A method and system for detection of voltage sag in a direct current distribution network

Abstract

This invention discloses a method and system for detecting voltage sags in DC distribution networks, comprising: acquiring a voltage signal from the DC distribution network; performing multi-resolution decomposition on the voltage signal according to a preset decomposition scale to obtain a maximum layer high-frequency coefficient; calculating the maximum, minimum, and average values ​​of the maximum layer high-frequency coefficient, and the times corresponding to the maximum and minimum values; obtaining the difference between the maximum and average values, calculating the ratio of the difference to the average value, and if the ratio is greater than a preset threshold, determining that a voltage sag has been detected in the DC distribution network. This solves the problem of poor applicability in existing detection methods, leading to inaccurate detection results.

Description

Technical Field

[0001] This invention relates to the field of power system automation technology, and specifically to a method and system for detecting voltage sags in DC distribution networks. Background Technology

[0002] Due to factors such as source-load fluctuations, the power quality issues of DC distribution networks differ from those of existing AC power networks. In particular, voltage sags, the most serious hazard, are crucial to the safe and stable operation of DC distribution systems. Past research and application of voltage sag detection methods designed for AC systems have poor applicability to DC systems, leading to inaccurate detection results. Summary of the Invention

[0003] To overcome the shortcomings of existing technologies, this invention provides a method for detecting voltage sags in DC distribution networks, comprising:

[0004] Obtain the voltage signal from the DC distribution network;

[0005] According to the preset decomposition scale, the voltage signal is decomposed into multi-resolution components to obtain the maximum layer high frequency coefficient.

[0006] Find the maximum, minimum, and average values ​​of the highest layer high-frequency coefficients;

[0007] Obtain the difference between the maximum value and the average value, calculate the ratio of the difference to the average value, and if the ratio is greater than a preset threshold, then determine that the DC distribution network has detected a voltage sag.

[0008] Furthermore, after determining that the DC distribution network has detected a voltage sag, the method further includes:

[0009] Obtain the time of occurrence and duration of the DC distribution voltage sag.

[0010] Furthermore, acquiring the voltage signal of the DC distribution network includes:

[0011] Obtain the voltage signal detection and sampling values ​​of the DC distribution network;

[0012] Calculate the arithmetic average of the DC voltage of the voltage signal detection sample value within a preset time period, where the average value is the voltage signal of the DC distribution network.

[0013] Furthermore, according to a preset decomposition scale, the voltage signal is decomposed into multi-resolution components to obtain the maximum layer high-frequency coefficients, including:

[0014] Define a family of discrete wavelet functions:

[0015]

[0016] In the above formula, a0 and b0 are the discretization scaling and translation parameters, j and k are integers, and a binary wavelet is used, with a0 = 2 and b0 = 1. Therefore, the discrete wavelet transform is:

[0017]

[0018] In the above formula, <,> represent inner product operations;

[0019] The Mallat algorithm is used to perform multi-scale decomposition of the voltage signal, and a low-pass smoothing function ψ(t) is used to approximate the voltage signal step by step to obtain the maximum layer high-frequency coefficient D. i , where i represents the decomposition level.

[0020] Further, the difference between the maximum value and the average value is obtained, and the ratio of the difference to the average value is calculated. If the ratio is greater than a preset threshold, it is determined that the DC distribution network has detected a voltage sag, including:

[0021] The maximum value is denoted as max. i The average value is denoted as mean. i Calculate (max) i -mean i ) / mean i If the calculation result is greater than the preset threshold, it is determined that the DC distribution network has detected a voltage sag.

[0022] This invention also provides a detection system for voltage sag in DC distribution networks, comprising:

[0023] Voltage signal acquisition module, used to acquire voltage signals from DC distribution network;

[0024] The maximum layer high frequency coefficient acquisition module is used to perform multi-resolution decomposition on the voltage signal according to a preset decomposition scale to obtain the maximum layer high frequency coefficient.

[0025] The system calculation module is used to calculate the maximum, minimum, and average values ​​of the maximum layer high-frequency coefficients;

[0026] The voltage sag determination module is used to obtain the difference between the maximum value and the average value, calculate the ratio of the difference to the average value, and if the ratio is greater than a preset threshold, determine that the DC distribution network has detected a voltage sag.

