embodiment of the broadband oscillation parameter identification method based on Bayesian inference described in this invention, wherein: the initialization of VMD parameters and the obtaining of each oscillation mode through mode decomposition include the following formula describing the oscillation behavior of WBO in a power system: (1) In the formula, S n (t) represents the broadband oscillation component in the measured signal, N is the total number of modes of the broadband oscillation component, n is the mode number, and S dc (t) represents the DC component in the measurement signal, S noise (t) represents the noise term, N represents the number of broadband oscillation modes, A is the initial amplitude of the oscillation component, and ξ is the damping ratio. S is the angular frequency. WBO (t) represents a wideband oscillation signal, where t represents the time variable.

[0008] As a preferred embodiment of the broadband oscillation parameter identification method based on Bayesian inference described in this invention, wherein: the initialization of VMD parameters and the obtaining of each oscillation mode through mode decomposition include, assuming the original signal S WBO (t) is decomposed into k Each component ensures that the decomposed sequence consists of modal components with a center frequency and a finite bandwidth, while minimizing the sum of the estimated bandwidths of each mode. The constraint condition is that the sum of all modes is equal to the original signal. Then, the VMD constrained variational model is as shown in equation (5), which decomposes the target signal into k intrinsic mode functions (IMFs): (2) In the formula: {s k}={s1,…,s K} represents the k modal components obtained through VMD decomposition; {ω K}={ω1, … ,ω K} represents the center angular frequency of each modal component; k represents the index of the modal component; j is the imaginary unit; S k (t) represents the time-domain signal component of the k-th mode; s k The k-th mode component obtained by variational mode decomposition; s K ω represents the last IMF corresponding to the set total number of modes K; K The center angular frequency of the Kth modal component; (t) is the damping factor for the k-th mode, describing the exponential decay rate of each oscillation component. (t) is the Dirac function, used to highlight a specific moment or transition; Equation (2) is the optimal solution of the constrained variational model, thus making S WBO (t) is decomposed into k narrowband IMF components; the k IMF components contain a trend term, a noise term and the actual broadband oscillation component; ⨂ is the convolution operator, used to represent the convolution between the signal and the kernel function.

[0009] As a preferred embodiment of the broadband oscillation parameter identification method based on Bayesian inference described in this invention, the initialization of VMD parameters and the obtaining of each oscillation mode through mode decomposition include solving a constrained variational problem by introducing a quadratic penalty factor α and a Lagrange multiplier operator λ(t) to obtain the augmented Lagrange formula: (3) The variational problem of equation (3) is obtained by using the alternating direction method of multiplication operators, and by alternately updating the signal component s of the k-th mode in the (r+1)-th iteration. r +1k The angular frequency ω of the k-th mode in the (r+1)-th iteration r + 1k and the Lagrange multiplier λ in the (r+1)th iteration r + 1 Seeking saddle points for augmented Lagrange expressions. Let be the angular frequency, λ be the Lagrange multiplier, and L be the augmented Lagrangian function used to describe the objective function of the VMD variational optimization problem. Let π be the constant of pi, λ(t) be the Lagrange multiplier, and α be the penalty factor used to control the weight of modal bandwidth in the objective function. This represents the first derivative operator with respect to the time variable t.

[0010] As a preferred embodiment of the broadband oscillation parameter identification method based on Bayesian inference described in this invention, the initialization of VMD parameters and the obtaining of each oscillation mode through mode decomposition include defining SMR and R... 2 The number of oscillation modes, Kop, is determined by a criterion based on minimizing the SMR and maximizing R. 2 : (4) (5) (6) In the formula, SMR is the signal-to-mode residual ratio, used to measure the residual between the reconstructed signal and the reference broadband oscillation signal; M is the number of IMFs in the decomposition, Σ sk Summing the first k decomposed signals, IMF m R is the m-th component decomposed by VMD;2 The coefficient of determination measures the goodness of fit of the modal superposition signal to the reference signal y; y is the broadband oscillation reference value or the true value. y i For y in the first i The value at each sampling time, This represents the broadband oscillation estimate; Kop is the optimal value for the number of oscillation modes. Independent Bayesian inference is performed on the remaining IMFs (Integral Multivariable Factors) after removing the DC component and noise term (Kop). i Assign to ,Right now ←s i ;s i The first decomposed by VMD and selected for Bayesian inference i There are oscillating mode signals; i = 1,..., Kop∈k.

