A method and system for screening subsynchronous oscillation curves

Abstract

The present invention provides a method and system for screening subsynchronous oscillation curves, the method comprising obtaining a sequence of amplitude data points to be determined of electromagnetic transient simulation phasors in a continuous number of time windows; outputting the category of the sequence of amplitude data points to be determined based on a pre-established classifier; using a time-frequency domain wavelet packet decomposition method to analyze the sequence of amplitude data points to be determined with oscillating behavior, and obtaining the frequency and amplitude of the interharmonic component to be screened; comparing the amplitude percentage of the interharmonic component to be screened with a predetermined amplitude threshold, and taking the interharmonic component to be screened after filtering out noise as the remaining interharmonic component; and determining the output subsynchronous oscillation curve according to the frequency and amplitude percentage of the remaining interharmonic component. The method and system can quickly classify oscillation data, filter out noise for data with oscillating behavior, and respond to dangerous oscillation behavior in a timely manner, thereby improving the efficiency and accuracy of screening subsynchronous frequency oscillation data.

Description

Technical Field

[0001] The present invention relates to the field of analysis of power system safety and stability, and more particularly, to a method and system for screening subsynchronous oscillation curves. Background Art

[0002] With the rapid development of new energy technologies, the construction of large-capacity centralized grid-connected wind power / photovoltaic bases, using ultra-high voltage or flexible direct current transmission, is the dominant form of large-scale consumption and utilization of new energy. In large-scale new energy gathering areas, a large number of power electronic equipment need to be installed for new energy grid connection. The fast control devices used in power electronic equipment require current phase locking, which will introduce sub / super synchronous harmonic components into the power signal during the process of sudden increase or decrease in power, thereby causing subsynchronous oscillation of the power system. Therefore, the screening of subsynchronous oscillation curves is crucial to the safe and stable operation of the power grid.

[0003] The existing technology mainly uses two methods to screen subsynchronous oscillation curves. One method is to perform frequency domain spectrum analysis on a large number of data points and identify them according to the frequency, amplitude and number of consecutive occurrences of the obtained interharmonic components. However, this technical solution requires continuous analysis for each oscillation curve, with a large overall amount of calculation and a long analysis time. There is still room for improvement in the rapidity of subsynchronous oscillation screening. Another method is to use prony analysis. The prony method uses a complex exponential decay linear combination to fit the mathematical model of equally spaced sampling data. After appropriate expansion, it can directly estimate the frequency, attenuation factor, amplitude and phase of a given signal. It can more comprehensively express the characteristics of transient signals than the traditional Fourier analysis method. It shows good application prospects in power system signal analysis, especially low-frequency oscillation analysis. The Prony algorithm can extract valuable information from uniformly sampled signals and decompose a series of exponentially decaying sinusoidal curves. However, the Prony method assumes that the model is a combination of a series of exponential functions with arbitrary amplitudes, phases, frequencies and attenuation factors, that is, it is composed of some decayed sine / cosine signal components. The algorithm requires the calculation of least squares fitting and high-order algebraic equations to find complex roots. When noise is embedded in the signal, the Prony solution process will fail due to ill-conditioned equations. For example, if the input data is too long or the estimated number of effective modal terms is too large, it will cause calculation errors or fitting failures. Summary of the invention

[0004] In order to solve the technical problems that the existing technology for screening subsynchronous oscillation curves has large computational complexity, long analysis time, and easy screening failure when noise is embedded in the signal, the present invention provides a method and device for screening subsynchronous oscillation curves.

[0005] According to one aspect of the present invention, the present invention provides a method for screening a subsynchronous oscillation curve, comprising:

[0006] According to the custom time window length, a sequence of amplitude data points to be determined of electromagnetic transient simulation phasors in a continuous number of time windows is obtained;

[0007] Inputting the sequence of amplitude data points to be determined into a pre-established classifier, and outputting the category of the sequence of amplitude data points to be determined, wherein the category of the sequence of amplitude data points to be determined includes having oscillation behavior and having no oscillation behavior;

[0008] The wavelet packet decomposition method in the time-frequency domain is used to analyze the amplitude data point sequence with oscillating behavior to be determined, and the frequency and amplitude of the interharmonic component to be screened are obtained;

[0009] Comparing the amplitude percentage of the interharmonic component to be screened with a predetermined amplitude threshold, and taking the interharmonic component to be screened after noise is filtered out as the remaining interharmonic component, wherein the amplitude percentage data refers to the percentage of the amplitude of the interharmonic component to the fundamental wave;

[0010] The output subsynchronous oscillation curve is determined according to the frequency and amplitude percentage of the remaining interharmonic components.

[0011] According to another aspect of the present invention, the present invention provides a system for screening subsynchronous oscillation curves, the system comprising:

[0012] A data acquisition module is used to obtain a sequence of amplitude data points to be determined of electromagnetic transient simulation phasors in a continuous number of time windows according to a custom time window length;

[0013] A data classification module, used for inputting the sequence of amplitude data points to be determined into a pre-established classifier, and outputting the category of the sequence of amplitude data points to be determined, wherein the category of the sequence of amplitude data points to be determined includes having oscillation behavior and having no oscillation behavior;

[0014] A data analysis module is used to analyze the amplitude data point sequence to be determined with oscillating behavior by using a time-frequency domain wavelet packet decomposition method to obtain the frequency and amplitude of the interharmonic component to be screened;

[0015] A noise filtering module, used for comparing the amplitude percentage of the interharmonic component to be screened with a predetermined amplitude threshold, and taking the interharmonic component to be screened after the noise is filtered out as the remaining interharmonic component, wherein the amplitude percentage data refers to the percentage of the amplitude of the interharmonic component to the fundamental wave;

[0016] The result output module is used to determine the output subsynchronous oscillation curve according to the frequency and amplitude percentage of the remaining interharmonic components.

[0017] According to another aspect of the present invention, the present invention provides a computer-readable storage medium, wherein the storage medium stores a computer program, and the computer program is used to execute the method described in any one of the above aspects of the present invention.

[0018] According to another aspect of the present invention, an electronic device is provided, comprising: a processor; a memory for storing instructions executable by the processor; the processor is configured to read the executable instructions from the memory and execute the instructions to implement the method described in any one of the above aspects of the present invention.

