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

Abstract

The present application provides a method, device, equipment and medium for processing broadband measurement data of an electric power system. It relates to the field of data processing technology. The method collects broadband measurement data of the electric power system in the current preset time period, and decomposes the broadband measurement data into multiple 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, extracts 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 determines the corresponding broadband measurement data eigenvalue matrix according to the broadband measurement data signal decomposition matrix, and then determines 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.

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] The expansion and increasing complexity of power systems have placed higher demands on their monitoring and management. Traditional power system monitoring methods primarily rely on low-frequency measurement data. While these data can reflect the basic operating status of the power system, they are limited in capturing the high-frequency dynamic characteristics and potential faults within the power system. In modern power systems, the widespread use of power electronics and the integration of distributed energy resources have made the frequency characteristics of power systems more complex, and the impact of high-frequency components on system stability has become increasingly significant.

[0003] To more comprehensively monitor the operating status of power systems, broadband measurement technology has emerged. It can simultaneously collect data from power systems across multiple frequency ranges, providing richer information for assessing power system stability and security. However, the large volume of broadband measurement data and the wide frequency range make effective processing and analysis of this data a pressing challenge. 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, 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 a power system, comprising:

[0006] 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;

[0007] For any wideband measurement window data in the wideband measurement window data sequence, extracting frequency-divided signals at respective preset frequencies 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 represent 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 represent the frequency-divided signals at the same preset frequency for respective wideband measurement window data in the wideband measurement window data sequence;

[0008] determining 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;

[0009] 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.

[0010] In the above scheme, by collecting broadband measurement data from the power system during a preset time period and decomposing it in the time domain into multiple broadband measurement windows, comprehensive broadband monitoring of the power system is achieved. This decomposition method enables the capture of more detailed information during data processing. For each broadband measurement window, the frequency-divided signals at each preset frequency are extracted to generate a broadband measurement data signal decomposition matrix. The generation of the signal decomposition matrix simplifies complex signals in the power system, facilitating subsequent feature extraction and analysis. Next, by determining the broadband measurement data eigenvalue matrix, the signal fluctuation level of each frequency-divided signal can be quantified. Each eigenvalue in the eigenvalue matrix represents the fluctuation characteristics of the corresponding frequency-divided signal, providing a quantitative indicator for power system stability analysis, more intuitively reflecting the power system's fluctuations and providing a basis for developing targeted maintenance strategies. Next, a signal fluctuation vector is determined based on the broadband measurement data eigenvalue matrix, and the operating status of the power system is evaluated using the signal fluctuation vector. The signal fluctuation vector integrates the fluctuation characteristics of each frequency-divided signal and comprehensively reflects the overall operating status 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 there are potential risks, providing strong guarantees for the safe operation of the power system.

[0011] Optionally, each element of the signal fluctuation vector is the maximum eigenvalue in each row of the broadband measurement data eigenvalue matrix; or,

[0012] 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 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 the previous preset time period of the current preset time period.

[0013] In the first approach described above, when the elements of the signal fluctuation vector are the maximum eigenvalues ​​in each row of the wideband measurement data eigenvalue matrix, the most significantly fluctuating frequency-divided signal within each wideband measurement window is highlighted. This selection method helps quickly identify the dominant fluctuation component in the power system, providing important clues for analyzing power system stability. By directly focusing on the maximum eigenvalue, the data analysis process is simplified, enabling faster understanding of the core fluctuation characteristics of the power system and improving work efficiency.

[0014] In another way, the eigenvalue corresponding to the element with the largest eigenvalue change rate in the corresponding row of the eigenvalue matrix of the 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.

[0015] In summary, the above scheme, through the two proposed signal fluctuation vector construction methods, provides more options for processing power system broadband measurement data. In practical applications, the appropriate method can be selected based on specific needs and analysis objectives, thereby improving the flexibility and applicability of data processing methods. For example, when it is necessary to quickly identify the dominant fluctuation component of the power system, a method based on the maximum eigenvalue can be selected; when it is necessary to analyze the dynamic behavior of the power system or predict potential problems, a method based on the rate of change of the eigenvalue can be selected.

[0016] Optionally, determining a corresponding broadband measurement data eigenvalue matrix according to the broadband measurement data signal decomposition matrix includes:

[0017] Determining the characteristic instantaneous energy of each frequency-divided signal to be processed in the broadband measurement data signal decomposition matrix;

[0018] 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.

[0019] 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.

[0020] Furthermore, because characteristic instantaneous energy can sensitively reflect changes in signal fluctuations, the eigenvalue matrix generated based on characteristic instantaneous energy is also sensitive to changes in the power system's operating state. When the power system's operating state undergoes slight changes, the eigenvalues ​​in the eigenvalue matrix will also change accordingly, enabling real-time monitoring and early warning of the power system's operating state. By calculating characteristic instantaneous energy to generate the eigenvalue matrix, the complex transformations and calculations required by traditional methods such as waveform analysis are avoided, simplifying the data processing process. This optimization not only improves data processing efficiency but also reduces computing resource consumption, making this method more feasible and economical in practical applications. Furthermore, by subsequently analyzing the changing trends and patterns of the eigenvalue matrix, potential problems in the power system can be promptly identified and appropriate maintenance measures implemented, thereby ensuring the safe and stable operation of the power system.

