Broadband measurement method and device based on Prony algorithm

Abstract

The invention provides a broadband measurement method and device based on a Prony algorithm, and the method comprises the steps: collecting time window data of a target power signal, and constructing a sampling sequence; constructing a Hank matrix based on the sampling sequence, and performing frequency analysis on the frequency component to separate the Hank matrix into a low-frequency part and a high-frequency part; carrying out resampling processing on the low-frequency part of the Hank matrix; for the resampled low-frequency part, a Prony algorithm is utilized to construct a characteristic polynomial, and the initial frequency of each frequency component is obtained by solving the characteristic polynomial. The problem that an existing broadband measurement algorithm cannot accurately reflect dynamic change characteristics of signals in a short time window and cannot accurately measure the dynamic change characteristics is solved.

Description

Technical Field

[0001] The present application relates to the field of signal processing technology, and in particular to a broadband measurement method based on a Prony algorithm, a broadband measurement device based on a Prony algorithm, a computer-readable storage medium, and an electronic device. Background Art

[0002] With the massive access of renewable energy and power electronic equipment, a large number of harmonic and interharmonic components have been introduced into the power grid. The synchronous phasor measurement unit (PMU) can only measure the fundamental wave of the power system. Although the existing broadband measurement scheme can measure broadband harmonics and interharmonics, its measurement results of multi-scale electrical quantities are not accurate. Therefore, it is necessary to study a high-precision measurement method suitable for dynamic broadband to ensure the validity of the measurement data of broadband devices.

[0003] The existing broadband measurement algorithms are mainly divided into two categories. The first category is the frequency domain processing method represented by Fast Fourier Transform (FFT). The resolution is directly affected by the length of the time window. A longer time window can improve the frequency resolution, but increasing the time window length in a rapidly changing signal may cause a time domain response lag and fail to track the dynamic changes of frequency and phase angle in real time. The second category is the time domain processing method, including modern spectrum estimation methods represented by Prony analysis, which extract frequency, phase angle and amplitude information from the signal by fitting an exponential model. It is suitable for processing signals with multiple frequency components, but involves constructing matrices and eigenvalue decomposition, and has a high computational complexity and cannot meet the real-time processing requirements. In particular, when estimating multiple frequency components in a short time window, it is easy to have non-convergence or error accumulation. It can be seen that the methods of the prior art all have certain problems. No algorithm can accurately reflect the dynamic change characteristics of the signal and accurately measure it in a short time window. Summary of the invention

[0004] The main purpose of the present application is to provide a broadband measurement method based on the Prony algorithm, a broadband measurement device based on the Prony algorithm, a computer-readable storage medium and an electronic device, so as to at least solve the problem that the current broadband measurement algorithm cannot accurately reflect the dynamic change characteristics of the signal and accurately measure it within a short time window.

[0005] In order to achieve the above-mentioned purpose, according to one aspect of the present application, a wideband measurement method based on the Prony algorithm is provided, including: collecting time window data of the target power signal to construct a sampling sequence; constructing a Hank matrix based on the sampling sequence, and separating the Hank matrix into a low-frequency part and a high-frequency part by performing frequency analysis on the frequency components; resampling the low-frequency part of the Hank matrix; for the low-frequency part after the resampling process, constructing a characteristic polynomial using the Prony algorithm, and obtaining the initial frequency of each frequency component by solving the characteristic polynomial.

[0006] Optionally, after resampling the low-frequency part of the Hank matrix, the wideband measurement method based on the Prony algorithm further includes: performing denoising on the Hank matrix corresponding to the low-frequency part after the resampling process by using singular value decomposition (SVD) to obtain a denoised low-frequency part, wherein the denoised Hank matrix is ​​expressed as: Among them, U r ,∑ r , V r are the first r principal components of the singular value decomposition of the Hank matrix, respectively.

[0007] Optionally, the broadband measurement method based on the Prony algorithm further includes: using a first polynomial fitting model x for the low-frequency part after denoising i (t) = A i cos(2πf i t+φ i ), to estimate the dynamic amplitude and phase angle, where A i is the amplitude, f i is the frequency, φ i Is the phase angle.

[0008] Optionally, the broadband measurement method based on the Prony algorithm further includes: using a second polynomial fitting model A k (t)cos(θ k (t))≈B k0 +B k1 t+B k2 t 2 +…+B kn t n , modeling the time-varying behavior of the amplitude and phase angle of each frequency component, where B k0 , B k1 , ..., B kn is the fitting coefficient, which is used to describe the dynamic change characteristics of the frequency component.

[0009] Optionally, the wideband measurement method based on the Prony algorithm further includes: dynamically optimizing the frequency component using a weighted least square method WLS, wherein the dynamic optimization specifically includes: calculating a phase angle derivative θ′ of the frequency component k (t), frequency correction Δf k It is expressed as: Using the adjustment formula f k (t+1)=f k (t)+Δf k Dynamically adjust the frequency; the weighted least squares optimization objective function is Among them, y i is the actual measured value, is the fitted value, w i is the weighting factor adjusted according to the signal-to-noise ratio.

[0010] Optionally, the wideband measurement method based on the Prony algorithm further includes: performing real-time correction on the frequency, amplitude and phase angle of the low-frequency component in a weighted least squares framework using the IWMARP algorithm, and using the parameter update formula Iteratively update the signal model parameters, where a j is the fitting parameter, and a is j to update.

[0011] Optionally, collecting time window data of the target power signal and constructing a sampling sequence includes: sampling the target power signal, setting the time window length to T, setting the number of sampling points to N, and the sampling frequency to f s , the time window length is expressed as The sampling sequence is represented as X(t k )={x(t0),x(t1),...,x(t N )}, where t k ∈[t0, t0+T′].

[0012] According to another aspect of the present application, a wideband measurement device based on the Prony algorithm is provided, including: a construction unit, used to collect time window data of a target power signal and construct a sampling sequence; a construction separation unit, used to construct a Hank matrix based on the sampling sequence, and separate the Hank matrix into a low-frequency part and a high-frequency part by performing frequency analysis on the frequency components; a resampling unit, used to resample the low-frequency part of the Hank matrix; and a solving unit, used to construct a characteristic polynomial using the Prony algorithm for the low-frequency part after the resampling process, and obtain the initial frequency of each frequency component by solving the characteristic polynomial.

[0013] Optionally, the broadband measurement device based on the Prony algorithm further includes: a denoising unit, configured to perform denoising on the Hank matrix corresponding to the low-frequency part after the resampling process using singular value decomposition (SVD) after the low-frequency part of the Hank matrix is ​​resampled, so as to obtain a denoised low-frequency part, wherein the denoised Hank matrix is ​​expressed as: Among them, U r ,∑ r , V r are the first r principal components of the singular value decomposition of the Hank matrix, respectively.

[0014] Optionally, the broadband measurement device based on the Prony algorithm further includes: an estimation unit for using a first polynomial fitting model x for the low-frequency part after denoising. i (t) = A i cos(2πf i t+φ i ), to estimate the dynamic amplitude and phase angle, where A i is the amplitude, f i is the frequency, φ i Is the phase angle.

