Power system broadband measurement data processing method, device, equipment and medium

Abstract

The invention provides a power system broadband measurement data processing method and device, equipment and a medium. Relates to the technical field of data processing. According to the method, broadband measurement data of a power system in a current preset time period are collected, the broadband measurement data are decomposed into multiple pieces of broadband measurement window data in a time domain to form a broadband measurement window data sequence, and then the broadband measurement window data in the broadband measurement window data sequence are acquired according to any one piece of broadband measurement window data in the broadband measurement window data sequence. Extracting frequency division signals on each preset frequency to generate a broadband measurement data signal decomposition matrix corresponding to the broadband measurement data, and determining a corresponding broadband measurement data characteristic value matrix according to the broadband measurement data signal decomposition matrix; and then determining a signal fluctuation vector of the broadband measurement data in a time domain according to the broadband measurement data eigenvalue matrix, so as to determine the operation state of the power system according to the signal fluctuation vector, thereby judging whether the power system is in a normal operation state or has a potential risk.

Description

Technical Field

[0001] The present application relates to data processing technology, and in particular to a method, device, equipment and medium for processing broadband measurement data of an electric power system. Background Art

[0002] With the expansion of power system scale and the increase of complexity, higher requirements are put forward for the monitoring and management of power system. Traditional power system monitoring methods mainly rely on low-frequency measurement data. Although these data can reflect the basic operating status of the power system, they have limitations in capturing the high-frequency dynamic characteristics and potential faults in the power system. Especially in modern power systems, with the widespread application of power electronic equipment and the access of distributed energy, the frequency characteristics of power systems have become more complex, and the impact of high-frequency components on system stability has become increasingly significant.

[0003] In order to more comprehensively monitor the operating status of the power system, broadband measurement technology has emerged. Broadband measurement technology can simultaneously collect data from the power system in multiple frequency ranges, thereby providing richer information to evaluate the stability and security of the power system. However, the amount of broadband measurement data is large and the frequency range is wide. How to effectively process and analyze this data has become an urgent problem to be solved. Summary of the invention

[0004] The present application provides a method, device, equipment and medium for processing broadband measurement data of an electric power system, which can be used to determine in real time whether the electric power system is in a normal operating state or has potential risks with a relatively small amount of calculation, thereby providing a strong guarantee for the safe operation of the electric power system.

[0005] In a first aspect, the present application provides a method for processing broadband measurement data of an electric power system, comprising: Collecting broadband measurement data of the power system within a current preset time period, and decomposing the broadband measurement data into a plurality of broadband measurement window data in the time domain to form a broadband measurement window data sequence; For any wideband measurement window data in the wideband measurement window data sequence, extract the frequency division signals at each preset frequency to generate a wideband measurement data signal decomposition matrix corresponding to the wideband measurement data, wherein each element in a row of the wideband measurement data signal decomposition matrix is ​​used to characterize the frequency division signals at each preset frequency in the corresponding wideband measurement window data, and each element in a column of the wideband measurement data signal decomposition matrix is ​​used to characterize the frequency division signals of each wideband measurement window data in the wideband measurement window data sequence at the same preset frequency; Determine a corresponding wideband measurement data eigenvalue matrix according to the wideband measurement data signal decomposition matrix, wherein each eigenvalue in the wideband measurement data eigenvalue matrix is ​​used to characterize the signal fluctuation degree of the corresponding frequency-divided signal in the wideband measurement data signal decomposition matrix; A signal fluctuation vector of the broadband measurement data in the time domain is determined according to the broadband measurement data eigenvalue matrix, so as to determine the operating state of the power system according to the signal fluctuation vector.

[0006] In the above scheme, by collecting the broadband measurement data of the power system in the current preset time period and decomposing it into multiple broadband measurement window data in the time domain, it is possible to achieve comprehensive monitoring of the broadband range of the power system. Through the above decomposition method, more detailed information can be captured during the data processing process. For each broadband measurement window data, the frequency division signal at each preset frequency can be extracted to generate a broadband measurement data signal decomposition matrix. The generation of the signal decomposition matrix simplifies the complex signals in the power system, which is convenient for subsequent feature extraction and analysis. Then, by determining the broadband measurement data eigenvalue matrix, the signal fluctuation degree of each frequency division signal can be quantified, wherein each eigenvalue in the eigenvalue matrix represents the fluctuation characteristics of the corresponding frequency division signal, which provides a quantitative indicator for the stability analysis of the power system, thereby being able to more intuitively reflect the fluctuation of the power system and provide a basis for formulating targeted maintenance strategies. Then, the signal fluctuation vector is determined according to the broadband measurement data eigenvalue matrix, and the operating state of the power system is evaluated by the signal fluctuation vector. Among them, the signal fluctuation vector integrates the fluctuation characteristics of each frequency division signal and can comprehensively reflect the overall operating state of the power system. By comparing with preset thresholds or standards, it is possible to determine whether the power system is in normal operation or has potential risks, providing strong protection for the safe operation of the power system.

[0007] Optionally, each element of the signal fluctuation vector is the maximum eigenvalue in each row of the broadband measurement data eigenvalue matrix; or, Each element of the signal fluctuation vector is an eigenvalue corresponding to an element with the largest eigenvalue change rate in corresponding rows of the broadband measurement data eigenvalue matrix and another broadband measurement data eigenvalue matrix, wherein the other broadband measurement data eigenvalue matrix corresponds to the broadband measurement data corresponding to a preset time period before the current preset time period.

[0008] In the above scheme, in the first mode, when each element of the signal fluctuation vector is the maximum eigenvalue in each row of the eigenvalue matrix of the broadband measurement data, the frequency-divided signal with the most significant fluctuation in each broadband measurement window data can be highlighted. This selection method helps to quickly identify the dominant fluctuation components in the power system and provides important clues for analyzing the stability of the power system. By directly focusing on the maximum eigenvalue, the data analysis process is simplified, making it possible to grasp the core fluctuation characteristics of the power system more quickly and improve work efficiency.

[0009] In another way, the eigenvalue corresponding to the element with the largest eigenvalue change rate in the corresponding row of the eigenvalue matrix of broadband measurement data and the eigenvalue matrix corresponding to the previous preset time period can be selected as the element of the signal fluctuation vector, so as to capture the changes in the fluctuation characteristics of the power system between different time periods, thereby reflecting the dynamic behavior of the power system.

[0010] In summary, the above scheme provides more options for the power system broadband measurement data processing method by proposing two signal fluctuation vector formation methods. In practical applications, appropriate methods can be selected according to specific needs and analysis purposes, thereby improving the flexibility and applicability of data processing methods. For example, in scenarios where it is necessary to quickly identify the dominant fluctuation component of the power system, a method based on the maximum eigenvalue can be selected; in scenarios where it is necessary to analyze the dynamic behavior of the power system or predict potential problems, a method based on the eigenvalue change rate can be selected.

[0011] Optionally, determining a corresponding broadband measurement data eigenvalue matrix according to the broadband measurement data signal decomposition matrix includes: Determining the characteristic instantaneous energy of each frequency-divided signal to be processed in the broadband measurement data signal decomposition matrix; The broadband measurement data eigenvalue matrix is ​​generated according to the characteristic instantaneous energy of each frequency-divided signal to be processed, and each eigenvalue in the broadband measurement data eigenvalue matrix corresponds to the characteristic instantaneous energy of the corresponding frequency-divided signal to be processed in the broadband measurement data signal decomposition matrix.

[0012] In the above scheme, by determining the characteristic instantaneous energy of each frequency-divided signal to be processed in the broadband measurement data signal decomposition matrix, the fluctuation characteristics of each frequency-divided signal can be expressed more finely. As a quantitative indicator of signal fluctuation, the characteristic instantaneous energy can reflect the intensity and change rate of the signal at different times, thereby providing richer information for the stability analysis of the power system. The broadband measurement data eigenvalue matrix is ​​generated according to the characteristic instantaneous energy of each frequency-divided signal to be processed, ensuring that each eigenvalue in the eigenvalue matrix is ​​closely related to the corresponding characteristic instantaneous energy of the frequency-divided signal. This generation method improves the accuracy and reliability of the eigenvalue matrix, making the subsequent analysis results based on the eigenvalue matrix more credible.

[0013] In addition, since the characteristic instantaneous energy can sensitively reflect the changes in signal fluctuations, the eigenvalue matrix generated based on the characteristic instantaneous energy is also sensitive to changes in the operating state of the power system. When the operating state of the power system changes slightly, the eigenvalues ​​in the eigenvalue matrix will also change accordingly, thereby realizing real-time monitoring and early warning of the operating state of the power system. By calculating the characteristic instantaneous energy to generate the eigenvalue matrix, the complex transformation and calculation process that may exist in the traditional method of waveform analysis is avoided, and the data processing process is simplified. This optimization not only improves the data processing efficiency, but also reduces the consumption of computing resources, making this method more feasible and economical in practical applications. In addition, by analyzing the changing trends and laws of the eigenvalue matrix, potential problems in the power system can be discovered in time and corresponding maintenance measures can be taken to ensure the safe and stable operation of the power system.

[0014] Optionally, determining the characteristic instantaneous energy of each to-be-processed frequency-divided signal in the broadband measurement data signal decomposition matrix includes: Determine a corresponding first transformed frequency-divided signal according to a first frequency-divided signal to be processed in the wideband measurement data signal decomposition matrix, wherein the first frequency-divided signal to be processed is any signal in the wideband measurement data signal decomposition matrix; Determine a first characteristic frequency-divided signal according to the first frequency-divided signal to be processed and the first transformed frequency-divided signal; A first characteristic instantaneous energy corresponding to the first frequency-divided signal to be processed is determined according to the first characteristic frequency-divided signal, wherein the first characteristic instantaneous energy is the maximum modulus of the first characteristic frequency-divided signal at each moment.

