Power distribution network traveling wave front arrival time calibration method based on issa-vmd-mteo

Abstract

This invention relates to a method for calibrating the arrival time of traveling wave fronts (TWF) in distribution networks based on ISSA-VMD-MTEO, comprising: extracting line-mode traveling wave signals; constructing a parameter optimization model for variational mode decomposition to obtain the optimal parameter combination; obtaining several intrinsic mode components; constructing a traveling wave instantaneous energy sequence; and calibrating the corresponding time as the initial arrival time of the traveling wave TWF at the fault based on the energy mutation characteristics in the instantaneous energy sequence. The beneficial effects of this invention are: by introducing an improved sparrow search algorithm, this invention adaptively optimizes the number of modes and penalty factor in VMD, avoiding manual parameter selection based on experience, and improving the algorithm's adaptability under different fault conditions.

Description

Technical Field

[0001] This invention relates to the field of distribution network automation technology, and more specifically, to a method for calibrating the arrival time of traveling wavefronts in distribution networks based on ISSA-VMD-MTEO. Background Technology

[0002] Fault location in distribution networks is one of the key technical challenges in distribution automation and smart grids. When a single-phase ground fault or phase-to-phase short circuit occurs in a distribution line, a high-frequency transient traveling wave signal is generated at the fault point, which propagates along the line to both sides. By detecting the arrival time of the traveling wave at the monitoring point and combining it with the propagation speed, the location of the fault point can be quickly determined. Compared with the traditional impedance method based on steady-state quantities, the traveling wave location method has advantages such as fast response speed, high location accuracy, and low dependence on system parameters, and has become a key research direction both domestically and internationally in recent years. However, distribution network lines generally have characteristics such as low voltage levels, short line lengths, complex branch structures, mixed laying of cables and overhead lines, and diverse grounding methods, which lead to severe attenuation and significant dispersion of the traveling wave signal during propagation. Furthermore, it is susceptible to noise from load disturbances, switching operations, and electromagnetic interference, making the initial wavefront characteristics of the traveling wave weak and difficult to accurately identify. Therefore, how to accurately calibrate the arrival time of the traveling wave wavefront under complex noise backgrounds is a core technical challenge restricting the engineering application of traveling wave location in distribution networks. Therefore, how to reliably calibrate the arrival time of weak traveling wave fronts under complex operating conditions is a technical problem that urgently needs to be solved in the field of traveling wave fault location in distribution networks.

[0003] To address the problem of traveling wave wavefront detection, existing research has proposed various signal processing methods. Early methods often employed time-domain thresholding and differential methods to directly detect wavefronts on the original signal. However, these methods are highly sensitive to threshold settings, have weak noise resistance, and are difficult to adapt to different operating conditions. To improve detection accuracy, researchers have introduced wavelet transform, empirical mode decomposition (EMD), and their improved algorithms (such as EEMD, CEEMDAN, and ICEEMDAN) to perform time-frequency decomposition of traveling wave signals, and combined this with the Teager energy operator (TEO) or indices such as kurtosis and envelope entropy for wavefront identification. These methods enhance the transient characteristics of traveling waves to some extent, but still suffer from problems such as wavelet basis function dependence on experience, EMD methods being prone to mode aliasing, significant endpoint effects, and insufficient stability of decomposition results. In recent years, variational mode decomposition (VMD) has been introduced into the field of traveling wave signal analysis due to its good mathematical rigor and resistance to mode aliasing. By decomposing the signal into several modal components with finite bandwidth, high-frequency information in traveling waves can be extracted more effectively. However, existing VMD-based methods typically rely on manual experience to select the mode number K and penalty factor α, resulting in poor parameter adaptability under different operating conditions. This can easily lead to insufficient or excessive decomposition, thus affecting the wavefront detection accuracy. Furthermore, existing methods often use a single-scale energy operator to process a specific mode, which limits their ability to respond to transient changes under weak traveling waves and strong noise conditions. Summary of the Invention

[0004] The purpose of this invention is to overcome the shortcomings of the prior art and provide a method for calibrating the arrival time of traveling wave fronts in distribution networks based on ISSA-VMD-MTEO.

