Method and device for identifying subsynchronous / supersynchronous oscillation signals of power system

Abstract

The invention discloses a method and a device for identifying sub-synchronous / super-synchronous oscillation signals of a power system, which are used for solving the technical problem that sub-synchronous and super-synchronous oscillation signals cannot be effectively monitored and accurately distinguished in the prior art. Collecting multi-source time sequence information of the power system, and constructing a fusion representation vector according to the multi-source time sequence information; performing spectral analysis and enhancement on the multi-source time sequence information to obtain an oscillation signal containing a sub / super-synchronous frequency band; on the basis of a spectral analysis result in the spectral analysis and enhancement process, identifying an oscillation modal parameter of the oscillation signal in the sub / super-synchronous frequency band, and evaluating an oscillation risk level on the basis of the oscillation modal parameter; performing fusion verification on the fusion representation vector according to multi-source time sequence information and oscillation modal parameters, and identifying a sub / super-synchronous oscillation region and a time sequence evolution path; and performing reverse verification on the oscillation modal parameter, and when the reverse verification is passed, outputting various types of identification information obtained in the previous steps as a multi-dimensional identification result of the oscillation signal.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of power system signal processing, and in particular to a power system sub / super-synchronous oscillation signal identification method, a power system sub / super-synchronous oscillation signal identification device, an electronic device and a storage medium. BACKGROUND

[0002] With the continuous expansion of the scale of the power system and the continuous improvement of the intelligence level of power equipment, the stability of the power system is facing increasingly severe challenges. Especially the dynamic phenomena such as sub-synchronous and super-synchronous oscillation. These oscillations not only cause the power system to operate inefficiently, but also may cause safety accidents such as damage and shutdown of power equipment. Therefore, accurately identifying and monitoring the oscillation signals in the power system, especially the sub-synchronous and super-synchronous frequency band oscillation signals, is crucial for the safe and stable operation of the power system.

[0003] At present, the oscillation signals in the power system are usually collected by devices such as synchronous phasor measurement units (PMU) and wave recorders. The synchronous phasor measurement unit can monitor the key parameters such as voltage and current in the power grid in real time. However, it can only capture low-frequency synchronous oscillation signals and cannot effectively monitor sub-synchronous (frequency lower than system fundamental frequency) and super-synchronous (frequency higher than system fundamental frequency) oscillation signals. Although the wave recorder can obtain high-frequency oscillation signals, its monitoring range is limited by factors such as sampling frequency and device accuracy.

[0004] In the extraction and identification of oscillation signals, current methods mostly rely on traditional frequency spectrum analysis techniques. Such as Fast Fourier Transform (FFT). However, these methods often have difficulty in accurately distinguishing sub-synchronous and super-synchronous oscillations when faced with spectral overlap or complex signals. SUMMARY

[0005] The present application provides a power system sub / super-synchronous oscillation signal identification method, a power system sub / super-synchronous oscillation signal identification device, an electronic device and a storage medium, which are used to solve or partially solve the technical problem that current related technologies cannot effectively monitor and accurately distinguish sub-synchronous and super-synchronous oscillation signals.

[0006] The present application provides a power system sub / super-synchronous oscillation signal identification method, which comprises:

[0007] Collecting multi-source time series information of the power system, and constructing a fusion representation vector according to the multi-source time series information;

[0008] Performing frequency spectrum analysis and enhancement on the multi-source time series information to obtain oscillation signals containing sub / super-synchronous frequency bands;

[0009] identifying, based on the spectrum analysis results obtained in the spectrum analysis and enhancement process, oscillation modal parameters of the oscillation signal in the subsynchronous / supersynchronous frequency band, and assessing the oscillation risk level based on the oscillation modal parameters;

[0010] Performing fusion verification on the fusion representation vector according to the multi-source time series information and the oscillation modal parameters to identify subsynchronous / supersynchronous oscillation regions and time series evolution paths;

[0011] The oscillation modal parameters are reversely checked, and when the reverse check passes, the oscillation modal parameters, the oscillation risk level, the subsynchronous / supersynchronous oscillation region and the timing evolution path are output as a multi-dimensional identification result of the oscillation signal.

[0012] Optionally, the multi-source time series information includes a recorded signal, a synchronized phasor signal, a protection event action signal, and a device status quantity; and constructing a fusion representation vector based on the multi-source time series information includes:

[0013] Using the timestamp of the synchronized phasor signal as a reference axis, time alignment and time reconstruction are performed on the recorded signal, the synchronized phasor signal, the protection event action signal, and the device status quantity to construct a plurality of multi-source data corresponding to different time periods;

[0014] For multi-source data in each time period, time series feature variables of the multi-source data are extracted, and time mapping is performed based on the time series feature variables to construct a fusion representation vector.

[0015] Optionally, the multi-source timing information includes a recorded signal; and performing spectrum analysis and enhancement on the multi-source timing information to obtain an oscillation signal including a subsynchronous / supersynchronous frequency band includes:

[0016] Filtering the recorded signal through a pre-built bandpass filter to obtain a sub-synchronous / super-synchronous frequency band;

[0017] Performing center frequency shift processing on the recorded signal, and mapping the center frequency of the sub- / super-synchronous frequency band to the zero frequency point to obtain a frequency-shifted recorded signal;

[0018] Resampling the frequency-shifted recorded signal, and performing spectrum analysis on the resampled signal by short-time Fourier transform;

[0019] The frequency shift recording signal is subjected to spectrum enhancement according to the spectrum analysis result to obtain the oscillation signal of the sub-synchronous / super-synchronous frequency band.

[0020] Optionally, the identifying, based on the spectrum analysis results obtained in the spectrum analysis and enhancement process, oscillation modal parameters of the oscillation signal in the subsynchronous / supersynchronous frequency band, and evaluating the oscillation risk level based on the oscillation modal parameters includes:

[0021] The frequency, amplitude, attenuation coefficient and initial phase angle of multimodal oscillation are considered simultaneously to construct a multimodal signal model;

[0022] constructing a loss function with the goal of minimizing the square error between the oscillation signal and the model signal output by the multimodal signal model;

[0023] Constructing the constraint conditions of the loss function based on amplitude constraints, frequency constraints and damping ratio constraints;

[0024] Obtaining a spectrum analysis result during the spectrum analysis and enhancement process, extracting the frequency of the spectrum analysis result as the initial frequency, and obtaining the frequency, amplitude, initial phase angle, and damping ratio of the multimodal oscillation by optimizing and solving the loss function under the constraints as the oscillation modal parameters of the oscillation signal in the subsynchronous / supersynchronous frequency band;

[0025] A comprehensive analysis is performed based on the frequency, amplitude, initial phase angle and damping ratio of the multi-modal oscillation to evaluate the oscillation risk level of the oscillation signal.