[0011] As a preferred embodiment of the broadband oscillation parameter identification method based on Bayesian inference described in this invention, the step of obtaining broadband oscillation mode parameters through MCMC solution includes, in the inference process, MCMC sampling. (7) In the formula, A min A max For FFT(s) i The neighborhood values ​​of the amplitude in the result, f min f max For FFT(s) i The results show the neighborhood values ​​of the frequency; FFT(s) i ) represents the modal signal s i The spectrum obtained by performing a Fast Fourier Transform; D and f represent the attenuation factor and frequency of the oscillation mode, respectively; v is the variance correlation constant of the normal prior distribution; τ is the scaling parameter of the semi-normal prior distribution; Uniform is the uniform distribution; Normal is the normal distribution; HalfNormal is the semi-normal distribution; (8) Among them, A i D is the initial amplitude of the i-th oscillation mode; i =ξ i ω i For the first i The attenuation factor of each oscillation mode, ξ i For the first i The damping ratio of each oscillation mode, ω i For the first i The angular frequency of each oscillation mode; f i t is the frequency of the i-th oscillation mode;start To select the starting time of the oscillation to be evaluated, t end The end time of the selected oscillation to be evaluated is given; y(t) is the actual broadband oscillation value; A and f adopt strong prior distributions and are taken from uniform distributions based on the FFT results; D is assumed to follow a normal distribution Normal(0, v); an additional noise term σ ~ HalfNormal(τ) is added to enhance the robustness of the inference model; the likelihood function is specified as the decay model A of the oscillation signal. i e -Dit cos (2πf i At this point, the broadband oscillation mode identification problem is transformed into a data regression problem, that is, inferring A within the time window t. i D i and f i The combination of these factors further makes the likelihood estimate... The error between y(t) and y(t) is minimized: (9) In the formula, θ m Let M be the m-th parameter sample drawn from the prior distribution, where M is the number of samples used to estimate the likelihood target; θ={A, D, f} is the set of oscillation mode parameters; Let be the posterior approximate distribution, and let represent the parameter. The following observation data The conditional probability distribution, q Φ For the parameterized model of the probability distribution, y m Let be the sample points, and Ф be the objective function for estimating the model parameters. is the estimated value of the learnable parameter in the posterior approximate distribution; log is the natural logarithm.

[0012] As a preferred embodiment of the broadband oscillation parameter identification method based on Bayesian inference described in this invention, the posterior parameter distribution and mean include the modal parameters of the oscillation obtained by the Bayesian inference posterior parameter distribution as specifically shown in equation (10), that is, the frequency, damping ratio and amplitude of the modal components contained in the broadband oscillation measurement signal are obtained: (10) in, To determine the approximate model based on the posterior perspective, given the observed data y. Estimation of oscillation parameters; f m , m A m These are the frequency estimate, damping ratio estimate, and initial amplitude parameter sample of the m-th group obtained through Bayesian inference.

[0013] As a preferred embodiment of the wideband oscillation parameter identification system based on Bayesian inference described in this invention, it includes: a data acquisition module, a data processing unit, and an algorithm module; the data acquisition module is used to acquire parameter data; the data processing unit is used to process the acquired data information; and the algorithm module processes the required algorithm.

[0014] A computer device includes a memory and a processor, the memory storing a computer program, characterized in that the processor executes the computer program to implement the steps of a broadband oscillation parameter identification method based on Bayesian inference.