[0005] Firstly, a method for calibrating the arrival time of traveling wave fronts in distribution networks based on ISSA-VMD-MTEO is provided, including:

[0006] S1. Collect transient electrical quantity signals when a fault occurs in the distribution network line, and perform phase mode transformation on the transient electrical quantity signals to extract the line mode traveling wave signal;

[0007] S2. Construct a parameter optimization model for variational mode decomposition (VMD), using the traveling wave characteristics of the line mode traveling wave signal as the fitness function, and adopt the improved sparrow search algorithm (ISSA) to adaptively optimize the key parameters of variational mode decomposition to obtain the optimal parameter combination.

[0008] S3. Using the optimal parameter combination, perform variational mode decomposition on the linear mode traveling wave signal to obtain several intrinsic mode components;

[0009] S4. Select a target modal component from the plurality of intrinsic modal components, and apply a multi-scale Teager energy operator to the target modal component to construct a traveling wave instantaneous energy sequence; the target modal component is the intrinsic modal component in S2 that makes the fitness function obtain the optimal value;

[0010] S5. Based on the energy mutation characteristics in the instantaneous energy sequence of the traveling wave, the corresponding time is calibrated as the arrival time of the initial traveling wave head of the fault.

[0011] Preferably, in S1, the transient electrical quantity signal is a three-phase transient current signal or a three-phase transient voltage signal.

[0012] Preferably, the phase mode transformation in S1 is the Clarke transformation, which converts the three-phase transient electrical quantity signal into α-mode, β-mode and zero-mode components, and selects the α-mode component as the line-mode traveling wave signal.

[0013] Preferably, in S2, the key parameters of the variational mode decomposition include the number of mode components K and the penalty factor α. The improved sparrow search algorithm ISSA achieves adaptive optimization of VMD parameters by jointly searching the number of mode components K and the penalty factor α.

[0014] Preferably, in S2, the fitness function of the parameter optimization model is constructed based on the statistical characteristics of the intrinsic mode components, and the statistical characteristics include a ratio-type combination index of kurtosis and envelope entropy.

[0015] Preferably, in S4, the multi-scale Teager energy operator performs Teager energy calculations on the target modal components using multiple scale factors, and fuses the energy results at each scale to enhance the transient energy mutation characteristics of the traveling wave front and obtain the instantaneous energy sequence of the traveling wave.

[0016] As a preferred embodiment, S5 includes:

[0017] An adaptive threshold is constructed based on the background energy sequence, and peak significance constraints and peak spacing constraints are applied to filter local maxima points in the instantaneous energy sequence of the traveling wave, thereby obtaining a peak candidate set;

[0018] For each peak point in the candidate peak set, a dual discrimination condition is applied, and the earliest peak time among the peak points that satisfy the dual discrimination condition is marked as the arrival time of the initial traveling wave front of the fault; the dual discrimination condition includes:

[0019] Peak energy is greater than or equal to the adaptive threshold;

[0020] The peak energy is greater than or equal to the product of the global maximum value of the traveling wave energy sequence and the preset relative intensity threshold coefficient.

[0021] Secondly, a distribution network traveling wavefront arrival time calibration system based on ISSA-VMD-MTEO is provided for performing any of the methods described in the first aspect, including:

[0022] The acquisition module is used to acquire transient electrical quantity signals when a fault occurs in the distribution network line, and to perform phase mode transformation on the transient electrical quantity signals to extract the line mode traveling wave signal;

[0023] The module is used to construct a parameter optimization model for variational mode decomposition. It uses the traveling wave characteristics of the linear mode traveling wave signal as the fitness function and adopts an improved sparrow search algorithm to adaptively optimize the key parameters of variational mode decomposition to obtain the optimal parameter combination.

[0024] The decomposition module is used to perform variational mode decomposition on the line mode traveling wave signal using the optimal parameter combination to obtain several intrinsic mode components.