[0026] Optionally, the multi-source timing information includes a synchronized phasor signal; and performing fusion verification on the fused representation vector according to the multi-source timing information and the oscillation modal parameters to identify subsynchronous / supersynchronous oscillation regions and timing evolution paths includes:

[0027] Based on a preset sliding window length, the time correlation between the oscillation modal parameter and the synchronized phasor signal in different time windows is calculated in combination with window sliding;

[0028] Identifying the target time window when the temporal correlation exceeds a preset correlation threshold as the recording starting point of the sub / supersynchronous oscillation region and triggering the identification of the temporal evolution path;

[0029] Perform real-time conversion rate analysis on the first-order derivative of time correlation and determine the inflection point of the time series evolution path based on the conversion rate analysis results;

[0030] In the process of identifying the time series evolution path, a fusion verification analysis based on principal component analysis is performed on the fusion representation vector based on the oscillation modal parameters and the synchronized phasor signal to obtain a principal component contribution matrix and a principal component time series change matrix;

[0031] Performing contribution analysis on the principal component contribution matrix to determine the final subsynchronous / supersynchronous oscillation region;

[0032] The changing trend of the oscillation mode over time is tracked according to the principal component time series change matrix, and the final time series evolution path is determined according to the corresponding positions of the principal components at different times.

[0033] Optionally, performing a fusion verification analysis based on principal component analysis on the fusion representation vector based on the oscillation modal parameters and the synchronized phasor signal to obtain a principal component contribution matrix and a principal component time series change matrix includes:

[0034] Constructing a data matrix using the oscillation modal parameters and the synchronized phasor signals in a time series, and performing standardization processing on the data matrix to obtain a characteristic matrix;

[0035] Constructing a covariance matrix according to the characteristic matrix, and performing eigenvalue decomposition on the covariance matrix to obtain multiple eigenvalues ​​and eigenvectors corresponding to each eigenvalue;

[0036] Sorting the principal component contributions of the feature matrix according to the order of eigenvalues ​​from large to small, constructing a principal component contribution matrix, and constructing an eigenvector space matrix corresponding to the principal component contribution matrix based on each eigenvector;

[0037] The fusion representation vector is projected onto the eigenvector space matrix to obtain a principal component time series change matrix.

[0038] Optionally, the multi-source timing information includes a protection event action signal; the reverse verification of the oscillation modal parameters is performed, and when the reverse verification passes, the oscillation modal parameters, the oscillation risk level, the subsynchronous / supersynchronous oscillation region, and the timing evolution path are output as the multi-dimensional identification result of the oscillation signal, including:

[0039] Taking the triggering time of the protection event action signal as an anchor point, a time backtracking window is constructed based on the triggering time;

[0040] Calculating the power spectrum energy density of the oscillation modal parameters within the time lookback window;

[0041] When the power spectrum energy density is greater than the preset energy density threshold and the multimodal oscillation frequency is within the sub / supersynchronous frequency band, it is determined that the multimodal trajectory of the oscillation signal is associated with the protection action, and the oscillation modal parameters, the oscillation risk level, the sub / supersynchronous oscillation area and the timing evolution path are output as the multidimensional identification result of the oscillation signal.

[0042] The present invention also provides a device for identifying subsynchronous / supersynchronous oscillation signals in a power system, comprising:

[0043] The fusion representation vector construction unit is configured to collect multi-source time sequence information of a power system and construct a fusion representation vector according to the multi-source time sequence information.

[0044] The spectrum analysis and enhancement unit is configured to perform spectrum analysis and enhancement on the multi-source time sequence information to obtain an oscillation signal containing a sub- / ultra-synchronous frequency band.

[0045] The oscillation mode parameter identification unit is configured to identify an oscillation mode parameter of the oscillation signal in the sub- / ultra-synchronous frequency band based on a spectrum analysis result obtained in the spectrum analysis and enhancement process, and evaluate an oscillation risk level based on the oscillation mode parameter.

[0046] The fusion verification unit is configured to perform fusion verification on the fusion representation vector according to the multi-source time sequence information and the oscillation mode parameter, identify a sub- / ultra-synchronous oscillation region and a time sequence evolution path.

[0047] The reverse verification unit is configured to perform reverse verification on the oscillation mode parameter, and when the reverse verification passes, output the oscillation mode parameter, the oscillation risk level, the sub- / ultra-synchronous oscillation region and the time sequence evolution path as a multi-dimensional identification result of the oscillation signal.

[0048] The present application also provides an electronic device, the device comprising a processor and a memory:

[0049] The memory is configured to store program code and transmit the program code to the processor.

[0050] The processor is configured to execute the power system sub- / ultra-synchronous oscillation signal identification method according to the instructions in the program code.

[0051] The present application also provides a computer readable storage medium for storing program code, the program code being used to execute the power system sub- / ultra-synchronous oscillation signal identification method.

[0052] From the above technical solutions, the present application has the following advantages:

[0053] A method for identifying subsynchronous / supersynchronous oscillation signals in power systems is provided. First, multi-source time series information of the power system is collected and a fused representation vector is constructed based on this information. By organically integrating multiple monitored data sources, the identification of oscillation signals becomes more comprehensive and robust. Time alignment and fusion modeling between the data can also be achieved, providing a unified foundation for subsequent analysis. Spectral analysis and enhancement are then performed on the multi-source time series information to obtain oscillation signals containing subsynchronous / supersynchronous frequency bands. This spectral analysis and enhancement not only detects oscillation signals containing subsynchronous / supersynchronous frequency bands but also significantly improves the sensitivity of weak signal identification, facilitating the early detection of potential system instability. Based on the spectral analysis results obtained during the spectral analysis and enhancement process, the oscillation modal parameters of the oscillation signal within the subsynchronous / supersynchronous frequency bands are identified. The oscillation risk level is then assessed based on these modal parameters to assess the current potential system risk and enable timely response when high-risk situations arise. The fused representation vector is then verified based on multi-source time series information and oscillation modal parameters. This not only identifies sub- / super-synchronous oscillation regions and their temporal evolution paths, but also improves the accuracy and credibility of the identification results, reducing the risk of false positives and omissions. Finally, a reverse verification of the oscillation modal parameters is performed. If the reverse verification passes, the oscillation modal parameters, oscillation risk level, sub- / super-synchronous oscillation regions, and their temporal evolution paths are output as the multi-dimensional identification results of the oscillation signal, which has practical regulatory significance. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0055] Figure 1 A flowchart of a method for identifying subsynchronous / supersynchronous oscillation signals in a power system;

[0056] Figure 2 A schematic diagram of the overall process of a method for identifying subsynchronous / supersynchronous oscillation signals in a power system;

[0057] Figure 3 The structure block diagram of a device for identifying subsynchronous / supersynchronous oscillation signals in a power system. DETAILED DESCRIPTION

[0058] Embodiments of the present invention provide a method for identifying subsynchronous / supersynchronous oscillation signals in a power system, a device for identifying subsynchronous / supersynchronous oscillation signals in a power system, an electronic device, and a storage medium, which are used to solve or partially solve the technical problem that current related technologies cannot effectively monitor and accurately distinguish subsynchronous and supersynchronous oscillation signals.

