Oscillation mode detection and frequency measurement node selection method and related device

Abstract

The invention discloses an oscillation mode detection and frequency measurement node selection method and a related device, and the method comprises the steps: obtaining an actual measurement signal of a frequency time domain signal, setting that the actual measurement signal comprises p decomposition signals, and carrying out the sampling of the actual measurement signal, so as to obtain a discrete time sequence, discrete points of the discrete time sequence are fitted into a linear difference equation with undetermined coefficients; based on a linear difference equation, defining an error function of the actually measured signal and the simulated signal, and constructing a residual function of a least square method; solving the residual function of the least square method to obtain the total least square estimation of the coefficient, and identifying the oscillation mode of the power system based on the total least square estimation of the coefficient; and determining a frequency measurement node which is most sensitive to the frequency change of the power system according to the oscillation mode in combination with small-interference stability simulation. Therefore, the problem that frequency measurement node selection cannot be adjusted in time due to the fact that the oscillation mode in the power system cannot be dynamically identified and the operation condition of the power system cannot be flexibly handled in the prior art is solved.

Description

Technical Field

[0001] The present application relates to the technical field of power systems, and in particular to methods and related devices for detecting oscillation modes and selecting frequency measurement nodes. Background Art

[0002] The frequency stability of the power system is an important factor related to the safe and reliable operation of the power system. When the frequency fluctuates greatly, the system needs to take timely measures to prevent the frequency change. With the increase in the proportion of new energy access, the inertia of the power system decreases, the frequency regulation reserve decreases, the distribution of frequency regulation resources becomes more uneven, and the difference in the spatial distribution of frequency response expands. The frequency stability of the power system faces new challenges. How to quickly and correctly monitor and analyze the frequency oscillation characteristics of the system and select reasonable frequency measurement nodes as the object of frequency stability analysis is a key technology to achieve effective damping control and thus improve the stability of large power grids.

[0003] At present, the commonly used methods for analyzing the frequency oscillation characteristics of power systems are mainly divided into two categories: analysis methods based on system measured signals and analysis methods based on system mathematical models. Among them, the analysis methods based on system measured signals can apply actual measurement data to oscillation mode analysis and damping control research. The analysis methods based on system mathematical models need to establish a full-order model of the system according to the characteristics of each component in the system, and theoretically calculate the various modes of the system for analysis. Such methods usually include complex frequency domain methods and time domain simulation methods.

[0004] However, in the analysis method based on the measured signal of the system, the Fourier transform cannot accurately process non-stationary signals, and cannot capture transient oscillations or time-varying frequencies; and as the scale of the power system gradually increases and the model becomes increasingly complex, the analysis method based on the mathematical model of the system is relatively complex to solve and takes a long time to calculate, making it unsuitable for online analysis. Furthermore, when selecting frequency measurement nodes, the existing methods rely on experience or fixed node distribution, and usually only consider the performance of the power system in steady state or average operating state, ignoring the impact of the system's dynamic characteristics. When the system structure or operating conditions change, it is impossible to adapt and adjust the frequency measurement node selection in time. Summary of the invention

[0005] The present application provides an oscillation mode detection and frequency measurement node selection method and related devices, which are used to solve the problem that the prior art cannot dynamically identify the oscillation mode in the power system, and cannot flexibly respond to the operating conditions of the power system, resulting in the inability to timely adjust the frequency measurement node selection.

[0006] In view of this, the first aspect of the present application provides an oscillation mode detection and frequency measurement node selection method, the method comprising:

[0007] Acquire a measured signal of a frequency-time domain signal and assume that the measured signal includes p decomposition signals, sample the measured signal to obtain a discrete time series, and fit discrete points of the discrete time series into a linear difference equation with unknown coefficients;

[0008] Based on the linear difference equation, an error function between the measured signal and the simulated signal is defined, thereby constructing a residual function of the least square method, wherein the simulated signal is composed of p decomposed signals;

[0009] Solving the residual function of the least squares method to obtain a total least squares estimate of the coefficients, and identifying an oscillation mode of the power system based on the total least squares estimate of the coefficients;

[0010] According to the oscillation mode and in combination with small disturbance stability simulation, a frequency measurement node which is most sensitive to the frequency change of the power system is determined.