[0021] Optionally, determining the characteristic instantaneous energy of each to-be-processed frequency-divided signal in the broadband measurement data signal decomposition matrix includes:

[0022] determining 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, where the first frequency-divided signal to be processed is any signal in the wideband measurement data signal decomposition matrix;

[0023] determining a first characteristic frequency-divided signal according to the first frequency-divided signal to be processed and the first converted frequency-divided signal;

[0024] 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.

[0025] In the above scheme, determining the first transformed frequency-divided signal corresponding to the first frequency-divided signal to be processed effectively transforms the original signal to extract its instantaneous characteristics. This transformation preserves the signal's amplitude information and incorporates phase information, allowing the transformed signal to more comprehensively reflect the dynamic changes of the original signal. Combining the first frequency-divided signal to be processed and its transformed signal to generate the first characteristic frequency-divided signal 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 phase information, allowing for a more detailed representation of the signal's characteristics at different moments. The characteristic instantaneous energy is defined as the maximum modulus of the first characteristic frequency-divided signal at each moment, that is, the absolute value of the complex signal. Because the characteristic frequency-divided signal is complex, its modulus accurately reflects the instantaneous power or energy level of the signal at a given moment. By calculating the maximum modulus of the characteristic frequency-divided signal at each moment, we can obtain the instantaneous energy of the signal at each moment. This metric is important for assessing the dynamic characteristics and stability of power systems. 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.

[0026] Optionally, determining a corresponding broadband measurement data eigenvalue matrix according to the broadband measurement data signal decomposition matrix includes:

[0027] determining a characteristic signal point set according to frequency-divided signal measurement values ​​of a to-be-processed frequency-divided signal in the broadband measurement data signal decomposition matrix at each acquisition time node corresponding to the broadband measurement window, wherein the to-be-processed frequency-divided signal is any element in the broadband measurement data signal decomposition matrix;

[0028] Determining a first characteristic signal point set based on the characteristic signal point set, where 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;

[0029] Determining a second characteristic signal point set based on the characteristic signal point set, where 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;

[0030] 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;

[0031] The eigenvalue corresponding to the frequency-divided signal to be processed in the broadband measurement data eigenvalue matrix is ​​determined according to the first characteristic reference line and the second characteristic reference line.

[0032] In this scheme, characteristic signal point sets are generated by extracting measured values ​​of the frequency-divided signal at each acquisition time point within the corresponding wideband measurement window. This fully captures the time-domain fluctuation characteristics of the frequency-divided signal to be processed. Furthermore, by screening the extreme points within the characteristic signal point set (i.e., the first and second characteristic signal point sets), key turning points in the signal fluctuation are highlighted, more accurately reflecting the fluctuation trend and strength of the signal. Characteristic reference lines generated based on these extreme points effectively represent the signal fluctuation characteristics, providing a more precise basis for determining characteristic values. Subsequently, by separately determining the characteristic signal point sets on the rising and falling edges of the signal (i.e., the first and second characteristic signal point sets), the detailed characteristics of the signal fluctuation can be more accurately reflected, contributing to a deeper understanding of the dynamic behavior of the power system and providing richer information for power system stability analysis and fault prediction. This scheme does not rely on complex mathematical models or assumptions, but instead extracts features based on the inherent fluctuation characteristics of the signal, thus offering high flexibility and adaptability. In practical applications, the screening conditions for the characteristic signal point set and the method for generating characteristic reference lines 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 the characteristic signal point set and characteristic reference line 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 the broadband measurement data of the power system, providing strong data support for the state assessment of the power system.

[0033] Optionally, determining the eigenvalue corresponding to the frequency-divided signal to be processed in the wideband measurement data eigenvalue matrix according to the first characteristic reference line and the second characteristic reference line includes:

[0034] Determine the eigenvalue corresponding to the frequency-divided signal to be processed in the broadband measurement data eigenvalue matrix according to the maximum difference between the first characteristic reference line and the second characteristic reference line; or

[0035] The eigenvalue corresponding to the frequency-divided signal to be processed in the broadband measurement data eigenvalue matrix is ​​determined according to the area enclosed by the first characteristic reference line and the second characteristic reference line.

[0036] 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.

[0037] The second method is based on the eigenvalue determination 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.

[0038] 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 based on the signal fluctuation vector includes:

[0039] If each characteristic value in the signal fluctuation vector is less 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,

[0040] The overall fluctuation characteristic value of the broadband measurement data within the current preset time period is determined based on the signal fluctuation vector; if the overall fluctuation characteristic value is less than the preset overall fluctuation characteristic threshold, the operating state of the power system within the current preset time period is determined to be a normal operating state; if the overall fluctuation characteristic value is greater than the preset overall fluctuation characteristic threshold, the operating state of the power system within the current preset time period is determined to be an abnormal operating state.