[0059] In order to make the purpose, features, and advantages of the present invention more obvious and easy to understand, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the embodiments described below are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0060] In order to enable those skilled in the art to better understand the technical solutions provided by the embodiments of the present invention, some of the technical features involved in the solutions are briefly described first:

[0061] Subsynchronous / supersynchronous oscillation regions: These are terms used to describe the frequency range of oscillations in power systems, corresponding to oscillations below and above the synchronous frequency, respectively. These oscillations can affect power system stability, requiring special attention and precautions during system design and operation.

[0062] Temporal evolution path: The temporal evolution path is a multidimensional dynamic process in the identification of subsynchronous and supersynchronous oscillations in power systems. It not only includes amplitude changes but also reflects the temporal evolution of other oscillation modal characteristics (frequency, phase, damping ratio, etc.).

[0063] For example, oscillation signals in power systems are currently typically collected by devices such as synchronized phasor measurement units (PMUs) and oscilloscopes. Synchronized phasor measurement units (PMUs) can monitor key parameters such as voltage and current in the power grid in real time. However, they can only capture lower-frequency synchronous oscillation signals and are unable to effectively monitor subsynchronous (frequency below the system fundamental frequency) and supersynchronous (frequency above the system fundamental frequency) oscilloscopes. While oscilloscopes can capture high-frequency oscillation signals, their monitoring range is limited by factors such as sampling frequency and device accuracy.

[0064] Current methods for extracting and identifying oscillatory signals rely heavily on traditional spectrum analysis techniques, such as the Fast Fourier Transform (FFT). However, these methods often struggle to accurately distinguish between subsynchronous and supersynchronous oscillations when faced with overlapping spectra or complex signals.

[0065] Therefore, one of the core points of the embodiments of the present application is that, in view of the deficiencies of the prior art, a power system sub / super-synchronous oscillation signal accurate identification method based on multi-source data fusion is proposed. First, by organically integrating the monitored multi-type data sources, the identification of the oscillation signal is more comprehensive and robust, and the time alignment and fusion modeling between the data can also be achieved, providing a unified basis for subsequent analysis. Then, by using spectrum analysis and enhancement means, not only the oscillation signal containing the sub / super-synchronous frequency band can be obtained, but also the identification sensitivity to weak signals can be significantly improved, which is conducive to discovering potential system instability factors in the early stage. Then, based on the spectrum analysis results obtained in the spectrum analysis and enhancement process, the oscillation modal parameters of the oscillation signal in the sub / super-synchronous frequency band are identified by optimization, and the oscillation risk level is further evaluated based on the oscillation modal parameters, so as to evaluate the current risk situation of the system, so that the high-risk situation can be responded in time. Then, the multi-modal oscillation parameters and the multi-dimensional dynamic characteristics of the synchronous phasor signal are combined to realize fusion verification, identify the sub / super-synchronous oscillation region and time sequence evolution path, which can improve the accuracy and reliability of the identification result and reduce the risk of false alarm and missed alarm. Finally, combined with the protection action, the reverse verification based on the causal correlation analysis is carried out, and when the reverse verification passes, the oscillation modal parameters, the oscillation risk level, the sub / super-synchronous oscillation region and the time sequence evolution path are output as the multi-dimensional identification result of the oscillation signal, which has practical regulation significance.

[0066] Referring to Figure 1 , a step flowchart of a power system sub / super-synchronous oscillation signal identification method provided by the embodiments of the present application is shown, which can specifically include the following steps:

[0067] Step 101, collecting multi-source time sequence information of the power system, and constructing a fusion representation vector according to the multi-source time sequence information;

[0068] In actual application, various types of time sequence data from the wave recorder, the synchronous phasor measurement device, the protection action recording device and the equipment state data measurement device during the operation of the power system are collected.

[0069] Therefore, the multi-source time sequence information proposed in the embodiments of the present application can mainly include the wave recording signal from the wave recorder , the synchronous phasor signal from the synchronous phasor measurement device , the protection event action signal from the protection action recording , and the equipment state quantity from the equipment state measurement device .

[0070] After acquiring multi-source time series information, we can perform time alignment on this data to establish a unified time-scale data mapping relationship. We then construct a fused representation vector through feature extraction. This fused representation vector includes key variables such as voltage, current, frequency, phase angle, power, and interruption status.

[0071] In some embodiments, the multi-source time series information includes recorded signals, synchronized phasor signals, protection event action signals, and device status quantities. The implementation process of constructing a fusion representation vector based on the multi-source time series information may mainly include the following sub-steps S01 to S02:

[0072] Step S01: Using the timestamp of the synchronized phasor signal as a reference axis, time alignment and time reconstruction are performed on the recorded signal, synchronized phasor signal, protection event action signal, and device status quantity to construct multiple multi-source data corresponding to different time periods;

[0073] The timestamp of the synchronized phasor signal can be selected As a reference time axis, linear interpolation, polynomial interpolation or sliding window averaging are used to align and reconstruct other data sources. When performing time alignment and reconstruction, the synchronization error is less than the preset error threshold (such as ), the uniform resampling interval is set to , with window length Sliding builds cross-source data blocks for each time period (also understood as time slices), that is, multi-source data.

[0074] Step S02: For the multi-source data in each time period, extract the time series feature variables of the multi-source data, perform time mapping based on the time series feature variables, and construct a fusion representation vector.

[0075] Construct the time mapping function shown below for time mapping to obtain the fused representation vector:

[0076]

[0077] in, Represents a time-based mapping function For time series characteristic variables The fused representation vector obtained after time mapping processing; express Align from a low-sample-rate source to The reconstructed value of .

[0078] Therefore, for each multi-source data within the same time period, we can first extract the time series feature variables, and then build a fusion representation vector based on these time series feature variables according to the time mapping function.

[0079] Step 102: performing spectrum analysis and enhancement on the multi-source timing information to obtain an oscillation signal including a sub-synchronous / super-synchronous frequency band;

[0080] In this step, the spectrum analysis and enhancement of multi-source timing information are mainly realized to obtain an oscillation signal containing sub- / super-synchronous frequency bands.

[0081] In some embodiments, the implementation process of performing spectrum analysis and enhancement on multi-source timing information to obtain an oscillation signal containing a sub-synchronous / super-synchronous frequency band may mainly include the following sub-steps S11 to S14:

[0082] Step S11: filtering the recorded signal through a pre-built bandpass filter to obtain a sub-synchronous / super-synchronous frequency band;

[0083] You can select the frequency band As the sub / supersynchronous frequency band to be analyzed, construct a bandpass filter , to filter out unnecessary frequency bands and focus on sub / super synchronous frequency bands. is the lower frequency limit of the frequency band, is the upper frequency limit of this band. Bandpass filter Meet the following amplitude-frequency response characteristics:

[0084]

[0085] Step S12: performing center frequency shift processing on the recorded signal, and mapping the center frequency of the sub- / super-synchronous frequency band to the zero frequency point to obtain a frequency-shifted recorded signal;

[0086] By recording the signal sampling sequence Multiply it by the unit rotation factor to perform center frequency shift processing on the recorded signal, and move the sub- / super-synchronous frequency band to be analyzed to Center frequency Map to the zero frequency point to obtain the frequency-shifted recording signal As shown below:

[0087]

[0088] Where, is a unit imaginary number; the center frequency is .