[0015] In the above scheme, by determining the first transformed frequency-divided signal corresponding to the first frequency-divided signal to be processed, this process actually transforms the original signal to extract the instantaneous characteristics of the signal. This transformation can retain the amplitude information of the signal and introduce phase information, so that the transformed signal can more comprehensively reflect the dynamic changes of the original signal. The first characteristic frequency-divided signal is generated by combining the first frequency-divided signal to be processed and its transformed signal. This process further enhances the ability to capture signal characteristics. The characteristic frequency-divided signal not only contains the amplitude information of the original signal, but also incorporates the phase information, so that the characteristics of the signal at different times can be expressed more finely. The characteristic instantaneous energy is defined as the maximum modulus of the first characteristic frequency-divided signal at each time, that is, the absolute value of the complex signal. Since the characteristic frequency-divided signal is in complex form, its modulus can accurately reflect the instantaneous power or energy level of the signal at a certain moment. By calculating the maximum modulus of the characteristic frequency-divided signal at each time, we can obtain the instantaneous energy of the signal at different times. This indicator is of great significance for evaluating the dynamic characteristics and stability of the power system. Since the characteristic frequency division signal integrates the information of the original signal and its transformed signal, the calculated characteristic instantaneous energy can more accurately reflect the fluctuation characteristics and energy distribution of the signal, thereby improving the accuracy and reliability of feature extraction.

[0016] Optionally, determining a corresponding broadband measurement data eigenvalue matrix according to the broadband measurement data signal decomposition matrix includes: Determine a characteristic signal point set according to the frequency division signal measurement values ​​of the to-be-processed frequency division signal in the broadband measurement data signal decomposition matrix at each acquisition time node of the corresponding broadband measurement window, wherein the to-be-processed frequency division signal is any element in the broadband measurement data signal decomposition matrix; Determine a first characteristic signal point set according to the characteristic signal point set, the first characteristic signal point set is a subset of the characteristic signal point set, a first characteristic signal point in the first characteristic signal point set corresponds to a first original signal point in the characteristic signal point set, a first frequency-divided signal measurement value of the first original signal point is greater than a second frequency-divided signal measurement value of the second original signal point and greater than a third frequency-divided signal measurement value of the third original signal point, wherein the second original signal point is a previous acquisition node of the first original signal point in the characteristic signal point set, and the third original signal point is a subsequent acquisition node of the first original signal point in the characteristic signal point set; Determine a second characteristic signal point set according to the characteristic signal point set, the second characteristic signal point set is a subset of the characteristic signal point set, a fourth characteristic signal point in the second characteristic signal point set corresponds to a fourth original signal point in the characteristic signal point set, a fourth frequency-divided signal measurement value of the fourth original signal point is less than a fifth frequency-divided signal measurement value of the fifth original signal point and less than a sixth frequency-divided signal measurement value of the sixth original signal point, wherein the fifth original signal point is a previous acquisition node of the fourth original signal point in the characteristic signal point set, and the sixth original signal point is a subsequent acquisition node of the fourth original signal point in the characteristic signal point set; generating a first characteristic reference line according to the first characteristic signal point set, and generating a second characteristic reference line according to the second characteristic signal point set; The eigenvalue corresponding to the to-be-processed frequency-divided signal in the wideband measurement data eigenvalue matrix is ​​determined according to the first characteristic reference line and the second characteristic reference line.

[0017] In the above scheme, by extracting the measured values ​​of the frequency-divided signal at each acquisition time node of the corresponding wideband measurement window, a characteristic signal point set is formed, so as to comprehensively capture the fluctuation characteristics of the frequency-divided signal to be processed in the time domain. Furthermore, by screening the extreme points in the characteristic signal point set (i.e., the first characteristic signal point set and the second characteristic signal point set), the key turning points in the signal fluctuation are highlighted to more accurately reflect the fluctuation trend and intensity of the signal. The characteristic reference lines generated based on these extreme points can be used as an effective representation of the signal fluctuation characteristics, providing a more accurate basis for determining the characteristic values. Then, by respectively determining the characteristic signal point sets on the rising edge and the falling edge of the signal (i.e., the first characteristic signal point set and the second characteristic signal point set), the detailed characteristics of the signal fluctuation can be more meticulously reflected, which is helpful for a deeper understanding of the dynamic behavior of the power system and providing more abundant information for the stability analysis and fault prediction of the power system. Among them, the above scheme does not rely on complex mathematical models or assumptions, but extracts features based on the fluctuation characteristics of the signal itself, so it has high flexibility and adaptability. In practical applications, the screening conditions of the characteristic signal point set and the generation method of the characteristic reference line can be adjusted according to different analysis requirements and data characteristics to obtain an eigenvalue matrix that is more in line with the actual situation. Compared with the traditional eigenvalue extraction method, this method generates characteristic signal point sets and characteristic reference lines by directly analyzing the signal measurement values, avoiding complex mathematical transformations and calculation processes, thereby simplifying the data processing process. This optimization not only improves data processing efficiency, but also reduces the consumption of computing resources, making this method more feasible and economical in practical applications. In addition, the eigenvalue matrix generated based on this method can more accurately reflect the fluctuation characteristics of broadband measurement data of the power system, providing strong data support for the state assessment of the power system.

[0018] Optionally, the determining, according to the first characteristic reference line and the second characteristic reference line, the eigenvalue corresponding to the to-be-processed frequency-divided signal in the wideband measurement data eigenvalue matrix includes: Determine the eigenvalue corresponding to the to-be-processed frequency-divided signal in the wideband measurement data eigenvalue matrix according to the maximum difference between the first characteristic reference line and the second characteristic reference line; or The eigenvalue corresponding to the to-be-processed frequency-divided signal in the wideband measurement data eigenvalue matrix is ​​determined according to the area enclosed by the first characteristic reference line and the second characteristic reference line.

[0019] In the above scheme, the first method is to determine the eigenvalue based on the maximum difference. By calculating the maximum difference between the first characteristic reference line and the second characteristic reference line, the amplitude of the signal fluctuation can be intuitively reflected. It is suitable for signal conditions with large fluctuation amplitude and obvious changes, and can accurately capture significant fluctuations in the signal.

[0020] The second method is based on the determination of the eigenvalue of the enclosed area. By calculating the area enclosed by the first characteristic reference line and the second characteristic reference line, the amplitude and duration of the signal fluctuation are comprehensively considered. This method is particularly effective for evaluating signals with small fluctuation amplitudes but long durations, and can more comprehensively reflect the overall fluctuation of the signal.

[0021] Optionally, if each element of the signal fluctuation vector is an eigenvalue corresponding to an element having the largest eigenvalue change rate in a corresponding row of the broadband measurement data eigenvalue matrix and another broadband measurement data eigenvalue matrix; correspondingly, determining the operating state of the power system according to the signal fluctuation vector includes: If each characteristic value in the signal fluctuation vector is smaller than the preset characteristic threshold value of the corresponding frequency-divided signal, it is determined that the operating state of the power system in the current preset time period is a normal operating state; if each characteristic value in the signal fluctuation vector is greater than the preset characteristic threshold value of the corresponding frequency-divided signal, it is determined that the operating state of the power system in the current preset time period is an abnormal operating state; or, The overall fluctuation characteristic value of the broadband measurement data within the current preset time period is determined according to the signal fluctuation vector; if the overall fluctuation characteristic value is less than the preset overall fluctuation characteristic threshold, it is determined that the operating state of the power system within the current preset time period is a normal operating state; if the overall fluctuation characteristic value is greater than the preset overall fluctuation characteristic threshold, it is determined that the operating state of the power system within the current preset time period is an abnormal operating state.

[0022] In the above scheme, if each element of the signal fluctuation vector is the eigenvalue corresponding to the element with the largest eigenvalue change rate in the corresponding row of the eigenvalue matrix of the current broadband measurement data and the eigenvalue matrix of the broadband measurement data in the previous preset period, a signal fluctuation vector that can reflect the signal fluctuation of the power system is constructed. Then, the eigenvalue in the signal fluctuation vector can be compared with the preset characteristic threshold of the corresponding frequency-divided signal. If all eigenvalues ​​are less than the preset characteristic threshold, the power system is judged to be in a normal operating state; if there is an eigenvalue greater than the preset characteristic threshold, it is judged to be in an abnormal operating state.

[0023] In addition, the overall fluctuation characteristic value of the broadband measurement data in the current preset time period can be calculated based on the signal fluctuation vector to quantitatively evaluate the overall fluctuation of the power system, thereby judging the overall stability of the power system and improving the overall monitoring effect of the power system.

[0024] In a second aspect, the present application provides a power system broadband measurement data processing device, comprising: An acquisition module, used for acquiring broadband measurement data of the power system within a current preset time period, and decomposing the broadband measurement data into a plurality of broadband measurement window data in the time domain to form a broadband measurement window data sequence; an extraction module, for extracting frequency-divided signals at respective preset frequencies for any wideband measurement window data in the wideband measurement window data sequence, so as to generate a wideband measurement data signal decomposition matrix corresponding to the wideband measurement data, wherein each element in a row of the wideband measurement data signal decomposition matrix is ​​used to characterize the frequency-divided signals at respective preset frequencies in the corresponding wideband measurement window data, and each element in a column of the wideband measurement data signal decomposition matrix is ​​used to characterize the frequency-divided signals of respective wideband measurement window data in the wideband measurement window data sequence at the same preset frequency; A processing module, used to determine a corresponding wideband measurement data eigenvalue matrix according to the wideband measurement data signal decomposition matrix, wherein each eigenvalue in the wideband measurement data eigenvalue matrix is ​​used to characterize a signal fluctuation degree of a corresponding frequency-divided signal in the wideband measurement data signal decomposition matrix; A determination module is used to determine a signal fluctuation vector of the broadband measurement data in the time domain according to the broadband measurement data eigenvalue matrix, so as to determine an operating state of the power system according to the signal fluctuation vector.