[0041] 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 from the previous preset period, a signal fluctuation vector that reflects the power system signal fluctuation is constructed. The eigenvalues ​​in the signal fluctuation vector can then be compared with a 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 operating normally; if any eigenvalue is greater than the preset characteristic threshold, the power system is judged to be operating abnormally.

[0042] In addition, the overall fluctuation characteristic value of the broadband measurement data within 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.

[0043] In a second aspect, the present application provides a power system broadband measurement data processing device, comprising:

[0044] an acquisition module, configured to acquire broadband measurement data of the 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;

[0045] an extraction module, configured to extract, for any wideband measurement window data in the wideband measurement window data sequence, frequency-divided signals at respective preset frequencies, 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 represent 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 represent the frequency-divided signals at the same preset frequency for respective wideband measurement window data in the wideband measurement window data sequence;

[0046] a processing module, configured 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;

[0047] A determination module is 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.

[0048] Optionally, each element of the signal fluctuation vector is the maximum eigenvalue in each row of the broadband measurement data eigenvalue matrix; or,

[0049] 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 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 the previous preset time period of the current preset time period.

[0050] Optionally, the processing module is specifically configured to:

[0051] Determining the characteristic instantaneous energy of each frequency-divided signal to be processed in the broadband measurement data signal decomposition matrix;

[0052] 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.

[0053] Optionally, the processing module is specifically configured to:

[0054] determining 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, where the first frequency-divided signal to be processed is any signal in the wideband measurement data signal decomposition matrix;

[0055] determining a first characteristic frequency-divided signal according to the first frequency-divided signal to be processed and the first converted frequency-divided signal;

[0056] 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.

[0057] Optionally, the processing module is specifically configured to:

[0058] determining a characteristic signal point set according to frequency-divided signal measurement values ​​of a to-be-processed frequency-divided signal in the broadband measurement data signal decomposition matrix at each acquisition time node corresponding to the broadband measurement window, wherein the to-be-processed frequency-divided signal is any element in the broadband measurement data signal decomposition matrix;

[0059] Determining a first characteristic signal point set based on the characteristic signal point set, where 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;

[0060] Determining a second characteristic signal point set based on the characteristic signal point set, where 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;

[0061] 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;

[0062] The eigenvalue corresponding to the frequency-divided signal to be processed in the broadband measurement data eigenvalue matrix is ​​determined according to the first characteristic reference line and the second characteristic reference line.

[0063] Optionally, the processing module is specifically configured to:

[0064] Determine the eigenvalue corresponding to the frequency-divided signal to be processed in the broadband measurement data eigenvalue matrix according to the maximum difference between the first characteristic reference line and the second characteristic reference line; or

[0065] The eigenvalue corresponding to the frequency-divided signal to be processed in the broadband measurement data eigenvalue matrix is ​​determined according to the area enclosed by the first characteristic reference line and the second characteristic reference line.

[0066] Optionally, the determining module is specifically configured to:

[0067] If each characteristic value in the signal fluctuation vector is less 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,

[0068] The overall fluctuation characteristic value of the broadband measurement data within the current preset time period is determined based on the signal fluctuation vector; if the overall fluctuation characteristic value is less than the preset overall fluctuation characteristic threshold, the operating state of the power system within the current preset time period is determined to be a normal operating state; if the overall fluctuation characteristic value is greater than the preset overall fluctuation characteristic threshold, the operating state of the power system within the current preset time period is determined to be an abnormal operating state.

[0069] In a third aspect, the present application provides an electronic device, comprising:

[0070] processor; and,

[0071] a memory for storing executable instructions of the processor;

[0072] The processor is configured to perform any possible method described in the first aspect by executing the executable instructions.

[0073] 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.

[0074] The method, apparatus, equipment, and medium for processing broadband measurement data of a power system provided in the present application collect broadband measurement data of the power system within a current preset time period, and decompose the broadband measurement data into multiple broadband measurement window data in the time domain to form a broadband measurement window data sequence. Then, for any broadband measurement window data in the broadband measurement window data sequence, the frequency-divided signals at each preset frequency are extracted to generate a broadband measurement data signal decomposition matrix corresponding to the broadband measurement data. The corresponding broadband measurement data eigenvalue matrix is ​​then determined based on the broadband measurement data signal decomposition matrix. The signal fluctuation vector of the broadband measurement data in the time domain is then determined based on the broadband measurement data eigenvalue matrix. The operating status of the power system is determined based on the signal fluctuation vector, thereby judging whether the power system is in a normal operating state or has potential risks, thereby providing a strong guarantee for the safe operation of the power system. BRIEF DESCRIPTION OF THE DRAWINGS

[0075] 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.

[0076] 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;

[0077] 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;

[0078] Figure 3 1 is a schematic structural diagram of a device for processing broadband measurement data of a power system according to an exemplary embodiment of the present application;

[0079] Figure 4 It is a structural diagram of an electronic device according to an exemplary embodiment of the present application.