[0015] A computer-readable storage medium having a computer program stored thereon, characterized in that, when the computer program is executed by a processor, it implements the steps of a broadband oscillation parameter identification method based on Bayesian inference.

[0016] The beneficial effects of this invention are as follows: This invention proposes a method and system for extracting broadband signal modes 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 power systems, there are many uncertainties originating from different sources, such as load variations and noise. The Bayesian method can represent these uncertainties through probability distributions, enabling more accurate estimation of model parameters and consideration of the impact of uncertainty on the results. Attached Figure Description

[0017] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the following description of the embodiments will be briefly introduced. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0018] Figure 1 This is a schematic flowchart of a broadband oscillation parameter identification method based on Bayesian inference, provided as an embodiment of the present invention.

[0019] Figure 2 This is a schematic diagram of a broadband oscillation signal with modal aliasing, provided as an embodiment of the present invention, for a broadband oscillation parameter identification method based on Bayesian inference.

[0020] Figure 3 This is a schematic diagram of VMD-FFT mode extraction, which is a broadband oscillation parameter identification method based on Bayesian inference, provided as an embodiment of the present invention.

[0021] Figure 4 This is a schematic diagram of the posterior distribution of subsynchronous oscillation (SSO) mode parameters, provided as an embodiment of the present invention, for a broadband oscillation parameter identification method based on Bayesian inference.

[0022] Figure 5 This is a schematic diagram of the posterior distribution of supersynchronous oscillation (SURSO) mode parameters, provided as an embodiment of the present invention, for a broadband oscillation parameter identification method based on Bayesian inference. Detailed Implementation

[0023] To make the above-mentioned objects, features, and advantages of the present invention more apparent and understandable, specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of them. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the protection scope of the present invention.

[0024] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and those skilled in the art can make similar extensions without departing from the spirit of the invention. Therefore, the invention is not limited to the specific embodiments disclosed below.

[0025] 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 phrase "in one embodiment" appearing in different places in this specification does not necessarily refer to the same embodiment, nor is it a single or selective embodiment that is mutually exclusive with other embodiments.

[0026] This invention is described in detail with reference to the schematic diagrams. When detailing the embodiments of this invention, for ease of explanation, the cross-sectional views illustrating the device structure may be partially enlarged, not adhering to the usual scale. Furthermore, the schematic diagrams are merely examples and should not be construed as limiting the scope of protection of this invention. In actual fabrication, the three-dimensional spatial dimensions of length, width, and depth should be included.

[0027] Furthermore, in the description of this invention, it should be noted that the terms "upper," "lower," "inner," and "outer," etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. These terms are used solely for the convenience of describing the invention and for simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on the invention. In addition, the terms "first," "second," or "third" are used for descriptive purposes only and should not be construed as indicating or implying relative importance.

[0028] Unless otherwise explicitly specified and limited, the terms "installation," "connection," and "joining" in this invention should be interpreted broadly. For example, they can refer to fixed connections, detachable connections, or integral connections; similarly, they can refer to mechanical connections, electrical connections, or direct connections, or indirect connections through an intermediate medium, or internal connections between two components. Those skilled in the art can understand the specific meaning of the above terms in this invention based on the specific circumstances.

[0029] Example 1, referring to Figures 1-5 This is the first embodiment of the present invention, which provides a broadband oscillation parameter identification method based on Bayesian inference, including: This embodiment proposes a broadband oscillation parameter identification method based on Bayesian inference. This method mainly consists of mode separation of broadband oscillation measurement data and Bayesian oscillation parameter inference, as shown in the flowchart below. Figure 1 As shown, firstly, PMU oscillation recording data is obtained by connecting to the WAMS database interface; secondly, the decomposition window length is set, and VMD parameters are initialized to perform modal decomposition and obtain each oscillation mode. Finally, Bayesian inference is performed on each oscillation mode, and the posterior parameter distribution and mean of the broadband oscillation mode parameters (including amplitude, frequency, and damping) are obtained by solving MCMC.