[0011] Optionally, solving the residual function of the least squares method to obtain an overall least squares estimate of the coefficients includes:

[0012] The residual function of the least squares method is solved using singular value decomposition, the effective rank of the matrix is ​​determined, and the overall least squares estimate of the coefficients is calculated.

[0013] Optionally, the identifying the oscillation mode of the power system based on the total least squares estimation of the coefficients comprises:

[0014] The key features of the frequency information of each node of the measured signal are obtained based on the total least squares estimation of the coefficients. The key features include: amplitude, attenuation coefficient, frequency, phase and damping ratio.

[0015] Optionally, determining the frequency measurement node most sensitive to the frequency change of the power system according to the oscillation mode and in combination with a small disturbance stability simulation includes:

[0016] According to the amplitude and the damping ratio, several nodes are selected as initial frequency measurement nodes, and the initial frequency measurement nodes are selected in combination with small disturbance stability simulation to obtain the frequency measurement nodes that are most sensitive to system frequency changes.

[0017] A second aspect of the present application provides an oscillation mode detection and frequency measurement node selection system, the system comprising:

[0018] an acquisition unit, configured to acquire a measured signal of a frequency-time domain signal and assume that the measured signal includes p decomposition signals, sample the measured signal to obtain a discrete time series, and fit discrete points of the discrete time series into a linear difference equation with undetermined coefficients;

[0019] A construction unit, used to define an error function between the measured signal and the simulated signal based on the linear difference equation, so as to construct a residual function of the least square method, wherein the simulated signal is composed of p decomposition signals;

[0020] A calculation unit, used for solving the residual function of the least square method to obtain a total least squares estimate of the coefficients, and identifying an oscillation mode of the power system based on the total least squares estimate of the coefficients;

[0021] A selection unit is used to determine the frequency measurement node that is most sensitive to the frequency change of the power system according to the oscillation mode and in combination with a small disturbance stability simulation.

[0022] Optionally, solving the residual function of the least squares method to obtain an overall least squares estimate of the coefficients includes:

[0023] The residual function of the least squares method is solved using singular value decomposition, the effective rank of the matrix is ​​determined, and the overall least squares estimate of the coefficients is calculated.

[0024] Optionally, the identifying the oscillation mode of the power system based on the total least squares estimation of the coefficients comprises:

[0025] The key features of the frequency information of each node of the measured signal are obtained based on the total least squares estimation of the coefficients. The key features include: amplitude, attenuation coefficient, frequency, phase and damping ratio.

[0026] Optionally, the selection unit is specifically configured to:

[0027] According to the amplitude and the damping ratio, several nodes are selected as initial frequency measurement nodes, and the initial frequency measurement nodes are selected in combination with small disturbance stability simulation to obtain the frequency measurement nodes that are most sensitive to system frequency changes.

[0028] A third aspect of the present application provides an oscillation mode detection and frequency measurement node selection device, the device comprising a processor and a memory:

[0029] The memory is used to store program codes and transmit the program codes to the processor;

[0030] The processor is used to execute the steps of the oscillation mode detection and frequency measurement node selection method as described in the first aspect according to the instructions in the program code.

[0031] A fourth aspect of the present application provides a computer-readable storage medium, wherein the computer-readable storage medium is used to store program codes, and the program codes are used to execute the oscillation mode detection and frequency measurement node selection method described in the first aspect.

[0032] It can be seen from the above technical solutions that this application has the following advantages:

[0033] The present application provides a method for detecting an oscillation mode and selecting a frequency measurement node. The Prony method is used for analysis, and the amplitude, attenuation coefficient, frequency, and phase information of the signal can be quickly and accurately extracted. The method can be seamlessly integrated with the phasor measurement unit, and the real-time collected data can be used for analysis to dynamically identify the oscillation mode in the power system, and the changes in the operating conditions of the power system can be more flexibly responded to, which is suitable for online analysis of frequency stability. Furthermore, by analyzing the oscillation mode of the system, a small number of key nodes that are highly sensitive to frequency changes can be screened out, thereby reducing the number of frequency measurement nodes, reducing the complexity of the monitoring system, reducing the cost of data processing, and improving the overall efficiency of the frequency measurement system. Finally, according to the dynamic response of the actual operation of the system, the measurement node is selected and adjusted in real time, which can more flexibly respond to the changes in the operating conditions of the power system, ensure that the measurement node is always in the most sensitive position to the system frequency stability, thereby reducing system risks and improving the reliability and monitoring accuracy of the system. The problem that the existing technology cannot dynamically identify the oscillation mode in the power system, and cannot flexibly respond to the operating conditions of the power system, resulting in the inability to adjust the selection of frequency measurement nodes in time is solved. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] Figure 1 A flow chart of an oscillation mode detection and frequency measurement node selection method provided in an embodiment of the present application;