[0080] The above drawings illustrate specific embodiments of the present application, which will be described in more detail below. These drawings and the textual description are not intended to limit the scope of the present application in any way, but rather to illustrate the concepts of the present application to those skilled in the art by reference to specific embodiments. DETAILED DESCRIPTION

[0081] Exemplary embodiments will be described in detail herein, with examples illustrated in the accompanying drawings. In the following description, when referring to the drawings, identical numerals in different figures represent identical or similar elements, unless otherwise indicated. The embodiments described in the following exemplary embodiments are not intended to represent all embodiments consistent with the present application. Rather, they are merely examples of apparatus and methods consistent with certain aspects of the present application, as detailed in the appended claims.

[0082] In order to solve the above problems, the embodiments provided in this application have the following specific technical concepts:

[0083] Data Collection and Windowing: This step collects broadband measurement data from the power system within a preset time period and decomposes this data into multiple broadband measurement windows of equal length in the time domain, forming a broadband measurement window data sequence. This step aims to segment the continuous data stream into easily processable chunks while retaining sufficient information to reflect the dynamic changes in the power system.

[0084] Signal Decomposition and Matrix Generation: For each wideband measurement window, the frequency-divided signals at each preset frequency are extracted to generate a wideband measurement data signal decomposition matrix. Each row of this matrix represents the frequency-divided signals of a wideband measurement window at each preset frequency, and each column represents the frequency-divided signals of different wideband measurement windows at the same preset frequency. This step simplifies complex signal processing and provides a foundation for subsequent feature extraction and analysis.

[0085] Eigenvalue matrix generation: Decompose the matrix of the broadband measurement data signal according to the broadband measurement data to determine the corresponding broadband measurement data eigenvalue matrix. Each eigenvalue in the eigenvalue matrix is ​​used to characterize the degree of signal fluctuation 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, and characteristic reference lines are generated by extracting characteristic signal points on the rising and falling edges of the signal, and the eigenvalues ​​are determined based on the characteristic reference lines. These two methods each have their own advantages and can be selected according to actual needs.

[0086] Signal fluctuation vector determination and status assessment: Based on the eigenvalue matrix of the broadband measurement data, the signal fluctuation vector of the 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 of the eigenvalue matrix and the eigenvalue matrix of the previous preset time period. By comparing the eigenvalues ​​in the signal fluctuation vector with the preset threshold or historical data, the operating status of the power system is evaluated. If all eigenvalues ​​are less than the preset threshold, the power system is judged to be in normal operating status; if there is an eigenvalue greater than the preset threshold, it is judged to be in an abnormal operating state. In addition, a specific formula can be used to calculate the overall fluctuation eigenvalue of the broadband measurement data to quantitatively evaluate the overall fluctuation of the power system.

[0087] 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:

[0088] S101 : Collect broadband measurement data of a power system within a current preset time period, and decompose the broadband measurement data into a plurality of broadband measurement window data in a time domain to form a broadband measurement window data sequence.

[0089] Specifically, high-precision sensors and data acquisition equipment can be used to collect broadband measurement data, such as voltage and current, from the power system in real time. Preset time periods can be set based on actual needs, such as hourly, minutely, secondly, or even shorter intervals. The collected broadband measurement data is divided into multiple windows of equal length in the time domain, each containing a certain number of data points. The window length can be determined based on the characteristics of the power system and analysis requirements, ensuring that each window contains sufficient information to reflect the dynamic changes in the power system. All window data is arranged in chronological order to form a broadband measurement window data sequence, facilitating subsequent processing and analysis.

[0090] S102 : For any wideband measurement window data in the wideband measurement window data sequence, extract frequency-divided signals at respective preset frequencies to generate a wideband measurement data signal decomposition matrix corresponding to the wideband measurement data.

[0091] In this step, for any wideband measurement window data in the wideband measurement window data sequence, the frequency-divided 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 represent 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 represent the frequency-divided signals of each wideband measurement window data in the wideband measurement window data sequence at the same preset frequency.

[0092] Specifically, based on the characteristics and analysis requirements of the power system, a set of frequencies are pre-set. These frequencies 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, fast Fourier transform or other spectrum analysis methods are used to extract the frequency-division signals at the preset frequencies. These frequency-division signals 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 frequencies 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 different frequency-division signals at different preset frequencies.

[0093] S103 : Determine a corresponding broadband measurement data eigenvalue matrix according to the broadband measurement data signal decomposition matrix.

[0094] In this step, the corresponding wideband measurement data eigenvalue matrix is ​​determined according to the wideband measurement data signal decomposition matrix. 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.

[0095] Optionally, statistical analysis can be performed on each column of the broadband measurement data signal decomposition matrix (i.e., the frequency-divided signals at the same preset frequency) to calculate their standard deviation, variance, or other eigenvalues ​​that reflect the degree of signal fluctuation. These eigenvalues ​​are used to quantify the degree of fluctuation in the frequency-divided signals. The calculated eigenvalues ​​are arranged by column 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 signals at different preset frequencies.

[0096] In one possible implementation, characteristic instantaneous energies of each to-be-processed frequency-divided signal in the broadband measurement data signal decomposition matrix may be determined. Then, a broadband measurement data eigenvalue matrix is ​​generated based on the characteristic instantaneous energies of each to-be-processed frequency-divided signal, where 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.

