Broadband measurement method and device based on prony algorithm

Abstract

The application provides a wideband measurement method and device based on a Prony algorithm, the method comprising: collecting time window data of a target power signal and constructing a sampling sequence; constructing a Hankel matrix based on the sampling sequence, separating the Hankel matrix into a low-frequency part and a high-frequency part through frequency analysis on frequency components; performing resampling processing on the low-frequency part of the Hankel matrix; for the resampling-processed low-frequency part, constructing a characteristic polynomial by using the Prony algorithm, and obtaining an initial frequency of each frequency component by solving the characteristic polynomial. The application solves the problem that current wideband measurement algorithms cannot accurately reflect the dynamic change characteristics of signals in a short time window and cannot accurately measure.

Description

Technical Field

[0001] This application relates to the field of signal processing technology, and more specifically, to 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. Background Technology

[0002] With the large-scale integration of renewable energy and power electronic equipment, a large number of harmonic and interharmonic components have been introduced into the power grid. However, the phasor measurement unit (PMU) can only measure the fundamental frequency of the power system. Although the existing broadband measurement scheme can measure broadband harmonics and interharmonics, its measurement results for 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 broadband device measurement data.

[0003] Existing broadband measurement algorithms are mainly divided into two categories. The first category is frequency domain processing methods, represented by the Fast Fourier Transform (FFT). The resolution is directly affected by the length of the time window; a longer time window can improve frequency resolution. However, increasing the time window length in rapidly changing signals may lead to time-domain response lag, making it impossible to track dynamic changes in frequency and phase in real time. The second category is time-domain processing methods, including modern spectral estimation methods such as Prony analysis. These methods extract frequency, phase, and amplitude information from the signal by fitting an exponential model. They are suitable for processing signals with multiple frequency components, but involve constructing matrices and eigenvalue decomposition, resulting in high computational complexity and failing to meet real-time processing requirements. Especially when estimating multiple frequency components within a short time window, convergence or error accumulation is likely to occur. Therefore, existing methods all have certain problems; no single algorithm can accurately reflect the dynamic characteristics of a signal and perform precise measurement within a short time window. Summary of the Invention

[0004] The main objective of this 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 current broadband measurement algorithms cannot accurately reflect the dynamic change characteristics of signals and perform precise measurement within a short time window.

[0005] To achieve the above objectives, according to one aspect of this application, a broadband measurement method based on the Prony algorithm is provided, comprising: acquiring time window data of a target power signal and constructing 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; constructing a characteristic polynomial using the Prony algorithm for the resampled low-frequency part, and obtaining the initial frequency of each frequency component by solving the characteristic polynomial.

[0006] Optionally, after resampling the low-frequency portion of the Hank matrix, the broadband measurement method based on the Prony algorithm further includes: using singular value decomposition (SVD) to denoise the Hank matrix corresponding to the resampled low-frequency portion, obtaining the denoised low-frequency portion, wherein the denoised Hank matrix is ​​represented as: Among them, U r , ∑ r V r These are the first r principal components of the singular value decomposition of the Hank matrix.

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

[0008] Optionally, the broadband measurement method based on the Prony algorithm further includes: 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 The time-varying behavior of the amplitude and phase angle of each frequency component is modeled, where B k0 B k1 B kn These are the fitting coefficients, used to describe the dynamic variation characteristics of the frequency components.

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

[0010] Optionally, this broadband measurement method based on the Prony algorithm further includes: real-time correction of the frequency, amplitude, and phase angle of the low-frequency components using the IWMARP algorithm within a weighted least squares framework, employing a parameter update formula. The signal model parameters are iteratively updated, where a j To fit the parameters, a is adjusted in each iteration. j 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 this application, a broadband measurement device based on the Prony algorithm is provided, comprising: a construction unit for acquiring time window data of a target power signal and constructing a sampling sequence; a construction separation unit 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 for resampling the low-frequency part of the Hank matrix; and a solution unit for constructing a characteristic polynomial using the Prony algorithm for the resampling low-frequency part and obtaining 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, used to perform singular value decomposition (SVD) on the Hank matrix corresponding to the resampled low-frequency portion after resampling the low-frequency portion of the Hank matrix, to obtain the denoised low-frequency portion, wherein the denoised Hank matrix is ​​represented as: Among them, U r , ∑ r V r These are the first r principal components of the singular value decomposition of the Hank matrix.

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

[0015] Optionally, the broadband measurement device based on the Prony algorithm further includes: a modeling unit for fitting 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 The time-varying behavior of the amplitude and phase angle of each frequency component is modeled, where B k0 B k1 B kn These are the fitting coefficients, used to describe the dynamic variation characteristics of the frequency components.