[0015] Optionally, the broadband measurement device based on the Prony algorithm further includes: a modeling unit for fitting the model A using a second polynomial k (t)cos(θ k (t))≈B k0 +B k1 t+B k2 t 2 +…+B kn t n , modeling the time-varying behavior of the amplitude and phase angle of each frequency component, where B k0 , B k1 , ..., B kn is the fitting coefficient, which is used to describe the dynamic change characteristics of the frequency component.

[0016] Optionally, the broadband measurement device based on the Prony algorithm further includes: a dynamic optimization unit for dynamically optimizing the frequency component using a weighted least square method WLS, wherein the dynamic optimization unit specifically includes: a calculation module for calculating a phase angle derivative θ′ of the frequency component k (t), frequency correction Δf k It is expressed as: Dynamic adjustment module, used to adopt the adjustment formula f k (t+1)=f k (t)+Δf k Dynamically adjust the frequency; the weighted least squares optimization objective function is Among them, y i is the actual measured value, is the fitted value, w i is the weighting factor adjusted according to the signal-to-noise ratio.

[0017] Optionally, the broadband measurement device based on the Prony algorithm further includes: an updating unit for performing real-time correction of the frequency, amplitude and phase angle of the low-frequency component in a weighted least squares framework using the IWMARP algorithm, using the parameter updating formula Iteratively update the signal model parameters, where a j is the fitting parameter, and a is j to update.

[0018] Optionally, the construction unit includes: a sampling module, which is used to sample the target power signal, set the time window length to T, set the number of sampling points to N, and the sampling frequency to f s , the time window length is expressed as The sampling sequence is represented as X(t k )={x(t0),x(t1),...,x(t N )}, where t k ∈[t0, t0+T].

[0019] According to another aspect of the present application, a computer-readable storage medium is provided, wherein the computer-readable storage medium includes a stored program, wherein when the program is executed, the device where the computer-readable storage medium is located is controlled to execute any one of the above-mentioned broadband measurement methods based on the Prony algorithm.

[0020] According to another aspect of the present application, an electronic device is provided, comprising: one or more processors, a memory, and one or more programs, wherein the one or more programs are stored in the memory and are configured to be executed by the one or more processors, and the one or more programs include a method for executing any one of the above-mentioned wideband measurement methods based on the Prony algorithm.

[0021] By applying the technical solution of the present application, a relatively accurate broadband measurement is achieved by constructing a Hank matrix, performing resampling processing, and applying the Prony algorithm. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] The drawings constituting part of the present application are used to provide a further understanding of the present application. The exemplary embodiments and descriptions of the present application are used to explain the present application and do not constitute an improper limitation on the present application. In the drawings:

[0023] Figure 1A hardware structure block diagram of a mobile terminal for a wideband measurement method based on the Prony algorithm according to an embodiment of the present invention is shown;

[0024] Figure 2 A schematic diagram of a process of a wideband measurement method based on the Prony algorithm according to an embodiment of the present application is shown;

[0025] Figure 3 A schematic flow chart of an optional wideband measurement method based on the Prony algorithm according to an embodiment of the present application is shown;

[0026] Figure 4 The structure block diagram of a broadband measurement device based on the Prony algorithm according to an embodiment of the present application is shown.

[0027] The above drawings include the following reference numerals:

[0028] 102, processor; 104, memory; 106, transmission device; 108, input and output devices. DETAILED DESCRIPTION

[0029] It should be noted that, in the absence of conflict, the embodiments and features in the embodiments of the present application can be combined with each other. The present application will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

[0030] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present application.

[0031] It should be noted that the terms "first", "second", etc. in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequential order. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments of the present application described here. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device comprising a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0032] As introduced in the background technology, the current wideband measurement algorithm is unable to accurately reflect the dynamic change characteristics of the signal and accurately measure it within a short time window. To solve the above problems, the embodiments of the present application provide a wideband measurement method based on the Prony algorithm, a wideband measurement device based on the Prony algorithm, a computer-readable storage medium and an electronic device.

[0033] The technical solutions in the embodiments of the present invention will be described clearly and completely below in conjunction with the accompanying drawings in the embodiments of the present invention.

[0034] The method embodiments provided in the embodiments of the present application can be executed in a mobile terminal, a computer terminal or a similar computing device. Taking running on a mobile terminal as an example, Figure 1 FIG. 1 is a hardware structure block diagram of a mobile terminal of a broadband measurement method based on the Prony algorithm according to an embodiment of the present invention. Figure 1 As shown, the mobile terminal may include one or more ( Figure 1 Only one is shown in the figure) a processor 102 (the processor 102 may include but is not limited to a processing device such as a microprocessor MCU or a programmable logic device FPGA) and a memory 104 for storing data, wherein the mobile terminal may also include a transmission device 106 and an input / output device 108 for communication functions. It can be understood by those skilled in the art that Figure 1 The structure shown is only for illustration and does not limit the structure of the mobile terminal. Figure 1 More or fewer components as shown, or with Figure 1 Different configurations are shown.

[0035] The memory 104 can be used to store computer programs, for example, software programs and modules of application software, such as the computer program corresponding to the broadband measurement method based on the Prony algorithm in the embodiment of the present invention. The processor 102 executes various functional applications and data processing by running the computer program stored in the memory 104, that is, to implement the above method. The memory 104 may include a high-speed random access memory, and may also include a non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some examples, the memory 104 may further include a memory remotely arranged relative to the processor 102, and these remote memories can be connected to the mobile terminal via a network. Examples of the above network include but are not limited to the Internet, an intranet, a local area network, a mobile communication network, and a combination thereof. The transmission device 106 is used to receive or send data via a network. The specific example of the above network may include a wireless network provided by a communication provider of the mobile terminal. In one example, the transmission device 106 includes a network adapter (Network Interface Controller, referred to as NIC), which can be connected to other network devices through a base station so as to communicate with the Internet. In one example, the transmission device 106 may be a radio frequency (RF) module, which is used to communicate with the Internet wirelessly.

[0036] In this embodiment, a broadband measurement method based on the Prony algorithm running on a mobile terminal, a computer terminal or a similar computing device is provided. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and although a logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0037] Figure 2 FIG. 1 is a flow chart of a broadband measurement method based on the Prony algorithm according to an embodiment of the present application. Figure 2 As shown, the method comprises the following steps:

[0038] Step S201 : collecting time window data of a target power signal and constructing a sampling sequence.

[0039] Specifically, collecting time window data of the target power signal can help determine the frequency and amplitude components of the signal. By constructing a sampling sequence, the signal can be analyzed and reconstructed more accurately, thereby helping to identify abnormal conditions or faults in the power system. This can help improve the accuracy and reliability of signal analysis, thereby improving the safety and stability of the power system.

[0040] Step S202: construct a Hank matrix based on the sampling sequence, and separate the Hank matrix into a low-frequency part and a high-frequency part by performing frequency analysis on the frequency components.