[0025] Optionally, each element of the signal fluctuation vector is the maximum eigenvalue in each row of the broadband measurement data eigenvalue matrix; or, Each element of the signal fluctuation vector is an eigenvalue corresponding to an element with the largest eigenvalue change rate in corresponding rows of the broadband measurement data eigenvalue matrix and another broadband measurement data eigenvalue matrix, wherein the other broadband measurement data eigenvalue matrix corresponds to the broadband measurement data corresponding to a preset time period before the current preset time period.

[0026] Optionally, the processing module is specifically used to: Determining the characteristic instantaneous energy of each frequency-divided signal to be processed in the broadband measurement data signal decomposition matrix; The broadband measurement data eigenvalue matrix is ​​generated according to the characteristic instantaneous energy of each frequency-divided signal to be processed, and each eigenvalue in the broadband measurement data eigenvalue matrix corresponds to the characteristic instantaneous energy of the corresponding frequency-divided signal to be processed in the broadband measurement data signal decomposition matrix.

[0027] Optionally, the processing module is specifically used to: Determine a corresponding first transformed frequency-divided signal according to a first frequency-divided signal to be processed in the wideband measurement data signal decomposition matrix, wherein the first frequency-divided signal to be processed is any signal in the wideband measurement data signal decomposition matrix; Determine a first characteristic frequency-divided signal according to the first frequency-divided signal to be processed and the first transformed frequency-divided signal; A first characteristic instantaneous energy corresponding to the first frequency-divided signal to be processed is determined according to the first characteristic frequency-divided signal, wherein the first characteristic instantaneous energy is the maximum modulus of the first characteristic frequency-divided signal at each moment.

[0028] Optionally, the processing module is specifically used to: Determine a characteristic signal point set according to the frequency division signal measurement values ​​of the to-be-processed frequency division signal in the broadband measurement data signal decomposition matrix at each acquisition time node of the corresponding broadband measurement window, wherein the to-be-processed frequency division signal is any element in the broadband measurement data signal decomposition matrix; Determine a first characteristic signal point set according to the characteristic signal point set, the first characteristic signal point set is a subset of the characteristic signal point set, a first characteristic signal point in the first characteristic signal point set corresponds to a first original signal point in the characteristic signal point set, a first frequency-divided signal measurement value of the first original signal point is greater than a second frequency-divided signal measurement value of the second original signal point and greater than a third frequency-divided signal measurement value of the third original signal point, wherein the second original signal point is a previous acquisition node of the first original signal point in the characteristic signal point set, and the third original signal point is a subsequent acquisition node of the first original signal point in the characteristic signal point set; Determine a second characteristic signal point set according to the characteristic signal point set, the second characteristic signal point set is a subset of the characteristic signal point set, a fourth characteristic signal point in the second characteristic signal point set corresponds to a fourth original signal point in the characteristic signal point set, a fourth frequency-divided signal measurement value of the fourth original signal point is less than a fifth frequency-divided signal measurement value of the fifth original signal point and less than a sixth frequency-divided signal measurement value of the sixth original signal point, wherein the fifth original signal point is a previous acquisition node of the fourth original signal point in the characteristic signal point set, and the sixth original signal point is a subsequent acquisition node of the fourth original signal point in the characteristic signal point set; generating a first characteristic reference line according to the first characteristic signal point set, and generating a second characteristic reference line according to the second characteristic signal point set; The eigenvalue corresponding to the to-be-processed frequency-divided signal in the wideband measurement data eigenvalue matrix is ​​determined according to the first characteristic reference line and the second characteristic reference line.

[0029] Optionally, the processing module is specifically used to: Determine the eigenvalue corresponding to the to-be-processed frequency-divided signal in the wideband measurement data eigenvalue matrix according to the maximum difference between the first characteristic reference line and the second characteristic reference line; or The eigenvalue corresponding to the to-be-processed frequency-divided signal in the wideband measurement data eigenvalue matrix is ​​determined according to the area enclosed by the first characteristic reference line and the second characteristic reference line.

[0030] Optionally, the determining module is specifically used to: If each characteristic value in the signal fluctuation vector is smaller than the preset characteristic threshold value of the corresponding frequency-divided signal, it is determined that the operating state of the power system in the current preset time period is a normal operating state; if each characteristic value in the signal fluctuation vector is greater than the preset characteristic threshold value of the corresponding frequency-divided signal, it is determined that the operating state of the power system in the current preset time period is an abnormal operating state; or, The overall fluctuation characteristic value of the broadband measurement data within the current preset time period is determined according to the signal fluctuation vector; if the overall fluctuation characteristic value is less than the preset overall fluctuation characteristic threshold, it is determined that the operating state of the power system within the current preset time period is a normal operating state; if the overall fluctuation characteristic value is greater than the preset overall fluctuation characteristic threshold, it is determined that the operating state of the power system within the current preset time period is an abnormal operating state.

[0031] In a third aspect, the present application provides an electronic device, including: processor; and, A memory, configured to store executable instructions of the processor; The processor is configured to perform any possible method described in the first aspect by executing the executable instructions.

[0032] In a fourth aspect, the present application provides a computer-readable storage medium, wherein the computer-readable storage medium stores computer-executable instructions, and when the computer-executable instructions are executed by a processor, they are used to implement any possible method described in the first aspect.

[0033] The method, device, equipment and medium for processing broadband measurement data of an electric power system provided in the present application collect broadband measurement data of an electric power system within a current preset time period, and decompose the broadband measurement data into a plurality of broadband measurement window data in the time domain to form a broadband measurement window data sequence, and then, for any broadband measurement window data in the broadband measurement window data sequence, extract the frequency-divided signals at each preset frequency to generate a broadband measurement data signal decomposition matrix corresponding to the broadband measurement data, and then determine the corresponding broadband measurement data eigenvalue matrix according to the broadband measurement data signal decomposition matrix, and then determine the signal fluctuation vector of the broadband measurement data in the time domain according to the broadband measurement data eigenvalue matrix, so as to determine the operating state of the electric power system according to the signal fluctuation vector, thereby judging whether the electric power system is in a normal operating state or has potential risks, and providing a strong guarantee for the safe operation of the electric power system. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and, together with the description, serve to explain the principles of the present application.

[0035] Figure 1 This is a flow chart of a method for processing broadband measurement data of a power system according to an exemplary embodiment of the present application; Figure 2 is a flow chart of a method for processing broadband measurement data of a power system according to another exemplary embodiment of the present application; Figure 3 is a schematic structural diagram of a power system broadband measurement data processing device according to an exemplary embodiment of the present application; Figure 4 It is a schematic diagram of the structure of an electronic device according to an exemplary embodiment of the present application.

[0036] The above drawings have shown clear embodiments of the present application, which will be described in more detail later. These drawings and text descriptions are not intended to limit the scope of the present application in any way, but to illustrate the concept of the present application to those skilled in the art by referring to specific embodiments. DETAILED DESCRIPTION

[0037] Exemplary embodiments will be described in detail herein, examples of which are shown in the accompanying drawings. When the following description refers to the drawings, the same numbers in different drawings represent the same or similar elements unless otherwise indicated. The implementations described in the following exemplary embodiments do not represent all implementations consistent with the present application. Instead, they are merely examples of devices and methods consistent with some aspects of the present application as detailed in the appended claims.

[0038] In order to solve the above problems, the embodiments provided in this application have the following specific technical concepts: Data collection and windowing: Collect broadband measurement data of the power system in the current preset period, and decompose these data into multiple broadband measurement window data of equal length in the time domain to form a broadband measurement window data sequence. The purpose of this step is to divide the continuous data stream into easy-to-process data blocks while retaining enough information to reflect the dynamic changes of the power system.

[0039] Signal decomposition and matrix generation: For each broadband measurement window data, extract the frequency division signal at each preset frequency and generate a broadband measurement data signal decomposition matrix. A row of the matrix represents the frequency division signal of a broadband measurement window data at each preset frequency, and a column represents the frequency division signal of different broadband measurement window data at the same preset frequency. This step realizes the simplified processing of complex signals and provides a basis for subsequent feature extraction and analysis.

[0040] Eigenvalue matrix generation: Determine the corresponding eigenvalue matrix of the wideband measurement data according to the wideband measurement data signal decomposition matrix. Each eigenvalue in the eigenvalue matrix is ​​used to characterize the signal fluctuation degree of the corresponding frequency-divided signal. The embodiment of the present application provides two methods for generating eigenvalue matrices: one is based on characteristic instantaneous energy, and the eigenvalue matrix is ​​generated by calculating the characteristic instantaneous energy of each frequency-divided signal; the other is based on signal fluctuation characteristics, by extracting characteristic signal points on the rising and falling edges of the signal, generating characteristic reference lines, and determining eigenvalues ​​according to the characteristic reference lines. These two methods have their own advantages and can be selected according to actual needs.

