Fault distance measurement analysis method and system for three-core cable of power distribution network

Abstract

The invention discloses a power distribution network three-core cable fault distance measurement analysis method and system, and relates to the technical field of fault distance measurement, and the key points of the technical scheme are that the method comprises the steps: carrying out the empirical mode decomposition of three-phase voltage and current signals; generating a dynamic correction wave velocity based on the cable type, the environment temperature and the soil humidity parameter; determining the first arrival time of the wave head of the initial traveling wave according to the instantaneous frequency abrupt change point of the alpha-mode component, screening the second arrival time of the first reflected wave by using the amplitude gradient of the zero-mode component, and calculating the arrival time difference; and calculating an initial fault distance, and calling the impedance matrix to iteratively correct the initial fault distance. According to the method, the dynamic wave velocity is coupled with multiple factors, the limitation of single parameter correction is broken through, and the problem of transient drift of the wave velocity is solved; and the STFT high-frequency resolution and the wavelet low-frequency stability are fused, and the ridge continuity is optimized in combination with dynamic programming, so that the traveling wave head detection precision can be improved.

Description

Technical Field

[0001] The present invention relates to the technical field of fault location, and more specifically, it relates to a method and system for analyzing the fault location of three-core cables in a distribution network. Background Art

[0002] With the acceleration of the urbanization process, the scale and complexity of three-core cables in the distribution network continue to increase, and the rapid and accurate location of cable faults has become a key technology to ensure power supply reliability. In the field of fault location of three-core cables in the distribution network, the traveling wave method is widely used due to its high precision and fast response characteristics.

[0003] However, the complex cable structure, multi-branch network, and dynamic environmental factors still pose significant challenges to the existing technologies in practical engineering. When the empirical mode decomposition method in the existing technologies processes cable transient signals, serious mode mixing often occurs due to the fixed white noise injection mechanism, making it difficult to effectively separate the α-mode component and the zero-mode component; especially in a multi-branch cable network, high-frequency noise interference and cross-band signal coupling will significantly reduce the reliability of feature component extraction, directly affecting the subsequent wavehead detection accuracy. In addition, in the existing technologies, the Hilbert transform or the Teager energy operator is generally used for wavehead detection, which is vulnerable to high-frequency noise in a strong electromagnetic interference environment, resulting in misjudgment of pseudo-mutation points; especially for severely attenuated reflected wave signals, it is easy to cause the measurement error of the arrival time difference between the initial traveling wave and the reflected wave to exceed 0.5 μs, directly affecting the ranging accuracy.

[0004] Therefore, how to research and design a method and system for analyzing the fault location of three-core cables in a distribution network that can overcome the above defects is an urgent problem for us to solve at present. Summary of the Invention

[0005] To solve the deficiencies in the existing technologies, the purpose of the present invention is to provide a method and system for analyzing the fault location of three-core cables in a distribution network. By coupling the dynamic wave velocity with multiple factors, breaking through the limitation of single-parameter correction, and constructing a dynamic compensation model of cable type - environmental parameters - historical data, the problem of transient drift of the wave velocity is solved; in addition, by integrating the high-frequency resolution of STFT and the low-frequency stability of wavelets, and combining dynamic programming to optimize the ridge line continuity, the accuracy of traveling wavehead detection can be improved.

[0006] The above technical purpose of the present invention is achieved through the following technical solutions:

[0007] In the first aspect, a method for analyzing the fault location of three-core cables in a distribution network is provided, including the following steps:

[0008] Perform empirical mode decomposition on the three-phase voltage and current signals to separate the α-mode component and the zero-mode component;

[0009] Generate a dynamically corrected wave velocity based on cable type, ambient temperature, and soil moisture parameters;

[0010] Determine the first arrival time of the initial traveling wave head according to the instantaneous frequency mutation point of the α-mode component, and use the amplitude gradient of the zero-mode component to screen the second arrival time of the first reflected wave, and calculate the arrival time difference according to the difference between the second arrival time and the first arrival time;

[0011] Based on the dynamically corrected wave velocity and the arrival time difference, calculate the initial fault distance using a single-ended ranging method, and call the impedance matrix to iteratively correct the initial fault distance to obtain the final ranging result.