[0097] 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 more precisely expressed. As a quantitative indicator of signal fluctuation, characteristic instantaneous energy can reflect the signal's strength and rate of change at different moments, thereby providing richer information for power system stability analysis. Generating the broadband measurement data eigenvalue matrix based on the characteristic instantaneous energy of each frequency-divided signal to be processed ensures that each eigenvalue in the eigenvalue matrix is ​​closely related to the characteristic instantaneous energy of the corresponding frequency-divided signal. This generation method improves the accuracy and reliability of the eigenvalue matrix, making subsequent analysis results based on the eigenvalue matrix more reliable. Furthermore, because characteristic instantaneous energy can sensitively reflect changes in signal fluctuations, the eigenvalue matrix generated based on characteristic instantaneous energy is also sensitive to changes in the power system's operating state. When the power system's operating state undergoes slight changes, the eigenvalues ​​in the eigenvalue matrix will also change accordingly, enabling real-time monitoring and early warning of the power system's operating state. Generating the eigenvalue matrix by calculating characteristic instantaneous energy avoids the complex transformations and calculations required by traditional methods such as waveform analysis, simplifying the data processing process. This optimization not only improves data processing efficiency but also reduces computing resource consumption, making this approach more feasible and economical in practical applications. Furthermore, by analyzing the changing trends and patterns of the eigenvalue matrix, potential problems in the power system can be promptly identified and appropriate maintenance measures can be taken, thereby ensuring the safe and stable operation of the power system.

[0098] 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:

[0099] Using formula 1, and according to the broadband measurement data signal decomposition matrix, the first frequency-divided signal to be processed is 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:

[0100] 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 , where Formula 2 is:

[0101] in, is an imaginary unit;

[0102] Frequency-divided signal according to the first characteristic Determine the first frequency-divided signal to be processed The corresponding first characteristic instantaneous energy, wherein the first characteristic instantaneous energy is the first characteristic frequency division signal The maximum modulus at each moment.

[0103] 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.

[0104] Then, using Equation 2, the processed frequency-divided signal is combined with the transformed frequency-divided signal to generate a complex characteristic frequency-divided signal. This complex signal contains the amplitude and phase information of the original signal and is the basis for the subsequent calculation of the characteristic instantaneous energy.

[0105] 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 given moment and is an important indicator for evaluating the dynamic characteristics of power systems.

[0106] Through the above steps, the characteristic instantaneous energy of each processed frequency-divided signal in the broadband measurement data signal decomposition matrix can be effectively determined. This energy information is important for analyzing the frequency components of the power system, monitoring dynamic changes in the power grid, and preventing potential faults. 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.

[0107] In the above scheme, the characteristic instantaneous energy of each frequency-divided signal to be processed is calculated using Formulas 1 and 2. Formula 1 converts the original frequency-divided signal into the first transformed frequency-divided signal of the analytical signal, a step that is key to extracting the signal's instantaneous characteristics. Formula 2 combines the original signal and its transformed signal to construct a complex first characteristic frequency-divided signal, which contains the signal's amplitude and phase information. 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 accurately reflects the energy state of the signal at a specific moment, providing a data foundation for in-depth analysis of broadband measurement data from power systems. The transformation in Formula 1 preserves the signal's amplitude information and introduces phase information, thereby more comprehensively describing the signal's instantaneous characteristics. This transformation in Formula 1 enables conversion from the time domain to the frequency domain without losing signal information, improving signal processing accuracy and efficiency. Furthermore, by directly calculating the characteristic instantaneous energy, the complex transformation and calculation processes that may involve waveform analysis in traditional methods are avoided, simplifying the data processing process and improving processing efficiency.

[0108] Furthermore, characteristic instantaneous energy, as a quantitative indicator of signal fluctuations, can sensitively reflect the dynamic behavior of the power system at different moments. By precisely calculating the characteristic instantaneous energy of each frequency-divided signal to be processed, it is possible to more accurately capture the fluctuation characteristics of the power system at different frequencies and time scales, providing strong support for power system stability analysis and fault prediction.

[0109] 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.

[0110] Specifically, each row of the eigenvalue matrix of the broadband measurement data (i.e., the eigenvalues ​​of data from 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. The calculated signal fluctuation vector is then compared with preset thresholds or historical data to assess the operating status of the power system. For example, if an element in the signal fluctuation vector exceeds a certain threshold, it may indicate unstable or abnormal operation of the power system. Based on the evaluation results, appropriate measures can be taken to ensure the safe and stable operation of the power system.

[0111] In one possible implementation, the elements of the signal fluctuation vector can be the maximum eigenvalues ​​in each row of the eigenvalue matrix of the broadband measurement data. When the elements of the signal fluctuation vector are the maximum eigenvalues ​​in each row of the eigenvalue matrix of the broadband measurement data, the frequency-divided signal with the most significant fluctuations 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.