[0041] Specifically, by constructing a Hank matrix for the sampling sequence and performing frequency analysis on the frequency components, the Hank matrix can be separated into a low-frequency part and a high-frequency part, so that the frequency characteristics of the signal can be better understood, thereby more accurately analyzing and processing the signal. By performing frequency analysis on the frequency components, the signal can be better decomposed and processed, thereby improving the accuracy and precision of phasor measurement. By using the improved Prony method to measure phasors in real time, specific frequency components can be extracted from the signal, thereby realizing signal spectrum analysis and signal reconstruction.

[0042] Step S203, resampling the low-frequency part of the Hank matrix.

[0043] Specifically, resampling the low-frequency part of the Hank matrix can improve the accuracy and stability of phasor measurement. Through resampling, the sampling error of the low-frequency part can be reduced, making the phasor measurement result more accurate. Resampling can also reduce noise interference in the signal and improve the signal-to-noise ratio of phasor measurement, thereby improving the performance and reliability of the system.

[0044] Step S204: construct a characteristic polynomial for the low-frequency part after resampling using the Prony algorithm, and obtain the initial frequency of each frequency component by solving the characteristic polynomial.

[0045] Specifically, the characteristic polynomial constructed using the Prony algorithm can accurately extract the frequency components of the low-frequency part, thereby achieving more accurate frequency component measurement. By solving the characteristic polynomial, the initial frequency of each frequency component can be obtained, thereby realizing real-time monitoring and measurement of the frequency, improving the accuracy and stability of phasor measurement, and effectively reducing the error of frequency measurement. By improving the Prony method, the frequency components can be extracted more accurately, reducing the frequency estimation error caused by factors such as uneven sampling or noise interference.

[0046] Through this embodiment, relatively accurate broadband measurement can be achieved by constructing a Hank matrix, performing resampling processing, and applying the Prony algorithm.

[0047] Specifically, the above-mentioned Hankel matrix is ​​a special form of matrix with a specific arrangement, and the elements on each diagonal are equal. Its special structural properties enable more efficient calculation and processing in signal processing problems; the above-mentioned Prony algorithm is a method for fitting discrete data points in a signal. The algorithm is based on the Prony formula, which describes that a signal can be represented by a linear combination of a set of exponential functions and can be used in signal processing to estimate the frequency, amplitude and phase of the signal.

[0048] Specifically, in real-time phasor measurement, resampling can reduce the error caused by sampling rate mismatch or instability. The sampling rate of the signal is adjusted by interpolation or decimation to match the sampling rate of the system. The resampling process steps are as follows:

[0049] 1. Collect original signal data: First, collect the original signal data, which may be collected at an unstable sampling rate or an unmatched sampling rate.

[0050] 2. Determine sampling rate mismatch or instability: Determine the sampling rate mismatch or instability of the signal by analyzing the original signal data.

[0051] 3. Resampling: According to the sampling rate mismatch or instability, select the appropriate resampling method, such as linear interpolation, nearest neighbor interpolation, polynomial interpolation, etc., to adjust the sampling rate of the signal.

[0052] 4. Perform phasor measurement: After completing the resampling process, phasor measurement can be performed based on the improved Prony method to obtain accurate phasor information.

[0053] Specifically, after the signal is sampled and the time window is selected, the obtained time window data can be rearranged into a Hank matrix H. The dimension of the Hank matrix is ​​K×P, where K is the number of matrix rows, P is the number of columns, and K·P≈N is satisfied. The columns of the Hank matrix are then analyzed to separate the matrix into high-frequency and low-frequency parts. For the low-frequency part, the low-frequency signal component can be processed by resampling (i.e., reducing the sampling rate) to improve the frequency resolution of the low-frequency component.

[0054] Afterwards, according to the extracted signal components and the calculation results of the Hank matrix, the Prony method is used to construct a characteristic polynomial to estimate the initial value of each frequency component. The characteristic polynomial can be expressed as P(z)=z p +a1z p-1 +…+a p , where p is the number of signal components, a iis the characteristic polynomial coefficient, i.e., the parameter to be estimated. In the process of solving the characteristic polynomial, the characteristic polynomial is constructed separately for the resampled data of the low-frequency signal to ensure the accuracy of the initial value estimation of the frequency of the low-frequency interharmonics. By solving the root z of the characteristic polynomial k , the initial frequency value f can be calculated k : Set the error limit ∈ and the maximum number of iterations N according to each frequency initial value iter , so as to gradually optimize the frequency estimation in the future.

[0055] In the specific implementation process, after resampling the low-frequency part of the Hank matrix, the method further includes: using singular value decomposition SVD to denoise the Hank matrix corresponding to the low-frequency part after the resampling process to obtain the denoised low-frequency part, wherein the denoised Hank matrix is ​​expressed as: Among them, U r ,∑ r , V r are the first r principal components of the singular value decomposition of the Hank matrix, respectively.

[0056] In this scheme, by using SVD to denoise the Hank matrix corresponding to the low-frequency part after resampling, the interference of noise on the signal can be effectively removed, thereby obtaining a more accurate and stable low-frequency part. After SVD denoising, the main components in the Hank matrix of the low-frequency part obtained can better describe the characteristics of the signal, so that the real-time phasor measurement method based on the improved Prony method can more accurately estimate the amplitude and phase information of the signal, thereby improving the accuracy and stability of phasor measurement.

[0057] Specifically, the above-mentioned singular value decomposition (SVD) is a matrix decomposition method, which can decompose a matrix into the product of three matrices to obtain the singular values, left singular vectors and right singular vectors of the matrix, so as to analyze and process the matrix; the above-mentioned denoising process refers to the process of removing noise in signal processing. Noise is any unwanted signal that interferes with the signal. The quality of the signal can be improved by denoising, making the original signal clearer and more accurate. Filtering is the most common denoising method. Noise signals are removed by selecting appropriate filter parameters. The denoising algorithm is based on mathematical models and statistical methods to identify and remove noise signals. Wavelet transform is a frequency domain analysis method that can decompose the signal into wavelet components of different frequencies and then remove the noise.

[0058] Specifically, singular value decomposition is introduced into the polynomial fitting model of the low-frequency component for denoising. SVD denoising can effectively filter out the noise component and retain only the main signal component, ensuring that the amplitude and phase angle estimation of the low-frequency interharmonics are more accurate. First, the Hank matrix is ​​subjected to singular value decomposition to extract the main signal components and remove the noise: H = U∑V T , retain the components corresponding to the larger singular values ​​to form a denoising matrix for the low-frequency components. Through SVD denoising, the noise interference in the low-frequency components can be effectively reduced and the fitting accuracy of the signal can be enhanced.

[0059] In addition, by setting the dynamic threshold ξ to adapt to the noise level of the signal, the number of signal components p can be extracted more accurately, and the calculation formula is: Among them, μ is the mean of the singular values, δ is the variance of the singular values, and k is an adjustment parameter that can control the threshold value.

