Power grid fault traveling wave head calibration method and device and storage medium

Abstract

The invention discloses a power grid fault traveling wave head calibration method and device and a storage medium, and the method comprises the steps: collecting an initial three-phase voltage in a normal operation state and a fault three-phase voltage in a fault state for a power transmission line of a power grid, and separating a fault component relative to the initial three-phase voltage in the fault three-phase voltage, carrying out phase-mode transformation on the fault component to obtain an independent sequence component, decomposing the independent sequence component into a plurality of initial intrinsic mode components, optimizing the plurality of initial intrinsic mode components according to a preset optimization condition to obtain a plurality of target intrinsic mode components, calculating kurtosis values of the plurality of target intrinsic mode components, and calculating the kurtosis values of the plurality of target intrinsic mode components according to the kurtosis values of the plurality of target intrinsic mode components. And according to the kurtosis value, calibrating a traveling wave head generated when the power grid breaks down. By optimizing the initial intrinsic mode component and calculating the kurtosis value of the target intrinsic mode component, the signal with the most fault feature can be effectively identified, so that the traveling wave head is accurately calibrated, and the efficiency and precision of power grid fault positioning and identification are improved.

Description

Technical Field

[0001] The embodiments of the present invention relate to the field of power grid fault location, and in particular to a power grid fault traveling wave head calibration method, device and storage medium. Background Art

[0002] As the scale of power systems continues to expand, the safety and stability of power grids are becoming increasingly important. Rapid detection and accurate positioning of power grid faults are one of the factors to ensure the stable operation of power systems. When a power grid fault occurs, the fault signal propagates along the transmission line in the form of a traveling wave. Accurate calibration of the traveling wave head can help engineers find the fault point in time, reduce system downtime and fault impact, and improve the reliability of the power system.

[0003] Traditional power grid fault detection methods mainly rely on wavelet modulus maximum method and Hilbert-Huang transform, which have low computational efficiency and may produce different results in analyzing traveling waves and have poor adaptability to multiple fault modes in complex power grids. Especially in multi-phase power grids, the types and characteristics of faults are diverse, and traditional fault detection technologies often have low efficiency and accuracy in locating and identifying power grid faults. Summary of the invention

[0004] The present invention provides a method, a device and a storage medium for calibrating a traveling wave head of a power grid fault, so as to improve the efficiency and accuracy of locating and identifying power grid faults.

[0005] In a first aspect, an embodiment of the present invention provides a method for calibrating a traveling wave head of a power grid fault, comprising:

[0006] The initial three-phase voltage under normal operation and the fault three-phase voltage under fault state are collected from the transmission lines of the power grid respectively;

[0007] separating a fault component relative to the initial three-phase voltage from the fault three-phase voltage;

[0008] Performing phase mode transformation on the fault component to obtain independent sequence components;

[0009] Decomposing the independent sequence components into a plurality of initial intrinsic mode components;

[0010] Optimizing the multiple initial intrinsic modal components according to preset optimization conditions to obtain multiple target intrinsic modal components;

[0011] Calculating kurtosis values ​​of a plurality of target intrinsic modal components;

[0012] The traveling wave head generated when a fault occurs in the power grid is calibrated according to the kurtosis value.

[0013] In a second aspect, an embodiment of the present invention further provides a power grid fault traveling wave head calibration device, comprising:

[0014] The three-phase voltage acquisition module is used to respectively acquire the initial three-phase voltage in the normal operation state and the fault three-phase voltage in the fault state of the power transmission line of the power grid;

[0015] A fault component separation module, used for separating the fault component relative to the initial three-phase voltage in the fault three-phase voltage;

[0016] An independent sequence component acquisition module, used for performing phase mode transformation on the fault component to obtain an independent sequence component;

[0017] An initial intrinsic modal component decomposition module, used for decomposing the independent sequence component into a plurality of initial intrinsic modal components;

[0018] A target intrinsic modal component optimization module, used for optimizing the multiple initial intrinsic modal components according to preset optimization conditions to obtain multiple target intrinsic modal components;

[0019] A kurtosis value calculation module, used for calculating the kurtosis values ​​of a plurality of said target intrinsic modal components;

[0020] The traveling wave head calibration module is used to calibrate the traveling wave head generated when the power grid fails according to the kurtosis value.

[0021] In a third aspect, an embodiment of the present invention further provides a computer device, the computer device comprising:

[0022] one or more processors;

[0023] A storage device for storing one or more programs;

[0024] When the one or more programs are executed by the one or more processors, the one or more processors implement the method for calibrating the traveling wave head of a power grid fault as provided in the first aspect of the present invention.

[0025] In a fourth aspect, an embodiment of the present invention further provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the method for calibrating the traveling wave head of a power grid fault as provided in the first aspect of the present invention.

[0026] In a fifth aspect, an embodiment of the present invention further provides a computer program product, which includes a computer program. When the computer program is executed by a processor, it implements the method for calibrating the traveling wave head of a power grid fault provided in the first aspect of the present invention.

[0027] In the embodiment of the present invention, the initial three-phase voltage under normal operation and the faulty three-phase voltage under fault state are collected from the transmission line of the power grid respectively, the fault component relative to the initial three-phase voltage is separated from the faulty three-phase voltage, the fault component is subjected to phase mode transformation to obtain independent sequence components, the independent sequence components are decomposed into multiple initial intrinsic modal components, the multiple initial intrinsic modal components are optimized according to preset optimization conditions to obtain multiple target intrinsic modal components, the kurtosis values ​​of the multiple target intrinsic modal components are calculated, and the traveling wave head generated when the power grid fails is calibrated according to the kurtosis value. By comparing the three-phase voltages under normal operation and fault state, the fault component is accurately separated, providing a clear signal basis for subsequent analysis. After phase mode transformation, the fault component is decomposed into independent sequence components, which is convenient for further analyzing the voltage characteristics of different sequences, optimizing the initial intrinsic modal components, removing noise and enhancing the key features of the signal, laying the foundation for accurate traveling wave head calibration, calculating the kurtosis value of the target intrinsic modal component, and being able to effectively identify the signal with the most fault characteristics, thereby accurately calibrating the traveling wave head and improving the efficiency and accuracy of power grid fault location and identification. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] Figure 1 This is a flow chart of a method for calibrating a traveling wave head of a power grid fault provided in accordance with the first embodiment of the present invention;

[0029] Figure 2 is a schematic diagram of a fault model provided according to Embodiment 1 of the present invention;

[0030] Figure 3 is a schematic diagram of an original signal of a faulty three-phase voltage provided according to the first embodiment of the present invention;

[0031] Figure 4 is a schematic diagram of a variational mode decomposition result provided according to the first embodiment of the present invention;

[0032] Figure 5 is a schematic diagram of a target natural modal component provided according to Embodiment 1 of the present invention;

[0033] Figure 6 is a calibration diagram of an energy operator provided according to the first embodiment of the present invention;

[0034] Figure 7 This is a structural example diagram of a power grid fault traveling wave head calibration device provided in accordance with Embodiment 2 of the present invention;

[0035] Figure 8 It is a structural diagram of a computer device provided according to Embodiment 3 of the present invention. DETAILED DESCRIPTION

[0036] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present invention.