[0016] Optionally, the broadband measurement device based on the Prony algorithm further includes: a dynamic optimization unit, used to dynamically optimize the frequency components using weighted least squares (WLS), wherein the dynamic optimization unit specifically includes: a calculation module, used to calculate the phase derivative θ′ of the frequency components. k (t), frequency correction Δf k Represented as: The dynamic adjustment module is used to apply the adjustment formula f. k (t+1)=f k (t)+Δf k The frequency is dynamically adjusted; the weighted least squares optimization objective function is: Among them, y i These are actual measured values. w is the fitted value. i This is a weighting factor adjusted according to the signal-to-noise ratio.

[0017] Optionally, the broadband measurement device based on the Prony algorithm further includes: an update unit, used to perform real-time correction of the frequency, amplitude, and phase angle of the low-frequency components using the IWMARP algorithm within a weighted least squares framework, employing a parameter update formula. The signal model parameters are iteratively updated, where a j To fit the parameters, a is adjusted in each iteration. j Update.

[0018] Optionally, the construction unit includes: a sampling module, used to sample the target power signal, setting a time window length of T, setting the number of sampling points to N, and setting 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 this application, a computer-readable storage medium is provided, the computer-readable storage medium including a stored program, wherein, when the program is executed, it controls the device on which the computer-readable storage medium is located to perform any of the above-described broadband measurement methods based on the Prony algorithm.

[0020] According to another aspect of this 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 configured to be executed by the one or more processors, the one or more programs including methods for performing broadband measurement methods based on the Prony algorithm described above.

[0021] By applying the technical solution of this application, relatively accurate broadband measurement can be achieved by constructing the Hank matrix, resampling processing, and applying the Prony algorithm. Attached Figure Description

[0022] The accompanying drawings, which form part of this application, are used to provide a further understanding of this application. The illustrative embodiments and descriptions of this application are used to explain this application and do not constitute an undue limitation of this application. In the drawings:

[0023] Figure 1The diagram shows a hardware structure block diagram of a mobile terminal for a broadband measurement method based on the Prony algorithm according to an embodiment of the present invention.

[0024] Figure 2 A flowchart illustrating a broadband measurement method based on the Prony algorithm according to an embodiment of this application is shown.

[0025] Figure 3 A flowchart illustrating an optional broadband measurement method based on the Prony algorithm according to an embodiment of this application is shown;

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

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

[0028] 102. Processor; 104. Memory; 106. Transmission device; 108. Input / output device. Detailed Implementation

[0029] It should be noted that, unless otherwise specified, the embodiments and features described in this application can be combined with each other. This application will now be described in detail with reference to the accompanying drawings and embodiments.

[0030] To enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present application, and not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative effort 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, claims, and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate for the embodiments of this application described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0032] As described in the background section, current broadband measurement algorithms cannot accurately reflect the dynamic changes of signals and perform precise measurements within a short time window. To address these issues, embodiments of this application 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.

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

[0034] The methods and embodiments provided in this application can be executed on a mobile terminal, computer terminal, or similar computing device. Taking running on a mobile terminal as an example, Figure 1 This is a hardware structure block diagram of a mobile terminal for a broadband measurement method based on the Prony algorithm according to an embodiment of the present invention. Figure 1 As shown, a mobile terminal may include one or more ( Figure 1 Only one is shown in the diagram. A processor 102 (which may include, but is not limited to, a microprocessor MCU or a programmable logic device FPGA, etc.) and a memory 104 for storing data are also shown. The mobile terminal may further include a transmission device 106 for communication functions and an input / output device 108. Those skilled in the art will understand that... Figure 1 The structure shown is for illustrative purposes only and does not limit the structure of the mobile terminal described above. For example, the mobile terminal may also include components that are more... Figure 1 The more or fewer components shown, or having the same Figure 1 The different configurations shown.

[0035] The memory 104 can be used to store computer programs, such as application software programs and modules, like the computer program corresponding to the broadband measurement method based on the Prony algorithm in this embodiment of the invention. The processor 102 executes various functional applications and data processing by running the computer program stored in the memory 104, thereby implementing the above-described method. The memory 104 may include high-speed random access memory and may also include non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some instances, the memory 104 may further include memory remotely located relative to the processor 102, and these remote memories can be connected to the mobile terminal via a network. Examples of the aforementioned networks include, but are not limited to, the Internet, corporate intranets, local area networks, mobile communication networks, and combinations thereof. The transmission device 106 is used to receive or send data via a network. Specific examples of the aforementioned networks may include wireless networks provided by the mobile terminal's communication provider. In one instance, the transmission device 106 includes a network interface controller (NIC), which can be connected to other network devices via a base station 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] This embodiment provides a broadband measurement method based on the Prony algorithm that runs on a mobile terminal, computer terminal, or similar computing device. It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions. Also, although the logical order is shown in the flowchart, in some cases, the steps shown or described may be executed in a different order than that shown here.

[0037] Figure 2 This is a flowchart illustrating a broadband measurement method based on the Prony algorithm according to an embodiment of this application. Figure 2 As shown, the method includes the following steps:

[0038] Step S201: Collect time window data of the target power signal and construct 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 sampling sequences, the signal can be analyzed and reconstructed more accurately, thereby helping to identify abnormalities or faults in the power system. This can help improve the accuracy and reliability of signal analysis, and thus improve the safety and stability of the power system.

