A power grid state identification method and an identification system

Abstract

The present invention discloses a power grid state identification method and an identification system, belonging to the fields of power grid power quality analysis and signal analysis. According to the unique characteristic harmonics that appear in the low-frequency band of the series-compensated power grid and the fact that unstable power grids will have harmonics in the middle frequency band compared with strong power grids, this method uses the S-transform to extract features from the grid-connected current; in order to reduce the data window length, a lower-resolution S-transform is used for feature extraction in the middle frequency band, and in order to accurately identify the series-compensated power grid and the weak power grid, a high-resolution S-transform is used for feature extraction in the low-frequency band to obtain complex time-domain elements with different resolutions; finally, the S-transform energy is obtained from the complex time-domain elements, and the S-transform energy is compared with a set threshold, and finally the series-compensated power grid and the weak power grid can be accurately identified. This method accurately extracts feature quantities, and the feature quantities of different power grid states are quite different, and there will be no misjudgment of the power grid state.

Description

Technical Field

[0001] The present invention relates to the technical fields of power quality analysis and signal analysis, and particularly to the stability detection technology of new energy inverters. Specifically, it is a power grid state identification method and identification system, and the power grid is a power grid connected to a new energy power generation system. Background Technique

[0002] In recent years, the large-scale transmission of new energy power generation has led to a high-penetration condition in the local power grid, and many new energy power generation accidents at home and abroad are related to high penetration. The complex power grid state caused by high new energy penetration has seriously threatened the stable and safe operation of new energy power generation systems.

[0003] To solve the new energy stability problem, it is necessary to select adaptive control strategies for different power grid states. Therefore, how to quickly and accurately identify the power grid state is particularly important. At present, the research on power grid state identification focuses on the measurement of power grid impedance. The measurement of power grid impedance can directly distinguish between strong power grids and weak power grids, but it cannot identify weak power grids and series-compensated power grids. For example:

[0004] 1. In the article "An Improved Impedance Measurement Method for Grid-Connected Inverter Systems Considering the Background Harmonics and Frequency Deviation" in the 2021 journal "IEEE Journal of Emerging and Selected Topics in Power Electronics", active injection of non-characteristic sub-harmonics is used. The harmonic impedance is measured by extracting the injected non-characteristic sub-harmonics and then converted into fundamental impedance. Due to the existence of background harmonics in the power grid, a two-stage series complex coefficient filter is proposed in this article to accurately extract the disturbance. This active injection of harmonics also affects the power quality of the power grid, and its engineering application remains to be discussed.

[0005] 2. In addition to the active injection perturbation method, some scholars have proposed the passive method. The literature "Grid impedance estimation for islanding detection and adaptive control of converters" published in "IET Power Electronics" in 2017 proposed to estimate the equivalent resistance value and inductance value by using the switching frequency ripple in the inverter output current. Although the passive method does not inject non-characteristic sub-harmonics, due to the low-pass filter in the grid-connected inverter, the current content at the switching frequency is very small, making it difficult to detect in practical applications.

[0006] 3. The "Grid state identification method based on power spectrum" (publication number: CN114944649A) of the Chinese invention patent published on August 26, 2022, proposed a grid state identification method based on the recursive discrete Fourier transform. The Fourier transform has defects in processing non-stationary signals and cannot obtain the time-frequency characteristics of non-stationary signals well. The method proposed in this patent may result in misjudgment during grid state identification.

[0007] Based on the above literature, the following deficiencies exist in the prior art:

[0008] 1. Most of the existing literature focuses on the measurement of grid impedance, and there is little research on the identification of weak grids and series-compensated grids.

[0009] 2. For the existing grid impedance measurement technologies, the active perturbation method has the defect that the injected harmonics affect the power quality; for the passive method, if it depends on the characteristics of the inverter itself, the characteristic quantities are not obvious, resulting in difficult measurement; therefore, both the active method and the passive method have inevitable disadvantages.