[0037] It should be noted that the terms "first", "second", etc. in the specification and claims of the present invention 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 invention described herein can cover sequential implementations other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof 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.

[0038] Embodiment 1

[0039] See also Figure 1 , shows a flow chart of a method for calibrating a traveling wave head for a power grid fault provided by a first embodiment of the present invention. The method can be executed by a calibrating device for calibrating a traveling wave head for a power grid fault. The calibrating device for calibrating a traveling wave head for a power grid fault can be implemented in the form of hardware and / or software. The calibrating device for calibrating a traveling wave head for a power grid fault can be configured in a computer device. Figure 1 As shown, the method includes:

[0040] Step 101: collecting the initial three-phase voltage in a normal operating state and the fault three-phase voltage in a fault state from the transmission line of the power grid.

[0041] In this embodiment, the three-phase voltage is a parameter characterizing the operation state of the power grid, and the waveform characteristics and dynamic changes of the three-phase voltage can reflect the operation state and fault characteristics of the power grid. By collecting the initial three-phase voltage under normal conditions, a benchmark can be established as reference data; and by collecting the faulty three-phase voltage under fault conditions, the abnormal changes in the voltage in the power grid after the fault occurs can be captured. This abnormal change is an important manifestation of the fault traveling wave, which is used for subsequent analysis and fault location.

[0042] The reason for choosing to collect three-phase voltage instead of current is that the fluctuation of three-phase voltage when a fault occurs is direct and significant, which can clearly reflect the propagation characteristics of the fault waveform and the characteristics of the traveling wave head. At the same time, compared with the current signal, the three-phase voltage signal is usually not interfered by the line load and power supply characteristics, which is suitable for high-precision traveling wave analysis. However, the current signal is greatly affected by the load and the inductance and resistance of the fault point, which may mask some traveling wave characteristics, which is not conducive to accurate fault location and traveling wave analysis. In addition, the measurement equipment of three-phase voltage signals (such as voltage transformers) is more common in transmission lines, which is convenient for obtaining three-phase voltage data.

[0043] For example, Figure 2 The figure shows the fault model of the power transmission line in the power grid under fault condition. In the figure, Line / 100km means that the length of a line in the power grid is 100km and the two sections of Line 1 are connected to a voltage of 220kV, and a grounding resistor is connected below Line 1. Line3 / 150km means that the length of a line in the power grid is 150km and one end of Line3 is connected to a voltage of 220kV.

[0044] like Figure 3 The figure shows the original signal of the faulty three-phase voltage collected by the transmission line of the power grid under the fault state. The waveform in the figure shows that the original signal of the faulty three-phase voltage presents different fluctuations over time. The horizontal axis in the figure represents time, and the vertical axis represents frequency.

[0045] Step 102: Separate the fault component relative to the initial three-phase voltage from the faulty three-phase voltage.

[0046] In this embodiment, the part of the abnormal voltage change in the power grid after the fault occurs is extracted from the fault three-phase voltage, that is, the fault component. The fault component directly reflects the dynamic characteristics of the power grid fault, including the propagation characteristics of the traveling wave head, the disturbance of the waveform, and the time and space characteristics of the fault point. By separating the fault component, the interference of the initial three-phase voltage can be reduced, and the three-phase voltage information in the normal state can be prevented from covering the characteristic signal of the fault.

[0047] For example, the difference between the faulty three-phase voltage and the initial three-phase voltage is used as the fault component of the faulty three-phase voltage, which can effectively eliminate the voltage component under normal operating conditions, thereby highlighting the voltage change of the power grid caused by the fault, providing a high-purity fault component input for subsequent fault analysis, and facilitating the accurate positioning of the fault traveling wave head. The initial three-phase voltage includes the initial A-phase voltage, the initial B-phase voltage, and the initial C-phase voltage; the faulty three-phase voltage includes the faulty A-phase voltage, the faulty B-phase voltage, and the faulty C-phase voltage.

[0048] Among them, the fault component of the fault three-phase voltage is expressed as:

[0049] V m,a=V sag,a -V nor,a ;

[0050] V m,b =V sag,b -V nor,b ;

[0051] V m,c =V sag,c -V nor,c ;

[0052] Where V m,a Represents the fault component of the fault phase A voltage, V m,b Indicates the fault component of the fault B phase voltage, V m,c Represents the fault component of the fault C phase voltage, V sag，a Indicates the fault A phase voltage, V sag，b Indicates the fault B phase voltage, V sag,c Indicates the fault C phase voltage, V nor，a Represents the initial A phase voltage, V nor，b Represents the initial B phase voltage, V nor，c Represents the initial C-phase voltage.

[0053] Step 103: Perform phase transformation on the fault component to obtain independent sequence components.

[0054] In this embodiment, when a transmission line of the power grid fails (such as a single-phase grounding fault), due to the coupling between the phase voltages in the three-phase voltage, not only the faulty three-phase voltage will generate traveling waves, but the initial three-phase voltage will also generate traveling waves. In order to extract the traveling wave information when the power grid fails from the three-phase voltage, the fault component is transformed into a phase mode, and the non-independent phase components of the faulty three-phase voltage are converted into independent sequence components.

[0055] Exemplarily, the Karenbauer matrix is ​​used to perform phase transformation on the fault component to obtain an independent sequence component, and the coupling information between the three-phase voltages in the power grid after the power grid fault occurs is separated and extracted, so as to more clearly identify the power grid fault characteristics. The Karenbauer matrix is ​​a standard tool for symmetrical component transformation. The non-independent voltage components (such as phase A, phase B, and phase C) of the three-phase system in the power grid can be converted into independent sequence components through the Karenbauer matrix. The independent sequence components include positive sequence voltage components, negative sequence voltage components, and zero sequence voltage components. The positive sequence voltage component (represents the main component under the symmetrical and balanced operating state), the negative sequence voltage component (represents the asymmetrical fault characteristics in the power grid), and the zero sequence voltage component (used to capture the characteristics of ground faults or imbalances).