[0041] Signal fluctuation vector determination and status evaluation: According to the eigenvalue matrix of broadband measurement data, the signal fluctuation vector of broadband measurement data in the time domain is determined. Each element of the signal fluctuation vector can be the maximum eigenvalue in the eigenvalue matrix, or it can be the eigenvalue corresponding to the element with the largest eigenvalue change rate in the corresponding row between the eigenvalue matrix and the eigenvalue matrix of the previous preset time period. The operating status of the power system is evaluated by comparing the eigenvalues ​​in the signal fluctuation vector with the preset threshold or historical data. If all eigenvalues ​​are less than the preset threshold, the power system is judged to be in normal operating state; if there is an eigenvalue greater than the preset threshold, it is judged to be in abnormal operating state. In addition, the overall fluctuation eigenvalue of the broadband measurement data can be calculated using a specific formula to quantitatively evaluate the overall fluctuation of the power system.

[0042] Figure 1 FIG. 1 is a flow chart of a method for processing broadband measurement data of a power system according to an exemplary embodiment of the present application. Figure 1 As shown, the method for processing broadband measurement data of a power system provided in this embodiment includes: S101 . Collect broadband measurement data of a power system in a current preset time period, and decompose the broadband measurement data into a plurality of broadband measurement window data in the time domain to form a broadband measurement window data sequence.

[0043] Specifically, high-precision sensors and data acquisition equipment can be used to collect broadband measurement data such as voltage and current of the power system in real time. The preset time period can be set according to actual needs, such as every hour, every minute, every second or a shorter time interval. The collected broadband measurement data is divided into multiple windows of equal length in the time domain, and each window contains a certain number of data points. The window length can be determined according to the characteristics of the power system and the analysis requirements to ensure that each window contains enough information to reflect the dynamic changes of the power system. All window data are arranged in chronological order to form a broadband measurement window data sequence for subsequent processing and analysis.

[0044] S102 . For any wideband measurement window data in the wideband measurement window data sequence, extract the frequency division signals at each preset frequency to generate a wideband measurement data signal decomposition matrix corresponding to the wideband measurement data.

[0045] In this step, for any wideband measurement window data in the wideband measurement window data sequence, the frequency division signals at each preset frequency are extracted to generate a wideband measurement data signal decomposition matrix corresponding to the wideband measurement data, each element in a row of the wideband measurement data signal decomposition matrix is ​​used to characterize the frequency division signals at each preset frequency in the corresponding wideband measurement window data, and each element in a column of the wideband measurement data signal decomposition matrix is ​​used to characterize the frequency division signals of each wideband measurement window data in the wideband measurement window data sequence at the same preset frequency.

[0046] Specifically, according to the characteristics and analysis requirements of the power system, a set of frequencies are pre-set, which will be used to extract the frequency division signals, wherein the frequency selection should cover the main spectrum range of the power system. For each broadband measurement window data, use fast Fourier transform or other spectrum analysis methods to extract the frequency division signals at the preset frequency, which reflect the dynamic characteristics of the power system at different frequencies. Then, the frequency division signals of each broadband measurement window data at the preset frequency are arranged in rows to form a broadband measurement data signal decomposition matrix. Different rows of the matrix represent different broadband measurement window data, and different columns represent frequency division signals at different preset frequencies.

[0047] S103 . Determine a corresponding broadband measurement data eigenvalue matrix according to the broadband measurement data signal decomposition matrix.

[0048] In this step, the corresponding wideband measurement data eigenvalue matrix is ​​determined according to the wideband measurement data signal decomposition matrix, and each eigenvalue in the wideband measurement data eigenvalue matrix is ​​used to characterize the signal fluctuation degree of the corresponding frequency-divided signal in the wideband measurement data signal decomposition matrix.

[0049] Optionally, each column of the broadband measurement data signal decomposition matrix (i.e., the frequency-divided signal at the same preset frequency) can also be statistically analyzed to calculate its standard deviation, variance, or other eigenvalues ​​that can reflect the degree of signal fluctuation. These eigenvalues ​​will be used to quantify the degree of fluctuation of the frequency-divided signal. The calculated eigenvalues ​​are arranged in columns to form a broadband measurement data eigenvalue matrix. The rows of the matrix represent different broadband measurement window data, and the columns represent the eigenvalues ​​of the frequency-divided signal at different preset frequencies.

[0050] In a possible implementation, the characteristic instantaneous energy of each to-be-processed frequency-divided signal in the wideband measurement data signal decomposition matrix may be determined. Then, a wideband measurement data eigenvalue matrix is ​​generated according to the characteristic instantaneous energy of each to-be-processed frequency-divided signal, wherein each eigenvalue in the wideband measurement data eigenvalue matrix corresponds to the characteristic instantaneous energy of the corresponding to-be-processed frequency-divided signal in the wideband measurement data signal decomposition matrix.

[0051] By determining the characteristic instantaneous energy of each frequency-divided signal to be processed in the broadband measurement data signal decomposition matrix, the fluctuation characteristics of each frequency-divided signal can be expressed more finely. As a quantitative indicator of signal fluctuation, the characteristic instantaneous energy can reflect the strength and change rate of the signal at different times, thereby providing more abundant information for the stability analysis of the power system. The broadband measurement data eigenvalue matrix is ​​generated according to the characteristic instantaneous energy of each frequency-divided signal to be processed, ensuring that each eigenvalue in the eigenvalue matrix is ​​closely related to the corresponding characteristic instantaneous energy of the frequency-divided signal. This generation method improves the accuracy and reliability of the eigenvalue matrix, making the subsequent analysis results based on the eigenvalue matrix more credible. In addition, since the characteristic instantaneous energy can sensitively reflect the changes in signal fluctuations, the eigenvalue matrix generated based on the characteristic instantaneous energy also has sensitivity to changes in the operating state of the power system. When the operating state of the power system changes slightly, the eigenvalues ​​in the eigenvalue matrix will also change accordingly, thereby realizing real-time monitoring and early warning of the operating state of the power system. By calculating the characteristic instantaneous energy to generate the eigenvalue matrix, the complex transformation and calculation process that may exist in the traditional method of waveform analysis is avoided, and the data processing process is simplified. This optimization not only improves data processing efficiency, but also reduces the consumption of computing resources, making this method more feasible and economical in practical applications. In addition, by analyzing the changing trends and rules of the eigenvalue matrix, potential problems in the power system can be discovered in a timely manner and corresponding maintenance measures can be taken to ensure the safe and stable operation of the power system.

[0052] Furthermore, determining the characteristic instantaneous energy of each to-be-processed frequency-divided signal in the broadband measurement data signal decomposition matrix may specifically include: Using formula 1, and according to the first frequency-divided signal to be processed in the broadband measurement data signal decomposition matrix Determine the corresponding first conversion frequency division signal , the first frequency-divided signal to be processed is any signal in the broadband measurement data signal decomposition matrix, and formula 1 is:

[0053] Using formula 2, and according to the first frequency-divided signal to be processed and the first conversion frequency division signal Determine the first characteristic frequency division signal , where Formula 2 is:

[0054] in, is an imaginary unit; The frequency division signal is based on the first characteristic Determine the first frequency division signal to be processed The first characteristic instantaneous energy corresponding to the first characteristic instantaneous energy is the first characteristic frequency division signal The largest model at each moment.

[0055] Specifically, a frequency-divided signal to be processed is selected from the broadband measurement data signal decomposition matrix. This signal can be any signal in the matrix, representing the time series data of a specific frequency component in the power system. Next, the selected frequency-divided signal to be processed is processed using Formula 1 to generate the corresponding transformed frequency-divided signal. This formula converts the original signal into a new signal through an integral operation, which is 90 degrees out of phase with the original signal, thereby revealing the instantaneous frequency and amplitude information of the original signal. This step is the key to feature extraction because it allows us to extract a complex signal containing instantaneous frequency and amplitude information from the original signal.

[0056] Then, the frequency-divided signal to be processed is combined with the transformed frequency-divided signal using Formula 2 to generate a complex characteristic frequency-divided signal. This complex signal contains the amplitude and phase information of the original signal, which is the basis for the subsequent calculation of the characteristic instantaneous energy.

[0057] Finally, the first characteristic instantaneous energy corresponding to the first frequency-divided signal to be processed is calculated based on the characteristic frequency-divided signal. This energy is defined as the maximum modulus of the characteristic frequency-divided signal at each moment, that is, the absolute value of the complex signal. It reflects the instantaneous power or energy level of the signal at a certain moment and is an important indicator for evaluating the dynamic characteristics of the power system.

[0058] Through the above steps, the characteristic instantaneous energy of each frequency-divided signal to be processed in the broadband measurement data signal decomposition matrix can be effectively determined. This energy information is of great significance for analyzing the frequency components of the power system, monitoring the dynamic changes of the power grid, and preventing potential failures. In practical applications, the above processing can be performed on multiple frequency components in the power system as needed to obtain comprehensive broadband measurement data analysis results.

[0059] In the above scheme, the characteristic instantaneous energy of each frequency-divided signal to be processed is calculated by using formula 1 and formula 2. Among them, formula 1 converts the original frequency-divided signal into the first transformed frequency-divided signal of the analytical signal, and this step is the key to extracting the instantaneous characteristics of the signal. Formula 2 constructs a complex first characteristic frequency-divided signal by combining the original signal and its transformed signal, which contains the amplitude and phase information of the signal. Finally, the characteristic instantaneous energy is obtained by calculating the maximum modulus of the first characteristic frequency-divided signal at each moment. This calculation method can accurately reflect the energy state of the signal at a specific moment and provide a data basis for the in-depth analysis of broadband measurement data of the power system. Among them, the transformation of the above formula 1 can retain the amplitude information of the signal and introduce phase information, thereby more comprehensively describing the instantaneous characteristics of the signal. Through the transformation of formula 1, the conversion of the signal from the time domain to the frequency domain can be realized without losing the signal information, thereby improving the accuracy and efficiency of signal processing. In addition, by directly calculating the characteristic instantaneous energy, the complex transformation and calculation process that may involve waveform analysis in the traditional method is avoided, the data processing process is simplified, and the processing efficiency is improved.