[0027] Furthermore, it also includes:

[0028] The occurrence time and duration acquisition module is used to acquire the occurrence time and duration of the DC distribution voltage sag.

[0029] Furthermore, the voltage signal acquisition module includes:

[0030] The sampling value acquisition submodule is used to acquire the voltage signal detection sampling values ​​of the DC distribution network;

[0031] The average value calculation submodule is used to calculate the arithmetic average value of the DC voltage of the voltage signal detection sample value within a preset time period, wherein the average value is the voltage signal of the DC distribution network.

[0032] Furthermore, the module for obtaining the highest layer high-frequency coefficients includes:

[0033] The function definition submodule is used to define families of discrete wavelet functions:

[0034]

[0035] In the above formula, a0 and b0 are the discretization scaling and translation parameters, j and k are integers, and a binary wavelet is used, with a0 = 2 and b0 = 1. Therefore, the discrete wavelet transform is:

[0036]

[0037] In the above formula, <,> represent inner product operations;

[0038] The decomposition submodule is used to perform multi-scale decomposition of the voltage signal using the Mallat algorithm. It uses a low-pass smoothing function ψ(t) to approximate the voltage signal step by step, obtaining the maximum layer high-frequency coefficient D. i , where i represents the decomposition level.

[0039] Furthermore, the voltage sag determination module includes:

[0040] The voltage sag determination sub-unit is used to denot the maximum value as max. i The average value is denoted as mean. i Calculate (max) i -mean i ) / mean i If the calculation result is greater than the preset threshold, it is determined that the DC distribution network has detected a voltage sag.

[0041] The present invention also provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the steps of the method as described in any one of the preceding claims.

[0042] The present invention provides a method and system for detecting voltage sags in DC distribution networks, employing a family of cubic B-spline wavelet functions as basis functions. The characteristics of wavelet functions have a significant impact on wavelet analysis, including orthogonality, support length, symmetry, smoothness, and the order of vanishing moments. B-spline wavelet functions possess orthogonality, tight support, symmetry, smoothness, and high-order vanishing moments, thus exhibiting fast convergence speed and strong detection capability for abrupt singularities in transient signal analysis. This improves the reliability and sensitivity of DC voltage sag detection and allows for precise location of the start and end times of DC voltage sags. It addresses the problem of poor applicability and inaccurate detection results in existing detection methods. Attached Figure Description

[0043] Figure 1 This is a schematic flowchart of a method for detecting voltage sags in a DC distribution network provided by an embodiment of the present invention;

[0044] Figure 2 This is a schematic diagram of the DC distribution network topology involved in an embodiment of the present invention;

[0045] Figure 3 This is a comparison chart of wavelet basis analysis results involved in the embodiments of the present invention;

[0046] Figure 4 This is a diagram showing the voltage sag waveform and wavelet analysis results involved in the embodiments of the present invention;

[0047] Figure 5 This is a flowchart of the DC distribution network voltage sag detection steps according to an embodiment of the present invention;

[0048] Figure 6 This is a schematic diagram of a detection system for voltage sag in a DC distribution network provided in an embodiment of the present invention. Detailed Implementation

[0049] Numerous specific details are set forth in the following description to provide a full understanding of the invention. However, the invention can be practiced in many other ways different from those described herein, and those skilled in the art can make similar extensions without departing from the spirit of the invention. Therefore, the invention is not limited to the specific embodiments disclosed below.

[0050] Example 1

[0051] Figure 2This is a schematic diagram of the DC distribution network topology involved in the embodiments of the invention. The constructed DC distribution network topology includes two AC power sources, which are rectified into 20kV DC power sources by AC / DC rectifiers VSC1 and VSC2 respectively and connected to the ±10kV DC grid; it includes one energy storage device and one photovoltaic power generation element, which are connected to the ±10kV DC bus through converters; it includes one AC load, which is connected to the 750V DC bus through one DC / AC inverter; it includes one DC load, which is directly connected in parallel to the 750V DC bus; and the 20kV DC bus is connected to the 750V DC bus through a DC / DC chopper.