[0025] The selection module is used to select a target modal component from the plurality of intrinsic modal components, and apply a multi-scale Teager energy operator to the target modal component to construct a traveling wave instantaneous energy sequence; the target modal component is the intrinsic modal component in S2 that makes the fitness function obtain the optimal value;

[0026] The calibration module is used to calibrate the corresponding time as the arrival time of the initial traveling wave front of the fault, based on the energy mutation characteristics in the instantaneous energy sequence of the traveling wave.

[0027] Thirdly, a computer storage medium is provided, wherein a computer program is stored therein; when the computer program is run on a computer, the computer causes the computer to perform any of the methods described in the first aspect.

[0028] Fourthly, an electronic device is provided, comprising:

[0029] Memory, used to store computer programs;

[0030] A processor for executing the computer program to implement the method as described in any of the first aspects.

[0031] The beneficial effects of this invention are:

[0032] 1. This invention introduces the improved Sparrow Search Algorithm (ISSA) to adaptively optimize the number of modes K and the penalty factor α in VMD, avoiding manual parameter selection based on experience and improving the algorithm's adaptability under different fault conditions.

[0033] 2. This invention utilizes the variational decomposition mechanism of VMD to achieve robust decomposition of traveling wave signals, effectively suppressing mode aliasing and endpoint effects, and providing a high-quality signal foundation for subsequent wavefront detection.

[0034] 3. This invention employs a multi-scale Teager energy operator to enhance the energy of the target modal components, which can significantly amplify the instantaneous energy mutation characteristics of the initial traveling wave of the fault and improve the detection robustness under weak traveling wave conditions.

[0035] 4. The method provided by this invention has a clear calculation process and moderate computational complexity, making it suitable for integration into distribution network fault location terminals or distribution automation systems for online application. Attached Figure Description

[0036] Figure 1 A flowchart illustrating the ISSA-VMD-MTEO-based method for calibrating the time of arrival (TOA) of traveling wavefronts in distribution networks, provided by this invention.

[0037] Figure 2 This is a diagram of the 10KV radial distribution network topology used in the embodiments of the present invention;

[0038] Figure 3 The fault traveling wave signal at the measuring point at the beginning of the line and its VMD adaptive decomposition result;

[0039] Figure 4 The fault traveling wave signal at the end-of-line measuring point and its VMD adaptive decomposition result;

[0040] Figure 5 The MTEO results are shown for the measurement points at the beginning and end of the line.

[0041] Figure 6 This is a graph showing the MTEO results for all measurement points along the line. Detailed Implementation

[0042] The present invention will be further described below with reference to embodiments. The description of the embodiments below is only for the purpose of helping to understand the present invention. It should be noted that those skilled in the art can make several modifications to the present invention without departing from the principle of the present invention, and these improvements and modifications also fall within the protection scope of the claims of the present invention.

[0043] Example 1:

[0044] The existing technology has the following problems:

[0045] (1) VMD parameters rely on manual experience to set and lack an adaptive optimization mechanism, making it difficult to adapt to the multi-condition and multi-noise environment of the power distribution network;

[0046] (2) The existing Teager energy operator has limited ability to enhance weak traveling wavefronts, and is prone to missed detections or misjudgments, resulting in insufficient robustness.

[0047] (3) The existing methods are not robust enough and are difficult to operate stably in complex power distribution network structures for a long time.

[0048] To address the problems of the prior art, Embodiment 1 of this application provides a fault traveling wave detection method that can adaptively optimize decomposition parameters, effectively enhance the transient characteristics of weak traveling waves, and improve the accuracy and stability of wavefront arrival time calibration.

[0049] Specifically, such as Figure 1 As shown, the method includes:

[0050] The method for determining the time of arrival (TOA) of traveling wave fronts in distribution networks based on ISSA-VMD-MTEO includes:

[0051] S1. Collect transient electrical quantity signals when a fault occurs in the distribution network line, and perform phase mode transformation on the transient electrical quantity signals to extract the line mode traveling wave signal.

[0052] In S1, the transient electrical quantity signal is a three-phase transient current signal or a three-phase transient voltage signal.