[0056] Among them, the independent sequence components are expressed as:

[0057]

[0058] In the formula, u α represents the positive sequence voltage component, u β represents the negative sequence voltage component, u0 represents the zero sequence voltage component, V m，a Represents the fault component of the fault phase A voltage, V m，b Indicates the fault component of the fault B phase voltage, V m，c Represents the fault component of the fault C phase voltage.

[0059] The distribution parameters of the power transmission lines of the power grid are shown in Table 1.

[0060] Table 1 Transmission line distribution parameters

[0061]

[0062] Step 104: Decompose the independent sequence components into a plurality of initial intrinsic mode components.

[0063] In this embodiment, the positive sequence voltage component in the independent sequence component is decomposed into multiple initial intrinsic modal components, and the dynamic characteristics hidden in the power grid fault are further extracted, and the time-frequency characteristics and propagation laws of the signal after the fault occur are accurately captured.

[0064] The positive sequence voltage component is the main manifestation of the symmetrical component in the three-phase system of the power grid under normal operating conditions, and is also the part of power transmission in the operation of the power grid. After a fault occurs, the change of the positive sequence component reflects the impact of the fault on the main transmission channel of the three-phase system of the power grid. Therefore, decomposing the positive sequence component can capture the main impact of the fault on the main characteristics of the power grid, which is helpful for rapid positioning and analysis of the fault. The negative sequence component mainly reflects the characteristics of asymmetric faults, such as single-phase grounding faults or two-phase short circuit faults. However, the characteristics of these asymmetric faults are often concentrated in the lower frequency range, the change law is relatively simple, and the benefit of decomposing the negative sequence component is low. The zero sequence component is mainly closely related to the ground fault and is used to capture the unbalanced characteristics of the grounding loop. However, the zero sequence component is usually small in amplitude, low in signal-to-noise ratio, and its spectral characteristics are not complex, so it is not suitable for extracting more information through decomposition.

[0065] In one embodiment of the present invention, step 104 may include the following steps:

[0066] Step 1041: randomly select a plurality of parameter combinations of positive sequence voltage components in the decomposed independent sequence components, and initialize the plurality of parameter combinations to positions in the double ice population.

[0067] In this embodiment, by randomly initializing multiple parameter combinations as candidate solutions, a search group (i.e., a double ice group) is formed to provide a diverse initial solution space for decomposing the positive sequence voltage component. The random initialization of candidate solutions can increase the globality of the search, avoid falling into a local optimal solution, and provide sufficient flexibility and diversity for subsequent optimization algorithms.

[0068] Step 1042: Calculate the fitness of each position.

[0069] In this embodiment, the fitness function is used to evaluate the quality of each parameter combination. The fitness function is usually related to the physical meaning of the decomposition result or the target optimization index (such as the kurtosis value of the intrinsic modal component, signal sparsity, etc.), and the purpose is to find the optimal parameter combination through quantitative criteria. This process is the core of the optimization, and the effect of different parameter combinations on the decomposition of the positive sequence voltage component can be measured by calculating the fitness.

[0070] Step 1043, determine whether the preset optimization conditions are met; if so, execute step 1044; if not, execute 1045.

[0071] In this embodiment, the best position and the best fitness of the current parameter combination are obtained by greedy selection, and it is determined whether the parameter combination meets the preset optimization conditions (such as the fitness reaches a certain threshold or the number of iterations reaches an upper limit), and whether to stop the optimization process. The preset conditions ensure that the optimization process will neither terminate prematurely nor fall into meaningless and lengthy calculations. If the optimization conditions are not met, the parameter combination needs to be further adjusted to continuously improve the decomposition results.

[0072] Step 1044: Use the parameter combination corresponding to the position with the highest fitness as the parameter combination for decomposing the positive sequence voltage component.

[0073] In this embodiment, during the optimization process, the position with the highest fitness is selected to represent the current optimal solution. By selecting the optimal parameter combination, it can be ensured that the result of variational mode decomposition (VMD) is closest to the target optimization index. This step is a phased result of the entire optimization process and provides the input of the optimal parameter combination for the final decomposition.

[0074] Step 1045, execute the soft frost search strategy and hard frost puncture mechanism to update the positions of multiple positions, and return to execute steps 1042-1043.

[0075] In this embodiment, the position of the parameter combination is dynamically adjusted through the double ice mechanism (soft ice search strategy and hard ice penetration mechanism) to jump out of the local optimal solution and further explore the search space. The soft ice strategy emphasizes the fine adjustment of a smaller range in the search space, while the hard ice mechanism breaks through the local optimal trap through a large range of parameter perturbations. This combination of double ice mechanism strategies can improve the globality and efficiency of optimization, help find a better parameter combination, return to recalculate the fitness of each position, and select the optimal parameter combination to replace the suboptimal parameter combination through the positive greedy selection mechanism.

[0076] Positive Greedy Selection Mechanism is a strategy commonly used in optimization algorithms, which aims to build a solution by gradually selecting the option that is most beneficial to the current goal. Specifically, the positive greedy selection mechanism selects a local optimal solution at each step, hoping to gradually approach the global optimal solution through the local optimal selection. The positive greedy strategy usually tends to select the option that is most beneficial to the current state without considering the impact in the future, so it may not be able to obtain the global optimal solution, but in some problems, the greedy strategy can quickly provide a good approximate solution.

[0077] Step 1046: Decompose the positive sequence voltage component into a plurality of initial natural mode components according to the parameter combination.

[0078] In this embodiment, by determining the optimal parameter combination, variational mode decomposition (VMD) is performed to decompose the positive sequence voltage component into multiple initial intrinsic mode components with physical significance. The initial intrinsic mode components provide a basis for the subsequent extraction of characteristic information related to power grid faults. These initial intrinsic mode components can reflect the various frequency characteristics and local characteristics of the positive sequence voltage component, and provide data support for the calibration of the traveling wave head.

[0079] The embodiment of the present invention establishes the optimal parameter combination of variational mode decomposition (VMD) through the relevance iteration (RIME) algorithm, and the parameter combination includes the optimal mode number K and penalty factor α for decomposing the positive sequence voltage component. The minimum value of the envelope entropy is used as the fitness function in the process of RIME optimization of VMD parameters. The envelope entropy can be used as a measure of the sparsity of the positive sequence voltage component. The lower the aliasing degree between the initial natural mode components obtained by signal decomposition, the smaller the value of the envelope entropy.