[0052] Based on the above topology diagram, this invention provides a method for detecting voltage sags in DC distribution networks, the process of which is as follows: Figure 1 As shown, it includes the following steps:

[0053] Step S101: Obtain the voltage signal of the DC distribution network.

[0054] This invention acquires voltage signal sampling values ​​from a DC distribution network through simulation. A DC distribution network model is built in the Simulink simulation environment, with a simulation step size of 1 μs. Disturbances such as faults and distributed power source fluctuations are introduced to obtain continuous sampling values ​​of the DC bus voltage. The sampled signals are exported to Matlab, and the arithmetic mean u of the DC voltage over a preset time period of 20 ms is calculated. dc (t), the average value u dc (t) represents the voltage signal of the DC distribution network.

[0055] Step S102: According to the preset decomposition scale, the voltage signal is decomposed into multi-resolution components to obtain the maximum layer high frequency coefficient.

[0056] Define a family of discrete wavelet functions:

[0057]

[0058] In the above formula, a0 and b0 are the discretization scaling and translation parameters, j and k are integers, and a binary wavelet is used, with a0 = 2 and b0 = 1. Therefore, the discrete wavelet transform is:

[0059]

[0060] In the above formula, <,> represent inner product operations;

[0061] The Mallat algorithm is used to perform multi-scale decomposition of the voltage signal, and a low-pass smoothing function ψ(t) is used to approximate the voltage signal step by step to obtain the maximum layer high-frequency coefficient D. i'i' represents the decomposition level. During this process, the voltage signal is decomposed into low-frequency and high-frequency components. While keeping the high-frequency information unchanged, the low-frequency information is further decomposed into even lower-frequency and higher-frequency components. This process is repeated until the decomposition scale reaches the set value.

[0062] Let the sag signal u dc The expansion of (t) in the finite frequency band is:

[0063]

[0064] In the formula ψ i,k (t) and φ j,k (t) represents the wavelet function and the scaling function, respectively, and the two satisfy orthogonality.

[0065] The wavelet coefficients and scaling coefficients are respectively

[0066] d j,k =f(t),ψ j,k (t) (2)

[0067] c j,k =f(t),φ j,k (t) (3)

[0068] In Mallat's algorithm, the recursive decomposition coefficients are related as follows:

[0069]

[0070]

[0071] By repeatedly decomposing the scaling coefficients, multi-resolution analysis of the signal is achieved. In equations (4) and (5), h[n] and g[n] are low-pass and high-pass digital filter banks, respectively.

[0072] The properties of wavelet functions include:

[0073] (1) Orthogonality: It can ensure that no information redundancy is generated during decomposition and that there is no information aliasing during the basis function translation process;

[0074] (2) Compact support: If the basis wavelet has compact support, it is called a compactly supported wavelet, and the better the time-domain localization ability;

[0075] (3) Symmetry: This makes the filter have a linear phase;

[0076] (4) Smoothness: Fast convergence reduces the truncation error generated by the filter;

[0077] (5) Vanishing moment: The larger the order of the vanishing moment, the stronger the localization ability of the wavelet frequency domain and the faster the convergence speed.

[0078] Choosing a high-performance wavelet basis (wavelet function) for signal decomposition requires satisfying orthogonality, compact support, symmetry, smoothness, and a high vanishing moment order. Using different wavelet basis functions to perform wavelet analysis on the same set of data can yield vastly different results. Haar wavelets, dbN wavelets, and B-spline wavelets are commonly used wavelet bases. Among them, Haar wavelets have poor regularity, dbN wavelets have poor symmetry, while B-spline wavelets possess excellent smoothness and compact support, are symmetric wavelets, and have good linear phase properties, making them suitable for feature extraction of fault signals. In the example application of this invention, the formula for the cubic B-spline binary wavelet is as follows:

[0079]

[0080] The 3rd order B-spline binary wavelet has the following characteristics: it is asymptotically optimal when detecting the singularity of a signal under noisy conditions; it has linear phase; the wavelet transform of the trigonometric function is still a trigonometric function of the same frequency; and the reconstruction algorithm is relatively accurate.