[0053] The phase mode transformation described in S1 is the Clarke transformation, which converts the three-phase transient electrical quantity signal into α-mode, β-mode and zero-mode components, and selects the α-mode component as the line-mode traveling wave signal.

[0054] S2. Construct a parameter optimization model for variational mode decomposition, using the traveling wave characteristics of the linear mode traveling wave signal as the fitness function, and employ an improved sparrow search algorithm to adaptively optimize the key parameters of variational mode decomposition to obtain the optimal parameter combination.

[0055] In S2, the key parameters of the variational mode decomposition include the number of mode components K and the penalty factor α. The improved sparrow search algorithm ISSA achieves adaptive optimization of VMD parameters by jointly searching the number of mode components K and the penalty factor α.

[0056] In S2, the fitness function of the parameter optimization model is constructed based on the statistical characteristics of the intrinsic mode components, including a ratio-type combination index of kurtosis and envelope entropy.

[0057] S3. Using the optimal parameter combination, perform variational mode decomposition on the linear mode traveling wave signal to obtain several intrinsic mode components.

[0058] S4. Select a target modal component from the plurality of intrinsic modal components, and apply a multi-scale Teager energy operator to the target modal component to construct a traveling wave instantaneous energy sequence. The target modal component is the intrinsic modal component in S2 that makes the fitness function obtain the optimal value.

[0059] In S4, the multi-scale Teager energy operator performs Teager energy calculations on the target modal components using multiple scale factors, and fuses the energy results at each scale to enhance the transient energy mutation characteristics of the traveling wave front, and obtains the instantaneous energy sequence of the traveling wave.

[0060] S5. Based on the energy mutation characteristics in the instantaneous energy sequence of the traveling wave, the corresponding time is calibrated as the arrival time of the initial traveling wave head of the fault.

[0061] S5 includes:

[0062] An adaptive threshold is constructed based on the background energy sequence, and peak significance constraints and peak spacing constraints are applied to filter local maxima points in the instantaneous energy sequence of the traveling wave, thereby obtaining a peak candidate set;

[0063] For each peak point in the candidate peak set, a dual discrimination condition is applied, and the earliest peak time among the peak points that satisfy the dual discrimination condition is marked as the arrival time of the initial traveling wave front of the fault; the dual discrimination condition includes:

[0064] Peak energy is greater than or equal to the adaptive threshold;

[0065] The peak energy is greater than or equal to the product of the global maximum value of the traveling wave energy sequence and the preset relative intensity threshold coefficient.

[0066] Example 2:

[0067] Based on Example 1, Example 2 of this application provides a specific method for determining the time of arrival (TOA) of traveling wavefronts in a distribution network based on ISSA-VMD-MTEO, including:

[0068] S1. Collect transient electrical quantity signals when a fault occurs in the distribution network line, and perform phase mode transformation on the transient electrical quantity signals to extract the line mode traveling wave signal.

[0069] For example, in Figure 2 The 10kV distribution network line shown is equipped with current traveling wave detection devices at its beginning, end, and all branch ends. The sampling rate is 5MHz. The fault is located at kilometer 14 of the main line, with the initial phase angle of the fault set to 60 degrees and the transition resistance set to 100Ω. Three-phase current traveling wave signals are collected at 200 points before and 800 points after the fault occurrence. , , Phase-mode transformation of the three-phase current traveling wave signal is obtained , , .

[0070] The phase-mode transformation uses the Clarke transform, whose expression is:

[0071]

[0072] in, , , This refers to the three-phase current signal in the circuit. , , The α-mode, β-mode, and 0-mode components are obtained after the three-phase current undergoes the Clarke transform. The decomposed α-mode current is selected as the object of subsequent analysis. The 0-mode component forms a loop with the ground; while... and Forming loops between lines, unaffected by the ground, effectively highlights the high-frequency characteristics of fault traveling waves; therefore, the transformed circuit is selected... They will be the subject of subsequent research.