[0060] In addition, characteristic instantaneous energy, as a quantitative indicator of signal fluctuation, can sensitively reflect the dynamic behavior of the power system at different times. By accurately calculating the characteristic instantaneous energy of each frequency-divided signal to be processed, the fluctuation characteristics of the power system at different frequencies and time scales can be more accurately captured, providing strong support for the stability analysis and fault prediction of the power system.

[0061] S104 . Determine a signal fluctuation vector of the broadband measurement data in the time domain according to the broadband measurement data eigenvalue matrix, so as to determine an operating state of the power system according to the signal fluctuation vector.

[0062] Specifically, each row of the eigenvalue matrix of the broadband measurement data (i.e., the eigenvalues ​​of the data in different broadband measurement windows) is analyzed to calculate its signal fluctuation vector in the time domain. This can be achieved by calculating the average, maximum, minimum, or other statistical indicators of the eigenvalues. Then, the calculated signal fluctuation vector is compared with the preset threshold or historical data to evaluate the operating status of the power system. For example, if the elements in the signal fluctuation vector exceed a certain threshold, it may indicate that the power system is unstable or operating abnormally. Based on the evaluation results, appropriate measures can be taken to ensure the safe and stable operation of the power system.

[0063] In one possible implementation, each element of the signal fluctuation vector may be the maximum eigenvalue in each row of the eigenvalue matrix of the broadband measurement data. When each element of the signal fluctuation vector is the maximum eigenvalue in each row of the eigenvalue matrix of the broadband measurement data, the frequency-divided signal with the most significant fluctuation in each broadband measurement window data can be highlighted. This selection method helps to quickly identify the dominant fluctuation components in the power system and provides important clues for analyzing the stability of the power system. By directly focusing on the maximum eigenvalue, the data analysis process is simplified, making it possible to grasp the core fluctuation characteristics of the power system more quickly and improving work efficiency.

[0064] In another possible implementation, each element of the signal fluctuation vector is an eigenvalue corresponding to an element with the largest eigenvalue change rate in a corresponding row of a broadband measurement data eigenvalue matrix and another broadband measurement data eigenvalue matrix, wherein the other broadband measurement data eigenvalue matrix corresponds to broadband measurement data corresponding to a preset period before the current preset period. The eigenvalue corresponding to an element with the largest eigenvalue change rate in a corresponding row of an eigenvalue matrix of broadband measurement data and an eigenvalue matrix corresponding to a previous preset period may be selected as an element of the signal fluctuation vector, so as to capture the change in the fluctuation characteristics of the power system between different time periods, and thus reflect the dynamic behavior of the power system.

[0065] In this embodiment, broadband measurement data of the power system in the current preset time period is collected, and the broadband measurement data is decomposed into a plurality of broadband measurement window data in the time domain to form a broadband measurement window data sequence, and then, for any broadband measurement window data in the broadband measurement window data sequence, the frequency division signals at each preset frequency are extracted to generate a broadband measurement data signal decomposition matrix corresponding to the broadband measurement data, and then the corresponding broadband measurement data eigenvalue matrix is ​​determined according to the broadband measurement data signal decomposition matrix, and then the signal fluctuation vector of the broadband measurement data in the time domain is determined according to the broadband measurement data eigenvalue matrix, so as to determine the operating state of the power system according to the signal fluctuation vector, thereby judging whether the power system is in a normal operating state or has potential risks, and providing a strong guarantee for the safe operation of the power system.

[0066] Figure 2 FIG. 1 is a flow chart of a method for processing broadband measurement data of a power system according to another exemplary embodiment of the present application. Figure 2 As shown, the method for processing broadband measurement data of a power system provided in this embodiment includes: S201 , collecting broadband measurement data of the power system in a current preset time period, and decomposing the broadband measurement data into a plurality of broadband measurement window data in the time domain to form a broadband measurement window data sequence.

[0067] S202 . For any wideband measurement window data in the wideband measurement window data sequence, extract the frequency division signals at each preset frequency to generate a wideband measurement data signal decomposition matrix corresponding to the wideband measurement data.

[0068] S203: Determine a characteristic signal point set according to the frequency-divided signal measurement values ​​of the to-be-processed frequency-divided signal in the wideband measurement data signal decomposition matrix at each acquisition time node of the corresponding wideband measurement window.

[0069] In this step, a characteristic signal point set is determined according to the frequency division signal measurement values ​​of the to-be-processed frequency division signal in the broadband measurement data signal decomposition matrix at each acquisition time node of the corresponding broadband measurement window, and the to-be-processed frequency division signal is any element in the broadband measurement data signal decomposition matrix.

[0070] Specifically, the frequency-divided signal to be processed can be extracted from the wideband measurement data signal decomposition matrix. The signal decomposition matrix contains multiple frequency-divided signals collected by the power system within a wideband range, and each frequency-divided signal corresponds to a specific frequency component. For each frequency-divided signal to be processed (i.e., any element in the matrix), the frequency-divided signal measurement values ​​at each acquisition time node in the wideband measurement window are traversed. These measurement values ​​constitute a time series, in which each measurement value corresponds to a characteristic signal point. These characteristic signal points are grouped together to form an initial characteristic signal point set, which contains all the measurement values ​​of the frequency-divided signal to be processed at all acquisition time nodes.

[0071] S204. Determine a first characteristic signal point set and a second characteristic signal point set according to the characteristic signal point set.

[0072] In this step, the first characteristic signal point set can be determined based on the characteristic signal point set, the first characteristic signal point set is a subset of the characteristic signal point set, the first characteristic signal point in the first characteristic signal point set corresponds to the first original signal point in the characteristic signal point set, the first frequency-divided signal measurement value of the first original signal point is greater than the second frequency-divided signal measurement value of the second original signal point and greater than the third frequency-divided signal measurement value of the third original signal point, wherein the second original signal point is the previous acquisition node of the first original signal point in the characteristic signal point set, and the third original signal point is the next acquisition node of the first original signal point in the characteristic signal point set.

[0073] Specifically, in the characteristic signal point set, each original signal point (i.e., the measured value of the frequency-divided signal at each acquisition time node) is compared with the size relationship of the adjacent original signal points before and after it. Specifically, if the measured value of the frequency-divided signal of a certain original signal point is greater than the measured values ​​of the previous and next original signal points, the original signal point is regarded as a local maximum point and added to the first characteristic signal point set. In this way, the first characteristic signal point set contains all local maximum points, which reflect the significant rising edges of the frequency-divided signal to be processed in the time series.

[0074] In addition, a second characteristic signal point set is determined based on the characteristic signal point set, the second characteristic signal point set is a subset of the characteristic signal point set, the fourth characteristic signal point in the second characteristic signal point set corresponds to the fourth original signal point in the characteristic signal point set, the fourth frequency-divided signal measurement value of the fourth original signal point is less than the fifth frequency-divided signal measurement value of the fifth original signal point and less than the sixth frequency-divided signal measurement value of the sixth original signal point, wherein the fifth original signal point is the previous acquisition node of the fourth original signal point in the characteristic signal point set, and the sixth original signal point is the next acquisition node of the fourth original signal point in the characteristic signal point set.

[0075] Specifically, the method is similar to that of determining the first characteristic signal point set, but in this step, what is sought is the local minimum point. That is, if the measured value of the frequency-divided signal of a certain original signal point is less than the measured values ​​of the previous and next original signal points, the original signal point is regarded as a local minimum point and added to the second characteristic signal point set. The second characteristic signal point set contains all local minimum points, which reflect the significant falling edges of the frequency-divided signal to be processed in the time series.

[0076] S205 . Generate a first characteristic reference line according to the first characteristic signal point set, and generate a second characteristic reference line according to the second characteristic signal point set.

[0077] Specifically, for the first characteristic signal point set, a first characteristic reference line passing through all the first characteristic signal points can be generated by linear interpolation or other suitable fitting methods. This line can be regarded as an upper envelope of the frequency-divided signal to be processed in the time series. Similarly, for the second characteristic signal point set, a second characteristic reference line passing through all the second characteristic signal points is also generated as a lower envelope.

[0078] S206 , determining the eigenvalue corresponding to the frequency-divided signal to be processed in the eigenvalue matrix of the broadband measurement data according to the first characteristic reference line and the second characteristic reference line.

[0079] Specifically, the eigenvalue corresponding to the frequency-divided signal to be processed in the eigenvalue matrix of the broadband measurement data can be determined according to the first characteristic reference line and the second characteristic reference line. This eigenvalue can be defined as a certain metric between the upper envelope and the lower envelope, such as the average distance, the maximum distance, or a certain area metric. This eigenvalue reflects the fluctuation amplitude or energy distribution characteristics of the frequency-divided signal to be processed in the time series, and is an important basis for subsequent power system state analysis or fault diagnosis.