[0081] In the established simulation model of a dual-terminal medium-low voltage DC distribution network, continuous disturbances are introduced to cause multiple voltage dips in the DC bus. Wavelet decomposition of the sampled DC voltage signal is then performed using third-order B-spline dyadic wavelet, Haar wavelet, and db3 wavelet, respectively. Clearly, as... Figure 3 As shown, the 3rd order B-spline binary wavelet has the best analysis effect on signal abrupt change points, while the detection effect of other wavelet basis functions all have omissions.

[0082] Step S103: Calculate the maximum, minimum and average values ​​of the maximum layer high-frequency coefficient.

[0083] For coefficient D i The maximum value is denoted as max. i Find the minimum value and record the corresponding time t1; find the minimum value and record the corresponding time t2; simultaneously, for D... i Find the average, denoted as mean. i .

[0084] The principle of modulus maxima is briefly described as follows:

[0085] If there is |f on one side near x0 w (a0,x)|<|f w (a0,x0)|, ​​on the other side near x0, there is |f w (a0,x)|≤|f w (a0,x0)|, ​​then |f w (a0,x0)| is called the wavelet transform modulus maxima near x0 at the a0 scale.

[0086] For a smooth function θ(t), ψ(t) is its first derivative, i.e., ψ(t) = dθ / dt, which satisfies the admissibility condition of wavelet functions:

[0087]

[0088] The scaling factor a is used as the scaling factor of θ(t). The wavelet function corresponding to the scaling factor is:

[0089]

[0090] The wavelet basis function ψ(t) is used to analyze the DC voltage signal u. dc (t) Perform wavelet transform on scale a

[0091]

[0092] For DC voltage signal u dc (t) After performing wavelet transform, the wavelet transform extremum point will be obtained, which exactly corresponds to the DC voltage sampling signal u. dc The abrupt change point (t) is due to the small abrupt changes in the voltage waveform during the occurrence and recovery of the voltage sag. After the voltage signal undergoes wavelet transform, the modulus maxima of the wavelet coefficients correspond to the signal singular points (discontinuities in the signal or discontinuities in the first-order derivative of the signal). Therefore, taking advantage of the close relationship between singular points and modulus maxima of wavelet coefficients, this invention detects voltage sags by finding modulus maxima and locates the start and end times of the sag.

[0093] Step S104: Obtain the difference between the maximum value and the average value, calculate the ratio of the difference to the average value, and if the ratio is greater than a preset threshold, determine that the DC distribution network has detected a voltage sag.

[0094] Calculate (max) i -mean i ) / mean i If the calculated result is greater than a preset threshold, it is determined that a voltage sag has been detected in the DC distribution network. At this time, the time of occurrence of the voltage sag (t1 and t2) is recorded and the duration (t1-t2) is calculated; otherwise, it is considered that no voltage sag has been detected.

[0095] Example 2

[0096] To verify the effectiveness of the voltage sag detection method for DC distribution networks involved in this invention, an electromagnetic transient simulation model of a two-terminal DC distribution network was built. The simulation parameters are shown in Table 1, and the system topology is as follows: Figure 2 As shown.

[0097] Table 1 System Simulation Parameters

[0098]

[0099] An inter-electrode short-circuit fault is set at the 20kV medium-voltage busbar in the model.

[0100] A permanent metallic inter-pole short circuit fault is set to occur sequentially at 35m from the VSC1 outlet. The fault duration is 0.02s and the fault occurrence time is 0.3s.

[0101] The DC voltage curve after the fault can be obtained, and the present invention can be applied to this curve.

[0102] Wavelet analysis was implemented using MATLAB. The 3rd order B-spline binary wavelet was imported into the wavelet function library. The simulated voltage amplitude curve was decomposed into 11 levels using B-spline wavelets to obtain the approximation and reconstruction coefficients for each level. The high-frequency wavelet coefficients of the 11th level were then selected. (and (Distinguish) values.

[0103] right Find the extreme values ​​and average values ​​and record the corresponding times, and obtain the results respectively. and (respectively with) and distinguish).