[0112] In another possible implementation, 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 broadband measurement data eigenvalue matrix and another broadband measurement data eigenvalue matrix, where the other broadband measurement data eigenvalue matrix corresponds to the broadband measurement data corresponding to the preset time period before the current preset time period. By selecting the eigenvalue corresponding to the element with the largest eigenvalue change rate in the corresponding row of the broadband measurement data eigenvalue matrix and the eigenvalue matrix corresponding to the previous preset time period as the element of the signal fluctuation vector, changes in the fluctuation characteristics of the power system between different time periods can be captured, thereby reflecting the dynamic behavior of the power system.

[0113] In this embodiment, broadband measurement data of the power system within a current preset time period is collected and decomposed into a plurality of broadband measurement window data in the time domain to form a broadband measurement window data sequence. Then, for any broadband measurement window data in the broadband measurement window data sequence, the frequency-divided signals at each preset frequency are extracted to generate a broadband measurement data signal decomposition matrix corresponding to the broadband measurement data. Then, a corresponding broadband measurement data eigenvalue matrix is ​​determined based on the broadband measurement data signal decomposition matrix. Then, a signal fluctuation vector of the broadband measurement data in the time domain is determined based on the broadband measurement data eigenvalue matrix. The operating status of the power system is determined based on the signal fluctuation vector, thereby determining whether the power system is in a normal operating state or has potential risks, thereby providing a strong guarantee for the safe operation of the power system.

[0114] 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:

[0115] S201 : Collect broadband measurement data of the 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.

[0116] S202 : For any wideband measurement window data in the wideband measurement window data sequence, extract the frequency-divided signals at each preset frequency to generate a wideband measurement data signal decomposition matrix corresponding to the wideband measurement data.

[0117] 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 broadband measurement data signal decomposition matrix at each acquisition time node of the corresponding broadband measurement window.

[0118] In this step, a characteristic signal point set is determined based on the frequency-divided signal measurement values ​​of the to-be-processed frequency-divided signal in the broadband measurement data signal decomposition matrix at each acquisition time node of the corresponding broadband measurement window, where the to-be-processed frequency-divided signal is any element in the broadband measurement data signal decomposition matrix.

[0119] Specifically, the frequency-divided signal to be processed can be extracted from the broadband measurement data signal decomposition matrix. The signal decomposition matrix contains multiple frequency-divided signals collected by the power system within a broadband 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 within the broadband 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 gathered 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.

[0120] S204: Determine a first characteristic signal point set and a second characteristic signal point set according to the characteristic signal point set.

[0121] 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.

[0122] Specifically, within the set of characteristic signal points, each original signal point (i.e., the measured value of the frequency-divided signal at each acquisition time point) is sequentially compared with the magnitude of its preceding and succeeding original signal points. Specifically, if the measured value of the frequency-divided signal at a particular original signal point is greater than the measured values ​​of the preceding and succeeding original signal points, the original signal point is considered a local maximum point and added to the first set of characteristic signal points. This first set of characteristic signal points now contains all local maximum points, which reflect significant rising edges of the frequency-divided signal to be processed in the time series.

[0123] 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.

[0124] Specifically, the method for determining the first characteristic signal point set is similar to that used for the first characteristic signal point set, but in this step, local minimum points are sought. That is, if the measured value of the frequency-divided signal of a particular original signal point is less than the measured values ​​of the previous and next original signal points, the original signal point is considered 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 significant falling edges in the time series of the frequency-divided signal to be processed.

[0125] 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.

[0126] Specifically, for the first set of characteristic signal points, a first characteristic reference line passing through all first characteristic signal points can be generated through linear interpolation or other suitable fitting methods. This line can be considered an upper envelope of the frequency-divided signal to be processed in the time series. Similarly, for the second set of characteristic signal points, a second characteristic reference line passing through all second characteristic signal points is generated as a lower envelope.

[0127] S206 : Determine 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.

[0128] Specifically, the eigenvalue corresponding to the frequency-divided signal to be processed in the eigenvalue matrix of the broadband measurement data can be determined based on the first and second characteristic reference lines. This eigenvalue can be defined as a metric between the upper and lower envelopes, such as the average distance, maximum distance, or some 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 serves as an important basis for subsequent power system status analysis or fault diagnosis.

[0129] In one possible implementation, the eigenvalue corresponding to the frequency-divided signal to be processed in the broadband measurement data eigenvalue matrix is ​​determined based on 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, the point on the upper envelope and the point on the lower envelope are found, and the difference between these two points is calculated. The maximum difference value is found among all the time points. This maximum difference value 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 broadband measurement data eigenvalue matrix.

[0130] In another possible implementation, the eigenvalue corresponding to the frequency-divided signal to be processed in the eigenvalue matrix of the broadband measurement data is determined based on 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 can be calculated, which can be achieved by a numerical integration method, such as using the trapezoidal method or the Simpson method. Specifically, the time axis is divided into multiple 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 can also be recorded as an eigenvalue in the eigenvalue matrix of the broadband measurement data.

[0131] In practical applications, the two possible implementations described above can be used, depending on specific needs, as either the maximum difference or the enclosed area. If the maximum instantaneous fluctuation of the signal is of primary concern, the maximum difference can be used; if the overall energy distribution or fluctuation intensity of the signal is of primary concern, the enclosed area can be used. Furthermore, the two methods can be combined, with their combined value used as the eigenvalue, to more comprehensively describe the characteristics of the frequency-divided signal being processed.