[0080] In a possible implementation, the eigenvalue corresponding to the frequency-divided signal to be processed in the eigenvalue matrix of broadband measurement data is determined according to the maximum difference between the first characteristic reference line and the second characteristic reference line. Specifically, the maximum difference between the first characteristic reference line (upper envelope) and the second characteristic reference line (lower envelope) can be calculated. This can be achieved by traversing all corresponding points on the two reference lines and calculating the vertical distance between them. Specifically, for each time point, find the point on the upper envelope and the point on the lower envelope, and then calculate the difference between the two points. Find the maximum value of the difference among all time points, which is the maximum difference. This maximum difference reflects the maximum fluctuation amplitude of the frequency-divided signal to be processed in the time series, and can be recorded as an eigenvalue in the eigenvalue matrix of broadband measurement data.

[0081] In another possible implementation, the eigenvalue corresponding to the frequency-divided signal to be processed in the eigenvalue matrix of broadband measurement data is determined according to the area enclosed by the first characteristic reference line and the second characteristic reference line. Specifically, the area enclosed by the first characteristic reference line and the second characteristic reference line may be calculated, which may be achieved by numerical integration methods, such as using the trapezoidal method or the Simpson method. Specifically, the time axis is divided into a plurality of small intervals, and for each small interval, the area between the upper envelope and the lower envelope is calculated, and then the areas of all small intervals are added together to obtain the total area. This total area reflects the total energy or fluctuation intensity of the frequency-divided signal to be processed in the time series, and may also be recorded as an eigenvalue in the eigenvalue matrix of broadband measurement data.

[0082] In practical applications, the maximum difference or enclosed area can be selected as the eigenvalue according to specific needs for the above two possible implementation methods. If you are more concerned about the instantaneous maximum fluctuation of the signal, you can choose the maximum difference as the eigenvalue; if you are more concerned about the overall energy distribution or fluctuation intensity of the signal, you can choose the enclosed area as the eigenvalue. In addition, the two methods can be combined and their combined value can be used as the eigenvalue to more comprehensively describe the characteristics of the frequency-divided signal to be processed.

[0083] S207 . Determine a signal fluctuation vector of the broadband measurement data in the time domain according to the broadband measurement data eigenvalue matrix, so as to determine an operating state of the power system according to the signal fluctuation vector.

[0084] Each element of the signal fluctuation vector is an eigenvalue corresponding to an element with the largest eigenvalue change rate in a corresponding row of a broadband measurement data eigenvalue matrix and another broadband measurement data eigenvalue matrix, wherein the other broadband measurement data eigenvalue matrix corresponds to broadband measurement data corresponding to a preset period before the current preset period. The eigenvalue corresponding to an element with the largest eigenvalue change rate in a corresponding row of an eigenvalue matrix of broadband measurement data and an eigenvalue matrix corresponding to a previous preset period may be selected as an element of the signal fluctuation vector, so as to capture the change of fluctuation characteristics of the power system between different time periods, and thus reflect the dynamic behavior of the power system.

[0085] In one possible implementation, if each eigenvalue in the signal fluctuation vector is smaller than a preset characteristic threshold of the corresponding frequency-divided signal, then the operating state of the power system in the current preset time period is determined to be a normal operating state; if each eigenvalue in the signal fluctuation vector is greater than a preset characteristic threshold of the corresponding frequency-divided signal, then the operating state of the power system in the current preset time period is determined to be an abnormal operating state.

[0086] Specifically, if each characteristic value in the signal fluctuation vector is less than the preset characteristic threshold of the corresponding frequency-divided signal, it is determined that the operating state of the power system in the current preset time period is a normal operating state. This indicates that the signal fluctuation of the power system in this period is within the normal range and the system is stable. If there is a characteristic value in the signal fluctuation vector that is greater than the preset characteristic threshold of the corresponding frequency-divided signal, it is determined that the operating state of the power system in the current preset time period is an abnormal operating state. This indicates that the power system has abnormal signal fluctuations in this period, and further inspection or maintenance may be required.

[0087] In the above scheme, if each element of the signal fluctuation vector is the eigenvalue corresponding to the element with the largest eigenvalue change rate in the corresponding row of the eigenvalue matrix of the current broadband measurement data and the eigenvalue matrix of the broadband measurement data in the previous preset period, a signal fluctuation vector that can reflect the signal fluctuation of the power system is constructed. Then, the eigenvalue in the signal fluctuation vector can be compared with the preset characteristic threshold of the corresponding frequency-divided signal. If all eigenvalues ​​are less than the preset characteristic threshold, the power system is judged to be in a normal operating state; if there is an eigenvalue greater than the preset characteristic threshold, it is judged to be in an abnormal operating state.

[0088] In another possible implementation, Formula 3 can be used, and according to the signal fluctuation vector Determine the overall fluctuation characteristic value of broadband measurement data within the current preset time period , where Formula 3 is:

[0089] in, is the number of wideband measurement window data in the wideband measurement window data sequence, is the signal fluctuation vector The eigenvalues, is the signal fluctuation vector The The preset characteristic threshold of the frequency-divided signal corresponding to the characteristic value.

[0090] If the overall fluctuation characteristic value is less than the preset overall fluctuation characteristic threshold, it is determined that the operating state of the power system in the current preset time period is a normal operating state; if the overall fluctuation characteristic value is greater than the preset overall fluctuation characteristic threshold, it is determined that the operating state of the power system in the current preset time period is an abnormal operating state.

[0091] Specifically, in order to more comprehensively evaluate the fluctuation of the power system in the current preset period, the overall fluctuation characteristic value of the broadband measurement data can also be calculated using Formula 3. The overall fluctuation characteristic value obtained by calculation can further quantify the degree of fluctuation of the power system in the current preset period. If the overall fluctuation characteristic value exceeds a certain set threshold, it can also be determined that the power system is in an abnormal operating state.

[0092] Based on the above embodiment, Formula 3 can be used to calculate the overall fluctuation characteristic value of the broadband measurement data in the current preset time period according to the signal fluctuation vector to quantitatively evaluate the overall fluctuation of the power system, thereby judging the overall stability of the power system and improving the overall monitoring effect of the power system.

[0093] It is worth noting that if it is determined that the operating state of the power system in the current preset time period is an abnormal operating state, then after determining the operating state of the power system according to the signal fluctuation vector, it also includes: Beginning within a preset monitoring period, determining the similarity between signal fluctuation vectors of two adjacent preset time periods to form a similarity sequence, wherein the preset monitoring period includes a plurality of preset time periods; If each similarity in the similarity sequence is greater than a preset similarity threshold, the abnormal operation state is determined to be a continuous oscillation fault, wherein the continuous oscillation fault is used to indicate that the power equipment of the power system should be repaired; If each similarity in the similarity sequence is less than a preset similarity threshold, the abnormal operation state is determined to be a random interference fault, wherein the random interference fault is used to indicate that external interference factors of the power system should be checked.

[0094] Specifically, within a preset monitoring cycle, the cycle includes multiple preset time periods, and then the similarity between the signal fluctuation vectors of two adjacent preset time periods is determined. The similarity can be calculated using a variety of methods, such as cosine similarity, inverse Euclidean distance, etc. By traversing the entire preset monitoring cycle, a similarity sequence can be obtained. After obtaining the similarity sequence, the type of abnormal operating state can be determined based on the relationship between the similarity and the preset similarity threshold: If each similarity in the similarity sequence is greater than the preset similarity threshold: In this case, we determined that the abnormal operation state was a continuous oscillation fault. A continuous oscillation fault usually indicates that there is a stable oscillation source in the power system, such as equipment failure, load imbalance, etc. This fault may cause the voltage, current and other parameters of the power system to fluctuate continuously for a long time, posing a threat to the stable operation of the system. Therefore, the operation and maintenance personnel should promptly inspect and repair the power equipment to eliminate the oscillation source and restore the stable operation of the system.

[0095] If the similarities in the similarity sequence are less than the preset similarity threshold: In this case, we determined that the abnormal operation state was a random interference fault. Random interference faults are usually caused by external random factors, such as lightning strikes, electromagnetic pulses, and human errors. These interference factors may cause the signal fluctuations of the power system to change dramatically in a short period of time, and the change pattern is random. Therefore, the operation and maintenance personnel should investigate the external interference factors of the power system, such as checking whether the lightning protection facilities and electromagnetic shielding measures are complete, and whether there are human errors, and take corresponding measures to reduce or eliminate the impact of interference factors.

[0096] In the above scheme, by calculating the similarity between the signal fluctuation vectors of two adjacent preset time periods, the changing trend of the operating state of the power system can be captured more accurately. When each similarity in the similarity sequence is greater than the preset similarity threshold, it indicates that the abnormal state of the power system is continuous or trending, and is likely to be caused by a continuous oscillation fault. When there is a similarity in the similarity sequence that is less than the preset similarity threshold, it indicates that the abnormal state is sudden or random, and may be related to a random interference fault. This similarity-based analysis method can more accurately identify the type and cause of the abnormal state compared to the signal fluctuation analysis of a single time period.

[0097] According to the analysis results of the similarity sequence, the type of abnormal state can be determined, thereby guiding the operation and maintenance personnel to take targeted maintenance measures. For continuous oscillation faults, the operation and maintenance personnel can focus on checking whether there are faults or abnormal operation of the power equipment in the power system, such as generators and transformers, and carry out inspection and maintenance in a timely manner. For random interference faults, it is necessary to pay attention to the external interference factors of the power system, such as weather conditions, electromagnetic environment, etc., and take corresponding preventive measures to reduce or eliminate the impact of interference on the power system.

[0098] In addition, by timely discovering and handling abnormal conditions of the power system, the further expansion and deterioration of faults can be effectively avoided, thereby improving the stability and reliability of the power system. In particular, for continuous oscillation faults, if not handled in a timely manner, it may cause the voltage, current and other parameters of the power system to fluctuate for a long time, causing damage to the equipment and even causing greater faults. The above solution can timely discover and locate such faults, providing operation and maintenance personnel with sufficient time and basis for inspection and maintenance, thereby ensuring the safe and stable operation of the power system.