[0019] The method and system for screening subsynchronous oscillation curves described in the present invention include obtaining a sequence of amplitude data points to be determined of electromagnetic transient simulation phasors in a plurality of continuous time windows according to a custom time window length; inputting the sequence of amplitude data points to be determined into a pre-established classifier, and outputting the category of the sequence of amplitude data points to be determined; using a time-frequency domain wavelet packet decomposition method to analyze the sequence of amplitude data points to be determined with oscillating behavior, and obtaining the frequency and amplitude of the interharmonic component to be screened; comparing the amplitude percentage of the interharmonic component to be screened with a predetermined amplitude threshold, and using the interharmonic component to be screened after noise is filtered out as the remaining interharmonic component; and determining the output subsynchronous oscillation curve according to the frequency and amplitude percentage of the remaining interharmonic component. The method and system adopt a classifier obtained by training based on oscillation data, so that the oscillation data can be quickly divided into two categories: those with oscillation behavior and those without oscillation behavior. Moreover, for the data with oscillation behavior, noise is filtered out based on the amplitude of the acquired interharmonic component, and the method and system can respond in time to dangerous oscillation behaviors with sensitive frequencies, large energy accumulation, and fast oscillation divergence, thereby greatly improving the efficiency and accuracy of screening sub-frequency oscillation data. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] A more complete understanding of exemplary embodiments of the present invention may be obtained by referring to the following drawings:

[0021] Figure 1 A flowchart of a method for screening a subsynchronous oscillation curve according to a preferred embodiment of the present invention;

[0022] Figure 2 It is a structural schematic diagram of a system for screening subsynchronous oscillation curves according to a preferred embodiment of the present invention;

[0023] Figure 3 Schematic diagram of the structure of an electronic device according to a preferred embodiment of the present invention. DETAILED DESCRIPTION

[0024] Now, exemplary embodiments of the present invention are described with reference to the accompanying drawings. However, the present invention can be implemented in many different forms and is not limited to the embodiments described herein. These embodiments are provided to disclose the present invention in detail and completely and to fully convey the scope of the present invention to those skilled in the art. The terms used in the exemplary embodiments shown in the accompanying drawings are not intended to limit the present invention. In the accompanying drawings, the same units / elements are marked with the same reference numerals.

[0025] Unless otherwise specified, the terms (including technical terms) used herein have the commonly understood meanings to those skilled in the art. In addition, it is understood that the terms defined in commonly used dictionaries should be understood to have the same meanings as those in the context of the relevant fields, and should not be understood as idealized or overly formal meanings.

[0026] Exemplary Methods

[0027] Figure 1 Flow chart of a method for screening subsynchronous oscillation curves according to a preferred embodiment of the present invention. Figure 1 As shown, the method for screening subsynchronous oscillation curves described in this preferred embodiment starts from step 101.

[0028] In step 101, a sequence of amplitude data points to be determined of electromagnetic transient simulation phasors in a plurality of consecutive time windows is obtained according to a user-defined time window length.

[0029] There is no special restriction on the electromagnetic transient simulation phasor in this preferred embodiment, and both current phasor and voltage phasor are acceptable.

[0030] Preferably, according to the custom time window length, before obtaining the amplitude data point sequence of the electromagnetic transient simulation phasor of a plurality of consecutive time windows, it also includes establishing a classifier for classifying the amplitude data point sequence to be determined of the electromagnetic transient simulation phasor, specifically:

[0031] According to the custom time window length, obtain the historical amplitude data point sequence of the electromagnetic transient simulation phasor in several consecutive time windows;

[0032] According to the custom dimension number N, the discrete wavelet transform method is used to represent the historical amplitude data point sequence of the electromagnetic transient simulation phasor in each time window as the historical coordinate point in the N-dimensional space, wherein the number of decomposition layers of the discrete wavelet transform method used is equal to the custom dimension number N;

[0033] Using a preset coordinate point category criterion to determine the category of the historical coordinate point in the N-dimensional space, and assigning a corresponding category label according to the category, wherein the category of the historical coordinate point includes having an oscillating behavior and having no oscillating behavior;

[0034] The historical coordinate points in the N-dimensional space and their corresponding category labels are used as training data, and support vector machine training is used to generate the classifier.

[0035] In this preferred embodiment, when training the classifier, the time window length for collecting the historical amplitude data points of the electromagnetic transient simulation phasor is the same as the time window length for collecting the amplitude data points to be determined when the classifier is generated and used to classify the unknown oscillation data. The custom dimension number N is adjusted according to the complexity of the power grid, usually not less than 2, and the custom dimension number N is used as the decomposition layer number of the discrete wavelet transform method adopted.

[0036] The support vector machine is used to train the classifier. Its main principle is to select the kernel function, find the support vectors of the two categories of historical coordinate points in the N-dimensional space, and determine the interface between the historical coordinate points with oscillating behavior and the historical coordinate points without oscillating behavior based on the criterion of maximizing the sum of the distances from the support vectors of the two categories to the interface. By establishing a classifier, it is possible to quickly screen the amplitude data point sequence of the electromagnetic transient simulation phasor with oscillating behavior, thereby providing a better data source for further in-depth analysis of more dangerous oscillations.

[0037] Preferably, the discrete wavelet transform method is used to express the historical amplitude data point sequence of the electromagnetic transient simulation phasor in each time window as historical coordinate points in N-dimensional space according to the self-defined dimension number N, including:

[0038] The discrete wavelet transform algorithm is used to decompose and reconstruct the historical amplitude data point sequence of the electromagnetic transient simulation phasor in each window, and the wavelet decomposition coefficient d of the nth layer corresponding to the historical amplitude data point sequence of the electromagnetic transient simulation phasor in the i-th window is determined. in , where 1≤i≤I, I is the total number of time windows, 1≤n≤N;

[0039] According to the wavelet decomposition coefficient d in Generate the historical coordinate points (d i1 , d i2 , …d in ,…,d iN ).

[0040] In this preferred embodiment, assuming that the custom dimension number N is equal to 5, the discrete wavelet transform algorithm is used to decompose and reconstruct the historical amplitude data point sequence of the electromagnetic transient simulation phasor in the i-th window, and the corresponding historical coordinate point in the 5-dimensional space (d i1 ,d i2 , di3 , d i4 ,d i5 ).

[0041] Preferably, the category of the historical coordinate points in the N-dimensional space is determined by using a preset coordinate point category criterion, wherein the coordinate point category criterion is:

[0042] When the historical coordinate point in the N-dimensional space corresponding to the historical amplitude data point sequence of the electromagnetic transient simulation phasor in the i-th window is (d i1 , d i2 , …d in ,…,d iN ), if d ia With d ib When the absolute values ​​of the differences are all less than d0, the category of the historical coordinate point is determined to be without oscillation behavior, otherwise, the category of the historical coordinate point is with oscillation behavior, where d0 is a positive number less than 1, 1≤i≤I, 1≤a, b≤N, and a≠b.

[0043] In this preferred embodiment, according to research, when the oscillation of the curve of the electromagnetic transient simulation phasor in a time window is not obvious, the wavelet decomposition coefficients of each layer corresponding to the historical coordinate points in the corresponding N-dimensional space will be very close. Therefore, by determining that the wavelet decomposition coefficients of any two layers are less than a set constant, it can be relatively accurately determined whether the curve of the electromagnetic transient simulation phasor in a time window has oscillatory behavior.

[0044] Preferably, after determining the category of the historical coordinate points in the N-dimensional space by using a preset coordinate point category criterion, it also includes determining an amplitude threshold for filtering out noise in a window with oscillating behavior, specifically:

[0045] Using a time-frequency domain wavelet packet decomposition method to analyze a sequence of historical amplitude data points corresponding to historical coordinate points classified as having oscillatory behavior, obtain the amplitude of the corresponding historical interharmonic component, and calculate the amplitude percentage of the historical interharmonic component;

[0046] According to the set interharmonic component classification rule, the category of the historical interharmonic component is determined according to the amplitude percentage of the historical interharmonic component, and a corresponding category label is added, wherein the category of the historical interharmonic component includes a category requiring attention and a category not requiring attention;

[0047] According to the amplitude percentage and category label of the historical interharmonic component, by training a support vector machine learner, the dividing point of the amplitude percentage data of the class that needs attention and the class that does not need attention on the one-dimensional axis is obtained, and the dividing point is the determined amplitude threshold.