[0073] To simulate actual measurement noise, the noise level within the analysis window is... The fault traveling wave signal was injected with white noise with a signal-to-noise ratio of 25 dB to verify the robustness of the method.

[0074] S2. Construct a parameter optimization model for variational mode decomposition, using the traveling wave characteristics of the linear mode traveling wave signal as the fitness function, and employ an improved sparrow search algorithm to adaptively optimize the key parameters of variational mode decomposition to obtain the optimal parameter combination.

[0075] The specific process of S2 includes:

[0076] S21. Optimize variable definitions:

[0077] Using key parameters of VMD as optimization variables

[0078]

[0079] Where K is the number of modal components and a is the penalty factor.

[0080] S22, Construction of fitness function

[0081] Design Philosophy: Envelope entropy characterizes the distribution complexity of signal energy in the time domain. For transient traveling wave fronts, energy is released in a concentrated manner within a very short time, resulting in a highly concentrated envelope distribution and a relatively small envelope entropy value; while noise signals have dispersed energy and a larger envelope entropy. Kurtosis reflects the peak degree of the signal amplitude distribution and is highly sensitive to transient impact signals. Traveling wave fronts are typical short-duration impact processes, with energy concentrated in a very small number of sampling points; therefore, modes containing wavefront characteristics usually have significantly higher kurtosis values. Thus, the objective function is designed to minimize envelope entropy / kurtosis. Compared to weighted fusion methods, ratio-based objective functions do not require manual adjustment of weights.

[0082] For example, the specific parameters of the ISSA algorithm are: population size 20, maximum number of iterations 20, and the optimization variable is defined as follows: The parameter optimization range is: K∈[3,8], α∈[20,1500]. The fitness function is:

[0083]

[0084] in For the envelope entropy, It is a ravine.

[0085] The Kth current mode Envelope entropy The calculation process is as follows:

[0086] First, perform the Hilbert transform to construct the analytic signal:

[0087]

[0088] Its envelope is defined as:

[0089]

[0090] in Let K be the envelope amplitude of the Kth current mode. Hilbert transform

[0091] After normalizing the envelope amplitude, a probability distribution is constructed:

[0092]

[0093] in, Discrete sampling points The current envelope amplitude at point N, where N is the number of sampling points within the analysis window.

[0094] The current mode envelope entropy is defined as:

[0095]

[0096] in For the first Envelope entropy of each current mode

[0097] Current mode kurtosis definition

[0098]

[0099] in

[0100]

[0101] After VMD decomposition, the current signal is decomposed into K intrinsic mode functions (IMFs), and the corresponding constrained variational model expression is:

[0102]

[0103]

[0104] Where K is the number of VMD decomposition modes. Let K be the center frequency of the Kth mode. To facilitate numerical solutions, a penalty factor is introduced for the Kth modal component obtained from the decomposition. With Lagrange multipliers Construct the augmented Lagrange function:

[0105]

[0106] in, This is a penalty factor used to constrain modal bandwidth; Lagrange multipliers

[0107] S23, ISSA search mechanism

[0108] The improved Sparrow Search Algorithm (ISSA) is used to search the parameter space. The discoverer is responsible for the global search, while the followers are responsible for local exploration. Adaptive perturbation / backward learning is introduced to avoid premature convergence. The optimal parameters are obtained through iterative updates.

[0109]

[0110] In this embodiment, the line-end measuring point converges on the 14th time, with optimal parameters K=5, α=53, and minimum fitness function J=0.8125; the line-end measuring point converges on the 12th time, with optimal parameters K=4, α=32, and minimum fitness function J=0.5022.

[0111] S3. Using the optimal parameter combination, perform variational mode decomposition on the linear mode traveling wave signal to obtain several intrinsic mode components.

[0112] Specifically, using optimal parameters

[0113] VMD decomposition of traveling wave signal

[0114]

[0115] in This represents the k current mode components obtained under optimal parameter conditions.

[0116] like Figure 3 and 4As shown in the sub-figure below, after using the optimized parameters to perform VMD adaptive decomposition on the fault traveling wave signal, the low-frequency trend features, high-frequency traveling wave features, and noise features can be well distinguished, avoiding mode aliasing.