[0089] Step S13: resampling the frequency-shifted recorded signal, and performing spectrum analysis on the resampled signal through short-time Fourier transform;

[0090] Frequency shift recording signal Resampling is performed and short-time Fourier transform is performed on the resampled signal to achieve spectrum analysis.

[0091] Furthermore, the steps of performing spectrum analysis based on short-time Fourier transform include:

[0092] First, the resampled signal is divided into multiple short time window segments;

[0093] Then, for each time window data Perform Fourier transform to obtain the time spectrum of each time window segment :

[0094]

[0095] in, is the window function; is the frequency; is the moment; Hanning window is selected as the window function of Fourier transform. The expression of the window function is:

[0096]

[0097] in, .

[0098] Finally, the time-frequency spectrum of each time window segment is synthesized to obtain a time-frequency diagram, from which the oscillation components in a specific frequency band are extracted to obtain the frequency result. The frequency results obtained by short-time Fourier transform spectrum analysis are , which can be used as the initial frequency for subsequent recognition operations .

[0099] Step S14: performing spectrum enhancement on the frequency-shifted recorded signal according to the spectrum analysis result to obtain an oscillation signal in a sub-synchronous / super-synchronous frequency band.

[0100] Based on the spectrum analysis results of the short-time Fourier transform, the frequency-shifted recorded signal is spectrally enhanced to enhance the oscillation components in the sub- / super-synchronous frequency bands and obtain an oscillation signal in the sub- / super-synchronous frequency bands. The spectrum enhancement process mainly includes weighting the low-energy portion of the spectrum to increase the discernibility of the oscillation signal.

[0101] Step 103: identifying oscillation modal parameters of the oscillation signal in the subsynchronous / supersynchronous frequency band based on the spectrum analysis results obtained in the spectrum analysis and enhancement process, and evaluating the oscillation risk level based on the oscillation modal parameters;

[0102] This step mainly realizes the identification of the oscillation modal parameters of the oscillation signal in the sub- / super-synchronous frequency band based on the spectrum analysis results obtained in the spectrum analysis and enhancement process, and the assessment of the oscillation risk level based on the oscillation modal parameters.

[0103] In some embodiments, the implementation process of identifying the oscillation modal parameters of the oscillation signal in the subsynchronous / supersynchronous frequency band based on the spectrum analysis results and evaluating the oscillation risk level based on the oscillation modal parameters may mainly include the following sub-steps S21 to S25:

[0104] Step S21: Considering the frequency, amplitude, attenuation coefficient and initial phase angle of the multimodal oscillation, a multimodal signal model is constructed;

[0105] Constructed multimodal signal model for:

[0106]

[0107] in, is the number of modes; For the The amplitude of each mode; For the The attenuation coefficient of each mode; For the The frequency of each mode; For the The initial phase angle of each mode (reflecting the phase trajectory).

[0108] Step S22: constructing a loss function with the goal of minimizing the square error between the oscillation signal and the model signal output by the multimodal signal model;

[0109] The loss function is constructed to minimize the difference between the observed signal (i.e., the oscillation signal) and the model signal (i.e., the model The square error between the model signal output by the model. The loss function is defined as:

[0110]

[0111] in, To observe the signal; is the model signal, Represents the parameter combination to be optimized.

[0112] Step S23: constructing the constraint conditions of the loss function based on the amplitude constraint, frequency constraint and damping ratio constraint;

[0113] The constraints of the constructed loss function are:

[0114]

[0115] Step S24: Obtaining the spectrum analysis results during the spectrum analysis and enhancement process, extracting the frequency of the spectrum analysis results as the initial frequency, and optimizing and solving the loss function under the constraint conditions to obtain the frequency, amplitude, initial phase angle, and damping ratio of the multimodal oscillation as the oscillation modal parameters of the oscillation signal in the subsynchronous / supersynchronous frequency band;

[0116] Under the constraints, the amplitude of the multimodal oscillation can be obtained by optimizing the loss function. ,frequency , damping ratio (reflecting the damping characteristics) and the initial phase angle .

[0117] Specifically, the optimization process of the loss function is:

[0118] First, the frequency results of the spectrum analysis as the initial value for optimization.

[0119] Then the initial value of the frequency Substitute into the loss function and use the one-dimensional optimization method to obtain the attenuation coefficient The initial value of .

[0120] Then the amplitude is obtained according to the linear least squares solution The initial value of .

[0121] Finally, the amplitude of each modal oscillation is solved by cyclic minimization ,frequency , damping ratio and initial phase angle .in .

[0122] Step S25: Perform a comprehensive analysis based on the frequency, amplitude, initial phase angle, and damping ratio of the multi-modal oscillation to evaluate the oscillation risk level of the oscillation signal.

[0123] A comprehensive analysis of multi-modal oscillation parameters, such as frequency, amplitude, initial phase angle, and damping ratio, assesses the oscillation risk level. Oscillations are categorized as high-risk, medium-risk, or low-risk based on their fluctuations. High-risk oscillations are characterized by large amplitudes and frequencies close to the grid frequency or critical equipment frequencies.

[0124] Step 104: performing fusion verification on the fusion representation vector according to the multi-source time series information and the oscillation modal parameters to identify subsynchronous / supersynchronous oscillation regions and time series evolution paths;

[0125] As discussed previously, multi-source timing information includes synchronized phasor signals. This step primarily integrates the previously obtained modal identification results (i.e., oscillation modal parameters) with the dynamic characteristics of the synchronized phasor signals, such as frequency drift, phase angle perturbations, and power fluctuations, to verify the fused representation vector. This allows the identification of possible subsynchronous / supersynchronous oscillation regions and their temporal evolution paths.

[0126] In some embodiments, the implementation process of performing fusion verification on the fused representation vector based on multi-source timing information and oscillation modal parameters to identify subsynchronous / supersynchronous oscillation regions and timing evolution paths may mainly include the following sub-steps S31 to S36:

[0127] Step S31: Based on a preset sliding window length, the time correlation between the oscillation modal parameters and the synchronized phasor signal in different time windows is calculated in combination with the window sliding;

[0128] Specifically, the sliding window length and overlap rate can be pre-set. In each window, the frequency trajectory of each modal frequency of the oscillation modal parameter and the frequency change rate reflecting the frequency drift in the synchronized phasor signal are calculated. , phase angle velocity reflecting phase angle disturbance , power fluctuations that reflect power fluctuations Time correlation :

[0129]

[0130] Where, express and relevance; express and relevance; express and relevance; 、 Both are weight factors, which are used to adjust the impact of the corresponding items on the overall results.

[0131] Step S32: identifying the target time window when the time correlation exceeds the preset correlation threshold as the recording starting point of the subsynchronous / supersynchronous oscillation region, and triggering the identification of the temporal evolution path;

[0132] by Exceeding the set threshold is the criterion for triggering the identification of oscillation areas and evolution paths. When the set threshold is exceeded, the identification oscillation region begins and the evolution path identification is triggered.