[0080] RIME is an iteration-based method that is usually used to solve optimization problems. Its core idea is to continuously improve the quality of the solution by gradually adjusting and iterating the optimization solution until it converges to an optimal solution or a near-optimal solution.

[0081] VMD is a time-frequency decomposition method used to decompose complex signals into several modes, each with different frequency components. The VMD method is particularly suitable for the analysis of non-stationary signals, and it can effectively extract different frequency modes from the signal.

[0082] The optimal mode number K in VMD refers to the number of modes selected during the decomposition process, while the penalty factor α is a parameter that controls the frequency distribution of the mode. The selection of the optimal mode number and penalty factor has an important impact on the performance of VMD. Too many modes will lead to overfitting, while too few modes may not fully capture the characteristics of the signal.

[0083] For example, Figure 4 The figure shows the result of variational mode decomposition. In the figure, IMF1-IMF5 are all initial intrinsic mode components. The horizontal axis of each initial intrinsic mode component represents time, and the vertical axis represents frequency. Figure 4 It also includes the residual of the VMD decomposition result, each initial natural mode component and the change of the residual frequency within a preset time.

[0084] Step 105 : Optimize the multiple initial intrinsic modal components according to the preset optimization conditions to obtain multiple target intrinsic modal components.

[0085] In this embodiment, multiple initial intrinsic modal components are optimized according to preset optimization conditions, aiming to improve the accuracy and reliability of the target intrinsic modal components. The target intrinsic modal components can more accurately reflect the characteristics of the traveling wave head generated during the power grid fault process through optimization, thereby improving the accuracy of fault detection and location. The optimized target intrinsic modal components can better capture the dynamic changes when the fault occurs, provide more accurate data support for the subsequent traveling wave head calibration, and thus improve the efficiency and accuracy of power grid fault diagnosis and recovery.

[0086] Step 1051: construct a variational constraint model.

[0087] In this embodiment, constructing a variational constraint model is to link the characteristics of the initial natural modal components (such as instantaneous frequency, bandwidth, etc.) with the constraint conditions through mathematical modeling, thereby providing clear goals and constraints for subsequent optimization calculations. This process helps determine the frequency characteristics of the initial natural modal components, so that in the subsequent optimization process, it is possible to better find a mode that meets the characteristics of the power grid fault.

[0088] Exemplarily, by performing a Hilbert transform on the initial natural mode components, an analytical signal of the initial natural mode components is obtained, and the analytical signal is spectrally modulated according to the estimated center frequency. If the spectral modulation is completed, the norm of the square of the gradient of the analytical signal is calculated to obtain a variational constraint model, which helps analyze the frequency characteristics of the initial natural mode components, provides accurate frequency and bandwidth information for subsequent optimization steps, and processes traveling wave head data caused by power grid faults.

[0089] Among them, the variational constraint model is expressed as:

[0090]

[0091] In the formula, C k represents the set of initial natural mode components, ω k represents the set of instantaneous frequencies, K represents the number of initial natural mode components, represents partial derivative, δ(t) represents unit pulse signal, j represents imaginary number sign, t represents time, ω k (t) represents the instantaneous frequency of the initial natural mode component, C k (t) represents the initial natural mode component, represents the estimated center frequency, st represents the constraint condition, and X(t) represents the set of initial natural mode components.

[0092] Step 1052: Calculate the center frequency and bandwidth of the initial natural mode component according to the variational constraint model.

[0093] In this embodiment, the center frequency and bandwidth of the initial intrinsic modal component are calculated according to the variational constraint model, and the spectrum characteristics of the initial intrinsic modal component are accurately described. The center frequency reflects the main frequency of the initial intrinsic modal component, and the bandwidth provides the width of the spectrum of the initial intrinsic modal component.

[0094] Step 1053: Convert the variational constrained model into an unconstrained variational model according to the center frequency and bandwidth.

[0095] In this embodiment, the optimization problem may become complicated and difficult to solve under the condition of constraints. By converting the variational constraint model into an unconstrained model, optimization can be performed without complex constraints, thereby improving computational efficiency and adjusting the optimization strategy more flexibly.

[0096] Exemplarily, the unconstrained variational model is expressed as:

[0097]

[0098] Where L represents the augmented Lagrangian function, u k represents the set of bandwidths, ω krepresents the set of instantaneous frequencies, λ(t) represents the Lagrange multiplier, α represents the penalty factor, K represents the number of initial natural mode components, represents partial derivative, δ(t) represents unit pulse signal, j represents imaginary number sign, t represents time, ω k (t) represents the instantaneous frequency of the initial natural mode component, C k (t) represents the initial natural mode component, represents the estimated center frequency, and f(t) represents the center frequency.

[0099] Step 1054: Optimize the initial intrinsic modal components according to the unconstrained variational model and preset constraints to obtain target intrinsic modal components.

[0100] In this embodiment, on the basis of the unconstrained model, the preset optimization conditions are combined for optimization to obtain the target intrinsic modal components. Through optimization, the characteristics of the signal during power grid faults can be better captured, and the robustness and accuracy of the model can be enhanced. Finally, the optimized target intrinsic modal components will be used for subsequent traveling wave head calibration, thereby improving the accuracy of fault diagnosis and location.

[0101] Exemplarily, an alternating multiplication operator is used to solve the unconstrained variational model under preset constraints to optimize the initial intrinsic modal components and obtain target intrinsic modal components.

[0102] Specifically, C in the unconstrained variational model k (t),ω k (t), λ(t) are initialized, and the expressions are as follows:

[0103]

[0104] In the formula, C k (t) represents the kth natural mode component, Ak(t) represents the instantaneous amplitude of the kth natural mode component, represents the instantaneous phase function, ω k (t) represents the instantaneous frequency of the kth natural mode component. The envelope of the natural mode component is non-negative. k (t)≥0, the instantaneous phase function is a non-decreasing function,

[0105] The unconstrained variational model is solved iteratively using the alternating multiplication operator, and the cycle starts. The number of iterations is n=n+1. All variables in each initial intrinsic modal component are updated to obtain multiple target intrinsic modal components. The expression is as follows:

[0106] The frequency spectrum of the initial intrinsic modal component is iteratively calculated, and the specific iterative update expression is as follows:

[0107]

[0108] In the formula, represents the spectrum of the kth initial intrinsic mode component at the n+1th iteration, represents the spectrum of the center frequency, n represents the number of iterations, represents the spectrum of the Lagrange multiplier, ω represents the initial instantaneous frequency, represents the instantaneous frequency of the kth initial natural mode component at the nth iteration, K represents the number of initial natural modes, and α represents the penalty factor.