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

[0041] Specifically, by constructing a Hank matrix from the sampled sequence and performing frequency analysis on the frequency components, the Hank matrix can be separated into low-frequency and high-frequency parts, allowing for a better understanding of the signal's frequency characteristics and thus enabling more accurate signal analysis and processing. By performing frequency analysis on the frequency components, the signal can be better decomposed and processed, improving the accuracy and precision of phasor measurements. By using the improved Prony method to measure phasors in real time, specific frequency components can be extracted from the signal, thereby achieving spectral analysis and signal reconstruction.

[0042] Step S203: Resample the low-frequency part of the Hank matrix.

[0043] Specifically, resampling the low-frequency portion of the Hank matrix can improve the accuracy and stability of phasor measurements. Resampling reduces sampling errors in the low-frequency portion, making phasor measurement results more accurate. It also reduces noise interference in the signal, improves the signal-to-noise ratio of phasor measurements, and thus enhances system performance and reliability.

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

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

[0046] This embodiment demonstrates how to achieve relatively accurate broadband measurements by constructing the Hank matrix, resampling, and applying the Prony algorithm.

[0047] Specifically, the Hankel matrix mentioned above is a special form of matrix with a specific arrangement where all elements on each diagonal are equal. Its special structural properties allow for more efficient computation and processing in signal processing problems. The Prony algorithm mentioned above is a method for fitting discrete data points in a signal. This 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. It can be used in signal processing to estimate the frequency, amplitude, and phase of a signal.

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

[0049] 1. Acquire raw signal data: First, acquire raw signal data, which may be acquired at unstable or mismatched sampling rates.

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

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

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

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

[0054] Subsequently, based on 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 iThese are the coefficients of the characteristic polynomial, i.e., the parameters to be estimated. During the solution process for the characteristic polynomial, a separate characteristic polynomial is constructed for the resampled data of the low-frequency signal to ensure the accuracy of the initial frequency estimation of the low-frequency interharmonics. The root z of the characteristic polynomial is then solved. k It can calculate the initial value of frequency f. k : Set the error limit ∈ and the maximum number of iterations N based on the initial value of each frequency. iter This allows for gradual optimization of frequency estimation in subsequent steps.

[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 resampled low-frequency part, obtaining the denoised low-frequency part, wherein the denoised Hank matrix is ​​represented as: Among them, U r , ∑ r V r These are the first r principal components of the singular value decomposition of the Hank matrix.

[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, thus obtaining a more accurate and stable low-frequency part. After SVD denoising, the main components in the Hank matrix of the low-frequency part can better describe the characteristics of the signal, enabling the real-time phasor measurement method based on the improved Prony method to more accurately estimate the amplitude and phase information of the signal, thereby improving the accuracy and stability of phasor measurement.

[0057] Specifically, Singular Value Decomposition (SVD) is a matrix decomposition method that decomposes a matrix into the product of three matrices, obtaining the singular values, left singular vector, and right singular vector of the matrix, thereby enabling matrix analysis and processing. Denoising refers to the process of removing noise in signal processing. Noise is any unwanted signal that interferes with the signal. Denoising can improve signal quality, making the original signal clearer and more accurate. Filtering is the most common denoising method, removing noise signals by selecting appropriate filter parameters. Denoising algorithms are based on mathematical models and statistical methods to identify and remove noise signals. Wavelet transform is a frequency domain analysis method that decomposes a signal into wavelet components of different frequencies, and then removes noise.

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

[0059] Furthermore, by setting a dynamic threshold ξ to adapt to the noise level of the signal, the number of signal components p can be extracted more accurately. The calculation formula is as follows: Where μ is the mean of the singular values, δ is the variance of the singular values, and k is an adjustment parameter that controls the threshold size.

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

[0061] In this scheme, by using the first polynomial fitting model to estimate the amplitude and phase angle of the denoised low-frequency part, 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 phasor measurement, and reducing the influence of noise in the signal. This can help the system monitor the status of power equipment in real time, prevent potential faults, and improve the reliability and stability of the system.

[0062] In the specific implementation process, the above method also includes: 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 The time-varying behavior of the amplitude and phase angle of each frequency component is modeled, where B k0 B k1 B kn These are the fitting coefficients, used to describe the dynamic variation characteristics of the frequency components.

[0063] This scheme employs a second polynomial fitting model to model the time-varying behavior of the amplitude and phase angle of frequency components. This model can more accurately describe the dynamic characteristics of frequency component changes. Through the fitting coefficients, changes in frequency components can be monitored and tracked in real time, enabling timely detection of anomalies in the power system and providing strong support for fault diagnosis and prevention. This allows for real-time monitoring and diagnosis of various faults and anomalies in the power system, improving the accuracy and stability of phasor measurements and better reflecting the true situation of frequency components in the power system. By modeling the time-varying behavior of frequency components, the frequency variation patterns in the power system can be better understood and analyzed, providing a reference for system operation and maintenance.