[0048] It should be noted that when a new oscillation mode appears, in order to make the training of the amplitude threshold more effective, the support vector machine learner should be updated in time when both the data points that need attention and those that do not need attention exceed a certain value.

[0049] Preferably, the interharmonic component classification rules include:

[0050] Define the category of historical interharmonic components with amplitude percentage lower than L% as the category of no concern;

[0051] Define the historical interharmonic components with amplitude percentage higher than M% as the category requiring attention, where L <M;

[0052] For the historical interharmonic components that do not meet the above two definitions, when their amplitude percentage is higher than K times the average amplitude percentage of all historical interharmonic components in the same time window, the category is the class that needs attention; otherwise, the category is the class that does not need attention, where K is a positive number.

[0053] Preferably, the wavelet mother function used in the discrete wavelet transform method or the time-frequency domain wavelet packet decomposition method is a third-order Daubechies series wavelet, which is expressed as follows:

[0054]

[0055] Where w is the amplitude point sequence of the input electromagnetic transient simulation phasor with oscillatory behavior, and H(w) is the amplitude and frequency of the interharmonic component obtained after hierarchical decomposition of the input.

[0056] In the prior art, there are many wavelet mother functions that can be used in the discrete wavelet transform method or the time-frequency domain wavelet packet decomposition method, such as Haar wavelet, Coiflet series wavelet, Symlets series wavelet, Morlets series wavelet and MexicanHat series wavelet. In this preferred implementation, the third-order Daubechies series wavelet is selected and further refined into the expression in the above preferred implementation. This is because the Daubechies series wavelet supports wavelet packet transform, has a low vanishing moment order, and has a high oscillation frequency feature recognition rate compared to other wavelets. The specific comparison results are shown in Table 1.

[0057] Table 1

[0058] Wavelet Function Haar Daubechies Coiflets Symlets Morlet Mexican Hat Wavelet Packet Transform Can Can Can Can Can't Can't Vanishing moment order 1 N 2N N - - Oscillation frequency recognition ratio 20% 95% 88% 91% 80% 76%

[0059] From the comparison in Table 1, it can be seen that the Daubechies series wavelet as a wavelet mother function has obvious advantages over other wavelets in terms of wavelet packet transform, vanishing moment divisor and oscillation frequency recognition ratio.

[0060] In step 102, the amplitude data point sequence to be determined is input into a pre-established classifier, and the category of the amplitude data point sequence to be determined is output, wherein the category of the amplitude data point sequence to be determined includes having oscillating behavior and not having oscillating behavior.

[0061] In step 103, a time-frequency domain wavelet packet decomposition method is used to analyze the sequence of amplitude data points to be determined with oscillating behavior to obtain the frequency and amplitude of the interharmonic component to be screened.

[0062] In step 104, the amplitude percentage of the interharmonic component to be screened is compared with a predetermined amplitude threshold, and the interharmonic component to be screened after noise is filtered out is used as the remaining interharmonic component, wherein the amplitude percentage data refers to the percentage of the amplitude of the interharmonic component to the fundamental wave.

[0063] Preferably, the step of comparing the amplitude percentage of the interharmonic component to be screened with a predetermined amplitude threshold and taking the interharmonic component to be screened after noise is filtered out as the remaining interharmonic component comprises:

[0064] Comparing the amplitude percentage of the interharmonic component to be screened with a predetermined amplitude threshold, and filtering out the interharmonic component to be screened whose amplitude percentage is less than the amplitude threshold as noise;

[0065] The remaining part of the interharmonic component to be screened after the noise is filtered out is used as the remaining interharmonic component.

[0066] In this preferred embodiment, by filtering out the interharmonic components with an amplitude percentage threshold in the interharmonic components to be screened as noise, the interharmonic components with a small amplitude percentage and harmless caused by noise can be excluded, and the interharmonic components with a large amplitude and a higher risk can be focused on, thereby improving the screening efficiency.

[0067] In step 105, the output subsynchronous oscillation curve is determined according to the frequency and amplitude percentage of the remaining interharmonic components.

[0068] Preferably, the step of determining the output subsynchronous oscillation curve according to the frequency and amplitude percentage of the remaining interharmonic components comprises:

[0069] Step 1, according to the frequency of each interharmonic component in the sensitive frequency segment and the remaining interharmonic components, determine the energy accumulation reference threshold and energy accumulation weight value of each interharmonic component, wherein the sensitive frequency segment is a frequency interval composed of the upper limit value and the lower limit value obtained by adding and subtracting the custom frequency step length respectively to the difference between the power frequency and the modal frequency of the motor group during electromagnetic transient simulation, energy accumulation refers to the time integral of the amplitude percentage of the interharmonic component, the first energy accumulation reference threshold of the interharmonic component whose frequency is located in the sensitive frequency segment is less than the second energy accumulation reference threshold of the interharmonic component whose frequency is not in the sensitive frequency segment, the first energy accumulation weight value of the interharmonic component whose frequency is located in the sensitive frequency segment is greater than the second energy accumulation weight value of the interharmonic component whose frequency is not in the sensitive frequency segment, and the sum of the first energy accumulation weight value and the second energy accumulation weight value is between the frequency step length and twice the frequency step length, and the first energy accumulation weight value is k times the second energy accumulation weight value, and k is a positive number not less than 3;

[0070] Step 2: Calculate the energy accumulation value of each interharmonic component in the jth time interval according to the set time interval, and calculate the energy accumulation weighted sum value of the jth time interval according to the energy accumulation weight value of each interharmonic component and the energy accumulation value in the jth time interval, where j is a positive integer and the initial value of j is 1;

[0071] Step 3: When the energy accumulation value of each interharmonic component in the jth time interval is less than its corresponding energy accumulation analysis threshold, and the energy accumulation weighted sum value in the jth time interval is less than the set energy accumulation weighted analysis threshold, go to step 4; otherwise, go to step 7, wherein the energy accumulation weighted analysis threshold is any positive integer between [5, 50], when j=1, the energy accumulation analysis threshold is equal to the energy accumulation reference threshold, and the energy accumulation value and energy accumulation weighted sum value of the j-1th time interval are equal to the energy accumulation value and energy accumulation weighted sum value of the j-1th time interval, respectively;

[0072] Step 4, calculating the j-th energy cumulative change rate of each interharmonic component according to the energy cumulative value of each interharmonic component in the j-th time interval and the j-1-th time interval, and calculating the j-th energy cumulative weighted change rate according to the energy cumulative weighted sum value in the j-th time interval and the j-1-th time interval;

[0073] Step 5: According to the set change rate gradient threshold mapping relationship table, determine the first gradient threshold interval corresponding to the j-th energy cumulative change rate of each interharmonic component, and the second gradient threshold interval corresponding to the j-th energy cumulative weighted change rate, wherein the first gradient threshold interval is the P1 gradient thresholds set from small to large for the energy cumulative change rate, and the two adjacent gradient thresholds form a gradient threshold interval to generate P1-1 value intervals arranged from small to large, and the second gradient threshold interval is the P2 gradient thresholds set from small to large for the energy cumulative weighted change rate, and the two adjacent gradient thresholds form a gradient threshold interval to generate P2-1 value intervals arranged from small to large;