[0132] 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.

[0133] 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 broadband measurement data eigenvalue matrix and another broadband measurement data eigenvalue matrix, where the other broadband measurement data eigenvalue matrix corresponds to the broadband measurement data corresponding to the preset time period before the current preset time period. By selecting the eigenvalue corresponding to the element with the largest eigenvalue change rate in the corresponding row of the broadband measurement data eigenvalue matrix and the eigenvalue matrix corresponding to the previous preset time period as an element of the signal fluctuation vector, changes in the fluctuation characteristics of the power system between different time periods can be captured, thereby reflecting the dynamic behavior of the power system.

[0134] In one possible implementation, if each eigenvalue in the signal fluctuation vector is smaller than the preset characteristic threshold of the corresponding frequency-divided signal, 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 the preset characteristic threshold of the corresponding frequency-divided signal, the operating state of the power system in the current preset time period is determined to be an abnormal operating state.

[0135] Specifically, if each eigenvalue in the signal fluctuation vector is less than the preset characteristic threshold of the corresponding frequency-divided signal, the power system is determined to be operating normally during the current preset period. This indicates that the power system's signal fluctuations during this period are within a normal range and the system is stable. If any eigenvalue in the signal fluctuation vector is greater than the preset characteristic threshold of the corresponding frequency-divided signal, the power system is determined to be operating abnormally during the current preset period. This indicates that the power system has experienced abnormal signal fluctuations during this period and may require further inspection or maintenance.

[0136] 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 from the previous preset period, a signal fluctuation vector that reflects the power system signal fluctuation is constructed. The eigenvalues ​​in the signal fluctuation vector can then be compared with a 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 operating normally; if any eigenvalue is greater than the preset characteristic threshold, the power system is judged to be operating abnormally.

[0137] 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:

[0138] 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.

[0139] 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.

[0140] Specifically, to more comprehensively assess the power system's fluctuations within the current preset time period, Formula 3 can be used to calculate the overall fluctuation characteristic value of the broadband measurement data. This calculated overall fluctuation characteristic value can further quantify the degree of power system fluctuations within the current preset time period. If the overall fluctuation characteristic value exceeds a certain set threshold, it can also be determined that the power system is operating abnormally.

[0141] 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.

[0142] 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, the following steps are further included:

[0143] Initially, within a preset monitoring period, similarities between signal fluctuation vectors in two adjacent preset time periods are determined to form a similarity sequence, where the preset monitoring period includes a plurality of preset time periods;

[0144] 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;

[0145] 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.

[0146] Specifically, within a preset monitoring cycle, which includes multiple preset time periods, 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 and the inverse of the Euclidean distance. 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:

[0147] If each similarity in the similarity sequence is greater than the preset similarity threshold:

[0148] In this case, we determined the abnormal operating condition to be a persistent oscillation fault. A persistent oscillation fault typically indicates a stable oscillation source in the power system, such as a device failure or load imbalance. This fault can cause parameters like voltage and current in the power system to fluctuate continuously for an extended period of time, posing a threat to the system's stable operation. Therefore, operations and maintenance personnel should promptly inspect and repair the power equipment to eliminate the oscillation source and restore stable system operation.

[0149] If the similarities in the similarity sequence are less than the preset similarity threshold:

[0150] In this case, we determined that the abnormal operating state was a random interference fault. Random interference faults are typically caused by external random factors, such as lightning strikes, electromagnetic pulses, and human error. These interference factors can cause the power system's signal fluctuations to fluctuate dramatically in a short period of time, and the change pattern is random. Therefore, operations and maintenance personnel should investigate external interference factors in the power system, such as the integrity of lightning protection facilities and electromagnetic shielding measures, and any human error, and take appropriate measures to reduce or eliminate the impact of these interference factors.

[0151] In the above scheme, by calculating the similarity between the signal fluctuation vectors of two adjacent preset time periods, the changing trend of the power system operating state 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 has continuity or trend, and is likely 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.

[0152] Based on the analysis results of the similarity sequence, the type of abnormal condition can be determined, guiding operations and maintenance personnel to take targeted maintenance measures. For persistent oscillation faults, operations and maintenance personnel can focus on inspecting the power system's electrical equipment, such as generators and transformers, for faults or abnormal operation, and perform timely inspections and maintenance. For random interference faults, attention should be paid to external interference factors in the power system, such as weather conditions and the electromagnetic environment, and appropriate preventive measures should be taken to reduce or eliminate the impact of interference on the power system.

[0153] Furthermore, by promptly detecting and addressing abnormal power system conditions, further expansion and deterioration of faults can be effectively prevented, thereby improving the stability and reliability of the power system. In particular, persistent oscillation faults, if not promptly addressed, can cause long-term fluctuations in power system parameters such as voltage and current, damaging equipment and even leading to further failures. The aforementioned solution enables the timely detection and location of such faults, providing operators with ample time and evidence for inspection and maintenance, thereby ensuring the safe and stable operation of the power system.