[0104] calculate If the result is greater than the threshold of 10, it indicates that a voltage dip has occurred.

[0105] At this point, record the time when the voltage dip occurs. and And calculate the duration. The results are shown in Table 2:

[0106] Table 2. Deviation between theoretical and simulated values ​​of inter-electrode short circuit.

[0107]

[0108] An error of less than 0.01s indicates that this method can effectively identify fault voltage sag waveforms and record their start and end times. Figure 4 As shown.

[0109] The voltage sag detection steps of the DC distribution network involved in this invention are as follows: Figure 5 As shown, firstly, voltage signal sampling is performed. A decomposition level is selected and a threshold is set. The voltage signal is then decomposed using multi-resolution methods to obtain the maximum layer high-frequency coefficients. Wavelet decomposition is then performed on the maximum layer high-frequency coefficients to obtain their maxima, minima, and average values, as well as the times corresponding to the maxima and minima. The maxima is denoted as max. i The average value is denoted as mean. i Calculate (max)i -mean i ) / mean i If the calculation result is greater than the preset threshold, it is determined that the DC distribution network has detected a voltage sag; otherwise, no voltage sag has occurred.

[0110] Based on the same inventive concept, this invention also provides a detection system 600 for voltage sag in DC distribution networks, such as... Figure 6 As shown, it includes:

[0111] Voltage signal acquisition module 610 is used to acquire voltage signals from DC distribution networks;

[0112] The maximum layer high frequency coefficient acquisition module 620 is used to perform multi-resolution decomposition on the voltage signal according to a preset decomposition scale to obtain the maximum layer high frequency coefficient.

[0113] The system calculation module 630 is used to calculate the maximum, minimum and average values ​​of the maximum layer high frequency coefficient;

[0114] The voltage sag determination module 640 is used to obtain the difference between the maximum value and the average value, calculate the ratio of the difference to the average value, and if the ratio is greater than a preset threshold, determine that the DC distribution network has detected a voltage sag.

[0115] Furthermore, it also includes:

[0116] The occurrence time and duration acquisition module is used to acquire the occurrence time and duration of the DC distribution voltage sag.

[0117] Furthermore, the voltage signal acquisition module includes:

[0118] The sampling value acquisition submodule is used to acquire the voltage signal detection sampling values ​​of the DC distribution network;

[0119] The average value calculation submodule is used to calculate the arithmetic average value of the DC voltage of the voltage signal detection sample value within a preset time period, wherein the average value is the voltage signal of the DC distribution network.

[0120] Furthermore, the module for obtaining the highest layer high-frequency coefficients includes:

[0121] The function definition submodule is used to define families of discrete wavelet functions:

[0122]

[0123] In the above formula, a0 and b0 are the discretization scaling and translation parameters, j and k are integers, and a binary wavelet is used, with a0 = 2 and b0 = 1. Therefore, the discrete wavelet transform is:

[0124]

[0125] In the above formula, <,> represent inner product operations;

[0126] The decomposition submodule is used to perform multi-scale decomposition of the voltage signal using the Mallat algorithm. It uses a low-pass smoothing function ψ(t) to approximate the voltage signal step by step, obtaining the maximum layer high-frequency coefficient D. i , where i represents the decomposition level.

[0127] Furthermore, the voltage sag determination module includes:

[0128] The voltage sag determination sub-unit is used to denot the maximum value as max. i The average value is denoted as mean. i Calculate (max) i -mean i ) / mean i If the calculation result is greater than the preset threshold, it is determined that the DC distribution network has detected a voltage sag.

[0129] This application provides a method and system for detecting voltage sags in DC distribution networks. It employs a family of cubic B-spline wavelet functions as basis functions, minimizing susceptibility to external interference signals; improves the reliability and sensitivity of DC voltage sag detection; and accurately pinpoints the start and end times of DC voltage sags. This addresses the problem of poor applicability and inaccurate detection results in existing methods.

[0130] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied 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.

[0131] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0132] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0133] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0134] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit its scope of protection. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that after reading the present invention, they can still make various changes, modifications or equivalent substitutions to the specific implementation of the invention, but these changes, modifications or equivalent substitutions are all within the scope of protection of the pending claims of the invention.