[0012] Further, the empirical mode decomposition of the three-phase voltage and current signals includes:

[0013] Dynamically adjust the white noise amplitude through the local signal-to-noise ratio, and combine wavelet packet frequency band segmentation to achieve multi-scale mode aliasing suppression;

[0014] And / or, adopt a dual constraint of energy convergence criterion and maximum iteration number to ensure that the single-channel decomposition time is less than or equal to the set time threshold.

[0015] Further, the generation process of the dynamically corrected wave velocity is specifically as follows:

[0016] Determine a preset reference wave velocity according to the cable type and update it online through historical fault data;

[0017] Trigger at least one compensation factor according to the fault type, and adjust the weight coefficient of the compensation factor according to the environmental parameter change rate;

[0018] Combine the preset reference wave velocity, the compensation factor, and the weight coefficient to determine the dynamically corrected wave velocity.

[0019] Further, the determination of the first arrival time of the initial traveling wave head according to the instantaneous frequency mutation point of the α-mode component includes:

[0020] Perform adaptive STFT-wavelet fusion analysis on the α-mode component to generate a joint time-frequency distribution;

[0021] Perform time-frequency ridge line tracking on the joint time-frequency distribution to determine the optimal ridge line;

[0022] Perform mutation point enhancement detection on the optimal ridge line, and use the time corresponding to the detected mutation point as the first arrival time of the initial traveling wave head.

[0023] Further, the adaptive STFT-wavelet fusion analysis of the α-mode component includes:

[0024] Perform a short-time Fourier transform on the α-mode component to obtain a first energy matrix;

[0025] Perform a wavelet transform on the α-mode component to obtain a second energy matrix;

[0026] Perform weighted fusion on the first energy matrix and the second energy matrix to obtain a joint time-frequency distribution.

[0027] Furthermore, the time-frequency ridge tracking of the joint time-frequency distribution includes:

[0028] Search for energy extreme points from the joint time-frequency distribution to construct an initial ridge candidate set;

[0029] Optimize the ridge continuity based on dynamic programming to determine the optimal ridge from the initial ridge candidate set;

[0030] Among them, the cost function for optimizing the ridge continuity based on dynamic programming is:

[0031]

[0032] Among them, C(t k ,f k ) represents the optimal cumulative cost at the current time t k at the current frequency f k ; C(t k-1 ,f k-1 ) represents the optimal cumulative cost at the previous time t k-1 at the previous frequency f k-1 ; λ represents the frequency jump penalty factor; E(t k ,f k ) represents the energy value at the current time t k at the current frequency f k .

[0033] Furthermore, the mutation point enhancement detection of the optimal ridge includes:

[0034] Calculate the instantaneous frequency gradient along the optimal ridge and detect extreme points;

[0035] Repeat ridge tracking in different frequency bands 0.1 - 1 MHz, 1 - 5 MHz, 5 - 10 MHz, and perform weighted processing on the extreme points obtained from all ridge tracking to obtain mutation points.

[0036] Furthermore, the expression for screening the second arrival time of the first reflected wave using the amplitude gradient of the zero-mode component is:

[0037]

[0038] Among them, ▽A represents the amplitude gradient of the zero-mode component; S0(t2±Δt) represents the signal amplitude of the zero-mode component within the time window corresponding to the second arrival time t2; Δt represents the half-width of the time window; ▽A base represents the reference gradient value.

[0039] Further, the calling of the impedance matrix to iteratively correct the initial fault distance includes:

[0040] Locating the candidate fault interval according to the initial fault distance, and calling the topology database to obtain all branch parameters within the candidate fault interval;

[0041] Intercepting the reflected wave period through the reflection wave spectrum matching, and extracting the spectrum characteristics;

[0042] Calculating the theoretical reflected wave by introducing an equivalent reflection path synthesis model;

[0043] Taking the minimization of the second norm difference between the spectrum characteristics and the theoretical reflected wave as the optimization objective, and dynamically correcting the branch parameters;

[0044] Recalculating the initial fault distance based on the corrected branch parameters.