[0109] The instantaneous frequency is iteratively calculated, and the specific iterative update expression is as follows:

[0110]

[0111] In the formula, represents the kth instantaneous frequency of the n+1th iteration, ω represents the initial instantaneous frequency, represents the spectrum of the kth initial natural mode component at the n+1th iteration, where n represents the number of iterations.

[0112] The spectrum of the Lagrange multiplier is iteratively calculated, and the specific iterative update expression is as follows:

[0113]

[0114] In the formula, represents the Lagrange multiplier of the n+1th iteration, represents the Lagrange multiplier of the nth iteration, n represents the number of iterations, γ represents the noise tolerance parameter, represents the spectrum with the center frequency, represents the spectrum of the kth initial natural mode component at the n+1th iteration, and K represents the number of initial natural modes.

[0115] Finally, the preset constraints are given as follows. When the initial natural mode component meets the accuracy requirement, stop, otherwise repeat the above steps to continue solving. The preset constraints are expressed as:

[0116]

[0117] Where n represents the number of iterations of the alternating multiplication operator, K represents the number of initial natural mode components, represents the bandwidth at the n+1th iteration, represents the bandwidth of the nth iteration, and ∈ represents the preset constraint.

[0118] When the entire iterative process is completed, k target intrinsic modal components are obtained.

[0119] like Figure 5 The figure shows a schematic diagram of the target intrinsic modal components. In the figure, IMF1-IMF6 represent the target intrinsic modal components. The horizontal axis of each target intrinsic modal component represents time, and the vertical axis represents frequency. The frequency of each target intrinsic modal component changes differently at different times.

[0120] Step 106: Calculate the kurtosis values ​​of multiple target natural mode components.

[0121] In this embodiment, the sharpness and mutation characteristics of the target intrinsic mode are evaluated by analyzing the kurtosis value of each target intrinsic mode component. Kurtosis is an indicator used to measure the steepness and non-Gaussian characteristics of a signal waveform, which can effectively identify the characteristics of the traveling wave head generated when a power grid fault occurs. By calculating the kurtosis values ​​of multiple target intrinsic mode components, the energy concentration degree of different target intrinsic mode components and the significance of the fault signal can be accurately distinguished, thereby providing a key basis for the subsequent traveling wave head calibration.

[0122] Exemplarily, the kurtosis value is expressed as:

[0123]

[0124] In the formula, R k represents the kurtosis value of the kth target intrinsic modal component, N represents the number of parameters of the target intrinsic modal component, represents the i-th parameter of the k-th target intrinsic modal component, Represents the sum of the number of parameters of the kth target intrinsic modal component. When the output result contains only the initial traveling wave head, the sharper the waveform, the larger the R value; but when it contains multiple wave heads, the R value decreases.

[0125] Step 107: calibrate the traveling wave head generated when a power grid fault occurs according to the kurtosis value.

[0126] In this embodiment, the traveling wave head generated when a power grid fault occurs is calibrated according to the kurtosis value, and the modal component that best reflects the fault characteristics is selected by evaluating the kurtosis value of the target intrinsic modal component, thereby accurately calibrating the traveling wave head. The kurtosis value can measure the sharpness and mutation characteristics of the signal, and the intrinsic modal component with a higher kurtosis value can often better reflect the characteristics of the fault waveform.

[0127] Exemplarily, the Teager energy operator is used to calibrate the traveling wave head generated after a power grid fault based on the target natural mode component with the highest kurtosis value; when the initial traveling wave of the fault reaches the detection point, the frequency and amplitude of the high-frequency component change, thereby increasing the instantaneous energy amplitude of the Teager energy operator, and the moment when the instantaneous energy amplitude is maximum is the moment when the initial traveling wave of the fault reaches the detection point.

[0128] Among them, the energy operator is expressed as:

[0129] Ψ[f(N)]=[f(N)] 2 -f(N+1)f(N-1);

[0130] Where Ψ[f(N)] represents the energy operator of the target intrinsic modal component with the highest kurtosis value, f(N) represents the target intrinsic modal component with the highest kurtosis value, f(N+1) represents the next target intrinsic modal component of the target intrinsic modal component with the highest kurtosis value, and f(N-1) represents the previous target intrinsic modal component of the target intrinsic modal component with the highest kurtosis value.

[0131] For example, Figure 6 The figure shows the calibration diagram of the energy operator. There are multiple wave heads in the figure. The target inherent modal component with the highest kurtosis value is selected for calibration. The ordinate represents the size of the kurtosis value, and the abscissa represents time. According to the highest kurtosis value, the wave head with the abscissa of 37 and the ordinate of 290499000 is selected for calibration.

[0132] In an embodiment of the present invention, detecting traveling waves of power grid faults is one of the cores of traveling wave positioning technology, which directly affects the accuracy of positioning. In transmission lines, the application of traveling wave method has been relatively mature, and can be mainly divided into single-end method and double-end method. However, there are some challenges when applying the traveling wave method to the distribution network, because the distribution line usually has more branches, which makes the single-end method not effective enough. However, with the development of the Beidou positioning system, the double-end method has been widely used, and the synchronization time accuracy has been guaranteed.

[0133] In the embodiment of the present invention, the initial three-phase voltage under normal operation and the faulty three-phase voltage under fault state are collected from the transmission line of the power grid respectively, the fault component relative to the initial three-phase voltage is separated from the faulty three-phase voltage, the fault component is subjected to phase mode transformation to obtain independent sequence components, the independent sequence components are decomposed into multiple initial intrinsic modal components, the multiple initial intrinsic modal components are optimized according to preset optimization conditions to obtain multiple target intrinsic modal components, the kurtosis values ​​of the multiple target intrinsic modal components are calculated, and the traveling wave head generated when the power grid fails is calibrated according to the kurtosis value. By comparing the three-phase voltages under normal operation and fault state, the fault component is accurately separated, providing a clear signal basis for subsequent analysis. After phase mode transformation, the fault component is decomposed into independent sequence components, which is convenient for further analyzing the voltage characteristics of different sequences, optimizing the initial intrinsic modal components, removing noise and enhancing the key features of the signal, laying the foundation for accurate traveling wave head calibration, calculating the kurtosis value of the target intrinsic modal component, and being able to effectively identify the signal with the most fault characteristics, thereby accurately calibrating the traveling wave head and improving the efficiency and accuracy of power grid fault location and identification.