[0060] In the specific implementation process, the above method also includes: using the first polynomial fitting model x for the low-frequency part after denoising i (t) = A i cos(2πf i t+φ i ), to estimate the dynamic amplitude and phase angle, where A i is the amplitude, f i is the frequency, φ i Is the phase angle.

[0061] In this scheme, by using the first polynomial fitting model to estimate the amplitude and phase angle of the low-frequency part after denoising, the amplitude and phase angle information of the signal can be extracted more accurately, thereby realizing real-time phasor measurement of the signal, effectively improving the accuracy and stability of the phasor measurement, and reducing the impact of noise in the signal. It can help the system monitor the status of power equipment in real time, prevent potential failures, and improve the reliability and stability of the system.

[0062] In the specific implementation process, the above method also includes: using the second polynomial fitting model A k (t)cos(θ k (t))≈B k0 +B k1 t+B k2 t 2 +…+B kn t n , modeling the time-varying behavior of the amplitude and phase angle of each frequency component, where B k0 , B k1 , ..., B kn is the fitting coefficient, which is used to describe the dynamic change characteristics of the frequency component.

[0063] In this scheme, the second polynomial fitting model is used to model the time-varying behavior of the amplitude and phase angle of the frequency component, which can more accurately describe the dynamic change characteristics of the frequency component. Through the fitting coefficient, the change of the frequency component can be monitored and tracked in real time, and the abnormal situation in the power system can be discovered in time, providing strong support for fault diagnosis and prevention, thereby realizing real-time monitoring and diagnosis of various faults and abnormal situations in the power system, improving the accuracy and stability of phasor measurement, and better reflecting the actual situation of the frequency component in the power system. By modeling the time-varying behavior of the frequency component, the frequency change law in the power system can be better understood and analyzed, providing a reference basis for system operation and maintenance.

[0064] Specifically, according to the real-time broadband measurement requirements, a dynamic model of polynomial fitting is established to characterize the time-varying behavior of signal amplitude and phase angle. That is, the dynamic changes of amplitude and phase angle are represented by the polynomial fitting method, and the amplitude and phase angle of each frequency component k are represented as a second polynomial fitting model. The amplitude and phase angle are expanded into a polynomial form through the least squares method, so that the model can adapt to the dynamic changes of broadband signals.

[0065] In the specific implementation process, the above method also includes: using the weighted least squares method WLS to dynamically optimize the frequency component. The above dynamic optimization can be specifically implemented by the following steps: calculating the phase angle derivative θ of the frequency component k (t), frequency correction Δf k It is expressed as: Using the adjustment formula f k (t+1)=f k (t)+Δf k Dynamically adjust the frequency; the weighted least squares optimization objective function is Among them, y i is the actual measured value, is the fitted value, w i is the weighting factor adjusted according to the signal-to-noise ratio.

[0066] In this scheme, the weighted least squares method WLS is used to dynamically optimize the frequency component to improve the accuracy and stability of the phasor measurement. By calculating the phase angle derivative and frequency correction of the frequency component, the frequency can be dynamically adjusted according to the difference between the actual measured value and the fitted value, so as to more accurately estimate the frequency component of the signal. By adjusting the weighting factor in the weighted least squares optimization objective function according to the signal-to-noise ratio, the influence of noise on the frequency estimation can be effectively reduced, and the accuracy of the estimation can be improved. By dynamically optimizing the frequency component, the measurement accuracy and stability can be improved in real-time phasor measurement, so that it can be better applied to various real-time control and monitoring systems.

[0067] Specifically, the weighted least squares (WLS) method is a regression analysis method for fitting data. The basic idea is to add weight settings on the basis of the least squares method to improve the fitting effect of the data. Each data point is assigned a weight, which can reflect the reliability or importance of the data point.

[0068] Specifically, the sampling is based on the weighted least squares iteration method of resampling Prony to correct the frequency, amplitude and phase angle of the low-frequency component. For each frequency component, the frequency, amplitude and phase angle are updated in real time based on the polynomial fitting model, and the phase angle derivative of each frequency component is calculated to correct the initial value of the frequency. It is judged whether the error or the number of iterations reaches the preset termination condition. If the error exceeds the limit or the number of iterations does not reach the maximum value, the frequency value is updated to continue the iterative solution. In the process of frequency correction, the weighted least squares resampling Prony estimation algorithm is introduced to iteratively optimize the low-frequency signal, and the weighted least squares method is used to update the low-frequency component parameters. The updated frequency is calculated. If the termination condition is met, the final frequency, amplitude and phase angle are output to obtain the real-time broadband signal measurement result.

[0069] The weighted least squares update formula is as follows: Assume that the low-frequency signal sequence is Y = {y1, y2, ..., y n}, the fitting result is The least squares optimization problem is then expressed as the above weighted least squares optimization objective function, where the weighting factor adjusted according to the signal-to-noise ratio depends on the signal-to-noise ratio of each point.

[0070] In the specific implementation process, the above method also includes: using the IWMARP algorithm to perform real-time correction on the frequency, amplitude and phase angle of the low-frequency component in the weighted least squares framework, and using the parameter update formula Iteratively update the signal model parameters, where a j To fit the parameters, "j" is updated in each iteration.

[0071] In this scheme, the signal model parameters are iteratively updated through the parameter update formula, and the fitting parameters can be continuously optimized so that the model can better fit the actual signal, thereby improving the accuracy of phasor measurement. The frequency, amplitude and phase angle of the low-frequency component are corrected in real time through the IWMARP algorithm, which can effectively eliminate the errors caused by noise and interference and improve the reconstruction quality of the signal. The signal model parameters are iteratively updated using the parameter update formula, and the parameters can be dynamically adjusted during the real-time measurement process to adapt to changes in signal characteristics, thereby improving the stability and real-time performance of the phasor measurement.

[0072] Specifically, the above-mentioned IWMARP (Improved Weighted Matching Algorithm for ResourcePooling) algorithm is a weighted matching algorithm for resource pool management. By weighted matching the resources in the resource pool with the requests, effective allocation and utilization of resources are achieved. A weight value is assigned to each resource and request according to the attributes of the resources and the requirements of the requests, and then the resources are allocated to the requests through the weighted matching algorithm. During the operation of the algorithm, the best matching scheme is selected according to the weight values ​​of the resources and the requests to achieve effective utilization of resources and satisfaction of the requests. By adopting the weighted matching algorithm, the IWMARP algorithm can better solve the optimization problem in resource allocation, improve resource utilization and request response speed, and improve resource utilization and request response speed by weighted updating formula of the resampled Prony's weighted least squares iteration method in each iteration. j The parameters are updated to ensure that the accurate frequency and phase angle of the low-frequency component can be obtained under noise interference.

[0073] In some embodiments, the time window data of the target power signal is collected to construct a sampling sequence, which can be specifically implemented by the following steps: sampling the target power signal, setting the time window length to T, setting the number of sampling points to N, and the sampling frequency to f s , the time window length is expressed as The sampling sequence is represented as X(t k )={x(t0),x(t1),...,x(t N )}, where t k ∈[t0, t0+T].