[0064] Specifically, based on 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 polynomial fitting method. 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 polynomial form by least squares method, so that the model can adapt to the dynamic changes of broadband signal.

[0065] In specific implementation, the above method also includes: using weighted least squares (WLS) to dynamically optimize the frequency components. This dynamic optimization can be achieved through the following steps: calculating the phase derivative θ of the frequency components. k (t), frequency correction Δf k Represented as: Using the adjustment formula f k (t+1)=f k (t)+Δf k The frequency is dynamically adjusted; the weighted least squares optimization objective function is: Among them, y i These are actual measured values. w is the fitted value. i This is a weighting factor adjusted according to the signal-to-noise ratio.

[0066] In this scheme, the use of weighted least squares (WLS) to dynamically optimize frequency components can improve the accuracy and stability of phasor measurements. By calculating the phase derivative and frequency correction of the frequency components, the frequency can be dynamically adjusted according to the difference between the actual measured value and the fitted value, thereby estimating the frequency components of the signal more accurately. By adjusting the weighting factor in the objective function of weighted least squares optimization according to the signal-to-noise ratio, the influence of noise on frequency estimation can be effectively reduced, and the accuracy of estimation can be improved. Through dynamic optimization of frequency components, the accuracy and stability of measurement can be improved in real-time phasor measurements, thus making it better applicable to various real-time control and monitoring systems.

[0067] Specifically, the aforementioned Weighted Least Squares (WLS) is a regression analysis method used to fit data. Its basic idea is to add weight settings to the least squares method to improve the fitting effect on 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 Prony resampling weighted least squares iterative method to correct the frequency, amplitude, and phase of the low-frequency components. For each frequency component, the frequency, amplitude, and phase are updated in real time based on a polynomial fitting model. The phase derivative of each frequency component is calculated to correct the initial frequency value. The error or the number of iterations is judged to see if the preset termination condition is met. If the error exceeds the limit or the number of iterations does not reach the maximum value, the frequency value is updated and the iteration continues. In the process of frequency correction, the Prony weighted least squares resampling estimation algorithm is introduced to iteratively optimize the low-frequency signal. The low-frequency component parameters are updated using the weighted least squares method, and the updated frequency is calculated. If the termination condition is met, the final frequency, amplitude, and phase are output to obtain the real-time broadband signal measurement results.

[0069] The weighted least squares update formula is as follows: Assume 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 at each point.

[0070] In specific implementation, the above method also includes: using the IWMARP algorithm to correct the frequency, amplitude, and phase angle of the low-frequency components in real time within the weighted least squares framework, and employing a parameter update formula. The signal model parameters are iteratively updated, where a j To fit the parameters, "j" is updated in each iteration.

[0071] In this scheme, the signal model parameters are iteratively updated using a parameter update formula, which continuously optimizes the fitting parameters, allowing the model to better fit the actual signal and thus improve the accuracy of phasor measurement. The IWMARP algorithm is used to correct the frequency, amplitude, and phase angle of low-frequency components in real time, which can effectively eliminate errors caused by noise and interference and improve the signal reconstruction quality. The iterative update of the signal model parameters using the parameter update formula allows for dynamic adjustment of parameters during real-time measurement to adapt to changes in signal characteristics, thereby improving the stability and real-time performance of phasor measurement.

[0072] Specifically, the IWMARP (Improved Weighted Matching Algorithm for Resource Pooling) algorithm is a weighted matching algorithm for resource pool management. It achieves efficient resource allocation and utilization by weighted matching of resources and requests in the resource pool. Based on resource attributes and request requirements, a weight value is assigned to each resource and request. Then, the weighted matching algorithm allocates resources to requests. During algorithm execution, the optimal matching scheme is selected based on the weight values ​​of resources and requests to achieve efficient resource utilization and request fulfillment. By employing a weighted matching algorithm, the IWMARP algorithm can better solve optimization problems in resource allocation, improve resource utilization and request response speed. This is achieved by updating the weighted least squares iterative method of the resampled Prony algorithm in each iteration. j Updating the parameters ensures that the accurate frequency and phase angle of the low-frequency components can still be obtained under noise interference.

[0073] In some embodiments, time window data of the target power signal is collected to construct a sampling sequence, which can be achieved through the following steps: sampling the target power signal, setting the time window length to T, 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, number of sampling points, and sampling frequency are set. The signal sample sequence within the time window is extracted, which can obtain sufficient sampled data for subsequent signal processing and analysis. The time window data obtained by sampling can be decomposed in the frequency domain using the Prony method to extract the frequency and amplitude information of the signal, thereby realizing phasor measurement of the power signal. Setting the time window length can control the time domain range of the sampled data, ensuring that the complete signal cycle is sampled and avoiding problems such as spectral leakage and insufficient spectral 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 components 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 time window length, number of sampling points, and sampling frequency.