[0134] Embodiment 2

[0135] Figure 7 A schematic diagram of a power grid fault traveling wave head calibration device provided in the second embodiment of the present invention is shown in FIG. Figure 7 As shown, the device comprises:

[0136] The three-phase voltage acquisition module 701 is used to respectively acquire the initial three-phase voltage in the normal operation state and the fault three-phase voltage in the fault state of the power transmission line of the power grid;

[0137] A fault component separation module 702, configured to separate a fault component relative to the initial three-phase voltage from the faulty three-phase voltage;

[0138] An independent sequence component acquisition module 703 is used to perform phase mode transformation on the fault component to obtain an independent sequence component;

[0139] An initial intrinsic modal component decomposition module 704, configured to decompose the independent sequence components into a plurality of initial intrinsic modal components;

[0140] A target intrinsic modal component optimization module 705 is used to optimize the multiple initial intrinsic modal components according to a preset optimization condition to obtain multiple target intrinsic modal components;

[0141] A kurtosis value calculation module 706, used to calculate the kurtosis values ​​of a plurality of target intrinsic modal components;

[0142] The traveling wave head calibration module 707 is used to calibrate the traveling wave head generated when a fault occurs in the power grid according to the kurtosis value.

[0143] In one embodiment of the present invention, the fault component separation module 702 includes:

[0144] a fault component calculation module, configured to use the difference between the fault three-phase voltage and the initial three-phase voltage as the fault component of the fault three-phase voltage; the initial three-phase voltage includes an initial A-phase voltage, an initial B-phase voltage and an initial C-phase voltage; the fault three-phase voltage includes a fault A-phase voltage, a fault B-phase voltage and a fault C-phase voltage;

[0145] Wherein, the fault component of the faulty three-phase voltage is expressed as:

[0146] V m,a =V sag,a -V nor，a ;

[0147] V m，b =V sag,b -V nor,b ;

[0148] Vm,c =V sag,c -V nor,c ;

[0149] Where V m，a represents the fault component of the fault phase A voltage, V m,b represents the fault component of the fault B phase voltage, V m,c represents the fault component of the fault C phase voltage, V sag，a Indicates the fault A phase voltage, V sag,b Indicates the fault B phase voltage, V sag,c Represents the fault C phase voltage, V nor，a represents the initial A phase voltage, V nor,b represents the initial B-phase voltage, V nor,c Represents the initial C-phase voltage.

[0150] In one embodiment of the present invention, the independent sequence component acquisition module 703 includes:

[0151] An independent sequence component transformation module is used to perform phase mode transformation on the fault component using a matrix to obtain independent sequence components; the independent sequence components include positive sequence voltage components, negative sequence voltage components and zero sequence voltage components;

[0152] Wherein, the independent sequence component is expressed as:

[0153]

[0154] In the formula, u α represents the positive sequence voltage component, u β represents the negative sequence voltage component, u0 represents the zero sequence voltage component, V m,a represents the fault component of the fault A phase voltage, V m,b represents the fault component of the fault B phase voltage, V m，c Represents the fault component of the fault C phase voltage.

[0155] In one embodiment of the present invention, the initial intrinsic modal component decomposition module 704 includes:

[0156] A parameter combination selection module, used for randomly selecting a plurality of parameter combinations for decomposing the positive sequence voltage component in the independent sequence component, and initializing the plurality of parameter combinations as positions in a double ice population;

[0157] A fitness calculation module, used to calculate the fitness of each of the positions;

[0158] The optimization condition judgment module is used to judge whether the preset optimization condition is met; if so, the parameter combination optimal selection module is executed; if not, the operation module is executed;

[0159] The parameter combination optimal selection module is used to use the parameter combination corresponding to the position with the highest fitness as the parameter combination for decomposing the positive sequence voltage component;

[0160] The operation module is used to execute the soft frost search strategy and the hard frost puncture mechanism on the multiple locations to update the locations, and return to execute the fitness calculation module;

[0161] The initial component acquisition module is used to decompose the positive sequence voltage component into a plurality of initial natural mode components according to the parameter combination.

[0162] In one embodiment of the present invention, the target intrinsic modal component optimization module 705 includes:

[0163] A variational constraint model construction module, used to construct a variational constraint model;

[0164] A parameter calculation module, used for calculating the center frequency and bandwidth of the initial natural modal component according to the variational constraint model;

[0165] An unconstrained variational model conversion module, used for converting the variational constrained model into an unconstrained variational model according to the center frequency and the bandwidth;

[0166] The target component acquisition module is used to optimize the initial intrinsic modal components according to the unconstrained variational model and preset constraints to obtain target intrinsic modal components.

[0167] In one embodiment of the present invention, the variational constraint model construction module includes:

[0168] An analytical signal acquisition module, used for performing Hilbert transform on the initial intrinsic modal component to obtain an analytical signal of the initial intrinsic modal component;

[0169] A spectrum modulation module, used for performing spectrum modulation on the analytical signal according to the estimated center frequency;

[0170] A constraint model acquisition module, configured to calculate the norm of the square of the gradient of the analytical signal to obtain a variational constraint model if the spectrum modulation is completed;

[0171] Wherein, the variational constraint model is expressed as:

[0172]

[0173] In the formula, C k represents the set of initial natural mode components, ω k represents the set of instantaneous frequencies, K represents the number of the initial natural modal components, represents partial derivative, δ(t) represents unit pulse signal, j represents imaginary number sign, t represents time, ω k (t) represents the instantaneous frequency of the initial natural modal component, C k (t) represents the initial natural mode component, represents the estimated center frequency, st represents the constraint condition, and X(t) represents the set of initial natural mode components;

[0174] The unconstrained variational model is expressed as:

[0175]

[0176] Where L represents the augmented Lagrangian function, u k represents the set of bandwidths, ω k represents the set of instantaneous frequencies, λ(t) represents the Lagrange multiplier, α represents the penalty factor, K represents the number of the initial natural modal components, represents the partial derivative, δ(t) represents the unit pulse signal, j represents the imaginary number sign, t represents the time, ω k (t) represents the instantaneous frequency of the initial natural modal component, C k (t) represents the initial natural mode component, represents the estimated center frequency, and f(t) represents the center frequency;

[0177] The target component acquisition module comprises:

[0178] A target component optimization module, used for solving the unconstrained variational model by using an alternating multiplication operator under preset constraints to optimize the initial intrinsic modal components and obtain target intrinsic modal components;

[0179] The preset constraint condition is expressed as:

[0180]

[0181] Where n represents the number of iterations of the alternating multiplication operator, K represents the number of the initial natural mode components, represents the bandwidth at the n+1th iteration, represents the bandwidth of the nth iteration number, and ∈ represents a preset constraint condition.