[0074] In this scheme, the target power signal is sampled and the time window length, sampling points and sampling frequency are set. The signal sample sequence within the time window is extracted, and sufficient sampling data can be obtained for subsequent signal processing and analysis. The time window data obtained by sampling can be decomposed in the frequency domain by the Prony method, and the frequency and amplitude information of the signal can be extracted, thereby realizing the phasor measurement of the power signal. Setting the time window length can control the time domain range of the sampling data, ensure that the complete signal cycle is sampled, and avoid spectrum leakage and insufficient spectrum resolution. Setting the number of sampling points and sampling frequency affects the frequency resolution of the signal. Increasing the number of sampling points and sampling frequency can improve the frequency resolution and make the signal frequency component more accurate. In real-time phasor measurement, the selection and setting of the sampling sequence plays an important role in the accuracy and efficiency of signal processing. The accuracy and real-time performance of phasor measurement can be improved by optimizing parameters such as the time window length, the number of sampling points and the sampling frequency.

[0075] Specifically, data with a time window length of T is extracted from the power signal to be processed to capture the dynamic changes of the signal in a short time. The selection of the time window length T should take into account the sampling requirements of low-frequency signals so as to perform frequency differentiation in subsequent steps. It is recommended that the sampling rate f within the time window is high enough to capture the low-frequency and high-frequency components contained in the broadband signal.

[0076] As can be seen from the above, in an embodiment of the present invention, a high-precision dynamic broadband measurement method based on an improved Prony algorithm is proposed, which aims to improve the dynamic measurement accuracy of low-frequency interharmonics and broadband signals in the power system. First, a time window sampling sequence is constructed for the collected target power signal, and the frequency components are analyzed based on the Hank matrix to separate the high-frequency and low-frequency components. For the low-frequency components, a resampling strategy is adopted to enhance the frequency resolution. Then, a characteristic polynomial is constructed through the resampled low-frequency components, and the Prony method is used to estimate the initial value of the frequency. In the frequency estimation, the Hank matrix is ​​denoised in combination with the singular value decomposition (SVD) to remove noise interference and retain only the main signal components.

[0077] In addition, to further improve the measurement accuracy, a polynomial fitting model is used to dynamically estimate the amplitude, frequency and phase angle of each frequency component. The model optimizes the dynamic frequency parameters through the weighted least squares method (WLS), and uses the phase angle derivative of the frequency component to perform real-time frequency correction to ensure dynamic tracking accuracy. The weighted least squares low-frequency interharmonic measurement algorithm based on the resampling Prony algorithm is used to iteratively correct the frequency, amplitude and phase angle of the low-frequency component under the WLS framework to reduce the impact of noise and improve the measurement accuracy of low-frequency interharmonics. It is suitable for accurate real-time measurement of wide-band dynamic signals and can achieve high-precision measurement of low-frequency interharmonics and fundamental signals under dynamic conditions to meet the real-time monitoring needs of dynamic power systems.

[0078] That is, the technical solution provided by the embodiment of the present invention comprehensively considers various factors, combines short time windows, Hank matrix decomposition and improved Prony method, and realizes accurate measurement of various components in broadband signals, especially low-frequency components, through polynomial fitting models and rapid iterations. Through polynomial fitting and iterative optimization, the difficult problems of short time window dynamic measurement and low measurement accuracy of low-frequency signals are solved, and the needs of modern power systems for accurate measurement of dynamic broadband signals can be met.

[0079] In order to enable those skilled in the art to more clearly understand the technical solution of the present application, the implementation process of the wideband measurement method based on the Prony algorithm of the present application will be described in detail below in conjunction with specific embodiments.

[0080] This embodiment relates to a specific broadband measurement method based on the Prony algorithm, such as Figure 3 As shown, the following steps are included:

[0081] Step S1: Data collection and sample matrix construction, collecting sample data of the target power signal within a set time window, constructing an adopted sequence, and constructing the adopted sequence into a sample matrix (Hank matrix).

[0082] Step S2: Signal component extraction and frequency band separation. Perform frequency analysis on the signal in the Hank matrix to divide the signal into low-frequency and high-frequency components. According to the frequency range of the signal, resample the low-frequency and high-frequency components at different sampling rates to improve the frequency resolution of the low-frequency components.

[0083] Step S3: SVD denoising, apply singular value decomposition (SVD) to the resampled low-frequency components, determine the effective order of the components and remove the noise, return the singular values ​​of the noise components to zero, retain only the main signal components, and generate the denoised Hank matrix.

[0084] Step S4: constructing characteristic polynomial and estimating initial frequency value, using the denoised low-frequency Hank matrix to construct characteristic polynomial and solve the characteristic polynomial to obtain the initial frequency estimation value of the frequency component.

[0085] Step S5: Setting the frequency estimation threshold and the iteration number limit.

[0086] Step S6: Generate a multi-order diagonal matrix about frequency, and generate a multi-order diagonal matrix Vp according to the initial frequency estimation value for subsequent dynamic frequency correction.

[0087] Step S7: Dynamic frequency correction and weighted least squares optimization, using weighted least squares (WLS) to dynamically adjust the frequency, amplitude and phase angle, calculate the frequency correction amount, and update the frequency value according to the frequency correction amount. The optimization goal is to minimize the weighted least squares error.

[0088] Step S8: Iterative correction of the IWMARP algorithm. The algorithm iteratively corrects the parameters of the low-frequency component under the weighted least squares framework to further reduce the impact of noise. In each iteration, the parameter a is updated. j To ensure high-precision tracking of the frequency, amplitude and phase angle of the low-frequency components.

[0089] Step S9: Error judgment and iteration control, judging whether the error or the number of iterations meets the termination condition. If the condition is met, the broadband measurement result is output; if not, the frequency value is corrected.

[0090] Specifically, the sampling sequence X(t k ) is rearranged into the Hank matrix H, and satisfies: The columns of the Hank matrix are analyzed and separated into high-frequency and low-frequency components. For the low-frequency components, the Hank matrix is ​​downsampled to reduce the frequency estimation error. Specifically, the Hank matrix columns of the low-frequency signal are extracted by downsampling (reducing the sampling rate) to form a downsampled sequence of the low-frequency signal. For example, every other sampling point is retained to form a new low-frequency signal sequence: X low (t m )={x(t0),x(t2),x(t4),…,x(t 2M )}.

[0091] After that, the Prony method is used to construct the characteristic polynomial for the extracted resampled low-frequency signal component. In the process of solving the characteristic polynomial, the downsampled low-frequency signal is used for Prony calculation to obtain a higher-precision low-frequency frequency initial value. This initial value will be used for polynomial fitting and frequency correction in subsequent steps. By using a resampled signal with a low sampling rate, the frequency resolution of the low-frequency component is increased, and spectrum leakage is effectively avoided.