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

[0076] As described above, this invention proposes a high-precision dynamic broadband measurement method based on an improved Prony algorithm, aiming to improve the dynamic measurement accuracy of low-frequency interharmonics and broadband signals in power systems. First, a time window sampling sequence is constructed for the acquired target power signal, and the frequency components are analyzed based on the Hank matrix to separate high-frequency and low-frequency components. For the low-frequency components, a resampling strategy is adopted to enhance frequency resolution. Then, a characteristic polynomial is constructed through the resampled low-frequency components, and the initial frequency value is estimated using the Prony method. In the frequency estimation, singular value decomposition (SVD) is combined to denoise the Hank matrix to remove noise interference and retain only the main signal components.

[0077] Furthermore, to further improve measurement accuracy, a polynomial fitting model is used to dynamically estimate the amplitude, frequency, and phase angle of each frequency component. This model optimizes the dynamic frequency parameters using the weighted least squares (WLS) method and performs real-time frequency correction using the phase derivative of the frequency component to ensure dynamic tracking accuracy. Additionally, a weighted least squares low-frequency interharmonic measurement algorithm based on the resampling Prony algorithm is used within the WLS framework to iteratively correct the frequency, amplitude, and phase angle of the low-frequency component, reducing noise impact and improving the measurement accuracy of low-frequency interharmonics. This method is suitable for accurate real-time measurement of wideband 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] In other words, the technical solution provided by the embodiments of the present invention comprehensively considers multiple factors, combines short time windows, Hank matrix decomposition and the improved Prony method, and achieves accurate measurement of each component in broadband signals, especially low-frequency components, through polynomial fitting models and fast iteration. Through polynomial fitting and iterative optimization, it solves the problem of low accuracy in short-time window dynamic measurement and low-frequency signal measurement, and can meet the needs of modern power systems for accurate measurement of dynamic broadband signals.

[0079] To enable those skilled in the art to better understand the technical solution of this application, the implementation process of the broadband measurement method based on the Prony algorithm of this application will be described in detail below with reference to specific embodiments.

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

[0081] Step S1: Data acquisition and sample matrix construction. Collect sample data of the target power signal within a set time window, construct a sequence, and construct a sample matrix (Hank matrix) from the sequence.

[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. Based on 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 to determine the effective order of the components and remove noise. The singular values ​​of the noise components are zeroed out, and only the main signal components are retained to generate the denoised Hank matrix.

[0084] Step S4: Characteristic polynomial construction and initial frequency estimation. The characteristic polynomial is constructed using the denoised low-frequency Hank matrix and the characteristic polynomial is solved to obtain the initial frequency estimates of the frequency components.

[0085] Step S5: Set the threshold for frequency estimation and the limit on the number of iterations.

[0086] Step S6: Generate a multi-order diagonal matrix with respect to frequency. Generate a multi-order diagonal matrix Vp based on the initial frequency estimate for subsequent dynamic frequency correction.

[0087] Step S7: Dynamic frequency correction and weighted least squares optimization. Use weighted least squares (WLS) to dynamically adjust the frequency, amplitude and phase angle, calculate the frequency correction amount, update the frequency value according to the frequency correction amount, and the optimization objective 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 components within the weighted least squares framework to further reduce the impact of noise. In each iteration, parameter a is updated. j This ensures high-precision tracking of the frequency, amplitude, and phase angle of low-frequency components.

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

[0090] Specifically, the sampling sequence X(t) k The matrix is ​​rearranged into a Hank matrix H, satisfying the following conditions: The columns of the Hank matrix are analyzed, separating the matrix into high-frequency and low-frequency components. For the low-frequency components, the Hank matrix is ​​downsampled to reduce frequency estimation errors. Specifically, the columns of the Hank matrix for low-frequency signals are extracted using downsampling (reducing the sampling rate) to form a downsampled sequence of the low-frequency signal, with a downsampling rate of... 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] Subsequently, a characteristic polynomial is constructed using the Prony method for the extracted resampled low-frequency signal components. During the solution process, the downsampled low-frequency signal is used for Prony calculations to obtain higher-precision initial values ​​for the low-frequency frequencies. These initial values ​​will be used for polynomial fitting and frequency correction in subsequent steps. By employing a low-sampling-rate resampled signal, the frequency resolution of the low-frequency components is increased, effectively avoiding spectral leakage.

[0092] Specifically, the above output measurement results include the amplitude, phase angle and frequency of each frequency component, generating the final measurement results of the broadband signal. The verification steps are as follows: Simulate and verify the measurement accuracy of low-frequency interharmonics to ensure that the measurement results of low-frequency interharmonics and fundamental wave under dynamic signals are accurate.