[0074] Step 6. When the first gradient threshold interval where the j-th energy cumulative change rate is located is greater than the first gradient threshold interval where the j-1-th energy cumulative change rate is located, or when the second gradient threshold interval corresponding to the j-th energy cumulative weighted change rate is greater than the second gradient threshold interval corresponding to the j-1-th energy cumulative weighted change rate, according to the set gradient threshold energy cumulative threshold mapping relationship table, the energy cumulative threshold corresponding to the first gradient threshold interval where the j-1-th energy cumulative change rate is located is used as the energy cumulative analysis threshold for the j+1-th time interval; otherwise, the energy cumulative threshold corresponding to the first gradient threshold interval where the j-th energy cumulative change rate is located is used as the updated energy cumulative analysis threshold, let j=j+1, and go to step 2, wherein, when j=1, the first gradient threshold interval where the j-1-th energy cumulative change rate is located is equal to the first gradient threshold interval where the j-th energy cumulative change rate is located;

[0075] Step 7: Output the interharmonic components from the 1st time interval to the jth time interval as the screened sub-isochronous frequency oscillation curve.

[0076] In this preferred embodiment, when the modal frequency of the generator set is 20Hz, the complementary characteristic frequency on the power system side is 30Hz. Considering the error, the frequency step is set to 1.5Hz, and the sensitive frequency range is 28.5Hz to 31.5Hz. For the interharmonic components in the sensitive frequency range (28.5Hz to 31.5Hz), the first energy accumulation reference threshold is correspondingly reduced and set to 30, while the second energy accumulation threshold of the interharmonic components in the insensitive frequency range is higher than 30. An analysis threshold is set for the energy accumulation weighted sum value of each interharmonic component that exists simultaneously, wherein the first energy accumulation weight value of the interharmonic components in the sensitive frequency range is set to a1, and the second energy accumulation weight value of the interharmonic components in the insensitive frequency range is set to a2. Then, according to the assignment method of this preferred embodiment, a1 can be selected as 1.5 and a2 as 0.5, so that 1.5<a1+ a2<1.5*2 and a1=3a2 can be satisfied. The energy cumulative weighted sum value refers to the sum of the product of the first energy cumulative weight value and the energy cumulative value of the interharmonic component in the sensitive frequency range and the product of the second energy cumulative weight value and the energy cumulative value of the interharmonic component in the non-sensitive frequency range.

[0077] In this preferred embodiment, thresholds from small to large are set for the energy cumulative change rate and the energy cumulative weighted change rate in advance according to the gradient to form a gradient threshold interval, so that the energy cumulative change rate and the energy cumulative weighted change rate can form a corresponding relationship with the gradient threshold interval, thereby establishing a change rate gradient threshold mapping relationship table. During the screening process, it is found that any one of the energy cumulative change rate and the energy cumulative weighted change rate becomes larger, jumping from a small gradient threshold interval to a large gradient threshold interval, indicating that the oscillation divergence speed is fast. Then, according to the gradient threshold-energy cumulative threshold mapping relationship table established in advance, the energy cumulative analysis threshold can be reduced from the next energy accumulation measurement moment on the basis of the previous value, according to the energy cumulative threshold corresponding to the corresponding gradient threshold interval, thereby speeding up the analysis speed.

[0078] The method for screening subsynchronous oscillation curves described in the preferred embodiment uses a classifier obtained by training based on oscillation data, so that the oscillation data can be quickly divided into two categories: those with oscillation behavior and those without oscillation behavior. Moreover, for the data with oscillation behavior, noise is filtered out based on the amplitude of the acquired interharmonic component, and the dangerous oscillation behavior with sensitive frequency, large energy accumulation and fast oscillation divergence is responded to in time, thereby greatly improving the efficiency and accuracy of screening subsynchronous frequency oscillation data.

[0079] Exemplary Systems

[0080] Figure 2 FIG. 1 is a schematic diagram of the structure of a system for screening subsynchronous oscillation curves according to a preferred embodiment of the present invention. Figure 2 As shown, the system 200 for screening subsynchronous oscillation curves according to the preferred embodiment includes:

[0081] The data acquisition module 201 is used to obtain a sequence of amplitude data points to be determined of electromagnetic transient simulation phasors in a continuous number of time windows according to a custom time window length;

[0082] The data classification module 202 is used to input the amplitude data point sequence to be determined into a pre-established classifier, and output the category of the amplitude data point sequence to be determined, wherein the category of the amplitude data point sequence to be determined includes oscillating behavior and no oscillating behavior;

[0083] The data analysis module 203 is used to analyze the amplitude data point sequence to be determined with oscillating behavior by using the time-frequency domain wavelet packet decomposition method to obtain the frequency and amplitude of the interharmonic component to be screened;

[0084] The noise filtering module 204 is used to compare the amplitude percentage of the interharmonic component to be screened with a predetermined amplitude threshold, and use the interharmonic component to be screened after the noise is filtered as the remaining interharmonic component, wherein the amplitude percentage data refers to the percentage of the amplitude of the interharmonic component to the fundamental wave;

[0085] The result output module 205 is used to determine the output subsynchronous oscillation curve according to the frequency and amplitude percentage of the remaining interharmonic components.

[0086] Preferably, the system further comprises a classifier module, which is used to establish a classifier for classifying a sequence of amplitude data points to be determined of an electromagnetic transient simulation phasor, specifically:

[0087] According to the custom time window length, obtain the historical amplitude data point sequence of the electromagnetic transient simulation phasor in several consecutive time windows;

[0088] According to the custom dimension number N, the discrete wavelet transform method is used to represent the historical amplitude data point sequence of the electromagnetic transient simulation phasor in each time window as the historical coordinate point in the N-dimensional space, wherein the number of decomposition layers of the discrete wavelet transform method used is equal to the custom dimension number N;

[0089] Using a preset coordinate point category criterion to determine the category of the historical coordinate point in the N-dimensional space, and assigning a corresponding category label according to the category, wherein the category of the historical coordinate point includes having an oscillating behavior and having no oscillating behavior;

[0090] The historical coordinate points in the N-dimensional space and their corresponding category labels are used as training data, and support vector machine training is used to generate the classifier.

[0091] Preferably, the classifier module uses a discrete wavelet transform method to represent the historical amplitude data point sequence of the electromagnetic transient simulation phasor in each time window as historical coordinate points in N-dimensional space according to a custom dimension number N, including:

[0092] The discrete wavelet transform algorithm is used to decompose and reconstruct the historical amplitude data point sequence of the electromagnetic transient simulation phasor in each window, and the wavelet decomposition coefficient d of the nth layer corresponding to the historical amplitude data point sequence of the electromagnetic transient simulation phasor in the i-th window is determined. in , where 1≤i≤I, I is the total number of time windows, 1≤n≤N;

[0093] According to the wavelet decomposition coefficient d in Generate the historical coordinate points (d i1 , d i2 , …d in ,…,d iN ).