[0030] In new power systems, local periodic disturbances and even significant oscillations caused by load power fluctuations at individual nodes can be described by nonlinear equations. The general solution of these equations describes the changes in broadband system characteristics, while specific solutions are related to the disturbances. The oscillating behavior of the WBO in a power system can be described by the following formula: In the formula, S n (t) represents the broadband oscillation component in the measured signal, N is the total number of modes of the broadband oscillation component, n is the mode number, and S dc (t) represents the DC component in the measurement signal, S noise (t) represents the noise term, N represents the number of broadband oscillation modes, A is the initial amplitude of the oscillation component, and ξ is the damping ratio. S is the angular frequency. WBO (t) represents a wideband oscillation signal, where t represents the time variable.

[0031] VMD has good processing effect on non-stationary and nonlinear signals. Assuming the original signal S WBO (t) is decomposed into k Each component ensures that the decomposed sequence consists of modal components with a center frequency and a finite bandwidth, while minimizing the sum of the estimated bandwidths of each mode. The constraint condition is that the sum of all modes is equal to the original signal. Then, the VMD constrained variational model is as shown in equation (5), which decomposes the target signal into k intrinsic mode functions (IMFs): (2) In the formula: {s k}={s1,…,s K} represents the k modal components obtained through VMD decomposition; {ω K}={ω1, … ,ω K} represents the center angular frequency of each modal component; k represents the index of the modal component; j is the imaginary unit; S k (t) represents the time-domain signal component of the k-th mode; s k The k-th mode component obtained by variational mode decomposition; s K ω represents the last IMF corresponding to the set total number of modes K; K The center angular frequency of the Kth modal component; (t) is the damping factor for the k-th mode, describing the exponential decay rate of each oscillation component. (t) is the Dirac function, used to highlight a specific moment or transition; Equation (2) is the optimal solution of the constrained variational model, thus making S WBO (t) is decomposed into k narrowband IMF components; the k IMF components contain a trend term, a noise term and the actual broadband oscillation component; ⨂ is the convolution operator, used to represent the convolution between the signal and the kernel function.