[0035] Figure 2 This is a frequency deviation waveform diagram of bus B provided in an embodiment of the present application;

[0036] Figure 3 A schematic diagram of the structure of an oscillation mode detection and frequency measurement node selection system provided in an embodiment of the present application. DETAILED DESCRIPTION

[0037] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0038] See also Figure 1 , an oscillation mode detection and frequency measurement node selection method provided in an embodiment of the present application includes:

[0039] Step 101: Acquire a measured signal of a frequency-time domain signal and assume that the measured signal includes p decomposition signals, sample the measured signal to obtain a discrete time series, and fit the discrete points of the discrete time series into a linear difference equation with unknown coefficients.

[0040] It should be noted that the measured signal of the frequency time domain signal needs to be obtained through wave recording or power system simulation. The frequency data of each node under disturbance needs to be collected. The total number of sampling points is N, and the discrete sampling data of the measured signal is (measured signal), where . Assume that the measured frequency waveform contains decomposed signal, then The estimated value of the points is:

[0041] (1.5)

[0042] In the formula, is the sampling time interval, , , , Respectively represent The amplitude, phase, attenuation factor, and oscillation frequency of a signal are yet to be determined.

[0043] No. The decomposed signal of each point is expressed by the following linear difference equation, which is used to derive the following equation (1.9).

[0044] (1.6)

[0045] In the formula, , .

[0046] Step 102: Based on the linear difference equation, define the error function between the measured signal and the simulated signal, so as to construct the residual function of the least square method, wherein the simulated signal is composed of p decomposition signals.

[0047] It should be noted that the key point of the Prony analysis method is to realize that the fit of equation (1.6) is a homogeneous solution of a linear difference equation with constant coefficients, specifically:

[0048] First, define the characteristic polynomial as follows

[0049] (1.8)

[0050] In the formula, is the eigenvalue variable, are the coefficients of different powers of the eigenvalue variable (to be determined).

[0051] When satisfied hour, .

[0052] The error function can be defined as follows by formula (1.6):

[0053] (1.9)

[0054] In the formula, the meaning of physical quantities is the same as in formula (1.6), is an integer, For the The simulated signal of the sampling points.

[0055] Multiply both sides of formula (1.9) And By summing the items, we can get

[0056] (1.10)

[0057] In the formula, the meaning of physical quantities is the same as in formula (1.6) and formula (1.8).

[0058] because ,so Satisfies the recurrence relation:

[0059] (1.11)

[0060] Where the physical quantities are as shown before.

[0061] Construct the relationship between the measured signal and the simulated signal

[0062] (1.12)

[0063] In the formula, Represents the error between the measured signal and the fitted signal, that is, the error function.

[0064] Then the sum of squares of the errors, that is, the residual function, is:

[0065] (1.13)

[0066] In the formula, is the overall error, which is the sum of the squares of all point errors.

[0067] Step 103: Solve the residual function of the least square method to obtain the overall least squares estimate of the coefficients, and identify the oscillation mode of the power system based on the overall least squares estimate of the coefficients.

[0068] In one embodiment, step 103 includes the following steps:

[0069] The singular value decomposition method is used to solve the residual function of the least squares method, determine the effective rank of the matrix, and calculate the overall least squares estimate of the coefficients; the key features of the frequency information of each node of the measured signal are obtained based on the overall least squares estimate of the coefficients, and the key features include: amplitude, attenuation coefficient, frequency, phase and damping ratio.

[0070] It should be noted that in order to make the overall error in step 2 The value of is the smallest, let , then

[0071] (1.14)

[0072] In the formula, The overall error right The partial derivative of (used to describe the overall error With coefficient The rate of change of the change. When the rate of change is zero, it means that the overall error reaches the minimum value under this coefficient).