[0094] Preferably, the classifier module uses a preset coordinate point category criterion to determine the category of the historical coordinate point in the N-dimensional space, wherein the coordinate point category criterion is:

[0095] When the historical coordinate point in the N-dimensional space corresponding to the historical amplitude data point sequence of the electromagnetic transient simulation phasor in the i-th window is (d i1 , d i2 , …d in ,…,d iN ), if d ia With d ib When the absolute values ​​of the differences are all less than d0, the category of the historical coordinate point is determined to be without oscillation behavior, otherwise, the category of the historical coordinate point is with oscillation behavior, where d0 is a positive number less than 1, 1≤i≤I, 1≤a, b≤N, and a≠b.

[0096] Preferably, the system further comprises an amplitude threshold module, which determines an amplitude threshold for filtering out noise in a window with oscillatory behavior, specifically:

[0097] Using a time-frequency domain wavelet packet decomposition method to analyze a sequence of historical amplitude data points corresponding to historical coordinate points classified as having oscillatory behavior, obtain the amplitude of the corresponding historical interharmonic component, and calculate the amplitude percentage of the historical interharmonic component;

[0098] According to the set interharmonic component classification rule, the category of the historical interharmonic component is determined according to the amplitude percentage of the historical interharmonic component, and a corresponding category label is added, wherein the category of the historical interharmonic component includes a category requiring attention and a category not requiring attention;

[0099] According to the amplitude percentage and category label of the historical interharmonic component, by training a support vector machine learner, the dividing point of the amplitude percentage data of the class that needs attention and the class that does not need attention on the one-dimensional axis is obtained, and the dividing point is the determined amplitude threshold.

[0100] Preferably, the interharmonic component classification rules applied in the amplitude threshold module include:

[0101] Define the category of historical interharmonic components with amplitude percentage lower than L% as the category of no concern;

[0102] Define the historical interharmonic components with amplitude percentage higher than M% as the category requiring attention, where L <M;

[0103] For the historical interharmonic components that do not meet the above two definitions, when their amplitude percentage is higher than K times the average amplitude percentage of all historical interharmonic components in the same time window, the category is the class that needs attention; otherwise, the category is the class that does not need attention, where K is a positive number.

[0104] Preferably, the wavelet mother function used in the discrete wavelet transform method adopted in the classifier module or the time-frequency domain wavelet packet decomposition method adopted in the data analysis module and the amplitude threshold module is a third-order Daubechies series wavelet, which is expressed as follows:

[0105]

[0106] Where w is the amplitude point sequence of the input electromagnetic transient simulation phasor with oscillatory behavior, and H(w) is the amplitude and frequency of the interharmonic component obtained after hierarchical decomposition of the input.

[0107] Preferably, the noise filtering module 204 compares the amplitude percentage of the interharmonic component to be screened with a predetermined amplitude threshold, and uses the interharmonic component to be screened after the noise is filtered out as the remaining interharmonic component, including:

[0108] Comparing the amplitude percentage of the interharmonic component to be screened with a predetermined amplitude threshold, and filtering out the interharmonic component to be screened whose amplitude percentage is less than the amplitude threshold as noise;

[0109] The remaining part of the interharmonic component to be screened after the noise is filtered out is used as the remaining interharmonic component.

[0110] Preferably, the result output module 205 determines the output subsynchronous oscillation curve according to the frequency and amplitude percentage of the remaining interharmonic components, including:

[0111] Step 1, according to the frequency of each interharmonic component in the sensitive frequency segment and the remaining interharmonic components, determine the energy accumulation reference threshold and energy accumulation weight value of each interharmonic component, wherein the sensitive frequency segment is a frequency interval composed of the upper limit value and the lower limit value obtained by adding and subtracting the custom frequency step length respectively to the difference between the power frequency and the modal frequency of the motor group during electromagnetic transient simulation, energy accumulation refers to the time integral of the amplitude percentage of the interharmonic component, the first energy accumulation reference threshold of the interharmonic component whose frequency is located in the sensitive frequency segment is less than the second energy accumulation reference threshold of the interharmonic component whose frequency is not in the sensitive frequency segment, the first energy accumulation weight value of the interharmonic component whose frequency is located in the sensitive frequency segment is greater than the second energy accumulation weight value of the interharmonic component whose frequency is not in the sensitive frequency segment, and the sum of the first energy accumulation weight value and the second energy accumulation weight value is between the frequency step length and twice the frequency step length, and the first energy accumulation weight value is k times the second energy accumulation weight value, and k is a positive number not less than 3;

[0112] Step 2: Calculate the energy accumulation value of each interharmonic component in the jth time interval according to the set time interval, and calculate the energy accumulation weighted sum value of the jth time interval according to the energy accumulation weight value of each interharmonic component and the energy accumulation value in the jth time interval, where j is a positive integer and the initial value of j is 1;

[0113] Step 3: When the energy accumulation value of each interharmonic component in the jth time interval is less than its corresponding energy accumulation analysis threshold, and the energy accumulation weighted sum value in the jth time interval is less than the set energy accumulation weighted analysis threshold, go to step 4; otherwise, go to step 7, wherein the energy accumulation weighted analysis threshold is any positive integer between [5, 50], when j=1, the energy accumulation analysis threshold is equal to the energy accumulation reference threshold, and the energy accumulation value and energy accumulation weighted sum value of the j-1th time interval are equal to the energy accumulation value and energy accumulation weighted sum value of the j-1th time interval, respectively;

[0114] Step 4, calculating the j-th energy cumulative change rate of each interharmonic component according to the energy cumulative value of each interharmonic component in the j-th time interval and the j-1-th time interval, and calculating the j-th energy cumulative weighted change rate according to the energy cumulative weighted sum value in the j-th time interval and the j-1-th time interval;

[0115] Step 5: According to the set change rate gradient threshold mapping relationship table, determine the first gradient threshold interval corresponding to the j-th energy cumulative change rate of each interharmonic component, and the second gradient threshold interval corresponding to the j-th energy cumulative weighted change rate, wherein the first gradient threshold interval is the P1 gradient thresholds set from small to large for the energy cumulative change rate, and the two adjacent gradient thresholds form a gradient threshold interval to generate P1-1 value intervals arranged from small to large, and the second gradient threshold interval is the P2 gradient thresholds set from small to large for the energy cumulative weighted change rate, and the two adjacent gradient thresholds form a gradient threshold interval to generate P2-1 value intervals arranged from small to large;

[0116] Step 6. When the first gradient threshold interval where the j-th energy cumulative change rate is located is greater than the first gradient threshold interval where the j-1-th energy cumulative change rate is located, or when the second gradient threshold interval corresponding to the j-th energy cumulative weighted change rate is greater than the second gradient threshold interval corresponding to the j-1-th energy cumulative weighted change rate, according to the set gradient threshold energy cumulative threshold mapping relationship table, the energy cumulative threshold corresponding to the first gradient threshold interval where the j-1-th energy cumulative change rate is located is used as the energy cumulative analysis threshold for the j+1-th time interval; otherwise, the energy cumulative threshold corresponding to the first gradient threshold interval where the j-th energy cumulative change rate is located is used as the energy cumulative analysis threshold for the j+1-th time interval, let j=j+1, and go to step 2, wherein, when j=1, the first gradient threshold interval where the j-1-th energy cumulative change rate is located is equal to the first gradient threshold interval where the j-th energy cumulative change rate is located;

[0117] Step 7: Output the interharmonic components from the 1st time interval to the jth time interval as the screened sub-isochronous frequency oscillation curve.