[0010] 3. For the commonly used Fourier transform signal decomposition method, it does not have good time-frequency characteristics. When extracting harmonics for grid state identification, the medium and high-frequency harmonic amplitudes in weak grids and series-compensated grids may be similar, and misjudgment may occur. Summary of the Invention

[0011] To overcome the limitations of the above methods, the present invention proposes a grid state identification method. The S transform combines the advantages of the discrete Fourier transform and the wavelet transform and has good time-frequency localization characteristics. For the frequency band amplitudes and frequency band ranges of weak grids and series-compensated grids, different resolution S transforms are used to process the signals. On the one hand, it can reduce the data window length and improve the program operation efficiency. On the other hand, it can effectively extract the effective information of the signal in the characteristic frequency band and accurately identify the grid state.

[0012] The object of the present invention is achieved as follows. The present invention provides a method for identifying the grid state, where the grid is a grid connected to a new energy power generation system, and the method includes the following steps:

[0013] Sample the current at the point of common coupling (PCC) N times at equal time intervals T to obtain N grid-connected current sampling values x k , where k = 1, 2…, N, and N is the total number of sampling times;

[0014] Calculate the number of sampling times N1 when the low resolution is set to a value a, N1 = 1 / af s , f s is the sampling frequency, and starting from k = 1, extract N1 grid-connected current sampling values x k sequentially from the N grid-connected current sampling values x k to form a low-resolution grid-connected current sequence E1;

[0015] Perform a one-dimensional discrete S transform on the low-resolution grid-connected current sequence E1 to obtain low-resolution complex time-domain elements Sl jn , where j is the j-th sampling time point of the low-resolution grid-connected current sequence E1, j = 0, 1, 2…, N1 - 1, and n is the n-th frequency point of the low-resolution grid-connected current sequence E1, n = 0, 1, 2…, N1 - 1;

[0016] Calculate the S transform energy S jn in the middle frequency band based on the low-resolution complex time-domain elements Sl sum1 ;

[0017] Given a first threshold ε1 and make a preliminary judgment on the grid state: if S sum1 ≤ε1, it is a strong grid and the identification ends; if S sum1 >ε1, calculate the number of sampling times N2 required when the high resolution is set to a value b, N2 = 1 / bf s , and starting from k = 1, extract N2 grid-connected current sampling values x k sequentially from the N grid-connected current sampling values x k to form a high-resolution grid-connected current sequence E2;

[0018] Perform a one-dimensional discrete S transform on the high-resolution grid-connected current sequence E2 to obtain high-resolution complex time-domain elements Sh pq , where p is the p-th sampling time point of the high-resolution grid-connected current sequence E2, p = 0, 1, 2…, N2 - 1, and q is the q-th frequency point of the high-resolution grid-connected current sequence E2, q = 0, 1, 2…, N2 - 1;

[0019] Calculate the S transform energy S pq in the low frequency band based on the high-resolution complex time-domain elements Sh sum2 ;

[0020] Given a second threshold ε2, if S sum2 > ε2, series-compensated power grid, if S sum2 ≤ ε2, weak power grid.

[0021] Preferably, the process of performing one-dimensional discrete S-transform on the low-resolution grid-connected current sequence E1 to obtain the low-resolution complex time-domain element Sl jn is as follows::

[0022] Perform discrete Fourier transform on the low-resolution grid-connected current sequence E1, and the expression is:

[0023]

[0024] where X is the spectrum of the low-resolution grid-connected current sequence E1, and i is the imaginary unit;

[0025] According to the relationship between discrete Fourier transform and one-dimensional discrete S-transform, obtain the expression of the low-resolution complex time-domain element Sl jn :

[0026]

[0027] where m is the translation frequency quantity of the low-resolution grid-connected current sequence E1, m = 0, 1, 2…, N1 - 1.

[0028] Preferably, the calculation formula for calculating the S-transform energy S jn in the middle frequency band according to the low-resolution complex time-domain element Sl sum1 is:

[0029]

[0030] where is the ceiling symbol.