[0133] Step S33: performing a conversion rate analysis on the first-order derivative of the time correlation in real time, and determining the evolution inflection point of the time series evolution path based on the conversion rate analysis result;

[0134] Real-time versus time correlation The first-order derivative of is used for conversion rate analysis. , then it is determined that the oscillation path has an evolutionary inflection point. Among them, is a pre-set threshold.

[0135] In practical applications, the threshold can also be adjusted based on the system's oscillation intensity, frequency fluctuation, etc. Perform dynamic adjustments. When the system fluctuates dramatically (e.g., with large frequency or power change rates), the threshold can be appropriately increased to prevent misjudgment of inflection points due to inherent signal fluctuations. When the system is stable, the threshold can be appropriately lowered to enhance sensitivity to subtle changes and improve the sensitivity of identifying evolutionary inflection points.

[0136] Step S34: During the time series evolution path identification process, a principal component analysis-based fusion verification analysis is performed on the fusion representation vector based on the oscillation modal parameters and the synchronized phasor signal to obtain a principal component contribution matrix and a principal component time series change matrix;

[0137] Furthermore, based on the oscillation modal parameters and the synchronized phasor signals, a fusion verification analysis based on principal component analysis is performed on the fusion representation vector to obtain a principal component contribution matrix and a principal component time series change matrix. This can be achieved by executing the following sub-steps S341 to S344:

[0138] Step S341: constructing a data matrix from the oscillation modal parameters and the synchronized phasor signals in time series, and performing standardization on the data matrix to obtain a characteristic matrix;

[0139] The modal frequency trajectory of the oscillation modal parameters ( 、 、 ) and the multidimensional characteristics of synchronized phasor signals ( 、 、 ) forms a data matrix according to the time series. Each column represents a feature and each row represents a time point. After normalization, the feature matrix .

[0140] Step S342: constructing a covariance matrix based on the characteristic matrix, and performing eigenvalue decomposition on the covariance matrix to obtain multiple eigenvalues ​​and eigenvectors corresponding to each eigenvalue;

[0141] According to the feature matrix Calculate the covariance matrix Among them, the covariance matrix represents the variance and covariance between each pair of features, and its calculation formula is:

[0142]

[0143] Here, n represents the number of data points.

[0144] Then the covariance matrix Perform eigenvalue decomposition to obtain multiple eigenvalues and their corresponding eigenvectors . Eigenvalue Indicates the contribution of each principal component to the variability of the data. The larger the eigenvalue, the greater the variance contained in the principal component and the greater the contribution. Represents the direction of the principal component and defines the projection of the original features in the fusion representation vector into the new space.

[0145] Step S343: sorting the principal component contributions of the feature matrix according to the order of eigenvalues ​​from large to small, constructing a principal component contribution matrix, and constructing an eigenvector space matrix corresponding to the principal component contribution matrix based on each eigenvector;

[0146] According to the characteristic value The contribution of the principal components is sorted by the size of the principal components to obtain the principal component contribution matrix. The contribution of the principal component is determined by its corresponding eigenvalue Determine the eigenvalue The larger the value, the greater the contribution. Then based on each eigenvector Construct the eigenvector space matrix corresponding to the principal component contribution matrix .

[0147] In addition, in order to reduce the amount of calculation and speed up the calculation, the principal component with greater contribution can be selected according to actual needs. According to the selected principal component, the eigenvector (and the corresponding principal component with greater contribution) are combined into an eigenvector space matrix .

[0148] Step S344: Project the fusion representation vector to the eigenvector space matrix to obtain the principal component time series change matrix.

[0149] Project the fusion representation vector to the eigenvector space matrix (corresponding to the selected principal component space) to obtain the principal component time series change matrix:

[0150]

[0151] in, Indicates the The principal components at time The projection value reflects the performance of the oscillation signal in the direction of the principal component at this moment.

[0152] Step S35: performing contribution analysis on the principal component contribution matrix to determine the final subsynchronous / supersynchronous oscillation region;

[0153] By analyzing the contribution of the principal components, the final subsynchronous / supersynchronous oscillation regions are identified. Among them, the larger contribution of the principal components indicates the region with larger oscillation amplitude and greater disturbance of the system.

[0154] Step S36: Track the changing trend of the oscillation mode over time according to the principal component time series change matrix, and determine the final time series evolution path according to the corresponding positions of the principal components at different times.

[0155] According to the time series changes of the principal components , track the changing trend of the oscillation pattern over time, and identify the final temporal evolution path based on the corresponding positions of the principal components at different times.

[0156] Step 105 , reversely verify the oscillation modal parameters. When the reverse verification passes, the oscillation modal parameters, the oscillation risk level, the subsynchronous / supersynchronous oscillation region, and the timing evolution path are output as the multi-dimensional recognition result of the oscillation signal.

[0157] In conjunction with the previous discussion, multi-source timing information also includes protection event action signals. In this step, using the trigger time of the protection event action signal as an anchor point, the oscillation modal parameter identification results are reverse-checked within a window before the event trigger to determine whether the multimodal trajectory of the oscillation signal is causally related to the protection action trigger event. If a correlation is determined, a multi-dimensional identification result is output, including the oscillation modal parameters, the timing evolution path (reflecting the time-varying trajectory), the oscillation risk level, and the subsynchronous / supersynchronous oscillation regions that may be triggered.

[0158] In some embodiments, a reverse check is performed on the oscillation modal parameters. When the reverse check passes, the oscillation modal parameters, oscillation risk level, subsynchronous / supersynchronous oscillation region, and timing evolution path are output as the multi-dimensional identification result of the oscillation signal. The implementation process can mainly include the following sub-steps S41 to S43:

[0159] Step S41: Taking the triggering time of the protection event action signal as an anchor point, a time backtracking window is constructed based on the triggering time;

[0160] Assume that the triggering time of the protection event action signal is , the lookback window is .

[0161] Step S42: Calculating the power spectrum energy density of the oscillation modal parameters within the time lookback window;

[0162] The power spectrum energy density of the identified modalities in the time period is calculated by the following formula :

[0163]

[0164] Step S43: When the power spectrum energy density is greater than the preset energy density threshold and the multi-modal oscillation frequency is in the sub- / super-synchronous frequency band, it is determined that the multi-modal trajectory of the oscillation signal is associated with the protection action, and the oscillation modal parameters, the oscillation risk level, the sub- / super-synchronous oscillation region and the time sequence evolution path are output as the multi-dimensional identification result of the oscillation signal.

[0165] If and the corresponding , the multi-modal trajectory of the oscillation signal is considered as the modality associated with the protection action, and the final result output step is entered.

[0166] If the multi-modal trajectory of the oscillation signal has an energy overlap region near the time , the composite energy function is defined as:

[0167]

[0168] Wherein, is the modal weight function, which is related to the consistency degree of the synchronous phasor signal feature.