[0117] S4. Select a target mode component from the plurality of intrinsic mode components, and apply a multi-scale Teager energy operator to the target mode component to construct a traveling wave instantaneous energy sequence.

[0118] The specific process of S4 includes:

[0119] S41. Target Mode Selection

[0120] In the ISSA optimization process, the modal component that minimizes the objective function is considered to be the most impactful and energy-concentrated modal component. This modal component is denoted as...

[0121] S42, Multiscale Teager Energy Operator (MTEO)

[0122] Feature enhancement principle: Real traveling wave signals have energy responses at different scales, while random noise is usually strong only at a specific scale. By superimposing, the signal features are enhanced, and the noise is relatively suppressed.

[0123] For example, the optimal modal component of the measuring point at the beginning of the line is IMF3, and its J=0.8125; the optimal modal component of the measuring point at the end of the line is IMF3, and its J=0.5022; these correspond to the optimal fitness function values ​​in S2, respectively.

[0124] To enhance the transient response capability of traveling wave components at different frequencies, different scale factors are used. The following constructs a multi-scale Teager energy operator for... Applying multi-scale Teager energy operators

[0125]

[0126] in Here, the scale factor is... Calculate using 1, 2, and 3 respectively.

[0127] S43, Multi-scale fusion

[0128] Let the set of scale factors be

[0129]

[0130] Calculate the Teager energy sequence at each scale. The instantaneous comprehensive energy sequence of the traveling wave is constructed using a multi-resolution absolute value summation and fusion method.

[0131]

[0132] in The instantaneous energy sequence of the traveling wave after multi-scale fusion is obtained; the absolute value operation can avoid the cancellation of energy signs at different scales and ensure the monotonic enhancement of the abrupt change feature; the direct summation method does not require the introduction of artificial weight parameters, which enhances the versatility and engineering adaptability of the method. Figure 5 The diagram shows the MTEO results for measuring point A1 at the beginning of the line and measuring point A2 at the end of the line. Figure 6 The MTEO results are plotted for all measurement points.

[0133] S5. Based on the energy mutation characteristics in the instantaneous energy sequence of the traveling wave, the corresponding time is calibrated as the arrival time of the initial traveling wave head of the fault.

[0134] S5 includes:

[0135] S51. Define the statistical characteristics of background noise.

[0136] (1) Let the background energy sequence be

[0137]

[0138] Sort them in non-descending order according to their numerical values ​​to obtain the sequence.

[0139]

[0140] Define the background energy center value as

[0141] (2) Let the absolute deviation sequence of background energy be

[0142]

[0143] Will Reorder in non-descending order:

[0144]

[0145] The typical fluctuation amplitude of background energy is defined as follows:

[0146]

[0147] (3) The mean square dispersion of the background energy is

[0148]

[0149] S52, Adaptive Threshold Construction

[0150] The total threshold formula is:

[0151]

[0152] in This is the threshold adjustment coefficient. This is the lower limit coefficient of dispersion. In this embodiment, the background noise window length is 200 points. Take 20, Take 0.02.

[0153] S53, Peak Screening

[0154] For the traveling wave instantaneous energy sequence obtained after multi-scale energy fusion Local maxima are extracted across the entire time domain to construct a candidate peak set:

[0155]

[0156] To avoid minor oscillations caused by noise being misidentified as traveling wave fronts, the following constraints are applied to the local maxima:

[0157] (1) Peak significance constraint

[0158] Define peak value The spuriousness relative to its neighborhood background is:

[0159]

[0160] in This is the neighborhood scale parameter.

[0161] A peak value is retained as a candidate peak only if the peak protrusion meets the following conditions:

[0162]

[0163] in The robust fluctuation amplitude is obtained based on the statistical characteristics of background energy. For the global maximum energy, The relative significance coefficient is... In this embodiment, the value is 0.03.

[0164] (2) Peak spacing constraint

[0165] To suppress dense peaks caused by high-frequency oscillations or noise, the time interval between two adjacent candidate peaks must satisfy:

[0166]

[0167] in The minimum peak spacing parameter, In this embodiment, we take the value of 2.