[0031] Preferably, the process of performing one-dimensional discrete S-transform on the high-resolution grid-connected current sequence E2 to obtain the high-resolution complex time-domain element Sh pq is as follows:

[0032] Perform discrete Fourier transform on the high-resolution grid-connected current sequence E2, and its expression is:

[0033]

[0034] where Y is the spectrum of the high-resolution grid-connected current sequence E2, and i is the imaginary unit;

[0035] According to the relationship between discrete Fourier transform and one-dimensional discrete S-transform, obtain the expression of the high-resolution complex time-domain element Sh pq :

[0036]

[0037] Wherein, h is the translation frequency quantity of the high-resolution grid-connected current sequence E2, and h = 0, 1, 2…, N2-1.

[0038] Preferably, the S transform energy S in the low-frequency band is calculated according to the high-resolution complex time-domain element Sh pq The calculation formula is: sum2 The calculation formula is:

[0039]

[0040] Wherein, is the ceiling symbol.

[0041] Preferably, the frequency range of the middle frequency band is 50Hz-1000Hz, and the frequency range of the low frequency band is 0Hz-20Hz.

[0042] The present invention also provides a power grid state identification system, including:

[0043] A sampling module for obtaining the grid-connected current sampling value x k ;

[0044] A control module for performing low-resolution setting;

[0045] A calculation module for calculating the number of sampling times N1;

[0046] A control module for sequentially extracting N1 grid-connected current sampling values x from N grid-connected current sampling values x k to form a low-resolution grid-connected current sequence E1; k ;

[0047] A calculation module for performing one-dimensional discrete S transform on the low-resolution grid-connected current sequence E1 to obtain the low-resolution complex time-domain element Sl jn ;

[0048] A calculation module for calculating the S transform energy S in the middle frequency band according to the low-resolution complex time-domain element Sl jn ; sum1 ;

[0049] A control module for performing an initial judgment on the power grid state;

[0050] A control module for performing high-resolution setting;

[0051] A calculation module for calculating the number of sampling times N2;

[0052] A control module for sequentially extracting N2 grid-connected current sampling values x from N grid-connected current sampling values x kSequentially extract N2 grid-connected current sampling values x k to form a control module for a high-resolution grid-connected current sequence E2;

[0053] A calculation module for performing a one-dimensional discrete S transform on the high- and low-resolution grid-connected current sequences E2 to obtain high-resolution complex time-domain elements Sh pq ;

[0054] A calculation module for calculating the S transform energy S in the low-frequency band according to the high-resolution complex time-domain elements Sh pq ; sum2 ;

[0055] A control module for judging the power grid state;

[0056] And a microprocessor and a memory, and each of the modules and the microprocessor is programmed or configured to execute the steps of the power grid state identification method.

[0057] The present invention also proposes a computer-readable storage medium, in which a computer program programmed or configured to execute the power grid state identification method is stored.

[0058] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0059] 1. The identification method proposed by the present invention fills the technical gap in effectively distinguishing a series-compensated power grid and a weak power grid.

[0060] 2. The identification method proposed by the present invention does not require injecting disturbance harmonics. The passive feature quantities adopted do not come from the inverter itself, but effectively extract the current at the PCC. The feature quantities are obvious and can actually be applied to engineering.

[0061] 3. The identification method proposed by the present invention uses the S transform with good time-frequency characteristics, can accurately distinguish a weak power grid and a series-compensated power grid, and is not prone to misjudgment. Description of the Drawings

[0062] Figure 1 It is the main circuit topology diagram of a new energy power generation system applying the present invention.

[0063] Figure 2 It is the flowchart of a power grid state identification method proposed by the present invention.

[0064] Figure 3 It is the waveform diagram of the grid-connected current at the PCC point of the simulation experiment.

[0065] Figure 4 It is the S transform energy diagram in the medium frequency band of a strong power grid.

[0066] Figure 5It is the S - transform energy diagram in the medium frequency band of a weak power grid.

[0067] Figure 6 It is the S - transform energy diagram in the medium frequency band of a series - compensated power grid.

[0068] Figure 7 It is the S - transform energy diagram in the low - frequency band of a weak power grid.

[0069] Figure 8 It is the S - transform energy diagram in the low - frequency band of a series - compensated power grid. Specific implementation manners

[0070] The present invention will be further described in detail below in conjunction with the accompanying drawings and embodiments.