[0092] Specifically, the above-mentioned output measurement results include outputting the amplitude, phase angle and frequency of each frequency component to generate the final measurement results of the broadband signal. The verification steps are as follows: simulation verifies the measurement accuracy of low-frequency interharmonics to ensure the accuracy of the measurement results of low-frequency interharmonics and fundamental waves under dynamic signals.

[0093] The embodiment of the present application also provides a broadband measurement device based on the Prony algorithm. It should be noted that the broadband measurement device based on the Prony algorithm of the embodiment of the present application can be used to execute the broadband measurement method based on the Prony algorithm provided in the embodiment of the present application. The device is used to implement the above-mentioned embodiments and preferred implementation modes, and the descriptions that have been made will not be repeated. As used below, the term "module" can implement a combination of software and / or hardware for a predetermined function. Although the device described in the following embodiments is preferably implemented in software, the implementation of hardware, or a combination of software and hardware, is also possible and conceivable.

[0094] The following is an introduction to the broadband measurement device based on the Prony algorithm provided in the embodiments of the present application.

[0095] Figure 4 Schematic diagram of a broadband measurement device based on the Prony algorithm according to an embodiment of the present application. Figure 4 As shown, the device includes: a construction unit 41, a construction separation unit 42, a resampling unit 43 and a solution unit 44.

[0096] The construction unit 41 is used to collect time window data of the target power signal and construct a sampling sequence.

[0097] Specifically, collecting time window data of the target power signal can help determine the frequency and amplitude components of the signal. By constructing a sampling sequence, the signal can be analyzed and reconstructed more accurately, thereby helping to identify abnormal conditions or faults in the power system. This can help improve the accuracy and reliability of signal analysis, thereby improving the safety and stability of the power system.

[0098] The separation unit 42 is configured to construct a Hank matrix based on the sampling sequence, and separate the Hank matrix into a low-frequency part and a high-frequency part by performing frequency analysis on the frequency components.

[0099] Specifically, by constructing a Hank matrix for the sampling sequence and performing frequency analysis on the frequency components, the Hank matrix can be separated into a low-frequency part and a high-frequency part, so that the frequency characteristics of the signal can be better understood, thereby more accurately analyzing and processing the signal. By performing frequency analysis on the frequency components, the signal can be better decomposed and processed, thereby improving the accuracy and precision of phasor measurement. By using the improved Prony method to measure phasors in real time, specific frequency components can be extracted from the signal, thereby realizing signal spectrum analysis and signal reconstruction.

[0100] The resampling unit 43 performs a resampling process on the low frequency part of the Hank matrix.

[0101] Specifically, resampling the low-frequency part of the Hank matrix can improve the accuracy and stability of phasor measurement. Through resampling, the sampling error of the low-frequency part can be reduced, making the phasor measurement result more accurate. Resampling can also reduce noise interference in the signal and improve the signal-to-noise ratio of phasor measurement, thereby improving the performance and reliability of the system.

[0102] The solving unit 44 is used to construct a characteristic polynomial for the low-frequency part after the resampling process using the Prony algorithm, and obtain the initial frequency of each frequency component by solving the characteristic polynomial.

[0103] Specifically, the characteristic polynomial constructed using the Prony algorithm can accurately extract the frequency components of the low-frequency part, thereby achieving more accurate frequency component measurement. By solving the characteristic polynomial, the initial frequency of each frequency component can be obtained, thereby realizing real-time monitoring and measurement of the frequency, improving the accuracy and stability of phasor measurement, and effectively reducing the error of frequency measurement. By improving the Prony method, the frequency components can be extracted more accurately, reducing the frequency estimation error caused by factors such as uneven sampling or noise interference.

[0104] In a specific implementation process, the broadband measurement device based on the Prony algorithm further includes a denoising unit, which is used to denoise the Hank matrix corresponding to the low-frequency part after resampling by using singular value decomposition (SVD) to obtain the denoised low-frequency part, wherein the denoised Hank matrix is ​​expressed as: Among them, U r ,∑ r , V r are the first r principal components of the singular value decomposition of the Hank matrix, respectively.

[0105] In this scheme, by using SVD to denoise the Hank matrix corresponding to the low-frequency part after resampling, the interference of noise on the signal can be effectively removed, thereby obtaining a more accurate and stable low-frequency part. After SVD denoising, the main components in the Hank matrix of the low-frequency part obtained can better describe the characteristics of the signal, so that the real-time phasor measurement method based on the improved Prony method can more accurately estimate the amplitude and phase information of the signal, thereby improving the accuracy and stability of phasor measurement.

[0106] In a specific implementation process, the broadband measurement device based on the Prony algorithm further includes an estimation unit, which is used to use a first polynomial fitting model x for the low-frequency part after denoising. i (t) = A i cos(2πf i t+φ i ), to estimate the dynamic amplitude and phase angle, where A i is the amplitude, f i is the frequency, φ i Is the phase angle.

[0107] In this scheme, by using the first polynomial fitting model to estimate the amplitude and phase angle of the low-frequency part after denoising, the amplitude and phase angle information of the signal can be extracted more accurately, thereby realizing real-time phasor measurement of the signal, effectively improving the accuracy and stability of the phasor measurement, and reducing the impact of noise in the signal. It can help the system monitor the status of power equipment in real time, prevent potential failures, and improve the reliability and stability of the system.

[0108] In the specific implementation process, the broadband measurement device based on the Prony algorithm further includes a modeling unit, which is used to fit the model A using the second polynomial k (t)cos(θ k (t))≈B k0 +B k1 t+B k2 t 2 +…+B kn t n, modeling the time-varying behavior of the amplitude and phase angle of each frequency component, where B k0 , B k1 , ..., B kn is the fitting coefficient, which is used to describe the dynamic change characteristics of the frequency component.

[0109] In this scheme, the second polynomial fitting model is used to model the time-varying behavior of the amplitude and phase angle of the frequency component, which can more accurately describe the dynamic change characteristics of the frequency component. Through the fitting coefficient, the change of the frequency component can be monitored and tracked in real time, and the abnormal situation in the power system can be discovered in time, providing strong support for fault diagnosis and prevention, thereby realizing real-time monitoring and diagnosis of various faults and abnormal situations in the power system, improving the accuracy and stability of phasor measurement, and better reflecting the actual situation of the frequency component in the power system. By modeling the time-varying behavior of the frequency component, the frequency change law in the power system can be better understood and analyzed, providing a reference basis for system operation and maintenance.

[0110] In the specific implementation process, the broadband measurement device based on the Prony algorithm further includes a dynamic optimization unit, which is used to dynamically optimize the frequency component using the weighted least square method WLS. The dynamic optimization unit includes a calculation module and a dynamic adjustment module. The calculation module is used to calculate the phase angle derivative θ of the frequency component. k (t), frequency correction Δf k It is expressed as: The dynamic adjustment module is used to adopt the adjustment formula f k (t+1)=f k (t)+Δf k Dynamically adjust the frequency; the weighted least squares optimization objective function is Among them, y i is the actual measured value, is the fitted value, w i is the weighting factor adjusted according to the signal-to-noise ratio.