[0182] In one embodiment of the present invention, the traveling wave head calibration module 707 includes:

[0183] A wave head calibration module, used for calibrating the wave head of the traveling wave generated after the power grid fails by using an energy operator according to the target natural mode component with the highest kurtosis value;

[0184] Wherein, the kurtosis value is expressed as:

[0185]

[0186] In the formula, R k represents the kurtosis value of the kth target intrinsic modal component, N represents the number of parameters of the target intrinsic modal component, represents the i-th parameter of the k-th target intrinsic modal component, Represents the sum of the number of parameters of the kth target intrinsic modal component.

[0187] In one embodiment of the present invention, the energy operator is expressed as:

[0188] Ψ[f(N)]=[f(N)] 2 -f(N+1)f(N-1);

[0189] In the formula, Ψ[f(N)] represents the energy operator of the target intrinsic modal component with the highest kurtosis value, f(N) represents the target intrinsic modal component with the highest kurtosis value, f(N+1) represents the next target intrinsic modal component of the target intrinsic modal component with the highest kurtosis value, and f(N-1) represents the previous target intrinsic modal component of the target intrinsic modal component with the highest kurtosis value.

[0190] The power grid fault traveling wave head calibration device provided in the embodiment of the present invention can execute the power grid fault traveling wave head calibration method provided in any embodiment of the present invention, and has the corresponding functional modules and beneficial effects for executing the power grid fault traveling wave head calibration method.

[0191] Embodiment 3

[0192] See also Figure 8 , showing a schematic diagram of the structure of a computer device provided by an embodiment of the present invention. The computer device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workbenches, personal digital assistants, blade servers, mainframe computers, and other suitable computers. The computer device may also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices (such as helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present invention described and / or required herein.

[0193] like Figure 8As shown, the computer device 10 includes at least one processor 11, and a memory connected to the at least one processor 11, such as a read-only memory (ROM) 12, a random access memory (RAM) 13, etc., wherein the memory stores a computer program that can be executed by at least one processor, and the processor 11 can perform various appropriate actions and processes according to the computer program stored in the read-only memory (ROM) 12 or the computer program loaded from the storage unit 18 to the random access memory (RAM) 13. In the RAM 13, various programs and data required for the operation of the computer device 10 can also be stored. The processor 11, ROM 12 and RAM 13 are connected to each other through a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0194] A number of components in the computer device 10 are connected to the I / O interface 15, including: an input unit 16, such as a keyboard, a mouse, etc.; an output unit 17, such as various types of displays, speakers, etc.; a storage unit 18, such as a disk, an optical disk, etc.; and a communication unit 19, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 19 allows the computer device 10 to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks.

[0195] The processor 11 may be a variety of general and / or special processing components with processing and computing capabilities. Some examples of the processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any appropriate processor, controller, microcontroller, etc. The processor 11 executes the various methods and processes described above, such as the power grid fault traveling wave head calibration method.

[0196] In some embodiments, the method for calibrating the traveling wave head of a power grid fault may be implemented as a computer program, which is tangibly contained in a computer-readable storage medium, such as a storage unit 18. In some embodiments, part or all of the computer program may be loaded and / or installed on the computer device 10 via the ROM 12 and / or the communication unit 19. When the computer program is loaded into the RAM 13 and executed by the processor 11, one or more steps of the method for calibrating the traveling wave head of a power grid fault described above may be performed. Alternatively, in other embodiments, the processor 11 may be configured to execute the method for calibrating the traveling wave head of a power grid fault by any other appropriate means (e.g., by means of firmware).

[0197] Various implementations of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various implementations can include: being implemented in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0198] Computer programs for implementing the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, so that when the computer program is executed by the processor, the functions / operations specified in the flow chart and / or block diagram are implemented. The computer program may be executed entirely on the machine, partially on the machine, partially on the machine and partially on a remote machine as a stand-alone software package, or entirely on a remote machine or server.

[0199] In the context of the present invention, a computer-readable storage medium may be a tangible medium that may contain or store a computer program for use by or in combination with an instruction execution system, device or equipment. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices or equipment, or any suitable combination of the foregoing. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. A more specific example of a machine-readable storage medium may include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0200] To provide interaction with a user, the systems and techniques described herein can be implemented on a computer device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and a pointing device (e.g., a mouse or trackball) through which the user can provide input to the computer device. Other types of devices can also be used to provide interaction with the user; for example, the feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including acoustic input, voice input, or tactile input).

[0201] The systems and techniques described herein may be implemented in a computing system that includes backend components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes frontend components (e.g., a user computer with a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such backend components, middleware components, or frontend components. The components of the system may be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), a blockchain network, and the Internet.

[0202] A computing system may include a client and a server. The client and the server are generally remote from each other and usually interact through a communication network. The client and server relationship is generated by computer programs running on the corresponding computers and having a client-server relationship with each other. The server may be a cloud server, also known as a cloud computing server or cloud host, which is a host product in the cloud computing service system to solve the defects of difficult management and weak business scalability in traditional physical hosts and VPS services.

[0203] Embodiment 4

[0204] An embodiment of the present invention further provides a computer program product, which includes a computer program. When the computer program is executed by a processor, the method for calibrating the head of a power grid fault traveling wave provided in any embodiment of the present invention is implemented.

[0205] In the process of implementation, the computer program product can be written in one or more programming languages ​​or a combination thereof to perform the computer program code of the present invention, including object-oriented programming languages, such as Java, Smalltalk, C++, and conventional procedural programming languages, such as "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a separate software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer can be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or can be connected to an external computer (for example, using an Internet service provider to connect through the Internet).

[0206] It should be understood that the various forms of processes shown above can be used to reorder, add or delete steps. For example, the steps described in the present invention can be executed in parallel, sequentially or in different orders, as long as the desired results of the technical solution of the present invention can be achieved, and this document does not limit this.

[0207] The above specific implementations do not constitute a limitation on the protection scope of the present invention. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions can be made according to design requirements and other factors. Any modification, equivalent substitution and improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for calibrating the traveling wave head of a power grid fault, characterized in that: include: The initial three-phase voltage under normal operation and the fault three-phase voltage under fault state are collected from the transmission lines of the power grid respectively; separating a fault component relative to the initial three-phase voltage from the fault three-phase voltage; Performing phase mode transformation on the fault component to obtain independent sequence components; Decomposing the independent sequence components into a plurality of initial intrinsic mode components; Optimizing the multiple initial intrinsic modal components according to preset optimization conditions to obtain multiple target intrinsic modal components; Calculating kurtosis values ​​of a plurality of target intrinsic modal components; The traveling wave head generated when a fault occurs in the power grid is calibrated according to the kurtosis value.