[0169] Finally, when the reverse verification passes, the oscillation modal parameters, the oscillation risk level, the sub- / super-synchronous oscillation region and the time sequence evolution path are output as the multi-dimensional identification result of the oscillation signal.

[0170] In an embodiment of the present invention, a method for accurately identifying subsynchronous / supersynchronous oscillation signals in power systems based on multi-source data fusion is proposed. First, by organically integrating multiple monitored data sources, oscillation signal identification is made more comprehensive and robust. Time alignment and fusion modeling between data can also be achieved, providing a unified foundation for subsequent analysis. Spectral analysis and enhancement are then employed to not only obtain oscillation signals encompassing subsynchronous / supersynchronous frequency bands but also significantly improve the sensitivity of weak signal identification, facilitating the early detection of potential system instability. Based on the spectral analysis results obtained during the spectral analysis and enhancement process, the oscillation modal parameters of the oscillation signal within the subsynchronous / supersynchronous frequency bands are optimized and solved. The oscillation risk level is further assessed based on the oscillation modal parameters to assess the current potential system risk, enabling timely response when high-risk situations arise. Fusion verification is then performed by combining the multimodal oscillation parameters with the multidimensional dynamic characteristics of synchronized phasor signals to identify subsynchronous / supersynchronous oscillation regions and their temporal evolution paths. This improves the accuracy and reliability of the identification results and reduces the risk of false positives or omissions. Finally, a reverse check based on causal correlation analysis is performed in combination with the protection action. When the reverse check passes, the oscillation modal parameters, oscillation risk level, sub / supersynchronous oscillation area and time sequence evolution path are output as the multi-dimensional identification result of the oscillation signal, which has practical control significance.

[0171] For better explanation, refer to Figure 2 , which shows a schematic diagram of the overall process of a method for identifying subsynchronous / supersynchronous oscillation signals in a power system according to an embodiment of the present invention. It should be noted that this embodiment only briefly describes the general process of identifying subsynchronous / supersynchronous oscillation signals in a power system. The specific implementation of each step can be understood by referring to the relevant content in the aforementioned embodiments and will not be described here in detail. It should be understood that the present invention is not limited to this.

[0172] Step 201: Collect multi-source time series information of the power system, perform time alignment and time reconstruction on the multi-source time series information, and then construct a fusion representation vector through feature extraction and time mapping;

[0173] Step 202: performing spectrum analysis and enhancement on the multi-source time series information based on center frequency shift processing and short-time Fourier transform to obtain an oscillation signal including a sub- / super-synchronous frequency band;

[0174] Step 203: Based on the spectrum analysis results obtained in the spectrum analysis and enhancement process, identify the oscillation modal parameters of the oscillation signal in the subsynchronous / supersynchronous frequency band through optimization, and evaluate the oscillation risk level based on the oscillation modal parameters;

[0175] Step 204: performing a principal component analysis-based fusion verification analysis on the fusion representation vector according to the multi-source time series information and oscillation modal parameters to identify subsynchronous / supersynchronous oscillation regions and time series evolution paths;

[0176] Step 205: performing reverse verification on the oscillation modal parameters based on protection action correlation analysis;

[0177] Step 206: When the reverse check passes, the oscillation modal parameters, oscillation risk level, subsynchronous / supersynchronous oscillation region and time sequence evolution path are output as the multi-dimensional identification result of the oscillation signal.

[0178] Reference Figure 3 , shows a structural block diagram of a device for identifying subsynchronous / supersynchronous oscillation signals in a power system according to an embodiment of the present invention, which may specifically include:

[0179] A fusion representation vector construction unit 301 is configured to collect multi-source time series information of the power system and construct a fusion representation vector based on the multi-source time series information;

[0180] The spectrum analysis and enhancement unit 302 is configured to perform spectrum analysis and enhancement on the multi-source timing information to obtain an oscillation signal including a sub- / super-synchronous frequency band;

[0181] an oscillation modal parameter identification unit 303 for identifying oscillation modal parameters of the oscillation signal in the subsynchronous / supersynchronous frequency band based on the spectrum analysis results obtained in the spectrum analysis and enhancement process, and evaluating the oscillation risk level based on the oscillation modal parameters;

[0182] A fusion verification unit 304 is configured to perform fusion verification on the fusion representation vector according to the multi-source time series information and the oscillation modal parameters, and identify subsynchronous / supersynchronous oscillation regions and time series evolution paths;

[0183] The reverse verification unit 305 is used to perform reverse verification on the oscillation modal parameters. When the reverse verification passes, the oscillation modal parameters, the oscillation risk level, the subsynchronous / supersynchronous oscillation area and the timing evolution path are output as the multi-dimensional identification result of the oscillation signal.

[0184] In an optional embodiment, the multi-source time series information includes a recording signal, a synchronized phasor signal, a protection event action signal, and a device state quantity; and the fusion representation vector construction unit 301 includes:

[0185] a time alignment and reconstruction unit, configured to perform time alignment and time reconstruction on the recorded signal, the synchronized phasor signal, the protection event action signal, and the device status quantity using the timestamp of the synchronized phasor signal as a reference axis, and construct a plurality of multi-source data corresponding to different time periods;

[0186] a time mapping unit configured to extract a time sequence characteristic variable of the multi-source data in each time period, and perform time mapping based on the time sequence characteristic variable to construct a fusion representation vector.

[0187] In an optional embodiment, the multi-source time sequence information comprises a recorded wave signal; and the spectrum analysis and enhancement unit 302 comprises:

[0188] a filtering processing unit configured to perform filtering processing on the recorded wave signal by using a pre-constructed band-pass filter to obtain a sub- / super-synchronous frequency band;

[0189] a center frequency shift processing unit configured to perform center frequency shift processing on the recorded wave signal, and map a center frequency of the sub- / super-synchronous frequency band to a zero frequency point to obtain a frequency-shifted recorded wave signal;

[0190] a spectrum analysis unit configured to perform resampling on the frequency-shifted recorded wave signal, and perform spectrum analysis on the resampled signal by using a short-time Fourier transform;

[0191] a spectrum enhancement unit configured to perform spectrum enhancement on the frequency-shifted recorded wave signal according to the spectrum analysis result to obtain an oscillation signal in the sub- / super-synchronous frequency band.

[0192] In an optional embodiment, the oscillation modal parameter identification unit 303 comprises:

[0193] a multi-modal signal model construction unit configured to construct a multi-modal signal model by simultaneously considering frequency, amplitude, damping coefficient and initial phase angle of multi-modal oscillation;

[0194] a loss function construction unit configured to construct a loss function by taking minimizing a square error between the oscillation signal and a model signal output by the multi-modal signal model as an objective;

[0195] a constraint condition construction unit configured to construct a constraint condition of the loss function based on amplitude constraint, frequency constraint and damping ratio constraint;

[0196] an optimization solving unit configured to obtain a spectrum analysis result in a spectrum analysis and enhancement process, extract a frequency of the spectrum analysis result as an initial frequency, and obtain frequency, amplitude, initial phase angle and damping ratio of multi-modal oscillation as oscillation modal parameters of the oscillation signal in the sub- / super-synchronous frequency band by optimizing and solving the loss function under the constraint condition;

[0197] an oscillation risk level evaluation unit configured to comprehensively analyze the frequency, amplitude, initial phase angle and damping ratio of the multi-modal oscillation to evaluate an oscillation risk level of the oscillation signal.