Claims

1. A method for detection of voltage sag in a direct current power distribution network, characterized by, The method comprises the following steps: obtaining a voltage signal of a direct-current power distribution network; performing multi-resolution decomposition on the voltage signal according to a preset decomposition scale to obtain a maximum layer high-frequency coefficient; calculating a maximum value, a minimum value and an average value of the maximum layer high-frequency coefficient; obtaining a difference value between the maximum value and the average value, calculating a ratio of the difference value to the average value, and determining that the direct-current power distribution network detects a voltage sag if the ratio is greater than a preset threshold.

2. The method of claim 1, wherein, After the step of determining that the direct-current power distribution network detects a voltage sag, the method further comprises the following steps: obtaining a time of occurrence and a duration of the voltage sag of the direct-current power distribution network.

3. The method of claim 1, wherein, The method for obtaining a voltage signal of a direct-current power distribution network comprises the following steps: obtaining a sampling value of a voltage signal of a direct-current power distribution network; calculating an arithmetic average value of a direct-current voltage of the sampling value of the voltage signal within a preset time, and the average value being a voltage signal of the direct-current power distribution network.

4. The method of claim 1, wherein, Performing multi-resolution decomposition on the voltage signal according to a preset decomposition scale to obtain a maximum layer high-frequency coefficient comprises the following steps: defining a discrete wavelet function family: In the above formula, a0 and b0 are discrete scaling parameters and translation parameters, j and k are integers, and a binary wavelet is adopted, a0=2 and b0=1, so that the discrete wavelet transform is: In the above formula, <,> is inner product operation; u dc (t) is a direct current voltage signal; The voltage signal is decomposed by Mallat algorithm, and the voltage signal is approximated by low-pass smoothing function ψ(t) to obtain maximum layer high-frequency coefficient D i , i represents decomposition level.

5. A detection system for voltage sag in a direct current distribution network, characterized in that The method comprises the following steps: a voltage signal obtaining module, configured to obtain a voltage signal of a direct-current power distribution network; a maximum layer high-frequency coefficient obtaining module, configured to perform multi-resolution decomposition on the voltage signal according to a preset decomposition scale to obtain a maximum layer high-frequency coefficient; a system calculating module, configured to calculate a maximum value, a minimum value and an average value of the maximum layer high-frequency coefficient; a voltage sag determining module, configured to obtain a difference value between the maximum value and the average value, calculate a ratio of the difference value to the average value, and determine that the direct-current power distribution network detects a voltage sag if the ratio is greater than a preset threshold.

6. The system of claim 5, wherein, The method further comprises the following steps: a time of occurrence and a duration obtaining module, configured to obtain a time of occurrence and a duration of the voltage sag of the direct-current power distribution network.

7. The system of claim 5, wherein, The voltage signal obtaining module comprises the following steps: a sampling value obtaining sub-module, configured to obtain a sampling value of a voltage signal of a direct-current power distribution network; an average value calculating sub-module, configured to calculate an arithmetic average value of a direct-current voltage of the sampling value of the voltage signal within a preset time, and the average value being a voltage signal of the direct-current power distribution network.

8. The system of claim 5, wherein, The maximum layer high-frequency coefficient obtaining module comprises the following steps: a function defining sub-module, configured to define a discrete wavelet function family: In the above formula, a0 and b0 are discrete scaling parameters and translation parameters, j and k are integers, and a binary wavelet is adopted, a0=2 and b0=1, so that the discrete wavelet transform is: In the above formula, <,> is inner product operation; u dc (t) is a direct current voltage signal; The decomposition sub-module is configured to perform multi-scale decomposition on the voltage signal by using the Mallat algorithm, to perform step-by-step approximation on the voltage signal by using a low-pass smoothing function ψ(t), and to obtain a maximum layer high-frequency coefficient D i , where i represents a decomposition level.

9. A computer-readable storage medium having stored thereon a computer program, characterized in that, The computer program is executed by the processor to realize the steps of the method in any one of claims 1 to 4.

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