[0111] In this scheme, the weighted least squares method WLS is used to dynamically optimize the frequency component to improve the accuracy and stability of the phasor measurement. By calculating the phase angle derivative and frequency correction of the frequency component, the frequency can be dynamically adjusted according to the difference between the actual measured value and the fitted value, so as to more accurately estimate the frequency component of the signal. By adjusting the weighting factor in the weighted least squares optimization objective function according to the signal-to-noise ratio, the influence of noise on the frequency estimation can be effectively reduced, and the accuracy of the estimation can be improved. By dynamically optimizing the frequency component, the measurement accuracy and stability can be improved in real-time phasor measurement, so that it can be better applied to various real-time control and monitoring systems.

[0112] In the specific implementation process, the broadband measurement device based on the Prony algorithm further includes an updating unit, which is used to perform real-time correction on the frequency, amplitude and phase angle of the low-frequency component under the weighted least squares framework through the IWMARP algorithm, and adopts the parameter updating formula Iteratively update the signal model parameters, where a j is the fitting parameter, and a is j to update.

[0113] In this scheme, the signal model parameters are iteratively updated through the parameter update formula, and the fitting parameters can be continuously optimized so that the model can better fit the actual signal, thereby improving the accuracy of phasor measurement. The frequency, amplitude and phase angle of the low-frequency component are corrected in real time through the IWMARP algorithm, which can effectively eliminate the errors caused by noise and interference and improve the reconstruction quality of the signal. The signal model parameters are iteratively updated using the parameter update formula, and the parameters can be dynamically adjusted during the real-time measurement process to adapt to changes in signal characteristics, thereby improving the stability and real-time performance of the phasor measurement.

[0114] In some embodiments, the construction unit includes a sampling module, which is used to sample the target power signal, set the time window length to T, set the number of sampling points to N, and the sampling frequency to f. s , the time window length is expressed as The sampling sequence is represented as X(t k )={x(t0),x(t1),...,x(t N )}, where t k ∈[t0, t0+T].

[0115] In this scheme, the target power signal is sampled and the time window length, sampling points and sampling frequency are set. The signal sample sequence within the time window is extracted, and sufficient sampling data can be obtained for subsequent signal processing and analysis. The time window data obtained by sampling can be decomposed in the frequency domain by the Prony method, and the frequency and amplitude information of the signal can be extracted, thereby realizing the phasor measurement of the power signal. Setting the time window length can control the time domain range of the sampling data, ensure that the complete signal cycle is sampled, and avoid spectrum leakage and insufficient spectrum resolution. Setting the number of sampling points and sampling frequency affects the frequency resolution of the signal. Increasing the number of sampling points and sampling frequency can improve the frequency resolution and make the signal frequency component more accurate. In real-time phasor measurement, the selection and setting of the sampling sequence plays an important role in the accuracy and efficiency of signal processing. The accuracy and real-time performance of phasor measurement can be improved by optimizing parameters such as the time window length, the number of sampling points and the sampling frequency.

[0116] The broadband measurement device based on the Prony algorithm comprises a processor and a memory. The construction unit, the structure separation unit, the resampling unit and the solution unit are all stored in the memory as program units, and the processor executes the program units stored in the memory to realize the corresponding functions. The modules are all located in the same processor; or, the modules are located in different processors in any combination.

[0117] The memory may include non-permanent memory in a computer-readable medium, random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM, and the memory includes at least one memory chip.

[0118] An embodiment of the present invention provides a computer-readable storage medium, wherein the computer-readable storage medium includes a stored program, wherein when the program is executed, a device where the computer-readable storage medium is located is controlled to execute a broadband measurement method based on the Prony algorithm.

[0119] Specifically, the broadband measurement method based on the Prony algorithm includes:

[0120] Step S201 : collecting time window data of a target power signal and constructing a sampling sequence.

[0121] Specifically, collecting time window data of the target power signal can help determine the frequency and amplitude components of the signal. By constructing a sampling sequence, the signal can be analyzed and reconstructed more accurately, thereby helping to identify abnormal conditions or faults in the power system. This can help improve the accuracy and reliability of signal analysis, thereby improving the safety and stability of the power system.

[0122] Step S202: construct a Hank matrix based on the sampling sequence, and separate the Hank matrix into a low-frequency part and a high-frequency part by performing frequency analysis on the frequency components.

[0123] Specifically, by constructing a Hank matrix for the sampling sequence and performing frequency analysis on the frequency components, the Hank matrix can be separated into a low-frequency part and a high-frequency part, so that the frequency characteristics of the signal can be better understood, thereby more accurately analyzing and processing the signal. By performing frequency analysis on the frequency components, the signal can be better decomposed and processed, thereby improving the accuracy and precision of phasor measurement. By using the improved Prony method to measure phasors in real time, specific frequency components can be extracted from the signal, thereby realizing signal spectrum analysis and signal reconstruction.

[0124] Step S203, resampling the low-frequency part of the Hank matrix.

[0125] Specifically, resampling the low-frequency part of the Hank matrix can improve the accuracy and stability of phasor measurement. Through resampling, the sampling error of the low-frequency part can be reduced, making the phasor measurement result more accurate. Resampling can also reduce noise interference in the signal and improve the signal-to-noise ratio of phasor measurement, thereby improving the performance and reliability of the system.

[0126] Step S204: construct a characteristic polynomial for the low-frequency part after resampling using the Prony algorithm, and obtain the initial frequency of each frequency component by solving the characteristic polynomial.

[0127] Specifically, the characteristic polynomial constructed using the Prony algorithm can accurately extract the frequency components of the low-frequency part, thereby achieving more accurate frequency component measurement. By solving the characteristic polynomial, the initial frequency of each frequency component can be obtained, thereby realizing real-time monitoring and measurement of the frequency, improving the accuracy and stability of phasor measurement, and effectively reducing the error of frequency measurement. By improving the Prony method, the frequency components can be extracted more accurately, reducing the frequency estimation error caused by factors such as uneven sampling or noise interference.

[0128] Optionally, after resampling the low-frequency part of the Hank matrix, the wideband measurement method based on the Prony algorithm further includes: performing denoising on the Hank matrix corresponding to the low-frequency part after the resampling process by using singular value decomposition (SVD) to obtain a denoised low-frequency part, wherein the denoised Hank matrix is ​​expressed as: Among them, U r ,∑ r , V r are the first r principal components of the singular value decomposition of the Hank matrix, respectively.

[0129] Optionally, the broadband measurement method based on the Prony algorithm further includes: using a first polynomial fitting model x for the low-frequency part after denoising i (t) = A j cos(2πf i t+φ i ), to estimate the dynamic amplitude and phase angle, where A i is the amplitude, f i is the frequency, φ i Is the phase angle.

[0130] Optionally, the broadband measurement method based on the Prony algorithm further includes: using a second polynomial fitting model A k (t)cos(θ k (t))≈B k0 +B k1 t+B k2 t 2 +…+Bkn t n , modeling the time-varying behavior of the amplitude and phase angle of each frequency component, where B k0 , B k1 , ..., B kn is the fitting coefficient, which is used to describe the dynamic change characteristics of the frequency component.