[0198] In an optional embodiment, the multi-source timing information includes synchronized phasor signals; and the fusion verification unit 304 includes:

[0199] a time correlation calculation unit, configured to calculate the time correlation between the oscillation modal parameter and the synchronized phasor signal in different time windows based on a preset sliding window length and in combination with window sliding;

[0200] A timing path identification trigger unit is used to identify the target time window when the time correlation exceeds a preset correlation threshold as the recording starting point of the subsynchronous / supersynchronous oscillation region, and trigger the identification of the timing evolution path;

[0201] A conversion rate analysis unit is used to perform conversion rate analysis on the first-order derivative of the time correlation in real time, and to determine the evolution inflection point of the time series evolution path based on the conversion rate analysis result;

[0202] a fusion verification analysis unit, configured to perform a fusion verification analysis based on principal component analysis on the fusion representation vector based on the oscillation modal parameters and the synchronized phasor signal during the time series evolution path identification process to obtain a principal component contribution matrix and a principal component time series change matrix;

[0203] A contribution analysis unit, configured to perform contribution analysis on the principal component contribution matrix to determine a final subsynchronous / supersynchronous oscillation region;

[0204] The time series evolution path determination unit is used to track the change trend of the oscillation mode over time according to the principal component time series change matrix, and determine the final time series evolution path according to the corresponding positions of the principal components at different times.

[0205] In an optional embodiment, the fusion verification analysis unit includes:

[0206] a characteristic matrix construction unit, configured to construct a data matrix from the oscillation modal parameters and the synchronized phasor signals in a time series, and perform standardization processing on the data matrix to obtain a characteristic matrix;

[0207] an eigenvalue decomposition unit, configured to construct a covariance matrix according to the characteristic matrix, and perform eigenvalue decomposition on the covariance matrix to obtain a plurality of eigenvalues ​​and an eigenvector corresponding to each of the eigenvalues;

[0208] a principal component contribution ranking unit, configured to sort the characteristic matrix by principal component contribution according to the order of eigenvalues ​​from large to small, construct a principal component contribution matrix, and construct an eigenvector space matrix corresponding to the principal component contribution matrix based on each eigenvector;

[0209] The projection unit is used to project the fusion representation vector to the eigenvector space matrix to obtain the principal component time series change matrix.

[0210] In an optional embodiment, the multi-source timing information includes a protection event action signal; and the reverse check unit 305 includes:

[0211] A time backtracking window construction unit, configured to take the triggering time of the protection event action signal as an anchor point and construct a time backtracking window based on the triggering time;

[0212] A power spectrum energy density calculation unit, configured to calculate the power spectrum energy density of the oscillation modal parameters within the time lookback window;

[0213] An identification result output unit is used to determine that the multimodal trajectory of the oscillation signal is associated with the protection action when the power spectrum energy density is greater than a preset energy density threshold and the multimodal oscillation frequency is within the sub / supersynchronous frequency band, and output the oscillation modal parameters, the oscillation risk level, the sub / supersynchronous oscillation area and the timing evolution path as the multidimensional identification result of the oscillation signal.

[0214] As for the device embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the aforementioned method embodiment.

[0215] An embodiment of the present invention further provides an electronic device, the device including a processor and a memory:

[0216] The memory is used to store program codes and transmit the program codes to the processor;

[0217] The processor is configured to execute the method for identifying subsynchronous / supersynchronous oscillation signals of a power system according to any embodiment of the present invention according to the instructions in the program code.

[0218] An embodiment of the present invention further provides a computer-readable storage medium for storing program code, and the program code is used to execute the method for identifying subsynchronous / supersynchronous oscillation signals in a power system according to any embodiment of the present invention.

[0219] Those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0220] In several embodiments provided by the present application, it should be understood that the disclosed system, device and method can be implemented in other manners. For example, the described device embodiments are merely schematic. For example, the division of the units is only a logical function division. There can be another division manner for the actual implementation, for example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the displayed or discussed mutual couplings or direct couplings or communication connections between different units, can be indirect couplings or communication connections through some interfaces, devices or units, and can be in electrical, mechanical or other forms.

[0221] The units described as separate components can or can not be physically separate, and the components shown as units can or can not be physical units, i.e., can be located in one place, or can be distributed on multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the embodiment.

[0222] In addition, each functional unit in each embodiment of the present application can be integrated into a processing unit, or each unit can be physically present separately, or two or more units can be integrated into one unit. The integrated unit can be realized in the form of hardware or in the form of a software functional unit.

[0223] The integrated unit, if realized in the form of a software functional unit and sold or used as an independent product, can be stored in a computer readable storage medium. Based on this understanding, the technical solutions of the present application essentially or the part of the prior art that contributes to the technical solutions or all or part of the technical solutions can be embodied in the form of a software product. The computer software product is stored in a storage medium, and includes a plurality of instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes: a U disk, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, and various media that can store program codes.

[0224] The above embodiments are merely used to illustrate the technical solutions of the present application, rather than limit them. Although the present application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the foregoing embodiments, or make equivalent replacements to some technical features. Such modifications or replacements do not cause the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A method for identifying subsynchronous / supersynchronous oscillation signals in a power system, characterized in that: include: Collecting multi-source time series information of the power system and constructing a fusion representation vector based on the multi-source time series information; Performing spectrum analysis and enhancement on the multi-source timing information to obtain an oscillation signal including a sub- / super-synchronous frequency band; identifying, based on the spectrum analysis results obtained in the spectrum analysis and enhancement process, oscillation modal parameters of the oscillation signal in the subsynchronous / supersynchronous frequency band, and assessing the oscillation risk level based on the oscillation modal parameters; Performing fusion verification on the fusion representation vector according to the multi-source time series information and the oscillation modal parameters to identify subsynchronous / supersynchronous oscillation regions and time series evolution paths; The oscillation modal parameters are reversely checked, and when the reverse check passes, the oscillation modal parameters, the oscillation risk level, the subsynchronous / supersynchronous oscillation region and the timing evolution path are output as a multi-dimensional identification result of the oscillation signal.

2. The method for identifying subsynchronous / supersynchronous oscillation signals in a power system according to claim 1, wherein: The multi-source time series information includes a recording signal, a synchronized phasor signal, a protection event action signal, and a device state quantity; and constructing a fusion representation vector based on the multi-source time series information includes: Using the timestamp of the synchronized phasor signal as a reference axis, time alignment and time reconstruction are performed on the recorded signal, the synchronized phasor signal, the protection event action signal, and the device status quantity to construct a plurality of multi-source data corresponding to different time periods; For multi-source data in each time period, time series feature variables of the multi-source data are extracted, and time mapping is performed based on the time series feature variables to construct a fusion representation vector.