[0154] As can be seen, the above solution can enhance the power system's self-healing capabilities. When an abnormal condition is detected, the system can automatically determine the fault type and trigger the corresponding emergency response mechanism, such as switching to a backup power source and adjusting load distribution, to reduce the fault's impact on the system. At the same time, operations and maintenance personnel can quickly locate and address the fault based on the fault information and analysis results provided by the system, thereby shortening fault recovery time and improving the power system's self-healing capabilities.

[0155] 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:

[0156] The acquisition module 310 is configured to acquire broadband measurement data of the 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;

[0157] An extraction module 320 is configured to extract, for any wideband measurement window data in the wideband measurement window data sequence, frequency-divided signals at respective preset frequencies 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 represents a frequency-divided signal 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 represents a frequency-divided signal at the same preset frequency for respective wideband measurement window data in the wideband measurement window data sequence.

[0158] a processing module 330 configured to determine a corresponding wideband measurement data eigenvalue matrix based on the wideband measurement data signal decomposition matrix, wherein each eigenvalue in the wideband measurement data eigenvalue matrix is ​​used to represent a signal fluctuation degree of a corresponding frequency-divided signal in the wideband measurement data signal decomposition matrix;

[0159] The determination module 340 is configured 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.

[0160] Optionally, each element of the signal fluctuation vector is the maximum eigenvalue in each row of the broadband measurement data eigenvalue matrix; or,

[0161] 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 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 the previous preset time period of the current preset time period.

[0162] Optionally, the processing module 330 is specifically configured to:

[0163] Determining the characteristic instantaneous energy of each frequency-divided signal to be processed in the broadband measurement data signal decomposition matrix;

[0164] 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.

[0165] Optionally, the processing module 330 is specifically configured to:

[0166] 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:

[0167] Using formula 2, and according to the first frequency-divided signal to be processed and the first converted frequency-divided signal Determine the first characteristic frequency division signal , wherein the formula 2 is:

[0168] in, is an imaginary unit;

[0169] The frequency-divided signal is divided according to the first characteristic Determine the first frequency-divided signal to be processed The first characteristic instantaneous energy corresponding to the first characteristic instantaneous energy is the first characteristic frequency division signal The maximum modulus at each moment.

[0170] Optionally, the processing module 330 is specifically configured to:

[0171] determining a characteristic signal point set according to frequency-divided signal measurement values ​​of a to-be-processed frequency-divided signal in the broadband measurement data signal decomposition matrix at each acquisition time node corresponding to the broadband measurement window, wherein the to-be-processed frequency-divided signal is any element in the broadband measurement data signal decomposition matrix;

[0172] Determining a first characteristic signal point set based on the characteristic signal point set, where 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;

[0173] Determining a second characteristic signal point set based on the characteristic signal point set, where 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;

[0174] 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;

[0175] The eigenvalue corresponding to the frequency-divided signal to be processed in the broadband measurement data eigenvalue matrix is ​​determined according to the first characteristic reference line and the second characteristic reference line.

[0176] Optionally, the processing module 330 is specifically configured to:

[0177] Determine the eigenvalue corresponding to the frequency-divided signal to be processed in the broadband measurement data eigenvalue matrix according to the maximum difference between the first characteristic reference line and the second characteristic reference line; or

[0178] The eigenvalue corresponding to the frequency-divided signal to be processed in the broadband measurement data eigenvalue matrix is ​​determined according to the area enclosed by the first characteristic reference line and the second characteristic reference line.

[0179] Optionally, the determining module 340 is specifically configured to:

[0180] If each characteristic value in the signal fluctuation vector is less 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,

[0181] 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:

[0182] 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.

[0183] Figure 4 FIG. 1 is a schematic diagram of the structure of an electronic device according to an exemplary embodiment of the present application. Figure 4 As shown, this embodiment provides an electronic device 400 including: a processor 401 and a memory 402; wherein:

[0184] The memory 402 is used to store computer programs. The memory may also be a flash memory.

[0185] The processor 401 is configured 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.

[0186] Optionally, the memory 402 may be independent or integrated with the processor 401 .

[0187] When the memory 402 is a device independent of the processor 401, the electronic device 400 may further include:

[0188] The bus 403 is used to connect the memory 402 and the processor 401 .

[0189] 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 various aforementioned embodiments.

[0190] This embodiment further provides a program product, which includes a computer program stored in a readable storage medium. At least one processor of an electronic device can read the computer program from the readable storage medium, and at least one processor can execute the computer program to cause the electronic device to implement the methods provided in the various embodiments described above.

[0191] Those skilled in the art will readily appreciate other embodiments of the present application after considering the specification and practicing the invention disclosed herein. This application is intended to cover any variations, uses, or adaptations of the present application that follow the general principles of the present application and include common knowledge or customary techniques in the art not disclosed herein. The description and examples are to be considered merely as exemplary, and the true scope and spirit of the present application are indicated by the claims.

[0192] It should be understood that the present application is not limited to the exact structure 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 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 broadband measurement data processing method of the 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 broadband measurement data processing method of the power system according to claim 4, wherein 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 the power system according to any one of claims 2-5, 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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