2. The method according to claim 1, characterized in that The step of separating the fault component relative to the initial three-phase voltage from the fault three-phase voltage comprises: The difference between the faulty three-phase voltage and the initial three-phase voltage is used as the fault component of the faulty three-phase voltage; the initial three-phase voltage includes an initial A-phase voltage, an initial B-phase voltage and an initial C-phase voltage; the faulty three-phase voltage includes a faulty A-phase voltage, a faulty B-phase voltage and a faulty C-phase voltage; Wherein, the fault component of the faulty three-phase voltage is expressed as: V m,a =V sag,a -V nor,a ; V m,b =V sag,b -V nor,b ; V m,c =V sag,c -V nor,c ; Where V m,a represents the fault component of the fault phase A voltage, V m,b represents the fault component of the fault B phase voltage, V m,c represents the fault component of the fault C phase voltage, V sag,a Indicates the fault A phase voltage, V sag,b Indicates the fault B phase voltage, V sag,c Represents the fault C phase voltage, V nor,a represents the initial A phase voltage, V nor,b represents the initial B-phase voltage, V nor,c Represents the initial C-phase voltage.

3. The method according to claim 2, characterized in that The step of performing phase mode transformation on the fault component to obtain an independent sequence component includes: A matrix is ​​used to perform phase-mode transformation on the fault component to obtain independent sequence components; the independent sequence components include positive sequence voltage components, negative sequence voltage components and zero sequence voltage components; Wherein, the independent sequence component is expressed as: In the formula, u α represents the positive sequence voltage component, u β represents the negative sequence voltage component, u0 represents the zero sequence voltage component, V m,a represents the fault component of the fault phase A voltage, V m,b represents the fault component of the fault B phase voltage, V m,c Represents the fault component of the fault C phase voltage.

4. The method according to claim 1, characterized in that The step of decomposing the independent sequence component into a plurality of initial intrinsic modal components comprises: Randomly selecting a plurality of parameter combinations for decomposing the positive sequence voltage component in the independent sequence component, and initializing the plurality of parameter combinations to positions in a double ice population; Calculating the fitness of each of the positions; Determine whether the preset optimization condition is met; if so, use the parameter combination corresponding to the position with the highest fitness as the parameter combination for decomposing the positive sequence voltage component; if not, execute the soft frost search strategy and hard frost puncture mechanism for multiple positions to update the positions, and return to execute the calculation of the fitness of each position; The positive sequence voltage component is decomposed into a plurality of initial natural mode components according to the parameter combination.

5. The method according to claim 4, characterized in that The step of optimizing the multiple initial intrinsic modal components according to the preset optimization conditions to obtain multiple target intrinsic modal components includes: Construct variational constraint models; Calculating the center frequency and bandwidth of the initial natural modal component according to the variational constraint model; Converting the variational constrained model into an unconstrained variational model according to the center frequency and the bandwidth; The initial intrinsic modal components are optimized according to the unconstrained variational model and preset constraints to obtain target intrinsic modal components.

6. The method according to claim 5, characterized in that The constructing variational constraint model comprises: Performing Hilbert transform on the initial intrinsic modal component to obtain an analytical signal of the initial intrinsic modal component; Performing spectrum modulation on the analytical signal according to the estimated center frequency; If the spectrum modulation is completed, the norm of the square of the analytical signal gradient is calculated to obtain a variational constraint model; Wherein, the variational constraint model is expressed as: In the formula, C k represents the set of initial natural mode components, ω k represents the set of instantaneous frequencies, K represents the number of the initial natural modal components, represents partial derivative, δ(t) represents unit pulse signal, j represents imaginary number sign, t represents time, ω k (t) represents the instantaneous frequency of the initial natural modal component, C k (t) represents the initial natural mode component, represents the estimated center frequency, st represents the constraint condition, and X(t) represents the set of initial natural mode components; The unconstrained variational model is expressed as: Where L represents the augmented Lagrangian function, u k represents the set of bandwidths, ω k represents the set of instantaneous frequencies, λ(t) represents the Lagrange multiplier, α represents the penalty factor, K represents the number of the initial natural modal components, represents the partial derivative, δ(t) represents the unit pulse signal, j represents the imaginary number sign, t represents the time, ω k (t) represents the instantaneous frequency of the initial natural modal component, C k (t) represents the initial natural mode component, represents the estimated center frequency, and f(t) represents the center frequency; The step of optimizing the initial intrinsic modal components according to the unconstrained variational model and preset constraints to obtain target intrinsic modal components includes: Under preset constraints, an alternating multiplication operator is used to solve the unconstrained variational model to optimize the initial intrinsic modal components to obtain target intrinsic modal components; The preset constraint condition is expressed as: Where n represents the number of iterations of the alternating multiplication operator, K represents the number of the initial natural mode components, represents the bandwidth at the n+1th iteration, represents the bandwidth of the nth iteration number, and ∈ represents a preset constraint condition.

7. The method according to any one of claims 1 to 6, characterized in that The step of calibrating the traveling wave head generated when the power grid fails according to the kurtosis value includes: Calibrate the traveling wave head generated after the power grid fails using an energy operator according to the target natural mode component with the highest kurtosis value; Wherein, the kurtosis value is expressed as: In the formula, R k represents the kurtosis value of the kth target intrinsic modal component, N represents the number of parameters of the target intrinsic modal component, represents the i-th parameter of the k-th target intrinsic modal component, Represents the sum of the number of parameters of the kth target intrinsic modal component.

8. The method according to claim 7, characterized in that The energy operator is expressed as: Ψ[f(N)]=[f(N)] 2 -f(N+1)f(N-1); In the formula, Ψ[f(N)] represents the energy operator of the target intrinsic modal component with the highest kurtosis value, f(N) represents the target intrinsic modal component with the highest kurtosis value, f(N+1) represents the next target intrinsic modal component of the target intrinsic modal component with the highest kurtosis value, and f(N-1) represents the previous target intrinsic modal component of the target intrinsic modal component with the highest kurtosis value.

9. A computer device, characterized in that: The computer device comprises: one or more processors; A storage device for storing one or more programs; When the one or more programs are executed by the one or more processors, the one or more processors implement the power grid fault traveling wave head calibration method as described in any one of claims 1-8.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the method for calibrating the traveling wave head of a power grid fault as described in any one of claims 1 to 8 is implemented.

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