[0032] By introducing a quadratic penalty factor α and the Lagrange multiplication operator λ(t) to solve the constrained variational problem, we obtain the augmented Lagrange formula: The variational problem of equation (3) is obtained by using the alternating direction method of multiplication operators, and by alternately updating the signal component s of the k-th mode in the (r+1)-th iteration. r +1k The angular frequency ω of the k-th mode in the (r+1)-th iteration r + 1k and the Lagrange multiplier λ in the (r+1)th iteration r + 1 Seeking saddle points for augmented Lagrange expressions. Let be the angular frequency, λ be the Lagrange multiplier, and L be the augmented Lagrangian function used to describe the objective function of the VMD variational optimization problem. Let π be the constant of pi, λ(t) be the Lagrange multiplier, and α be the penalty factor used to control the weight of modal bandwidth in the objective function. Let SMR represent the first derivative operator with respect to the time variable t. As defined in equations (4) and (5), SMR and R are defined. 2 To find the number of oscillating modes, Kop. The Kop acquisition criterion is based on minimizing the SMR and maximizing R. 2 : (4) (5) (6) In the formula, SMR is the signal-to-mode residual ratio, used to measure the residual between the reconstructed signal and the reference broadband oscillation signal; M is the number of IMFs in the decomposition, Σ sk Summing the first k decomposed signals, IMF m R is the m-th component decomposed by VMD; 2 The coefficient of determination measures the goodness of fit of the modal superposition signal to the reference signal y; y is the broadband oscillation reference value or the true value. y i For y in the first i The value at each sampling time, This represents the broadband oscillation estimate; Kop is the optimal value for the number of oscillation modes. Independent Bayesian inference is performed on the remaining IMFs (Integral Multivariable Factors) after removing the DC component and noise term (Kop). i Assign to ,Right now ←s i ;s i The first decomposed by VMD and selected for Bayesian inference i There are oscillating mode signals; i = 1,..., Kop∈k. The inference process uses MCMC sampling as follows: (7) In the formula, A min A max For FFT(s) i The neighborhood values ​​of the amplitude in the result, f min f max For FFT(s) i The results show the neighborhood values ​​of the frequency; FFT(s) i ) represents the modal signal s i The spectrum obtained by performing a Fast Fourier Transform; D and f represent the attenuation factor and frequency of the oscillation mode, respectively; v is the variance correlation constant of the normal prior distribution; τ is the scaling parameter of the semi-normal prior distribution; Uniform is the uniform distribution; Normal is the normal distribution; HalfNormal is the semi-normal distribution; (8) Among them, A i D is the initial amplitude of the i-th oscillation mode; i =ξ i ω i For the first i The attenuation factor of each oscillation mode, ξ i For the first iThe damping ratio of each oscillation mode, ω i For the first i The angular frequency of each oscillation mode; f i t is the frequency of the i-th oscillation mode; start To select the starting time of the oscillation to be evaluated, t end The end time of the selected oscillation to be evaluated is given; y(t) is the actual broadband oscillation value; A and f adopt strong prior distributions and are taken from uniform distributions based on the FFT results; D is assumed to follow a normal distribution Normal(0, v); an additional noise term σ ~ HalfNormal(τ) is added to enhance the robustness of the inference model; the likelihood function is specified as the decay model A of the oscillation signal. i e -Dit cos (2πf i At this point, the broadband oscillation mode identification problem is transformed into a data regression problem, that is, inferring A within the time window t. i D i and f i The combination of these factors further makes the likelihood estimate... The error between y(t) and y(t) is minimized: (9) In the formula, θ m Let M be the m-th parameter sample drawn from the prior distribution, where M is the number of samples used to estimate the likelihood target; θ={A, D, f} is the set of oscillation mode parameters; Let be the posterior approximate distribution, and let represent the parameters. The following observation data The conditional probability distribution, q Φ For the parameterized model of the probability distribution, y m Let be the sample points, and Ф be the objective function for estimating the model parameters. is the estimated value of the learnable parameter in the posterior approximate distribution; log is the natural logarithm.

[0033] The posterior parameter distribution of Bayesian inference can yield the modal parameters of the oscillation, as shown in Equation (10), which gives the frequency, damping ratio, and amplitude of the modal components contained in the broadband oscillation measurement signal: (10) in, To determine the approximate model based on the posterior perspective, given the observed data y. Estimation of oscillation parameters; f m , m A m These are the frequency estimate, damping ratio estimate, and initial amplitude parameter sample of the m-th group obtained through Bayesian inference.

[0034] Using subsynchronous oscillation 0.49 e 0.06× tsin (2 π × 26.58× t Supersynchronous oscillation 0.32 e 0.17× tsin (2 π × 93.42 × t ) and a non-linear trend term 0.01 t 2 0.03 t +5 Construct a multimodal aliased broadband oscillation signal to demonstrate the effectiveness of this method, where t is time. Figure 2 To modify the time-domain curve of a broadband signal within 10 seconds, a relatively large decomposition value K=9 is set, and VMD-FFT processing is performed on the broadband signal. The result is as follows. Figure 3 As shown in the figure, the trend term at 0Hz, the SSO around 26Hz, the SURSO around 93Hz, and other higher frequency noise components were successfully separated. Bayesian inference of IMF2 and IMF3 yields the probability posterior distribution of their oscillation mode parameters as follows: Figure 4 , 5 As shown.

[0035] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

[0036] Example 3 The third embodiment of the present invention differs from the first two embodiments in that: If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this invention, essentially, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0037] 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.

[0038] 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.

[0039] 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.

[0040] Although preferred embodiments of this application have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments as well as all changes and modifications falling within the scope of this application.

[0041] Obviously, those skilled in the art can make various modifications and variations to this application without departing from the spirit and scope of this application. Therefore, if such modifications and variations fall within the scope of the claims of this application and their equivalents, this application also intends to include such modifications and variations.