[0073] The corresponding minimum for

[0074] (1.15)

[0075] In the formula, is the minimum overall error.

[0076] make , and construct the expanded order matrix :

[0077] (1.16)

[0078] (1.17)

[0079] in, is the order of the prediction model, usually taken as , which is much larger than what is actually needed .

[0080] Using the singular value decomposition (SVD) principle, the matrix Decompose so that

[0081] (1.18)

[0082] In the formula, and are all orthogonal matrices, that is, they satisfy and , For matrices where all diagonal elements are zero, each element on the main diagonal is called a singular value.

[0083] Solution Matrix, the matrix and Multiplying together, we can get a square matrix , the eigenvalue can be obtained by eigenvalue decomposition and the left eigenvector , all eigenvectors together form the matrix , similarly we can obtain Matrices and Right Eigenvectors .

[0084] By the following transformation:

[0085] (1.19)

[0086] In the formula, , , , It is a matrix used in mathematical derivation and has no actual physical meaning.

[0087] Each singular value can be found , so we can get the matrix At this time, the normalized singular value can be used to determine the moment The effective rank of

[0088] (1.20)

[0089] when When the value of is greater than a certain threshold (this threshold can be flexibly set according to the required accuracy), the rank of the matrix is ​​increased by one to determine the effective value order .

[0090] In the formula, For the matrix Middle Row, No. The singular values ​​of the column. is the calculated normalized value.

[0091] Then, from equations (1.16) and (1.18), we can solve , , , , the characteristic root of the characteristic polynomial can be obtained , That is, the overall least squares estimate of the coefficients, satisfy:

[0092] (1.21)

[0093] Equation (1.9) can be transformed into the unknown parameter The linear equation is:

[0094] (1.22)

[0095] (1.23)

[0096] This can be used to solve the amplitude of each decomposed signal , attenuation coefficient ,frequency , Phase and damping ratio , which is expressed as follows:

[0097] (1.24)

[0098] In the formula, all physical quantities are explained in front. for The natural logarithm of for The imaginary part of for The real part of .

[0099] Furthermore, it should be noted that, in a specific implementation, a person skilled in the art may write a Prony analysis program according to the principles in step 102 and step 103, input the node frequency information obtained in step 101 into the program to obtain the Prony analysis result, and calculate the amplitude, attenuation coefficient, frequency, phase and damping ratio of each node frequency signal.

[0100] Step 104: Determine the frequency measurement node that is most sensitive to the frequency change of the power system according to the oscillation mode and in combination with the small disturbance stability simulation.

[0101] In one embodiment, step 104 includes the following steps:

[0102] According to the amplitude and damping ratio, several nodes are selected as the initial frequency measurement nodes, and the initial frequency measurement nodes are selected in combination with the small disturbance stability simulation to obtain the frequency measurement nodes that are most sensitive to the system frequency change.

[0103] It should be noted that this application selects the point with the smallest damping ratio and the largest oscillation amplitude as the frequency measurement node of the whole system. Then, a simulation test is performed based on the IEEE standard system of 3 machines and 9 nodes. The disturbance is set as a three-phase short-circuit fault of the system, and the fault duration is 0.08 seconds. Among them, the IEEE standard system of 3 machines and 9 nodes is a classic test system in power system simulation and analysis, and is usually used to verify control strategies, stability analysis, fault simulation and other research. This system consists of 3 generators, 9 nodes (i.e., busbars) and 6 transmission lines. Its structure is relatively simple, but it is sufficient to demonstrate the key characteristics of the power system.

[0104] The small disturbance stability calculation results of the system are shown in Table 1:

[0105] Table 1 Small disturbance stability computer results

[0106]

[0107] Prony analysis of node frequency is performed, and the results are as follows Figure 2 As shown (taking busbar B as an example). Figure 2 As shown in Table 2, the original signal is fitted more accurately through Prony analysis, the signal-to-noise ratio is 62.3291dB (generally greater than 20dB is considered a good effect), and the oscillation mode with a frequency of 1.984Hz in the system is obtained:

[0108] Table 2 Busbar BProny analysis results

[0109]

[0110] From this, the oscillation mode of each node can be extracted as shown in Table 3:

[0111] Table 3 Dominant oscillation modes of each node

[0112]

[0113] From the above analysis, it can be seen that the damping ratio of node generator 3 is the smallest, and insufficient damping may occur. After simulation, the maximum frequency deviation of each node during the frequency oscillation is obtained as shown in Table 4, among which the frequency deviation of the generator 3 node is the largest, so the generator 3 node should be selected as the frequency measurement node.