[0071] The power grid described in the present invention is a power grid connected to a new - energy power generation system. Figure 1 It is the main - circuit topology diagram of the new - energy power generation system applying the present invention. It can be seen from Figure 1 that the new - energy power generation system applying this method includes a DC - side power supply, a three - phase full - bridge inverter, a three - phase LCL filter, and a three - phase power - grid impedance. The DC - side power supply is connected to the input end of the three - phase full - bridge inverter. The output end of the three - phase full - bridge inverter is connected to the three - phase LCL filter. The three - phase LCL filter is connected in series with the three - phase power - grid impedance. The connection point is the point of common coupling PCC. Finally, the three - phase power - grid impedance is connected to the power grid.

[0072] In Figure 1 10 is the DC - side power supply, U dc is the DC - side voltage, 20 is the full - bridge inverter, 30 is the three - phase LCL filter, 40 is the three - phase power - grid impedance. Among them, L g is the grid - side inductor, C g is the grid - side capacitor, and 50 is the power grid.

[0073] Figure 2 It is the flowchart of the power - grid state identification method. It can be seen from Figure 2 that the power - grid state identification method discriminates the power - grid state by analyzing the current - frequency characteristics at the point of common coupling PCC, including the following steps:

[0074] S1, sample the current at the point of common coupling PCC N times at equal - interval time T to obtain N grid - connection current sampling values x k , k = 1, 2…, N, and N is the total number of sampling times.

[0075] S2, calculate the number of sampling times N1 when the low resolution is the set value a, N1 = 1 / af s , f s is the sampling frequency, and starting from k = 1, sequentially extract N1 grid - connection current sampling values x k from the N grid - connection current sampling values x kForm a low-resolution grid-connected current sequence E1.

[0076] In this embodiment, a = 5Hz, N1 < N.

[0077] S3. Perform a one-dimensional discrete S-transform on the low-resolution grid-connected current sequence E1 to obtain the low-resolution complex time-domain element Sl jn , where j is the j-th sampling time point of the low-resolution grid-connected current sequence E1, j = 0, 1, 2…, N1 - 1, and n is the n-th frequency point of the low-resolution grid-connected current sequence E1, n = 0, 1, 2…, N1 - 1.

[0078] In this embodiment, the process of performing a one-dimensional discrete S-transform on the low-resolution grid-connected current sequence E1 to obtain the low-resolution complex time-domain element Sl jn is as follows:

[0079] Perform a discrete Fourier transform on the low-resolution grid-connected current sequence E1, and the expression is:

[0080]

[0081] where X is the spectrum of the low-resolution grid-connected current sequence E1, and i is the imaginary unit;

[0082] According to the relationship between the discrete Fourier transform and the one-dimensional discrete S-transform, obtain the expression of the low-resolution complex time-domain element Sl jn :

[0083]

[0084] where m is the translation frequency quantity of the low-resolution grid-connected current sequence E1, m = 0, 1, 2…, N1 - 1.

[0085] S4. Calculate the S-transform energy S in the middle frequency band according to the low-resolution complex time-domain element Sl jn . sum1

[0086] In this embodiment, the calculation formula for the S-transform energy S in the middle frequency band sum1 is:

[0087]

[0088] where is the ceiling symbol.

[0089] In this embodiment, the frequency range of the middle frequency band is 50Hz - 1000Hz.

[0090] S5. Given the first threshold ε1, and perform a primary judgment on the grid state: If S sum1≤ε1, it is a strong power grid and the identification ends; if S sum1 >ε1, calculate the number of sampling times N2 required when the high resolution is the set value b, N2 = 1 / bf s , and starting from k = 1, sequentially extract N2 grid-connected current sampling values x k from the N grid-connected current sampling values k to form a high-resolution grid-connected current sequence E2.

[0091] In this embodiment, b = 2Hz and N2 < N.

[0092] S6, perform a one-dimensional discrete S transform on the high-resolution grid-connected current sequence E2 to obtain the high-resolution complex time-domain element Sh pq , where p is the p-th sampling time point of the high-resolution grid-connected current sequence E2, p = 0, 1, 2…, N2 - 1, and q is the q-th frequency point of the high-resolution grid-connected current sequence E2, q = 0, 1, 2…, N2 - 1.