[0099] It can be seen that through the above solution, the power system can have stronger self-repair capabilities. When an abnormal state is detected, the system can automatically determine the fault type and trigger the corresponding emergency response mechanism, such as switching backup power supplies, adjusting load distribution, etc., to reduce the impact of the fault on the system. At the same time, operation and maintenance personnel can also quickly locate and handle faults based on the fault information and analysis results provided by the system, thereby shortening the fault recovery time and improving the self-repair capability of the power system.

[0100] Figure 3 FIG. 1 is a schematic diagram of a structure of a power system broadband measurement data processing device according to an exemplary embodiment of the present application. Figure 3 As shown, the power system broadband measurement data processing device 300 provided in this embodiment includes: The acquisition module 310 is used to acquire broadband measurement data of the power system in a current preset time period, and decompose the broadband measurement data into a plurality of broadband measurement window data in the time domain to form a broadband measurement window data sequence; An extraction module 320 is used to extract the frequency-divided signals at each preset frequency for any wideband measurement window data in the wideband measurement window data sequence, so as to generate a wideband measurement data signal decomposition matrix corresponding to the wideband measurement data, wherein each element in a row of the wideband measurement data signal decomposition matrix is ​​used to characterize the frequency-divided signals at each preset frequency in the corresponding wideband measurement window data, and each element in a column of the wideband measurement data signal decomposition matrix is ​​used to characterize the frequency-divided signals of each wideband measurement window data in the wideband measurement window data sequence at the same preset frequency; A processing module 330 is used to determine a corresponding wideband measurement data eigenvalue matrix according to the wideband measurement data signal decomposition matrix, wherein each eigenvalue in the wideband measurement data eigenvalue matrix is ​​used to characterize a signal fluctuation degree of a corresponding frequency-divided signal in the wideband measurement data signal decomposition matrix; The determination module 340 is used to determine the signal fluctuation vector of the broadband measurement data in the time domain according to the broadband measurement data eigenvalue matrix, so as to determine the operation state of the power system according to the signal fluctuation vector.

[0101] Optionally, each element of the signal fluctuation vector is the maximum eigenvalue in each row of the broadband measurement data eigenvalue matrix; or, Each element of the signal fluctuation vector is an eigenvalue corresponding to an element with the largest eigenvalue change rate in corresponding rows of the broadband measurement data eigenvalue matrix and another broadband measurement data eigenvalue matrix, wherein the other broadband measurement data eigenvalue matrix corresponds to the broadband measurement data corresponding to a preset time period before the current preset time period.

[0102] Optionally, the processing module 330 is specifically configured to: Determining the characteristic instantaneous energy of each frequency-divided signal to be processed in the broadband measurement data signal decomposition matrix; The broadband measurement data eigenvalue matrix is ​​generated according to the characteristic instantaneous energy of each frequency-divided signal to be processed, and each eigenvalue in the broadband measurement data eigenvalue matrix corresponds to the characteristic instantaneous energy of the corresponding frequency-divided signal to be processed in the broadband measurement data signal decomposition matrix.

[0103] Optionally, the processing module 330 is specifically configured to: Using formula 1, and according to the first frequency-divided signal to be processed in the broadband measurement data signal decomposition matrix Determine the corresponding first conversion frequency division signal , the first frequency-divided signal to be processed is any signal in the broadband measurement data signal decomposition matrix, and the formula 1 is:

[0104] Using formula 2, and according to the first frequency-divided signal to be processed and the first conversion frequency-divided signal Determine the first characteristic frequency division signal , wherein the formula 2 is:

[0105] in, is an imaginary unit; The frequency division signal is based on the first characteristic Determine the first frequency division signal to be processed The first characteristic instantaneous energy corresponding to the first characteristic instantaneous energy is the first characteristic frequency-divided signal The largest model at each moment.

[0106] Optionally, the processing module 330 is specifically configured to: Determine a characteristic signal point set according to the frequency division signal measurement values ​​of the to-be-processed frequency division signal in the broadband measurement data signal decomposition matrix at each acquisition time node of the corresponding broadband measurement window, wherein the to-be-processed frequency division signal is any element in the broadband measurement data signal decomposition matrix; Determine a first characteristic signal point set according to the characteristic signal point set, the first characteristic signal point set is a subset of the characteristic signal point set, a first characteristic signal point in the first characteristic signal point set corresponds to a first original signal point in the characteristic signal point set, a first frequency-divided signal measurement value of the first original signal point is greater than a second frequency-divided signal measurement value of the second original signal point and greater than a third frequency-divided signal measurement value of the third original signal point, wherein the second original signal point is a previous acquisition node of the first original signal point in the characteristic signal point set, and the third original signal point is a subsequent acquisition node of the first original signal point in the characteristic signal point set; Determine a second characteristic signal point set according to the characteristic signal point set, the second characteristic signal point set is a subset of the characteristic signal point set, a fourth characteristic signal point in the second characteristic signal point set corresponds to a fourth original signal point in the characteristic signal point set, a fourth frequency-divided signal measurement value of the fourth original signal point is less than a fifth frequency-divided signal measurement value of the fifth original signal point and less than a sixth frequency-divided signal measurement value of the sixth original signal point, wherein the fifth original signal point is a previous acquisition node of the fourth original signal point in the characteristic signal point set, and the sixth original signal point is a subsequent acquisition node of the fourth original signal point in the characteristic signal point set; generating a first characteristic reference line according to the first characteristic signal point set, and generating a second characteristic reference line according to the second characteristic signal point set; The eigenvalue corresponding to the to-be-processed frequency-divided signal in the wideband measurement data eigenvalue matrix is ​​determined according to the first characteristic reference line and the second characteristic reference line.

[0107] Optionally, the processing module 330 is specifically configured to: Determine the eigenvalue corresponding to the to-be-processed frequency-divided signal in the wideband measurement data eigenvalue matrix according to the maximum difference between the first characteristic reference line and the second characteristic reference line; or The eigenvalue corresponding to the to-be-processed frequency-divided signal in the wideband measurement data eigenvalue matrix is ​​determined according to the area enclosed by the first characteristic reference line and the second characteristic reference line.

[0108] Optionally, the determining module 340 is specifically configured to: If each characteristic value in the signal fluctuation vector is smaller than the preset characteristic threshold value of the corresponding frequency-divided signal, it is determined that the operating state of the power system in the current preset time period is a normal operating state; if each characteristic value in the signal fluctuation vector is greater than the preset characteristic threshold value of the corresponding frequency-divided signal, it is determined that the operating state of the power system in the current preset time period is an abnormal operating state; or, Using formula 3, and according to the signal fluctuation vector Determine the overall fluctuation characteristic value of the broadband measurement data within the current preset time period , wherein the formula 3 is:

[0109] in, is the number of broadband measurement window data in the broadband measurement window data sequence, is the signal fluctuation vector The eigenvalues, is the signal fluctuation vector The The preset characteristic threshold of the frequency-divided signal corresponding to the characteristic value.

[0110] Figure 4 is a schematic diagram of the structure of an electronic device according to an exemplary embodiment of the present application. Figure 4 As shown, an electronic device 400 provided in this embodiment includes: a processor 401 and a memory 402; wherein: The memory 402 is used to store computer programs, and the memory may also be a flash memory.

[0111] The processor 401 is used to execute the execution instructions stored in the memory to implement each step in the above method. For details, please refer to the relevant description in the above method embodiment.

[0112] Optionally, the memory 402 may be independent or integrated with the processor 401 .

[0113] When the memory 402 is a device independent of the processor 401, the electronic device 400 may further include: The bus 403 is used to connect the memory 402 and the processor 401 .

[0114] This embodiment further provides a readable storage medium, in which a computer program is stored. When at least one processor of an electronic device executes the computer program, the electronic device executes the methods provided in the above-mentioned various implementation modes.

[0115] This embodiment also provides a program product, which includes a computer program stored in a readable storage medium. At least one processor of the electronic device can read the computer program from the readable storage medium, and at least one processor executes the computer program so that the electronic device implements the methods provided in the above various embodiments.

[0116] Those skilled in the art will readily appreciate other embodiments of the present application after considering the specification and practicing the invention disclosed herein. The present application is intended to cover any modification, use or adaptation of the present application, which follows the general principles of the present application and includes common knowledge or customary techniques in the art that are not disclosed in the present application. The specification and examples are intended to be exemplary only, and the true scope and spirit of the present application are indicated by the claims.

[0117] It should be understood that the present application is not limited to the precise structures that have been described above and shown in the drawings, and that various modifications and changes may be made without departing from the scope thereof. The scope of the present application is limited only by the appended claims.