[0114] Table 4 Maximum frequency deviation of each node

[0115]

[0116] In the embodiment of the present application, a method for online frequency oscillation mode detection and frequency measurement node selection based on multiple data is provided. The Prony method is used for analysis, and the amplitude, attenuation coefficient, frequency, and phase information of the signal can be quickly and accurately extracted. It can also be seamlessly integrated with the phasor measurement unit, and the real-time collected data is used for analysis to dynamically identify the oscillation mode in the power system, so that the changes in the operating conditions of the power system can be more flexibly responded to. Furthermore, by analyzing the oscillation mode of the system, a small number of key nodes that are highly sensitive to frequency changes can be screened out, thereby reducing the number of frequency measurement nodes, reducing the complexity of the monitoring system, reducing the cost of data processing, and improving the overall efficiency of the frequency measurement system. Finally, the measurement nodes are selected and adjusted in real time according to the dynamic response of the system during actual operation, so that the changes in the operating conditions of the power system can be more flexibly responded to, and the measurement nodes are always in the position that is most sensitive to the stability of the system frequency.

[0117] The above is an oscillation mode detection and frequency measurement node selection method provided in an embodiment of the present application, and the following is an oscillation mode detection and frequency measurement node selection system provided in an embodiment of the present application.

[0118] See also Figure 3 , an oscillation mode detection and frequency measurement node selection system provided in an embodiment of the present application includes:

[0119] The acquisition unit 201 is used to acquire a measured signal of a frequency-time domain signal and assume that the measured signal includes p decomposition signals, sample the measured signal to obtain a discrete time series, and fit discrete points of the discrete time series into a linear difference equation with unknown coefficients.

[0120] The construction unit 202 is used to define the error function between the measured signal and the simulated signal based on the linear difference equation, so as to construct the residual function of the least square method, wherein the simulated signal is composed of p decomposition signals.

[0121] The calculation unit 203 is used to solve the residual function of the least square method to obtain the total least squares estimate of the coefficients, and identify the oscillation mode of the power system based on the total least squares estimate of the coefficients.

[0122] The selection unit 204 is used to determine the frequency measurement node that is most sensitive to the frequency change of the power system according to the oscillation mode and in combination with the small disturbance stability simulation.

[0123] Furthermore, an oscillation mode detection and frequency measurement node selection device is provided in an embodiment of the present application, and the device includes a processor and a memory:

[0124] The memory is used to store program codes and transmit the program codes to the processor;

[0125] The processor is used to execute the steps of the oscillation mode detection and frequency measurement node selection method as described in the above method embodiment according to the instructions in the program code.

[0126] Furthermore, a computer-readable storage medium is provided in an embodiment of the present application, and the computer-readable storage medium is used to store program code, and the program code is used to execute the oscillation mode detection and frequency measurement node selection method described in the above method embodiment.

[0127] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0128] The terms "first", "second", "third", "fourth", etc. (if any) in the specification of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein, for example. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0129] It should be understood that in this application, "at least one (item)" means one or more, and "plurality" means two or more. "And / or" is used to describe the association relationship of associated objects, indicating that three relationships may exist. For example, "A and / or B" can mean: only A exists, only B exists, and A and B exist at the same time, where A and B can be singular or plural. The character " / " generally indicates that the objects associated before and after are in an "or" relationship. "At least one of the following" or similar expressions refers to any combination of these items, including any combination of single or plural items. For example, at least one of a, b or c can mean: a, b, c, "a and b", "a and c", "b and c", or "a and b and c", where a, b, c can be single or multiple.

[0130] In the several embodiments provided in the present application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interfaces, devices or units, which can be electrical, mechanical or other forms.

[0131] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0132] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0133] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application is essentially or the part that contributes to the prior art or all or part of the technical solution can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions to enable a computer device (which can be a personal computer, server, or 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: U disk, mobile hard disk, read-only memory (full name in English: Read-Only Memory, English abbreviation: ROM), random access memory (full name in English: Random Access Memory, English abbreviation: RAM), disk or optical disk and other media that can store program codes.