[0093] In this embodiment, the process of performing a one-dimensional discrete S transform on the high-resolution grid-connected current sequence E2 to obtain the high-resolution complex time-domain element Sh pq is as follows:

[0094] Perform a discrete Fourier transform on the high-resolution grid-connected current sequence E2, and its expression is:

[0095]

[0096] In the formula, Y is the spectrum of the high-resolution grid-connected current sequence E2, and i is the imaginary unit;

[0097] According to the relationship between the discrete Fourier transform and the one-dimensional discrete S transform, obtain the expression of the high-resolution complex time-domain element Sh pq :

[0098]

[0099] In the formula, h is the translation frequency quantity of the high-resolution grid-connected current sequence E2, h = 0, 1, 2…, N2 - 1.

[0100] S7, calculate the S transform energy S in the low-frequency band according to the high-resolution complex time-domain element Sh pq . sum2 .

[0101] In this embodiment, the calculation formula of the S transform energy S in the low-frequency band is: sum2 :

[0102]

[0103] In the formula, is the ceiling symbol.

[0104] In this embodiment, the frequency range of the low frequency band is 0 Hz - 20 Hz.

[0105] S8, given the second threshold ε2, if S sum2 > ε2, series-compensated power grid, if S sum2 ≤ ε2, weak power grid.

[0106] The present invention also provides a power grid state identification system, including:

[0107] A sampling module for obtaining the sampled value x of the grid-connected current k ;

[0108] A control module for performing low-resolution setting;

[0109] A calculation module for calculating the number of samplings N1;

[0110] A control module for sequentially extracting N1 grid-connected current sampled values x k from N grid-connected current sampled values x k to form a low-resolution grid-connected current sequence E1;

[0111] A calculation module for performing one-dimensional discrete S transform on the low-resolution grid-connected current sequence E1 to obtain the low-resolution complex time-domain element Sl jn ;

[0112] A calculation module for calculating the S transform energy S of the middle frequency band according to the low-resolution complex time-domain element Sl jn ; sum1 ;

[0113] A control module for performing primary judgment on the power grid state;

[0114] A control module for performing high-resolution setting;

[0115] A calculation module for calculating the number of samplings N2;

[0116] A control module for sequentially extracting N2 grid-connected current sampled values x k from N grid-connected current sampled values x k to form a high-resolution grid-connected current sequence E2;

[0117] A calculation module for performing one-dimensional discrete S transform on the high-low resolution grid-connected current sequence E2 to obtain the high-resolution complex time-domain element Sh pq ;

[0118] A calculation module for calculating the S transform energy S of the low frequency band according to the high-resolution complex time-domain element Sh pq ;sum2 A calculation module;

[0119] A control module for judging the power grid state;

[0120] And a microprocessor and a memory, and each of the above modules and the microprocessor is programmed or configured to execute the steps of the power grid state identification method.

[0121] The present invention also provides a computer-readable storage medium, in which a computer program programmed or configured to execute the power grid state identification method is stored.

[0122] In order to prove the accuracy and superiority of the present invention, the identification method of the present invention is verified by simulation.

[0123] Figure 3 The waveform diagram of the grid-connected current at the PCC point for this simulation experiment, the abscissa is time, and the ordinate is the grid current amplitude. In the simulation experiment, the power grid is set to a strong grid state at 2 seconds, the power grid is switched to a weak grid state after 3 seconds, and the power grid is switched to a series-compensated grid state after 5 seconds.

[0124] Perform a lower-resolution S transform on the sampled current. The S transform energy in the middle frequency band of the strong power grid, weak power grid and series-compensated power grid is as Figure 4 , Figure 5 And Figure 6 shown. It can be seen that the S transform energy in the middle frequency band of the weak power grid and the series-compensated power grid has a magnitude difference from that of the strong power grid, and a threshold can be set to accurately judge the strong power grid state and the unstable state. If the power grid is in an unstable state, perform a high-resolution S transform on the sampled current. The S transform energy in the low frequency band of the weak power grid and the series-compensated power grid is as Figure 7 And Figure 8 shown. It can be seen that the S transform energy in the low frequency band of the series-compensated power grid also has a magnitude difference from that of the weak power grid, and a threshold can be set to accurately judge the weak power grid state and the series-compensated power grid state. Through the simulation experiment, the effectiveness of the identification method proposed by the present invention can be seen.