[0118] The system for screening sub-synchronous oscillation curves described in this preferred embodiment has the same steps as the method for screening sub-synchronous oscillation curves in screening sub-synchronous oscillation curves, and the technical effects achieved are also the same, which will not be repeated here.

[0119] Exemplary Electronic Devices

[0120] Figure 3 The electronic device according to the preferred embodiment of the present invention is a schematic diagram of the structure of the electronic device. The electronic device can be any one or both of the first device and the second device, or a stand-alone device independent of them, and the stand-alone device can communicate with the first device and the second device to receive the collected input signal from them. Figure 3 FIG. 1 is a block diagram of an electronic device according to an embodiment of the present disclosure. Figure 3 As shown, the electronic device includes one or more processors 301 and a memory 302 .

[0121] The processor 301 may be a central processing unit (CPU) or other forms of processing units having data processing capabilities and / or instruction execution capabilities, and may control other components in the electronic device to perform desired functions.

[0122] The memory 302 may include one or more computer program products, which may include various forms of computer-readable storage media, such as volatile memory and / or non-volatile memory. The volatile memory may include, for example, random access memory (RAM) and / or cache memory (cache), etc. The non-volatile memory may include, for example, read-only memory (ROM), hard disk, flash memory, etc. One or more computer program instructions may be stored on the computer-readable storage medium, and the processor 301 may run the program instructions to implement the energy consumption anomaly diagnosis method based on the enterprise energy consumption space of the various embodiments disclosed above and / or other desired functions. In one example, the electronic device may also include: an input device 303 and an output device 304, which are interconnected via a bus system and / or other forms of connection mechanisms (not shown).

[0123] In addition, the input device 303 may also include, for example, a keyboard, a mouse, and the like.

[0124] The output device 304 can output various information to the outside, and can include, for example, a display, a speaker, a printer, a communication network and a remote output device connected thereto.

[0125] Of course, to simplify, Figure 3 Only some of the components related to the present disclosure in the electronic device are shown, and components such as a bus, an input / output interface, etc. are omitted. In addition, according to specific application situations, the electronic device may further include any other appropriate components.

[0126] Exemplary computer program products and computer-readable storage media

[0127] In addition to the above-mentioned methods and devices, an embodiment of the present disclosure may also be a computer program product, which includes computer program instructions, which, when executed by a processor, enable the processor to execute the steps in the method for screening subsynchronous oscillation curves according to various embodiments of the present disclosure described in the above-mentioned "Exemplary Method" section of this specification.

[0128] The computer program product may be written in any combination of one or more programming languages ​​to write program code for performing the operations of the disclosed embodiments, including object-oriented programming languages ​​such as Java, C++, etc., and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code may be executed entirely on the user computing device, partially on the user device, as a separate software package, partially on the user computing device and partially on a remote computing device, or entirely on a remote computing device or server.

[0129] In addition, an embodiment of the present disclosure may also be a computer-readable storage medium having computer program instructions stored thereon, and when the computer program instructions are executed by a processor, the processor executes the steps of the method for screening subsynchronous oscillation curves according to various embodiments of the present disclosure described in the above “Exemplary Method” section of this specification.

[0130] The computer readable storage medium can adopt any combination of one or more readable media. The readable medium can be a readable signal medium or a readable storage medium. The readable storage medium can include, for example, but is not limited to, a system, device or device of electricity, magnetism, light, electromagnetic, infrared, or semiconductor, or any combination of the above. More specific examples (non-exhaustive list) of readable storage media include: an electrical connection with one or more wires, a portable disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above.

[0131] The basic principles of the present disclosure are described above in conjunction with specific embodiments. However, it should be noted that the advantages, strengths, effects, etc. mentioned in the present disclosure are only examples and not limitations, and it cannot be considered that these advantages, strengths, effects, etc. are required by each embodiment of the present disclosure. In addition, the specific details disclosed above are only for the purpose of illustration and ease of understanding, and are not limitations. The above details do not limit the present disclosure to the necessity of adopting the above specific details to be implemented.

[0132] Each embodiment in this specification is described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same or similar parts between the embodiments can be referred to each other. For the system embodiment, since it basically corresponds to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment.

[0133] The block diagrams of the devices, apparatuses, equipment, and systems involved in this disclosure are only illustrative examples and are not intended to require or imply that they must be connected, arranged, and configured in the manner shown in the block diagrams. As will be appreciated by those skilled in the art, these devices, apparatuses, equipment, and systems can be connected, arranged, and configured in any manner. Words such as "including," "comprising," "having," and the like are open words, referring to "including but not limited to," and can be used interchangeably therewith. The words "or" and "and" used herein refer to the words "and / or," and can be used interchangeably therewith, unless the context clearly indicates otherwise. The word "such as" used herein refers to the phrase "such as but not limited to," and can be used interchangeably therewith.

[0134] The apparatus and method of the present disclosure may be implemented in many ways. For example, the apparatus and method of the present disclosure may be implemented by software, hardware, firmware, or any combination of software, hardware, and firmware. The above order of steps for the method is for illustration only, and the steps of the method of the present disclosure are not limited to the order specifically described above, unless otherwise specifically stated. In addition, in some embodiments, the present disclosure may also be implemented as a program recorded in a recording medium, which includes machine-readable instructions for implementing the method according to the present disclosure. Therefore, the present disclosure also covers a recording medium storing a program for executing the method according to the present disclosure.

[0135] It should also be noted that in the apparatus, equipment and method of the present disclosure, each component or each step can be decomposed and / or recombined. These decompositions and / or recombinations should be regarded as equivalent schemes of the present disclosure. The above description of the disclosed aspects is provided to enable any technician in the field to make or use the present disclosure. Various modifications to these aspects are very obvious to those skilled in the art, and the general principles defined herein can be applied to other aspects without departing from the scope of the present disclosure. Therefore, the present disclosure is not intended to be limited to the aspects shown here, but to the widest scope consistent with the principles and novel features disclosed herein.

[0136] The above description has been given for the purpose of illustration and description. In addition, this description is not intended to limit the embodiments of the present disclosure to the forms disclosed herein. Although multiple example aspects and embodiments have been discussed above, those skilled in the art will recognize certain variations, modifications, changes, additions and sub-combinations thereof.