[0134] As described above, the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A method for oscillation mode detection and frequency measurement node selection, characterized in that: include: Acquire a measured signal of a frequency-time domain signal and assume that the measured signal includes p decomposition signals, sample the measured signal to obtain a discrete time series, and fit discrete points of the discrete time series into a linear difference equation with unknown coefficients; Based on the linear difference equation, an error function between the measured signal and the simulated signal is defined, thereby constructing a residual function of the least square method, wherein the simulated signal is composed of p decomposed signals; Solving the residual function of the least squares method to obtain a total least squares estimate of the coefficients, and identifying an oscillation mode of the power system based on the total least squares estimate of the coefficients; According to the oscillation mode and in combination with small disturbance stability simulation, a frequency measurement node which is most sensitive to the frequency change of the power system is determined.

2. The method for oscillation mode detection and frequency measurement node selection according to claim 1, characterized in that: The step of solving the residual function of the least squares method to obtain the overall least squares estimate of the coefficients includes: The residual function of the least squares method is solved using singular value decomposition, the effective rank of the matrix is ​​determined, and the overall least squares estimate of the coefficients is calculated.

3. The method for oscillation mode detection and frequency measurement node selection according to claim 1, characterized in that: The identifying of the oscillation mode of the power system based on the total least squares estimation of the coefficients comprises: The key features of the frequency information of each node of the measured signal are obtained based on the total least squares estimation of the coefficients. The key features include: amplitude, attenuation coefficient, frequency, phase and damping ratio.

4. The method for oscillation mode detection and frequency measurement node selection according to claim 3, characterized in that: The determining of the frequency measurement node most sensitive to the frequency change of the power system according to the oscillation mode and in combination with the small disturbance stability simulation includes: According to the amplitude and the damping ratio, several nodes are selected as initial frequency measurement nodes, and the initial frequency measurement nodes are selected in combination with small disturbance stability simulation to obtain the frequency measurement nodes that are most sensitive to system frequency changes.

5. An oscillation mode detection and frequency measurement node selection system, characterized in that: include: an acquisition unit, configured to acquire a measured signal of a frequency-time domain signal and assume that the measured signal includes p decomposition signals, sample the measured signal to obtain a discrete time series, and fit discrete points of the discrete time series into a linear difference equation with undetermined coefficients; A construction unit, used to define an error function between the measured signal and the simulated signal based on the linear difference equation, so as to construct a residual function of the least square method, wherein the simulated signal is composed of p decomposition signals; A calculation unit, used for solving the residual function of the least square method to obtain a total least squares estimate of the coefficients, and identifying an oscillation mode of the power system based on the total least squares estimate of the coefficients; A selection unit is used to determine the frequency measurement node that is most sensitive to the frequency change of the power system according to the oscillation mode and in combination with a small disturbance stability simulation.

6. The oscillation mode detection and frequency measurement node selection system according to claim 5, characterized in that: The step of solving the residual function of the least squares method to obtain the overall least squares estimate of the coefficients includes: The residual function of the least squares method is solved using singular value decomposition, the effective rank of the matrix is ​​determined, and the overall least squares estimate of the coefficients is calculated.

7. The oscillation mode detection and frequency measurement node selection system according to claim 5, characterized in that: The identifying of the oscillation mode of the power system based on the total least squares estimation of the coefficients comprises: The key features of the frequency information of each node of the measured signal are obtained based on the total least squares estimation of the coefficients. The key features include: amplitude, attenuation coefficient, frequency, phase and damping ratio.

8. The oscillation mode detection and frequency measurement node selection system according to claim 7, characterized in that: The selection unit is specifically used for: According to the amplitude and the damping ratio, several nodes are selected as initial frequency measurement nodes, and the initial frequency measurement nodes are selected in combination with small disturbance stability simulation to obtain the frequency measurement nodes that are most sensitive to system frequency changes.

9. An oscillation mode detection and frequency measurement node selection device, characterized in that: The device comprises a processor and a memory: The memory is used to store program code and transmit the program code to the processor; The processor is used to execute the oscillation mode detection and frequency measurement node selection method according to any one of claims 1 to 4 according to the 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 oscillation mode detection and frequency measurement node selection method according to any one of claims 1 to 4.

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