[0125] Those skilled in the art should understand that the embodiments of the present invention can be provided as a method, a system, or a computer program product. Therefore, the present invention can take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present invention can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code. The solutions in the embodiments of the present invention can be implemented in various computer languages, for example, object-oriented programming languages such as Java and interpreted scripting languages such as JavaScript.

[0126] The present invention is described with reference to the flowcharts and / or block diagrams of methods, apparatuses (systems), and computer program products according to embodiments of the present invention. It should be understood that each flow and / or block in the flowcharts and / or block diagrams, and the combination of flows and / or blocks in the flowcharts and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to the processors of general-purpose computers, special-purpose computers, embedded processors, or other programmable data processing devices to generate a machine, such that the instructions executed by the processors of the computer or other programmable data processing devices produce means for implementing the functions specified in one or more flows Figure 1 one or more flows and / or blocks Figure 1 or in one or more blocks.

[0127] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, such that the instructions stored in the computer-readable memory produce a manufactured article including instruction means for implementing the functions specified in one or more flows Figure 1 one or more flows and / or blocks Figure 1 or in one or more blocks.

[0128] These computer program instructions can also be loaded onto a computer or other programmable data processing device, such that a series of operation steps are executed on the computer or other programmable device to generate a computer-implemented process, and thus the instructions executed on the computer or other programmable device provide steps for implementing the functions specified in one or more flows Figure 1 one or more flows and / or blocks Figure 1 or in one or more blocks.

[0129] Although the preferred embodiments of the present invention have been described, those skilled in the art can make additional changes and modifications once they learn the basic creative concept. Therefore, the appended claims are intended to be construed as including the preferred embodiments and all changes and modifications falling within the scope of the present invention. Obviously, those skilled in the art can make various changes and variations to the present application without departing from the spirit and scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the claims of the present application and their equivalent technologies, the present application is also intended to include these changes and variations.

Claims

1. A power grid state identification method, wherein the power grid is a power grid interconnected with a new energy power generation system, characterized in that, Including: The current at the point of common coupling (PCC) is sampled N times at equal time intervals T to obtain N grid-connected current sampling values x k , where k = 1, 2..., N, and N is the total number of sampling times; Calculate the number of samplings N1 when the low resolution is the set value a, N1 = 1 / af s , f s is the sampling frequency, and starting from k = 1, extract N1 grid-connected current sampling values x k sequentially from the N grid-connected current sampling values x k to form a low-resolution grid-connected current sequence E1; Perform a one-dimensional discrete S-transform on the low-resolution grid-connected current sequence E1 to obtain the low-resolution complex time-domain element Sl jn , where j is the j-th sampling time point of the low-resolution grid-connected current sequence E1, j = 0, 1, 2…, N1 - 1, and n is the n-th frequency point of the low-resolution grid-connected current sequence E1, n = 0, 1, 2…, N1 - 1; According to the low-resolution complex time-domain element Sl jn calculate the S-transform energy S in the middle frequency band sum1 ; Given the first threshold ε1, the initial judgment of the power grid state is performed: if S sum1 ≤ε1, it is a strong power grid, and the identification is completed; if S sum1 >ε1, calculate the number of sampling times N2 required when the high resolution is the set value b, N2=1 / bf s , and starting from k=1, from N grid-connected current sampling values ​​x k Sequentially extract N2 grid-connected current sampling values ​​x k Form a high-resolution grid-connected current sequence E2; Perform a one-dimensional discrete S-transform on the high-resolution grid-connected current sequence E2 to obtain the high-resolution complex time-domain element Sh pq , where p is the p-th sampling time point of the high-resolution grid-connected current sequence E2, p = 0, 1, 2…, N2 - 1, and q is the q-th frequency point of the high-resolution grid-connected current sequence E2, q = 0, 1, 2…, N2 - 1; According to the high-resolution complex time-domain element Sh pq The S-transform energy S in the low-frequency band is calculated sum2 ; Given a second threshold ε2, if S sum2 > ε2, series-compensated power grid, if S sum2 ≤ ε2, weak power grid.