[0131] Optionally, the broadband measurement method based on the Prony algorithm further includes: dynamically optimizing the frequency component using a weighted least square method WLS, and the dynamic optimization specifically includes: calculating the phase angle derivative θ′ of the frequency component k (t), frequency correction Δf k It is expressed as: Using the adjustment formula f k (t+1)=f k (t)+Δf k Dynamically adjust the frequency; the weighted least squares optimization objective function is Among them, y i is the actual measured value, y i is the fitted value, w i is the weighting factor adjusted according to the signal-to-noise ratio.

[0132] Optionally, the wideband measurement method based on the Prony algorithm further includes: performing real-time correction on the frequency, amplitude and phase angle of the low-frequency component in a weighted least squares framework using the IWMARP algorithm, and using the parameter update formula Iteratively update the signal model parameters, where a j is the fitting parameter, and a is j to update.

[0133] Optionally, collecting time window data of the target power signal and constructing a sampling sequence includes: sampling the target power signal, setting the time window length to T, setting the number of sampling points to N, and the sampling frequency to f s , the time window length is expressed as The sampling sequence is represented as X(t k )={x(t0),x(t1),...,x(t N )}, where t k ∈[t0, t0+T].

[0134] An embodiment of the present invention provides an electronic device, comprising: one or more processors, a memory, and one or more programs, wherein the one or more programs are stored in the memory and are configured to be executed by the one or more processors, and the one or more programs include a method for executing any one of the above-mentioned wideband measurement methods based on the Prony algorithm.

[0135] Obviously, those skilled in the art should understand that the above modules or steps of the present invention can be implemented by a general computing device, they can be concentrated on a single computing device, or distributed on a network composed of multiple computing devices, they can be implemented by a program code executable by a computing device, so that they can be stored in a storage device and executed by the computing device, and in some cases, the steps shown or described can be executed in a different order than here, or they can be made into individual integrated circuit modules, or multiple modules or steps therein can be made into a single integrated circuit module for implementation. Thus, the present invention is not limited to any specific combination of hardware and software.

[0136] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented in one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that include computer-usable program code.

[0137] 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.

[0138] 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.

[0139] 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 A step that specifies a function in one or more boxes.

[0140] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0141] The memory may include non-permanent memory in a computer-readable medium, random access memory (RAM) and / or non-volatile memory in the form of read-only memory (ROM) or flash RAM. The memory is an example of a computer-readable medium.

[0142] Computer readable media include permanent and non-permanent, removable and non-removable media that can be implemented by any method or technology to store information. Information can be computer readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disk read-only memory (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic cassettes, magnetic tape disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer readable media does not include temporary computer readable media (transitory media), such as modulated data signals and carrier waves.

[0143] It should also be noted that the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, commodity or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, commodity or device. In the absence of more restrictions, the elements defined by the sentence "comprises a ..." do not exclude the existence of other identical elements in the process, method, commodity or device including the elements.

[0144] The above are only preferred embodiments of the present application and are not intended to limit the present application. For those skilled in the art, the present application may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included in the protection scope of the present application.

Claims

1. A broadband measurement method based on Prony algorithm, characterized in that: include: Collect time window data of target power signal and construct sampling sequence; Constructing a Hank matrix based on the sampling sequence, and separating the Hank matrix into a low-frequency part and a high-frequency part by performing frequency analysis on the frequency components; Resampling the low-frequency part of the Hank matrix; For the low-frequency part after resampling, the Prony algorithm is used to construct a characteristic polynomial, and the initial frequency of each frequency component is obtained by solving the characteristic polynomial.

2. The method according to claim 1, characterized in that After resampling the low-frequency part of the Hank matrix, the method further includes: Singular value decomposition (SVD) is used to perform denoising on the Hank matrix corresponding to the low-frequency part after the resampling process to obtain the denoised low-frequency part. Among them, the Hank matrix after denoising is expressed as: Among them, U r ,∑ r , V r are the first r principal components of the singular value decomposition of the Hank matrix, respectively.

3. The method according to claim 1, characterized in that The method further comprises: The first polynomial fitting model x is used for the low-frequency part after denoising i (t) = A i cos(2πf i t+φ i ), to estimate the dynamic amplitude and phase angle, where A i is the amplitude, f i is the frequency, φ i Is the phase angle.

4. The method according to claim 3, characterized in that The method further comprises: Use the second polynomial to fit model A k (t)cos(θ k (t))≈B k0 +B k1 t+B k2 t 2 +…+B kn t n , modeling the time-varying behavior of the amplitude and phase angle of each frequency component, where B k0 , B k1 , ..., B kn is the fitting coefficient, which is used to describe the dynamic change characteristics of the frequency component.

5. The method according to claim 1, characterized in that The method further includes: dynamically optimizing the frequency component using a weighted least square method WLS, wherein the dynamic optimization specifically includes: Calculate the phase angle derivative θ′ of the frequency component k (t), frequency correction Δf k It is expressed as: Using the adjustment formula f k (t+1)=f k (t)+Δf k Dynamically adjust the frequency; the weighted least squares optimization objective function is Among them, y i is the actual measured value, is the fitted value, w i is the weighting factor adjusted according to the signal-to-noise ratio.

6. The method according to claim 5, characterized in that The method further comprises: The frequency, amplitude and phase angle of the low-frequency component are corrected in real time under the weighted least squares framework using the IWMARP algorithm. Iteratively update the signal model parameters, where a j is the fitting parameter, and a is j to update.

7. The method according to any one of claims 1 to 5, characterized in that Collect the time window data of the target power signal and construct a sampling sequence, including: The target power signal is sampled, the time window length is set to T, the number of sampling points is set to N, and the sampling frequency is set to f s , the time window length is expressed as The sampling sequence is represented as X(t k )={x(t0),x(t1),...,x(t N )}, where t k ∈[t0, t0+T].

8. A broadband measurement device based on the Prony algorithm, characterized in that: include: A construction unit, used for collecting time window data of a target power signal and constructing a sampling sequence; Constructing a separation unit, used for constructing a Hank matrix based on the sampling sequence, and separating the Hank matrix into a low-frequency part and a high-frequency part by performing frequency analysis on the frequency components; A resampling unit, performing a resampling process on the low-frequency part of the Hank matrix; The solving unit is used to construct a characteristic polynomial for the low-frequency part after the resampling process by using the Prony algorithm, and obtain the initial frequency of each frequency component by solving the characteristic polynomial.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium includes a stored program, wherein when the program is executed, the device where the computer-readable storage medium is located is controlled to execute the wideband measurement method based on the Prony algorithm as claimed in any one of claims 1 to 7.

10. An electronic device, characterized in that: include: One or more processors, a memory, and one or more programs, wherein the one or more programs are stored in the memory and are configured to be executed by the one or more processors, and the one or more programs include a method for executing the wideband measurement method based on the Prony algorithm as described in any one of claims 1 to 7.

Images