Claims

1. A method for processing broadband measurement data of a power system, characterized in that: include: Collecting broadband measurement data of the power system within a current preset time period, and decomposing the broadband measurement data into a plurality of broadband measurement window data in the time domain to form a broadband measurement window data sequence; For any wideband measurement window data in the wideband measurement window data sequence, extract the frequency division signals at each preset frequency to generate a wideband measurement data signal decomposition matrix corresponding to the wideband measurement data, wherein each element in a row of the wideband measurement data signal decomposition matrix is ​​used to characterize the frequency division signals at each preset frequency in the corresponding wideband measurement window data, and each element in a column of the wideband measurement data signal decomposition matrix is ​​used to characterize the frequency division signals of each wideband measurement window data in the wideband measurement window data sequence at the same preset frequency; Determine a corresponding wideband measurement data eigenvalue matrix according to the wideband measurement data signal decomposition matrix, wherein each eigenvalue in the wideband measurement data eigenvalue matrix is ​​used to characterize the signal fluctuation degree of the corresponding frequency-divided signal in the wideband measurement data signal decomposition matrix; Determine a signal fluctuation vector of the broadband measurement data in the time domain according to the broadband measurement data eigenvalue matrix, so as to determine the operating state of the power system according to the signal fluctuation vector; The step of determining a corresponding broadband measurement data eigenvalue matrix according to the broadband measurement data signal decomposition matrix comprises: Determining the characteristic instantaneous energy of each frequency-divided signal to be processed in the broadband measurement data signal decomposition matrix; generating the broadband measurement data eigenvalue matrix according to the characteristic instantaneous energy of each to-be-processed frequency-divided signal, wherein each eigenvalue in the broadband measurement data eigenvalue matrix corresponds to the characteristic instantaneous energy of the corresponding to-be-processed frequency-divided signal in the broadband measurement data signal decomposition matrix; or, Determine a characteristic signal point set according to the frequency division signal measurement values ​​of the to-be-processed frequency division signal in the broadband measurement data signal decomposition matrix at each acquisition time node of the corresponding broadband measurement window; Determine a first characteristic signal point set and a second characteristic signal point set according to the characteristic signal point set, wherein a characteristic signal point in the first characteristic signal point set corresponds to an original signal point in the characteristic signal point set, and a characteristic signal point in the second characteristic signal point set corresponds to another original signal point in the characteristic signal point set, and frequency division signal measurement values ​​of the one original signal point and the other original signal point are in different measurement value intervals; generating a first characteristic reference line according to the first characteristic signal point set, and generating a second characteristic reference line according to the second characteristic signal point set; The eigenvalue corresponding to the to-be-processed frequency-divided signal in the wideband measurement data eigenvalue matrix is ​​determined according to the first characteristic reference line and the second characteristic reference line.

2. The method for processing broadband measurement data of a power system according to claim 1, characterized in that: Each element of the signal fluctuation vector is the maximum eigenvalue in each row of the broadband measurement data eigenvalue matrix; or, Each element of the signal fluctuation vector is an eigenvalue corresponding to an element with the largest eigenvalue change rate in corresponding rows of the broadband measurement data eigenvalue matrix and another broadband measurement data eigenvalue matrix, wherein the other broadband measurement data eigenvalue matrix corresponds to the broadband measurement data corresponding to a preset time period before the current preset time period.

3. The method for processing broadband measurement data of a power system according to claim 1, characterized in that: The step of determining the characteristic instantaneous energy of each to-be-processed frequency-divided signal in the broadband measurement data signal decomposition matrix comprises: Determine a corresponding first transformed frequency-divided signal according to a first frequency-divided signal to be processed in the wideband measurement data signal decomposition matrix, wherein the first frequency-divided signal to be processed is any signal in the wideband measurement data signal decomposition matrix; Determine a first characteristic frequency-divided signal according to the first frequency-divided signal to be processed and the first transformed frequency-divided signal; A first characteristic instantaneous energy corresponding to the first frequency-divided signal to be processed is determined according to the first characteristic frequency-divided signal, wherein the first characteristic instantaneous energy is the maximum modulus of the first characteristic frequency-divided signal at each moment.

4. The method for processing broadband measurement data of a power system according to claim 2, characterized in that: The determining a first characteristic signal point set and a second characteristic signal point set according to the characteristic signal point set comprises: Determine a first characteristic signal point set according to the characteristic signal point set, the first characteristic signal point set is a subset of the characteristic signal point set, a first characteristic signal point in the first characteristic signal point set corresponds to a first original signal point in the characteristic signal point set, a first frequency-divided signal measurement value of the first original signal point is greater than a second frequency-divided signal measurement value of the second original signal point and greater than a third frequency-divided signal measurement value of the third original signal point, wherein the second original signal point is a previous acquisition node of the first original signal point in the characteristic signal point set, and the third original signal point is a subsequent acquisition node of the first original signal point in the characteristic signal point set; A second characteristic signal point set is determined based on the characteristic signal point set, the second characteristic signal point set is a subset of the characteristic signal point set, the fourth characteristic signal point in the second characteristic signal point set corresponds to the fourth original signal point in the characteristic signal point set, the fourth frequency-divided signal measurement value of the fourth original signal point is less than the fifth frequency-divided signal measurement value of the fifth original signal point and less than the sixth frequency-divided signal measurement value of the sixth original signal point, wherein the fifth original signal point is the previous acquisition node of the fourth original signal point in the characteristic signal point set, and the sixth original signal point is the next acquisition node of the fourth original signal point in the characteristic signal point set.

5. The method for processing broadband measurement data of a power system according to claim 4, characterized in that: The determining, according to the first characteristic reference line and the second characteristic reference line, the eigenvalue corresponding to the to-be-processed frequency-divided signal in the wideband measurement data eigenvalue matrix comprises: Determine the eigenvalue corresponding to the to-be-processed frequency-divided signal in the wideband measurement data eigenvalue matrix according to the maximum difference between the first characteristic reference line and the second characteristic reference line; or The eigenvalue corresponding to the to-be-processed frequency-divided signal in the wideband measurement data eigenvalue matrix is ​​determined according to the area enclosed by the first characteristic reference line and the second characteristic reference line.

6. The method for processing broadband measurement data of a power system according to any one of claims 2 to 5, characterized in that: If each element of the signal fluctuation vector is an eigenvalue corresponding to an element having the largest eigenvalue change rate in a corresponding row of the broadband measurement data eigenvalue matrix and another broadband measurement data eigenvalue matrix; Correspondingly, determining the operating state of the power system according to the signal fluctuation vector includes: If each characteristic value in the signal fluctuation vector is smaller than the preset characteristic threshold value of the corresponding frequency-divided signal, it is determined that the operating state of the power system in the current preset time period is a normal operating state; if each characteristic value in the signal fluctuation vector is greater than the preset characteristic threshold value of the corresponding frequency-divided signal, it is determined that the operating state of the power system in the current preset time period is an abnormal operating state; or, The overall fluctuation characteristic value of the broadband measurement data within the current preset time period is determined according to the signal fluctuation vector; if the overall fluctuation characteristic value is less than the preset overall fluctuation characteristic threshold, it is determined that the operating state of the power system within the current preset time period is a normal operating state; if the overall fluctuation characteristic value is greater than the preset overall fluctuation characteristic threshold, it is determined that the operating state of the power system within the current preset time period is an abnormal operating state.

7. A power system broadband measurement data processing device, characterized in that: include: An acquisition module, used for acquiring broadband measurement data of the power system within a current preset time period, and decomposing the broadband measurement data into a plurality of broadband measurement window data in the time domain to form a broadband measurement window data sequence; an extraction module, for extracting frequency-divided signals at respective preset frequencies for any wideband measurement window data in the wideband measurement window data sequence, so as to generate a wideband measurement data signal decomposition matrix corresponding to the wideband measurement data, wherein each element in a row of the wideband measurement data signal decomposition matrix is ​​used to characterize the frequency-divided signals at respective preset frequencies in the corresponding wideband measurement window data, and each element in a column of the wideband measurement data signal decomposition matrix is ​​used to characterize the frequency-divided signals of respective wideband measurement window data in the wideband measurement window data sequence at the same preset frequency; A processing module, used to determine a corresponding wideband measurement data eigenvalue matrix according to the wideband measurement data signal decomposition matrix, wherein each eigenvalue in the wideband measurement data eigenvalue matrix is ​​used to characterize a signal fluctuation degree of a corresponding frequency-divided signal in the wideband measurement data signal decomposition matrix; A determination module, configured to determine a signal fluctuation vector of the broadband measurement data in a time domain according to an eigenvalue matrix of the broadband measurement data, so as to determine an operating state of the power system according to the signal fluctuation vector; The processing module is specifically used for: Determining the characteristic instantaneous energy of each frequency-divided signal to be processed in the broadband measurement data signal decomposition matrix; Generating the broadband measurement data eigenvalue matrix according to the characteristic instantaneous energy of each to-be-processed frequency-divided signal, wherein each eigenvalue in the broadband measurement data eigenvalue matrix corresponds to the characteristic instantaneous energy of the corresponding to-be-processed frequency-divided signal in the broadband measurement data signal decomposition matrix; or, Determine a characteristic signal point set according to the frequency division signal measurement values ​​of the to-be-processed frequency division signal in the broadband measurement data signal decomposition matrix at each acquisition time node of the corresponding broadband measurement window; Determine a first characteristic signal point set and a second characteristic signal point set according to the characteristic signal point set, wherein a characteristic signal point in the first characteristic signal point set corresponds to an original signal point in the characteristic signal point set, and a characteristic signal point in the second characteristic signal point set corresponds to another original signal point in the characteristic signal point set, and frequency division signal measurement values ​​of the one original signal point and the other original signal point are in different measurement value intervals; generating a first characteristic reference line according to the first characteristic signal point set, and generating a second characteristic reference line according to the second characteristic signal point set; The eigenvalue corresponding to the to-be-processed frequency-divided signal in the wideband measurement data eigenvalue matrix is ​​determined according to the first characteristic reference line and the second characteristic reference line.

8. An electronic device, characterized in that: include: processor; as well as, A memory, configured to store executable instructions of the processor; The processor is configured to perform the method of any one of claims 1 to 6 by executing the executable instructions.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer-executable instructions, which are used to implement the method according to any one of claims 1 to 6 when executed by a processor.

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