Claims

1. A method for screening subsynchronous oscillation curves, characterized in that: The method comprises: According to the custom time window length, a sequence of amplitude data points to be determined of electromagnetic transient simulation phasors in a continuous number of time windows is obtained; Inputting the sequence of amplitude data points to be determined into a pre-established classifier, and outputting the category of the sequence of amplitude data points to be determined, wherein the category of the sequence of amplitude data points to be determined includes having oscillation behavior and having no oscillation behavior; The wavelet packet decomposition method in the time-frequency domain is used to analyze the amplitude data point sequence with oscillating behavior to be determined, and the frequency and amplitude of the interharmonic component to be screened are obtained; Comparing the amplitude percentage of the interharmonic component to be screened with a predetermined amplitude threshold, and taking the interharmonic component to be screened after noise is filtered out as the remaining interharmonic component, wherein the amplitude percentage data refers to the percentage of the amplitude of the interharmonic component to the fundamental wave; The output subsynchronous oscillation curve is determined according to the frequency and amplitude percentage of the remaining interharmonic components, including: Step 1, according to the frequency of each interharmonic component in the sensitive frequency segment and the remaining interharmonic components, determine the energy accumulation reference threshold and energy accumulation weight value of each interharmonic component, wherein the sensitive frequency segment is a frequency interval composed of the upper limit value and the lower limit value obtained by adding and subtracting the custom frequency step length respectively to the difference between the power frequency and the modal frequency of the motor group during electromagnetic transient simulation, energy accumulation refers to the time integral of the amplitude percentage of the interharmonic component, the first energy accumulation reference threshold of the interharmonic component whose frequency is located in the sensitive frequency segment is less than the second energy accumulation reference threshold of the interharmonic component whose frequency is not in the sensitive frequency segment, the first energy accumulation weight value of the interharmonic component whose frequency is located in the sensitive frequency segment is greater than the second energy accumulation weight value of the interharmonic component whose frequency is not in the sensitive frequency segment, and the sum of the first energy accumulation weight value and the second energy accumulation weight value is between the frequency step length and twice the frequency step length, and the first energy accumulation weight value is k times the second energy accumulation weight value, and k is a positive number not less than 3; Step 2: Calculate the energy accumulation value of each interharmonic component in the jth time interval according to the set time interval, and calculate the energy accumulation weighted sum value of the jth time interval according to the energy accumulation weight value of each interharmonic component and the energy accumulation value in the jth time interval, where j is a positive integer and the initial value of j is 1; Step 3: When the energy accumulation value of each interharmonic component in the jth time interval is less than its corresponding energy accumulation analysis threshold, and the energy accumulation weighted sum value in the jth time interval is less than the set energy accumulation weighted analysis threshold, go to step 4; otherwise, go to step 7, wherein the energy accumulation weighted analysis threshold is any positive integer between [5, 50], when j=1, the energy accumulation analysis threshold is equal to the energy accumulation reference threshold, and the energy accumulation value and energy accumulation weighted sum value of the j-1th time interval are equal to the energy accumulation value and energy accumulation weighted sum value of the j-1th time interval, respectively; Step 4, calculating the j-th energy cumulative change rate of each interharmonic component according to the energy cumulative value of each interharmonic component in the j-th time interval and the j-1-th time interval, and calculating the j-th energy cumulative weighted change rate according to the energy cumulative weighted sum value in the j-th time interval and the j-1-th time interval; Step 5: According to the set change rate gradient threshold mapping relationship table, determine the first gradient threshold interval corresponding to the j-th energy cumulative change rate of each interharmonic component, and the second gradient threshold interval corresponding to the j-th energy cumulative weighted change rate, wherein the first gradient threshold interval is the P1 gradient thresholds set from small to large for the energy cumulative change rate, and the two adjacent gradient thresholds form a gradient threshold interval to generate P1-1 value intervals arranged from small to large, and the second gradient threshold interval is the P2 gradient thresholds set from small to large for the energy cumulative weighted change rate, and the two adjacent gradient thresholds form a gradient threshold interval to generate P2-1 value intervals arranged from small to large; Step 6. When the first gradient threshold interval where the j-th energy cumulative change rate is located is greater than the first gradient threshold interval where the j-1-th energy cumulative change rate is located, or when the second gradient threshold interval corresponding to the j-th energy cumulative weighted change rate is greater than the second gradient threshold interval corresponding to the j-1-th energy cumulative weighted change rate, according to the set gradient threshold energy cumulative threshold mapping relationship table, the energy cumulative threshold corresponding to the first gradient threshold interval where the j-1-th energy cumulative change rate is located is used as the energy cumulative analysis threshold for the j+1-th time interval; otherwise, the energy cumulative threshold corresponding to the first gradient threshold interval where the j-th energy cumulative change rate is located is used as the energy cumulative analysis threshold for the j+1-th time interval, let j=j+1, and go to step 2, wherein, when j=1, the first gradient threshold interval where the j-1-th energy cumulative change rate is located is equal to the first gradient threshold interval where the j-th energy cumulative change rate is located; Step 7: Output the interharmonic components from the 1st time interval to the jth time interval as the screened sub-isochronous frequency oscillation curve.

2. The method according to claim 1, characterized in that: According to the custom time window length, before obtaining the amplitude data point sequence of the electromagnetic transient simulation phasor in a plurality of consecutive time windows, it also includes establishing a classifier for classifying the amplitude data point sequence to be determined of the electromagnetic transient simulation phasor, specifically: According to the custom time window length, obtain the historical amplitude data point sequence of the electromagnetic transient simulation phasor in several consecutive time windows; According to the custom dimension number N, the discrete wavelet transform method is used to represent the historical amplitude data point sequence of the electromagnetic transient simulation phasor in each time window as the historical coordinate point in the N-dimensional space, wherein the number of decomposition layers of the discrete wavelet transform method used is equal to the custom dimension number N; Using a preset coordinate point category criterion to determine the category of the historical coordinate point in the N-dimensional space, and assigning a corresponding category label according to the category, wherein the category of the historical coordinate point includes having an oscillating behavior and having no oscillating behavior; The historical coordinate points in the N-dimensional space and their corresponding category labels are used as training data, and support vector machine training is used to generate the classifier.

3. The method according to claim 2, characterized in that According to the self-defined dimension number N, the discrete wavelet transform method is used to represent the historical amplitude data point sequence of the electromagnetic transient simulation phasor in each time window as historical coordinate points in the N-dimensional space, including: The discrete wavelet transform algorithm is used to decompose and reconstruct the historical amplitude data point sequence of the electromagnetic transient simulation phasor in each window, and the wavelet decomposition coefficient d of the nth layer corresponding to the historical amplitude data point sequence of the electromagnetic transient simulation phasor in the i-th window is determined. in , where 1≤i≤I, I is the total number of time windows, 1≤n≤N; According to the wavelet decomposition coefficient d in Generate the historical coordinate points (d i1 , d i2 , …d in ,…,d iN ).

4. The method according to claim 2, characterized in that: The category of the historical coordinate points in the N-dimensional space is determined by using a preset coordinate point category criterion, wherein the coordinate point category criterion is: When the historical coordinate point in the N-dimensional space corresponding to the historical amplitude data point sequence of the electromagnetic transient simulation phasor in the i-th window is (d i1 , d i2 , …d in ,…,d iN ), if d ia With d ib When the absolute values ​​of the differences are all less than d0, the category of the historical coordinate point is determined to be without oscillation behavior, otherwise, the category of the historical coordinate point is with oscillation behavior, where d0 is a positive number less than 1, 1≤i≤I, 1≤a, b≤N, and a≠b.

5. The method according to claim 2, characterized in that: After the category of the historical coordinate points in the N-dimensional space is determined by using the preset coordinate point category criterion, the method further includes determining an amplitude threshold for filtering out the noise in the window with oscillating behavior, specifically: Using a time-frequency domain wavelet packet decomposition method to analyze a sequence of historical amplitude data points corresponding to historical coordinate points classified as having oscillatory behavior, obtain the amplitude of the corresponding historical interharmonic component, and calculate the amplitude percentage of the historical interharmonic component; According to the set interharmonic component classification rule, the category of the historical interharmonic component is determined according to the amplitude percentage of the historical interharmonic component, and a corresponding category label is added, wherein the category of the historical interharmonic component includes a category requiring attention and a category not requiring attention; According to the amplitude percentage and category label of the historical interharmonic component, by training a support vector machine learner, the dividing point of the amplitude percentage data of the class that needs attention and the class that does not need attention on the one-dimensional axis is obtained, and the dividing point is the determined amplitude threshold.