2. The power grid state identification method according to claim 1, characterized in that Performing one-dimensional discrete S-transform on the low-resolution grid-connected current sequence E1 to obtain the low-resolution complex time-domain element Sl jn The process is as follows: Performing discrete Fourier transform on the low-resolution grid-connected current sequence E1, and the expression is: In the formula, X is the spectrum of the low-resolution grid-connected current sequence E1, and i is the imaginary unit; According to the relationship between the discrete Fourier transform and the one-dimensional discrete S transform, the expression of the low-resolution complex time-domain element Sl jn is obtained as follows: In the formula, m is the translation frequency quantity of the low-resolution grid-connected current sequence E1, and m = 0, 1, 2…, N1 - 1.

3. The power grid state identification method according to claim 1, characterized in that, The S transform energy S in the middle frequency band is calculated based on the low-resolution complex time-domain element Sl jn The calculation formula for sum1 is as follows: In the formula, is the ceiling symbol.

4. A power grid state identification method according to claim 1, characterized in that, Perform a one-dimensional discrete S-transform on the high-resolution grid-connected current sequence E2 to obtain the high-resolution complex time-domain element Sh pq The process is as follows: Performing discrete Fourier transform on the high-resolution grid-connected current sequence E2, and its expression is: In the formula, Y is the spectrum of the high-resolution grid-connected current sequence E2, and i is the imaginary unit; Based on the relationship between the discrete Fourier transform and the one-dimensional discrete S transform, the expression of the high-resolution complex time-domain element Sh pq is obtained as follows: In the formula, h is the translation frequency quantity of the high-resolution grid-connected current sequence E2, and h = 0, 1, 2…, N2 - 1.

5. A power grid state identification method according to claim 1, characterized in that, The S transform energy S in the low frequency band calculated according to the high-resolution complex time domain element Sh pq is calculated as follows: sum2 The calculation formula of S In the formula, is the ceiling symbol.

6. A power grid state identification method according to claim 1, characterized in that The frequency range of the medium frequency band is 50H Z -1000H Z , and the frequency range of the low frequency band is 0H Z -20H Z .

7. A power grid state identification system, characterized in that, Including: Sampling module for obtaining the sampled value x of grid-connected current k ; A control module for performing low-resolution setting; A calculation module for calculating the sampling times N1; A control module for sequentially extracting N1 grid-connected current sampling values x from N grid-connected current sampling values x k to form a low-resolution grid-connected current sequence E1; k ​ A calculation module for performing one-dimensional discrete S-transform on a low-resolution grid-connected current sequence E1 to obtain a low-resolution complex time-domain element Sl jn ; Calculation module for calculating the S-transform energy S in the middle frequency band according to the low-resolution complex time-domain element Sl jn and obtaining the S-transform energy S in the middle frequency band sum1 ; A control module for initially judging the grid state; A control module for performing high-resolution setting; A calculation module for calculating the sampling times N2; A control module for sequentially extracting N2 grid-connected current sampling values x from N grid-connected current sampling values x k to form a high-resolution grid-connected current sequence E2; k ​ A calculation module for performing one-dimensional discrete S transform on the high and low resolution grid-connected current sequences E2 to obtain high resolution complex time domain elements Sh pq ; Calculation module for calculating the S-transform energy S in the low-frequency band according to the high-resolution complex time-domain element Sh pq Calculating the S-transform energy S in the low-frequency band sum2 ; A control module for judging the grid state; And a microprocessor and a memory, and each of the above modules and the microprocessor is programmed or configured to execute the steps of the grid state identification method according to any one of claims 1 to 6.

8. A computer-readable storage medium, characterized in that, A computer program programmed or configured to execute the grid state identification method according to any one of claims 1 to 6 is stored in the computer-readable storage medium.

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