[0093] This 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 in this application can be used to execute the broadband measurement method based on the Prony algorithm provided in this application. This device is used to implement the above embodiments and preferred embodiments; details already described will not be repeated. As used below, the term "module" can refer to a combination of software and / or hardware that performs a predetermined function. Although the device described in the following embodiments is preferably implemented in software, hardware implementation, or a combination of software and hardware, is also possible and contemplated.

[0094] The following describes the broadband measurement device based on the Prony algorithm provided in the embodiments of this application.

[0095] Figure 4 This is a schematic diagram of a broadband measurement device based on the Prony algorithm according to an embodiment of this 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] 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 sampling sequences, the signal can be analyzed and reconstructed more accurately, thereby helping to identify abnormalities or faults in the power system. This can help improve the accuracy and reliability of signal analysis, and thus improve the safety and stability of the power system.

[0098] A separation unit 42 is constructed to construct the Hank matrix based on the sampling sequence. By performing frequency analysis on the frequency components, the Hank matrix is ​​separated into low-frequency and high-frequency components.

[0099] Specifically, by constructing a Hank matrix from the sampled sequence and performing frequency analysis on the frequency components, the Hank matrix can be separated into low-frequency and high-frequency parts, allowing for a better understanding of the signal's frequency characteristics and thus enabling more accurate signal analysis and processing. By performing frequency analysis on the frequency components, the signal can be better decomposed and processed, improving the accuracy and precision of phasor measurements. By using the improved Prony method to measure phasors in real time, specific frequency components can be extracted from the signal, thereby achieving spectral analysis and signal reconstruction.

[0100] Resampling unit 43 performs resampling processing on the low-frequency part of the Hank matrix.

[0101] Specifically, resampling the low-frequency portion of the Hank matrix can improve the accuracy and stability of phasor measurements. Resampling reduces sampling errors in the low-frequency portion, making phasor measurement results more accurate. It also reduces noise interference in the signal, improves the signal-to-noise ratio of phasor measurements, and thus enhances system performance and reliability.

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

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

[0104] In its specific implementation, the aforementioned broadband measurement device based on the Prony algorithm also includes a denoising unit. This denoising unit is used to denoise the Hank matrix corresponding to the resampled low-frequency portion after resampling the low-frequency part, using Singular Value Decomposition (SVD) to obtain the denoised low-frequency portion. The denoised Hank matrix is ​​represented as follows: Among them, U r , ∑ r V r These are the first r principal components of the singular value decomposition of the Hank matrix.

[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, thus obtaining a more accurate and stable low-frequency part. After SVD denoising, the main components in the Hank matrix of the low-frequency part can better describe the characteristics of the signal, enabling the real-time phasor measurement method based on the improved Prony method to more accurately estimate the amplitude and phase information of the signal, thereby improving the accuracy and stability of phasor measurement.

[0106] In its specific implementation, the aforementioned broadband measurement device based on the Prony algorithm also includes an estimation unit, which is used to fit the denoised low-frequency component using a first polynomial fitting model x. i (t)=A i cos(2πf i t+φ i ), to estimate the dynamic amplitude and phase angle, where A i f is the amplitude. i For frequency, φ i It is the phase angle.

[0107] In this scheme, by using the first polynomial fitting model to estimate the amplitude and phase angle of the denoised low-frequency part, 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 phasor measurement, and reducing the influence of noise in the signal. This can help the system monitor the status of power equipment in real time, prevent potential faults, and improve the reliability and stability of the system.

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

[0109] This scheme employs a second polynomial fitting model to model the time-varying behavior of the amplitude and phase angle of frequency components. This model can more accurately describe the dynamic characteristics of frequency component changes. Through the fitting coefficients, changes in frequency components can be monitored and tracked in real time, enabling timely detection of anomalies in the power system and providing strong support for fault diagnosis and prevention. This allows for real-time monitoring and diagnosis of various faults and anomalies in the power system, improving the accuracy and stability of phasor measurements and better reflecting the true situation of frequency components in the power system. By modeling the time-varying behavior of frequency components, the frequency variation patterns in the power system can be better understood and analyzed, providing a reference for system operation and maintenance.

[0110] In its implementation, the aforementioned broadband measurement device based on the Prony algorithm also includes a dynamic optimization unit. This unit uses weighted least squares (WLS) to dynamically optimize the frequency components. The dynamic optimization unit comprises a calculation module and a dynamic adjustment module. The calculation module calculates the phase derivative θ of the frequency components. k (t), frequency correction Δf k Represented as: The dynamic adjustment module is used to apply the adjustment formula f k (t+1)=f k (t)+Δf k The frequency is dynamically adjusted; the weighted least squares optimization objective function is: Among them, y i These are actual measured values. w is the fitted value. i This is a weighting factor adjusted according to the signal-to-noise ratio.