3. The method for identifying subsynchronous / supersynchronous oscillation signals in a power system according to claim 1, wherein: The multi-source timing information includes a recorded signal; and performing spectrum analysis and enhancement on the multi-source timing information to obtain an oscillation signal including a sub- / super-synchronous frequency band includes: Filtering the recorded signal through a pre-built bandpass filter to obtain a sub-synchronous / super-synchronous frequency band; Performing center frequency shift processing on the recorded signal, and mapping the center frequency of the sub- / super-synchronous frequency band to the zero frequency point to obtain a frequency-shifted recorded signal; Resampling the frequency-shifted recorded signal, and performing spectrum analysis on the resampled signal by short-time Fourier transform; The frequency shift recording signal is subjected to spectrum enhancement according to the spectrum analysis result to obtain the oscillation signal of the sub-synchronous / super-synchronous frequency band.

4. The method for identifying subsynchronous / supersynchronous oscillation signals in a power system according to claim 1, wherein: The step of identifying the oscillation modal parameters of the oscillation signal in the subsynchronous / supersynchronous frequency band based on the spectrum analysis results obtained in the spectrum analysis and enhancement process, and evaluating the oscillation risk level based on the oscillation modal parameters, includes: The frequency, amplitude, attenuation coefficient and initial phase angle of multimodal oscillation are considered simultaneously to construct a multimodal signal model; constructing a loss function with the goal of minimizing the square error between the oscillation signal and the model signal output by the multimodal signal model; Constructing the constraint conditions of the loss function based on amplitude constraints, frequency constraints and damping ratio constraints; Obtaining a spectrum analysis result during the spectrum analysis and enhancement process, extracting the frequency of the spectrum analysis result as the initial frequency, and obtaining the frequency, amplitude, initial phase angle, and damping ratio of the multimodal oscillation by optimizing and solving the loss function under the constraints as the oscillation modal parameters of the oscillation signal in the subsynchronous / supersynchronous frequency band; A comprehensive analysis is performed based on the frequency, amplitude, initial phase angle and damping ratio of the multi-modal oscillation to evaluate the oscillation risk level of the oscillation signal.

5. The method for identifying subsynchronous / supersynchronous oscillation signals in a power system according to claim 1, wherein: The multi-source timing information includes a synchronized phasor signal; and performing fusion verification on the fused representation vector according to the multi-source timing information and the oscillation modal parameters to identify subsynchronous / supersynchronous oscillation regions and timing evolution paths includes: Based on a preset sliding window length, the time correlation between the oscillation modal parameter and the synchronized phasor signal in different time windows is calculated in combination with window sliding; Identifying the target time window when the temporal correlation exceeds a preset correlation threshold as the recording starting point of the sub / supersynchronous oscillation region and triggering the identification of the temporal evolution path; Perform real-time conversion rate analysis on the first-order derivative of time correlation and determine the inflection point of the time series evolution path based on the conversion rate analysis results; In the process of identifying the time series evolution path, a fusion verification analysis based on principal component analysis is performed on the fusion representation vector based on the oscillation modal parameters and the synchronized phasor signal to obtain a principal component contribution matrix and a principal component time series change matrix; Performing contribution analysis on the principal component contribution matrix to determine the final subsynchronous / supersynchronous oscillation region; The changing trend of the oscillation mode over time is tracked according to the principal component time series change matrix, and the final time series evolution path is determined according to the corresponding positions of the principal components at different times.

6. The method for identifying subsynchronous / supersynchronous oscillation signals in a power system according to claim 5, characterized in that: The performing of a fusion verification analysis based on principal component analysis on the fusion representation vector based on the oscillation modal parameters and the synchronized phasor signal to obtain a principal component contribution matrix and a principal component time series change matrix includes: Constructing a data matrix using the oscillation modal parameters and the synchronized phasor signals in a time series, and performing standardization processing on the data matrix to obtain a characteristic matrix; Constructing a covariance matrix according to the characteristic matrix, and performing eigenvalue decomposition on the covariance matrix to obtain a plurality of eigenvalues ​​and eigenvectors corresponding to each of the eigenvalues; Sorting the principal component contributions of the feature matrix according to the order of eigenvalues ​​from large to small, constructing a principal component contribution matrix, and constructing an eigenvector space matrix corresponding to the principal component contribution matrix based on each eigenvector; The fusion representation vector is projected onto the eigenvector space matrix to obtain a principal component time series change matrix.

7. The method for identifying subsynchronous / supersynchronous oscillation signals in a power system according to claim 1, wherein: The multi-source timing information includes a protection event action signal; the reverse verification of the oscillation modal parameters is performed, and when the reverse verification passes, the oscillation modal parameters, the oscillation risk level, the subsynchronous / supersynchronous oscillation region, and the timing evolution path are output as the multi-dimensional identification result of the oscillation signal, including: Taking the triggering time of the protection event action signal as an anchor point, a time backtracking window is constructed based on the triggering time; Calculating the power spectrum energy density of the oscillation modal parameters within the time lookback window; When the power spectrum energy density is greater than the preset energy density threshold and the multimodal oscillation frequency is within the sub / supersynchronous frequency band, it is determined that the multimodal trajectory of the oscillation signal is associated with the protection action, and the oscillation modal parameters, the oscillation risk level, the sub / supersynchronous oscillation area and the timing evolution path are output as the multidimensional identification result of the oscillation signal.

8. A device for identifying subsynchronous / supersynchronous oscillation signals in a power system, characterized in that: include: a fusion representation vector construction unit, configured to collect multi-source time series information of the power system and construct a fusion representation vector based on the multi-source time series information; a spectrum analysis and enhancement unit, configured to perform spectrum analysis and enhancement on the multi-source time series information to obtain an oscillation signal containing a sub- / super-synchronous frequency band; an oscillation modal parameter identification unit, configured to identify oscillation modal parameters of the oscillation signal in the subsynchronous / supersynchronous frequency band based on the spectrum analysis results obtained in the spectrum analysis and enhancement process, and to assess an oscillation risk level based on the oscillation modal parameters; a fusion verification unit, configured to perform fusion verification on the fusion representation vector according to the multi-source time series information and the oscillation modal parameters, and identify subsynchronous / supersynchronous oscillation regions and time series evolution paths; A reverse verification unit is used to perform reverse verification on the oscillation modal parameters. When the reverse verification passes, the oscillation modal parameters, the oscillation risk level, the subsynchronous / supersynchronous oscillation area and the timing evolution path are output as the multi-dimensional identification result of the oscillation signal.

9. An electronic device, characterized in that: The device includes a processor and a memory: The memory is used to store program code and transmit the program code to the processor; The processor is configured to execute the method for identifying subsynchronous / supersynchronous oscillation signals in a power system according to any one of claims 1 to 7 according to instructions in the program code.

10. A computer-readable storage medium, characterized in that The computer-readable storage medium is used to store program codes, and the program codes are used to execute the method for identifying subsynchronous / supersynchronous oscillation signals in a power system according to any one of claims 1 to 7.

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