Claims

1. A method for wideband oscillation parameter identification based on Bayesian inference, characterized in that: Comprising, obtaining PMU oscillation recording data through a WAMS database interface; setting a decomposition window length, initializing VMD parameters to perform modal decomposition to obtain each oscillation mode; respectively performing Bayesian inference on the oscillation modes, and obtaining posterior parameter distribution and mean value of wideband oscillation mode parameters through MCMC solving.

2. The method of claim 1, wherein: The initialization VMD parameters to perform modal decomposition to obtain each oscillation mode comprises that the oscillation behavior of the WBO in the power system is described by the following formula: (1) where S n (t) represents the broadband oscillation component in the measurement signal, N is the total number of modes of the broadband oscillation component, n is the mode number, S dc (t) represents the DC component in the measurement signal, S noise (t) represents the noise term, N represents the number of modes of the broadband oscillation component, A is the initial amplitude of the oscillation component, ξ is the damping ratio, is the angular frequency, S WBO (t) is the broadband oscillation signal, and t represents the time variable.

3. A Bayesian inference based wideband oscillation parameter identification method according to claim 2, characterized in that: The initialization of the 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 of the center frequency, while the sum of the estimated bandwidths of each mode is minimized, and the constraint condition is that the sum of all modes is equal to the original signal. Then the VMD constrained variation model is as formula (5), and the target signal is decomposed into k intrinsic mode functions IMFs: (2) In the formula: {s k }={s1,…,s K } represents the k modal components obtained through VMD decomposition; {ω K }={ω1, … , ω K } represents the center angular frequency of each modal component; k represents the index of the modal component; j is the imaginary unit; S k (t) represents the time-domain signal component of the k-th mode; s k The k-th mode component obtained by variational mode decomposition; s K ω represents the last IMF corresponding to the set total number of modes K; K The center angular frequency of the Kth modal component; (t) is the damping factor for the k-th mode, describing the exponential decay rate of each oscillation component. (t) is the Dirac function, used to highlight a specific moment or transition; Equation (2) is the optimal solution of the constrained variational model, thus making S WBO (t) is decomposed into k narrowband IMF components; the k IMF components contain a trend term, a noise term and the actual broadband oscillation component; ⨂ is the convolution operator, used to represent the convolution between the signal and the kernel function.

4. The method of claim 3, wherein: The initialization VMD parameters to perform modal decomposition to obtain each oscillation mode comprises introducing a quadratic penalty factor α and a Lagrange multiplier λ(t) to solve a constrained variation problem, and obtaining an augmented Lagrange formula: (3) The variational problem of equation (3) is obtained by using the alternating direction method of multiplication operators, and by alternately updating the signal component s of the k-th mode in the (r+1)-th iteration. r +1k The angular frequency ω of the k-th mode in the (r+1)-th iteration r + 1k and the Lagrange multiplier λ in the (r+1)th iteration r + 1 Seeking saddle points for augmented Lagrange expressions. Let be the angular frequency, λ be the Lagrange multiplier, and L be the augmented Lagrangian function, used to describe the objective function of the VMD variational optimization problem. Let π be the constant of pi, λ(t) be the Lagrange multiplier, and α be the penalty factor used to control the weight of modal bandwidth in the objective function. This represents the first derivative operator with respect to the time variable t.