[0111] In this scheme, the use of weighted least squares (WLS) to dynamically optimize frequency components can improve the accuracy and stability of phasor measurements. By calculating the phase derivative and frequency correction of the frequency components, the frequency can be dynamically adjusted according to the difference between the actual measured value and the fitted value, thereby estimating the frequency components of the signal more accurately. By adjusting the weighting factor in the objective function of weighted least squares optimization according to the signal-to-noise ratio, the influence of noise on frequency estimation can be effectively reduced, and the accuracy of estimation can be improved. Through dynamic optimization of frequency components, the accuracy and stability of measurement can be improved in real-time phasor measurements, thus making it better applicable to various real-time control and monitoring systems.

[0112] In its implementation, the aforementioned broadband measurement device based on the Prony algorithm also includes an update unit. This update unit is used to perform real-time correction of the frequency, amplitude, and phase angle of the low-frequency components using the IWMARP algorithm within a weighted least squares framework, employing a parameter update formula. The signal model parameters are iteratively updated, where a j To fit the parameters, a is adjusted in each iteration. j Update.

[0113] In this scheme, the signal model parameters are iteratively updated using a parameter update formula, which continuously optimizes the fitting parameters, allowing the model to better fit the actual signal and thus improve the accuracy of phasor measurement. The IWMARP algorithm is used to correct the frequency, amplitude, and phase angle of low-frequency components in real time, which can effectively eliminate errors caused by noise and interference and improve the signal reconstruction quality. The iterative update of the signal model parameters using the parameter update formula allows for dynamic adjustment of parameters during real-time measurement to adapt to changes in signal characteristics, thereby improving the stability and real-time performance of phasor measurement.

[0114] In some embodiments, the above-mentioned building unit includes a sampling module, which is used to sample the target power signal, setting the time window length to T, 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, number of sampling points, and sampling frequency are set. The signal sample sequence within the time window is extracted, which can obtain sufficient sampled data for subsequent signal processing and analysis. The time window data obtained by sampling can be decomposed in the frequency domain using the Prony method to extract the frequency and amplitude information of the signal, thereby realizing phasor measurement of the power signal. Setting the time window length can control the time domain range of the sampled data, ensuring that the complete signal cycle is sampled and avoiding problems such as spectral leakage and insufficient spectral 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 components 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 time window length, number of sampling points, and sampling frequency.

[0116] The aforementioned broadband measurement device based on the Prony algorithm includes a processor and a memory. The aforementioned construction unit, construction separation unit, resampling unit, and solution unit are all stored as program units in the memory. The processor executes these program units stored in the memory to achieve the corresponding functions. All of the above modules reside in the same processor; alternatively, the modules may be located in different processors in any combination.

[0117] The memory may include non-permanent memory in computer-readable media, such as 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] This invention provides a computer-readable storage medium including a stored program, wherein, when the program is executed, it controls the device where the computer-readable storage medium is located to perform a broadband measurement method based on the Prony algorithm.

[0119] Specifically, broadband measurement methods based on the Prony algorithm include:

[0120] Step S201: Collect time window data of the target power signal and construct 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 sampling sequences, the signal can be analyzed and reconstructed more accurately, thereby helping to identify abnormalities or faults in the power system. This can help improve the accuracy and reliability of signal analysis, and thus improve the safety and stability of the power system.

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

[0123] Specifically, by constructing a Hank matrix from the sampled sequence and performing frequency analysis on the frequency components, the Hank matrix can be separated into low-frequency and high-frequency parts, allowing for a better understanding of the signal's frequency characteristics and thus enabling more accurate signal analysis and processing. By performing frequency analysis on the frequency components, the signal can be better decomposed and processed, improving the accuracy and precision of phasor measurements. By using the improved Prony method to measure phasors in real time, specific frequency components can be extracted from the signal, thereby achieving spectral analysis and signal reconstruction.

[0124] Step S203: Resample the low-frequency part of the Hank matrix.

[0125] Specifically, resampling the low-frequency portion of the Hank matrix can improve the accuracy and stability of phasor measurements. Resampling reduces sampling errors in the low-frequency portion, making phasor measurement results more accurate. It also reduces noise interference in the signal, improves the signal-to-noise ratio of phasor measurements, and thus enhances system performance and reliability.

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

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

[0128] Optionally, after resampling the low-frequency portion of the Hank matrix, the broadband measurement method based on the Prony algorithm further includes: using singular value decomposition (SVD) to denoise the Hank matrix corresponding to the resampled low-frequency portion, obtaining the denoised low-frequency portion, wherein the denoised Hank matrix is ​​represented as: Among them, U r , ∑ r V r These are the first r principal components of the singular value decomposition of the Hank matrix.

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

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

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

[0132] Optionally, this broadband measurement method based on the Prony algorithm further includes: real-time correction of the frequency, amplitude, and phase angle of the low-frequency components using the IWMARP algorithm within a weighted least squares framework, employing a parameter update formula. The signal model parameters are iteratively updated, where a j To fit the parameters, a is adjusted in each iteration. j Update.