[0168] After the above-mentioned constraint screening, the candidate set of traveling wave peak values ​​is obtained.

[0169]

[0170] S54 Candidate Peak Set Each peak point in Furthermore, dual discrimination conditions are applied to identify the true traveling wave head peak.

[0171] (1) The peak energy is required to exceed the adaptive threshold constructed based on the statistical characteristics of the background energy. This condition is used to exclude spurious peaks that are only slightly above the background noise level.

[0172]

[0173] (2) To avoid misjudging fault precursors or weak traveling waves as the main traveling wave front, the peak energy is further required to meet the following requirements:

[0174]

[0175] in This represents the global maximum value of the traveling wave energy sequence. In this embodiment, the value is set to 0.15. This criterion is used to ensure that the selected peak value has sufficient relative significance in the overall energy distribution.

[0176] Among the peak points that meet the above dual discrimination conditions, the earliest peak time is selected as the initial arrival time of the traveling wave front. In this embodiment, the fault location is set at 14km on the main line. The arrival time index of measuring point A1 is calibrated as 473, and the arrival time index of measuring point A2 is calibrated as 417. The positioning result calculated by the dual-end ranging formula is 13.932km, with an error of 68m.

[0177] It should be noted that the parts in this embodiment that are the same as or similar to those in Embodiment 1 can be referred to each other, and will not be repeated in this application.

[0178] Example 3:

[0179] Based on Embodiment 2, Embodiment 3 of this application provides a distribution network traveling wavefront arrival time calibration system based on ISSA-VMD-MTEO, including:

[0180] The acquisition module is used to acquire transient electrical quantity signals when a fault occurs in the distribution network line, and to perform phase mode transformation on the transient electrical quantity signals to extract the line mode traveling wave signal;

[0181] The module is used to construct a parameter optimization model for variational mode decomposition. It uses the traveling wave characteristics of the linear mode traveling wave signal as the fitness function and adopts an improved sparrow search algorithm to adaptively optimize the key parameters of variational mode decomposition to obtain the optimal parameter combination.

[0182] The decomposition module is used to perform variational mode decomposition on the line mode traveling wave signal using the optimal parameter combination to obtain several intrinsic mode components.

[0183] The selection module is used to select a target modal component from the plurality of intrinsic modal components, and apply a multi-scale Teager energy operator to the target modal component to construct a traveling wave instantaneous energy sequence; the target modal component is the intrinsic modal component in S2 that makes the fitness function obtain the optimal value;

[0184] The calibration module is used to calibrate the corresponding time as the arrival time of the initial traveling wave front of the fault, based on the energy mutation characteristics in the instantaneous energy sequence of the traveling wave.

[0185] It should be noted that the system provided in this embodiment is the system corresponding to the method provided in the embodiment. Therefore, the parts that are the same as or similar to those in embodiment 3 can be referred to each other, and will not be described again in this application.

Claims

1. A power distribution network traveling wave front arrival time calibration method based on ISSA-VMD-MTEO, characterized in that, include: S1. Acquire transient electrical quantity signals when a fault occurs in the distribution network line, and perform phase mode transformation on the transient electrical quantity signals to extract the line mode traveling wave signal; S2. Construct a parameter optimization model for variational mode decomposition, using the traveling wave characteristics of the linear mode traveling wave signal as the fitness function, and adopt an improved sparrow search algorithm to adaptively optimize the key parameters of variational mode decomposition to obtain the optimal parameter combination. S3. Using the optimal parameter combination, perform variational mode decomposition on the linear mode traveling wave signal to obtain several intrinsic mode components; S4. Select a target modal component from the plurality of intrinsic modal components, and apply a multi-scale Teager energy operator to the target modal component to construct a traveling wave instantaneous energy sequence; the target modal component is the intrinsic modal component in S2 that makes the fitness function obtain the optimal value; S5. Based on the energy mutation characteristics in the instantaneous energy sequence of the traveling wave, the corresponding time is calibrated as the arrival time of the initial traveling wave head of the fault.