5. A Bayesian inference based wideband oscillation parameter identification method according to claim 4, characterized in that: The initialization of the VMD parameters for modal decomposition to obtain each oscillation mode includes defining SMR and R 2 Finding the number of oscillation modes Kop, Kop acquisition criteria based on minimizing SMR and maximizing R 2 : (4) (5) (6) In the formula, SMR is the signal-to-mode residual ratio, used to measure the residual between the reconstructed signal and the reference broadband oscillation signal; M is the number of IMFs in the decomposition, Σ sk Summing the first k decomposed signals, IMF m R is the m-th component decomposed by VMD; 2 The coefficient of determination measures the goodness of fit of the modal superposition signal to the reference signal y; y is the broadband oscillation reference value or the true value. y i For y in the first i The value at each sampling time, This represents the broadband oscillation estimate; Kop is the optimal value for the number of oscillation modes. Independent Bayesian inference is performed on the remaining IMFs (Integral Multivariable Factors) after removing the DC component and noise term (Kop). The s values ​​in the remaining IMFs are then used to estimate the oscillation modes. i Assign to ,Right now ←s i ;s i The first decomposed by VMD and selected for Bayesian inference i There are oscillating mode signals; i = 1, ...,Kop∈k.

6. A Bayesian inference based wideband oscillation parameter identification method according to claim 5, characterized in that: The MCMC solving to obtain wideband oscillation mode parameters comprises that an inference process adopts MCMC sampling, (7) where A min , A max are the neighborhood values of the magnitude in the FFT(s i ) result, f min , f max are the neighborhood values of the frequency in the FFT(s i ) result; FFT(s i ) is the frequency spectrum obtained by performing a fast Fourier transform on the modal signal s i ; D, f are the damping factor and the frequency of the oscillation mode, respectively; v is the variance-related constant of the normal prior distribution; τ is the scale parameter of the half-normal prior distribution; Uniform is the uniform distribution; Normal is the normal distribution; HalfNormal is the half-normal distribution. (8) Among them, A i D is the initial amplitude of the i-th oscillation mode; i =ξ i ω i For the first i The attenuation factor of each oscillation mode, ξ i For the first i The damping ratio of each oscillation mode, ω i For the first i The angular frequency of each oscillation mode; f i t is the frequency of the i-th oscillation mode; start To select the starting time of the oscillation to be evaluated, t end The end time of the selected oscillation to be evaluated is given; y(t) represents the actual broadband oscillation value; A and f adopt strong prior distributions and are taken from uniform distributions based on the FFT results; D is assumed to follow a normal distribution Normal(0,v); an additional noise term σ ~ HalfNormal(τ) is added to enhance the robustness of the inference model; the likelihood function is specified as the decay model A of the oscillation signal. i e -Dit cos (2πf i At this point, the broadband oscillation mode identification problem is transformed into a data regression problem, that is, inferring A within the time window t. i D i and f i The combination of these factors further makes the likelihood estimate... The error between y(t) and y(t) is minimized: (9) where θ m is the m-th sample of parameters drawn from the prior distribution, M is the number of samples used to estimate the likelihood objective; θ = {A, D, f} is the set of oscillation modal parameters; is the posterior approximate distribution, representing the parameters is the conditional probability distribution of the posterior approximate distribution q of the observation data y Φ is the parameterized model of the probability distribution, y m is the sample point, Ф is the objective function of the model parameter estimation, is the estimated value of the learnable parameter in the posterior approximate distribution; log is the natural logarithm operation.

7. A Bayesian inference based wideband oscillation parameter identification method according to claim 6, characterized in that: The posterior parameter distribution and mean value comprise that the modal parameter of the oscillation can be obtained according to the posterior parameter distribution of the Bayesian inference, specifically as formula (10), that is, the frequency, damping ratio and amplitude of the modal component contained in the wideband oscillation measurement signal are obtained: (10) where, is the posterior approximation model for given observation data y Estimation of the oscillation parameters; f m , m , A m are the frequency estimate, damping ratio estimate, and initial amplitude parameter sample of the mth group, respectively, obtained by Bayesian inference.

8. A system employing a Bayesian inference based wideband oscillation parameter identification method according to any one of claims 1 to 7, characterized in that: Comprising, a data acquisition module, a data processing unit, an algorithm module; The data acquisition module is used for acquiring parameter data; The data processing unit is used for processing 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, and the computer device is configured to perform the method according to any one of claims 1-8 when the computer program is executed by the processor. The processor executes the computer program to realize the steps of the method in any one of claims 1 to 7.

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

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