[0133] Optionally, time window data of the target power signal is collected to construct a sampling sequence, including: 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] This invention provides an electronic device, including: one or more processors, a memory, and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors, and the one or more programs include a broadband measurement method based on the Prony algorithm for performing any of the above.

[0135] It is obvious to those skilled in the art that the modules or steps of the present invention described above can be implemented using general-purpose computing devices. They can be centralized on a single computing device or distributed across a network of multiple computing devices. They can be implemented using computer-executable program code, and thus can be stored in a storage device for execution by a computing device. In some cases, the steps shown or described can be performed in a different order than those described herein, or they can be fabricated as separate integrated circuit modules, or multiple modules or steps can be fabricated as a single integrated circuit module. Thus, the present invention is not limited to any particular combination of hardware and software.

[0136] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0137] 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.

[0138] 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.

[0139] 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.

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

[0141] Memory may include non-persistent memory in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM. Memory is an example of computer-readable media.

[0142] Computer-readable media includes both permanent and non-permanent, removable and non-removable media that can store information using any method or technology. Information can be computer-readable instructions, data structures, modules of programs, 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 technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other non-transferable medium that can be used to store information accessible by a computing device. As defined herein, computer-readable media does not include transient computer-readable media, such as modulated data signals and carrier waves.

[0143] It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element.

[0144] The above are merely preferred embodiments of this application and are not intended to limit this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the protection scope of this application.

Claims

1. A broadband measurement method based on the Prony algorithm, characterized in that, include: Collect time window data of the target power signal and construct a sampling sequence; Based on the sampling sequence, a Hank matrix is ​​constructed. By performing frequency analysis on the frequency components, the Hank matrix is ​​separated into a low-frequency part and a high-frequency part. The low-frequency portion of the Hank matrix is ​​resampled. For the low-frequency component after resampling, a characteristic polynomial is constructed using the Prony algorithm, and the initial frequency of each frequency component is obtained by solving the characteristic polynomial. After resampling the low-frequency portion of the Hank matrix, the method further includes: The Hank matrix corresponding to the low-frequency component after resampling is denoised using Singular Value Decomposition (SVD) to obtain the denoised low-frequency component. The denoised Hank matrix is ​​represented as follows: ,in, These are the first r principal components of the singular value decomposition of the Hank matrix; The first polynomial fitting model is used for the denoised low-frequency part. To estimate the dynamic amplitude and phase angle, For amplitude, For frequency, Phase angle; The second polynomial was used to fit the model. The time-varying behavior of the amplitude and phase angle of each frequency component is modeled, whereby... These are the fitting coefficients, used to describe the dynamic variation characteristics of the frequency components.

2. The method according to claim 1, characterized in that, After modeling the time-varying behavior of the amplitude and phase angle of each frequency component using a second polynomial fitting model, the method further includes: dynamically optimizing the frequency components using weighted least squares (WLS), wherein the dynamic optimization specifically includes: Calculate the phase derivative of the frequency component. Frequency correction amount Represented as: ; Use adjustment formula The frequency is dynamically adjusted; the weighted least squares optimization objective function is: ,in, These are actual measured values. These are the fitted values. This is a weighting factor adjusted according to the signal-to-noise ratio.

3. The method according to claim 2, characterized in that, After dynamically optimizing the frequency components using the weighted least squares (WLS) method, the method further includes: A weighted matching algorithm for resource pool management is used to correct the frequency, amplitude, and phase of low-frequency components in real time within a weighted least squares framework, employing a parameter update formula. The signal model parameters are iteratively updated, where, To fit the parameters, in each iteration... Update.

4. The method according to any one of claims 1 to 2, characterized in that, Time window data of the target power signal is collected to construct a sampling sequence, including: The target power signal is sampled, and the time window length is set to... Set the number of sampling points to The sampling frequency is The time window length is expressed as The sampling sequence is represented as ,in .

5. A broadband measurement device based on the Prony algorithm, characterized in that, The broadband measurement method based on the Prony algorithm as described in claim 1 is applicable to the broadband measurement device based on the Prony algorithm, comprising: The construction unit is used to collect time window data of the target power signal and construct a sampling sequence; A separation unit is constructed to construct a Hank matrix based on the sampling sequence, and the Hank matrix is ​​separated into a low-frequency part and a high-frequency part by performing frequency analysis on the frequency components; The resampling unit performs resampling processing on the low-frequency portion of the Hank matrix; The solving unit is used to construct a characteristic polynomial using the Prony algorithm for the low-frequency part after resampling, and to obtain the initial frequency of each frequency component by solving the characteristic polynomial.

6. A computer-readable storage medium, characterized in that, The computer-readable storage medium includes a stored program, wherein, when the program is executed, it controls the device on which the computer-readable storage medium is located to perform the broadband measurement method based on the Prony algorithm as described in any one of claims 1 to 4.

7. 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 configured to be executed by the one or more processors, the one or more programs including methods for performing the broadband measurement method based on the Prony algorithm as described in any one of claims 1 to 4.

Images