6. The method according to claim 5, characterized in that The interharmonic component classification rules include: Define the category of historical interharmonic components with amplitude percentage lower than L% as the category of no concern; Define the historical interharmonic components with amplitude percentage higher than M% as the category requiring attention, where L <M; For the historical interharmonic components that do not meet the above two definitions, when their amplitude percentage is higher than K times the average amplitude percentage of all historical interharmonic components in the same time window, the category is the class that needs attention; otherwise, the category is the class that does not need attention, where K is a positive number.

7. The method according to any one of claims 1, 2 and 5, characterized in that The wavelet mother function used in the discrete wavelet transform method or the time-frequency domain wavelet packet decomposition method is the third-order Daubechies series wavelet, which is expressed as follows: In the formula, is the amplitude point sequence of the input electromagnetic transient simulation phasor with oscillatory behavior, is the amplitude and frequency of the interharmonic components obtained after hierarchical decomposition of the input.

8. The method according to claim 1, characterized in that The step of comparing the amplitude percentage of the interharmonic component to be screened with a predetermined amplitude threshold and taking the interharmonic component to be screened after noise is filtered out as the remaining interharmonic component comprises: Comparing the amplitude percentage of the interharmonic component to be screened with a predetermined amplitude threshold, and filtering out the interharmonic component to be screened whose amplitude percentage is less than the amplitude threshold as noise; The remaining part of the interharmonic component to be screened after the noise is filtered out is used as the remaining interharmonic component.

9. A system for screening subsynchronous oscillation curves, characterized in that: The system comprises: A data acquisition module is used to obtain a sequence of amplitude data points to be determined of electromagnetic transient simulation phasors in a continuous number of time windows according to a custom time window length; A data classification module, used for inputting the sequence of amplitude data points to be determined into a pre-established classifier, and outputting the category of the sequence of amplitude data points to be determined, wherein the category of the sequence of amplitude data points to be determined includes having oscillation behavior and having no oscillation behavior; A data analysis module is used to analyze the amplitude data point sequence to be determined with oscillating behavior by using a time-frequency domain wavelet packet decomposition method to obtain the frequency and amplitude of the interharmonic component to be screened; A noise filtering module, used for comparing the amplitude percentage of the interharmonic component to be screened with a predetermined amplitude threshold, and taking the interharmonic component to be screened after the noise is filtered out as the remaining interharmonic component, wherein the amplitude percentage data refers to the percentage of the amplitude of the interharmonic component to the fundamental wave; The result output module is used to determine the output subsynchronous oscillation curve according to the frequency and amplitude percentage of the remaining interharmonic components, including: Step 1, according to the frequency of each interharmonic component in the sensitive frequency segment and the remaining interharmonic components, determine the energy accumulation reference threshold and energy accumulation weight value of each interharmonic component, wherein the sensitive frequency segment is a frequency interval composed of the upper limit value and the lower limit value obtained by adding and subtracting the custom frequency step length respectively to the difference between the power frequency and the modal frequency of the motor group during electromagnetic transient simulation, energy accumulation refers to the time integral of the amplitude percentage of the interharmonic component, the first energy accumulation reference threshold of the interharmonic component whose frequency is located in the sensitive frequency segment is less than the second energy accumulation reference threshold of the interharmonic component whose frequency is not in the sensitive frequency segment, the first energy accumulation weight value of the interharmonic component whose frequency is located in the sensitive frequency segment is greater than the second energy accumulation weight value of the interharmonic component whose frequency is not in the sensitive frequency segment, and the sum of the first energy accumulation weight value and the second energy accumulation weight value is between the frequency step length and twice the frequency step length, and the first energy accumulation weight value is k times the second energy accumulation weight value, and k is a positive number not less than 3; Step 2: Calculate the energy accumulation value of each interharmonic component in the jth time interval according to the set time interval, and calculate the energy accumulation weighted sum value of the jth time interval according to the energy accumulation weight value of each interharmonic component and the energy accumulation value in the jth time interval, where j is a positive integer and the initial value of j is 1; Step 3: When the energy accumulation value of each interharmonic component in the jth time interval is less than its corresponding energy accumulation analysis threshold, and the energy accumulation weighted sum value in the jth time interval is less than the set energy accumulation weighted analysis threshold, go to step 4; otherwise, go to step 7, wherein the energy accumulation weighted analysis threshold is any positive integer between [5, 50], when j=1, the energy accumulation analysis threshold is equal to the energy accumulation reference threshold, and the energy accumulation value and energy accumulation weighted sum value of the j-1th time interval are equal to the energy accumulation value and energy accumulation weighted sum value of the j-1th time interval, respectively; Step 4, calculating the j-th energy cumulative change rate of each interharmonic component according to the energy cumulative value of each interharmonic component in the j-th time interval and the j-1-th time interval, and calculating the j-th energy cumulative weighted change rate according to the energy cumulative weighted sum value in the j-th time interval and the j-1-th time interval; Step 5: According to the set change rate gradient threshold mapping relationship table, determine the first gradient threshold interval corresponding to the j-th energy cumulative change rate of each interharmonic component, and the second gradient threshold interval corresponding to the j-th energy cumulative weighted change rate, wherein the first gradient threshold interval is the P1 gradient thresholds set from small to large for the energy cumulative change rate, and the two adjacent gradient thresholds form a gradient threshold interval to generate P1-1 value intervals arranged from small to large, and the second gradient threshold interval is the P2 gradient thresholds set from small to large for the energy cumulative weighted change rate, and the two adjacent gradient thresholds form a gradient threshold interval to generate P2-1 value intervals arranged from small to large; Step 6. When the first gradient threshold interval where the j-th energy cumulative change rate is located is greater than the first gradient threshold interval where the j-1-th energy cumulative change rate is located, or when the second gradient threshold interval corresponding to the j-th energy cumulative weighted change rate is greater than the second gradient threshold interval corresponding to the j-1-th energy cumulative weighted change rate, according to the set gradient threshold energy cumulative threshold mapping relationship table, the energy cumulative threshold corresponding to the first gradient threshold interval where the j-1-th energy cumulative change rate is located is used as the energy cumulative analysis threshold for the j+1-th time interval; otherwise, the energy cumulative threshold corresponding to the first gradient threshold interval where the j-th energy cumulative change rate is located is used as the energy cumulative analysis threshold for the j+1-th time interval, let j=j+1, and go to step 2, wherein, when j=1, the first gradient threshold interval where the j-1-th energy cumulative change rate is located is equal to the first gradient threshold interval where the j-th energy cumulative change rate is located; Step 7: Output the interharmonic components from the 1st time interval to the jth time interval as the screened sub-isochronous frequency oscillation curve.

10. A computer-readable storage medium, characterized in that: The storage medium stores a computer program, and the computer program is used to execute the method according to any one of claims 1 to 8.

11. An electronic device, characterized in that: The electronic device comprises: processor; a memory for storing instructions executable by the processor; The processor is used to read the executable instructions from the memory and execute the instructions to implement the method described in any one of claims 1 to 8.

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