2. The method of claim 1, wherein the ISSA-VMD-MTEO based power distribution network traveling wave front arrival time calibration method further comprises: In S1, the transient electrical quantity signal is a three-phase transient current signal or a three-phase transient voltage signal.

3. The method of claim 2, wherein the ISSA-VMD-MTEO based power distribution network traveling wave front arrival time calibration method further comprises: The phase mode transformation described in S1 is the Clarke transformation, which converts the three-phase transient electrical quantity signal into α-mode, β-mode and zero-mode components, and selects the α-mode component as the line-mode traveling wave signal.

4. The method of claim 3, wherein the ISSA-VMD-MTEO based power distribution network traveling wave front arrival time calibration method further comprises: In S2, the key parameters of the variational mode decomposition include the number of mode components K and the penalty factor α. The improved sparrow search algorithm ISSA achieves adaptive optimization of VMD parameters by jointly searching the number of mode components K and the penalty factor α.

5. The method of claim 4, wherein the ISSA-VMD-MTEO based power distribution network traveling wave front arrival time calibration method further comprises: In S2, the fitness function of the parameter optimization model is constructed based on the statistical characteristics of the intrinsic mode components, including a ratio-type combination index of kurtosis and envelope entropy.

6. The method of claim 5, wherein the ISSA-VMD-MTEO based power distribution network traveling wave front arrival time calibration method further comprises: In S4, the multi-scale Teager energy operator performs Teager energy calculations on the target modal components using multiple scale factors, and fuses the energy results at each scale to enhance the transient energy mutation characteristics of the traveling wave front, and obtains the instantaneous energy sequence of the traveling wave.

7. The method for determining the arrival time of traveling wave front in a distribution network based on ISSA-VMD-MTEO according to claim 6, characterized in that, S5 include: An adaptive threshold is constructed based on the background energy sequence, and peak significance constraints and peak spacing constraints are applied to filter local maxima points in the instantaneous energy sequence of the traveling wave, thereby obtaining a peak candidate set; Apply dual discrimination conditions to each peak point in the peak candidate set, and mark the earliest peak time among the peak points that satisfy the dual discrimination conditions as the arrival time of the initial traveling wave front of the fault. The dual discrimination conditions include: Peak energy is greater than or equal to the adaptive threshold; The peak energy is greater than or equal to the product of the global maximum value of the traveling wave energy sequence and the preset relative intensity threshold coefficient.

8. A distribution network traveling wavefront arrival time calibration system based on ISSA-VMD-MTEO, characterized in that, For performing the method according to any one of claims 1 to 7, comprising: The acquisition module is used to acquire transient electrical quantity signals when a fault occurs in the distribution network line, and to perform phase mode transformation on the transient electrical quantity signals to extract the line mode traveling wave signal; The module is used to construct a parameter optimization model for variational mode decomposition. It uses the traveling wave characteristics of the linear mode traveling wave signal as the fitness function and adopts an improved sparrow search algorithm to adaptively optimize the key parameters of variational mode decomposition to obtain the optimal parameter combination. The decomposition module is used to perform variational mode decomposition on the line mode traveling wave signal using the optimal parameter combination to obtain several intrinsic mode components. The selection module is used to select a target modal component from the plurality of intrinsic modal components, and apply a multi-scale Teager energy operator to the target modal component to construct a traveling wave instantaneous energy sequence; the target modal component is the intrinsic modal component in S2 that makes the fitness function obtain the optimal value; The calibration module is used to calibrate the corresponding time as the arrival time of the initial traveling wave front of the fault, based on the energy mutation characteristics in the instantaneous energy sequence of the traveling wave.

9. A computer storage medium, characterized in that, The computer storage medium stores a computer program; When a computer program is run on a computer, it causes the computer to perform the method described in any one of claims 1 to 7.

10. An electronic device, characterized in that, include: Memory, used to store computer programs; A processor for executing the computer program to implement the method as described in any one of claims 1 to 7.

Images