[0045] In a second aspect, a fault location analysis system for a three-core cable in a distribution network is provided. The system is used to implement a fault location analysis method for a three-core cable in a distribution network as described in any one of the first aspects, including:

[0046] A modal decomposition module for performing empirical mode decomposition on three-phase voltage and current signals to separate the α-mode component and the zero-mode component;

[0047] A wave velocity correction module for generating a dynamically corrected wave velocity based on cable type, ambient temperature, and soil moisture parameters;

[0048] A time difference analysis module for determining the first arrival time of the initial traveling wave head according to the instantaneous frequency mutation point of the α-mode component, screening the second arrival time of the first reflected wave using the amplitude gradient of the zero-mode component, and calculating the arrival time difference according to the difference between the second arrival time and the first arrival time;

[0049] A ranging analysis module for calculating the initial fault distance using a single-end ranging method based on the dynamically corrected wave velocity and the arrival time difference, and calling the impedance matrix to iteratively correct the initial fault distance to obtain the final ranging result.

[0050] Compared with the prior art, the present invention has the following beneficial effects:

[0051] 1. A fault location analysis method for three - core cables in a distribution network provided by the present invention breaks through the limitation of single - parameter correction by coupling dynamic wave velocity with multiple factors, constructs a dynamic compensation model of cable type - environmental parameters - historical data, and solves the problem of transient drift of wave velocity.

[0052] 2. The present invention innovatively integrates the high - frequency resolution of STFT and the low - frequency stability of wavelets, combines dynamic programming to optimize the continuity of ridge lines, and can improve the detection accuracy of traveling - wave heads.

[0053] 3. The present invention realizes the high - purity separation of α - mode components and zero - mode components through local signal - to - noise ratio adaptive noise injection and wavelet packet frequency band segmentation.

[0054] 4. The present invention realizes the self - correction of parameter errors under complex topologies by establishing an iterative matching mechanism between the spectral characteristics of reflected waves and theoretical models. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] The drawings described herein are used to provide a further understanding of the embodiments of the present invention, form a part of this application, and do not limit the embodiments of the present invention. In the drawings:

[0056] Figure 1 is the flowchart in Embodiment 1 of the present invention;

[0057] Figure 2 is the system block diagram in Embodiment 2 of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0058] To make the objectives, technical solutions, and advantages of the present invention clearer and more understandable, the present invention will be further described in detail below in combination with embodiments and drawings. The illustrative embodiments and descriptions of the present invention are only used to explain the present invention and do not limit the present invention.

[0059] Embodiment 1: A fault location analysis method for three - core cables in a distribution network, as Figure 1 shown, includes the following steps:

[0060] S1: Perform empirical mode decomposition on three - phase voltage and current signals to separate α - mode components and zero - mode components;

[0061] S2: Generate a dynamically corrected wave velocity based on cable type, ambient temperature, and soil moisture parameters;

[0062] S3: Determine the first arrival time of the initial traveling - wave head according to the instantaneous frequency mutation point of the α - mode component, use the amplitude gradient of the zero - mode component to screen the second arrival time of the first reflected wave, and calculate the arrival - time difference according to the difference between the second arrival time and the first arrival time.

[0063] S4: Based on the dynamically corrected wave velocity and the time difference of arrival, use the single - end ranging method to calculate the initial fault distance, and call the impedance matrix to iteratively correct the initial fault distance to obtain the final ranging result.

[0064] In step S1, the empirical mode decomposition can be implemented by the CEEMDAN (Complete Ensemble Empirical Mode Decomposition with Adaptive Noise) method to obtain the α - mode component and the zero - mode component.

[0065] In some examples, considering that the fixed - amplitude white - noise injection in the traditional CEEMDAN is difficult to adapt to different scenarios when decomposing cable transient signals, such as weak signals in high - resistance faults and large environmental noise in submarine cables.

[0066] Therefore, the white - noise amplitude is dynamically adjusted by the local signal - to - noise ratio, and the specific formula is:

[0067]

[0068] where, A n represents the amplitude of the injected white noise; μ represents the attenuation factor, such as taking a value of 0.5; σ s represents the standard deviation of the current signal window; SNR l represents the local signal - to - noise ratio.

[0069] In addition, the noise frequency band can be restricted by wavelet packet decomposition, and noise is added only in the overlapping region with the cable fault characteristic frequency band (0.1 - 10 MHz) to reduce the interference of invalid frequency bands.

[0070] In some examples, considering the problem of cross - interference between the α - mode, β - mode and zero - mode components caused by frequency - band overlap, multi - scale mode aliasing suppression is achieved by combining wavelet packet frequency - band segmentation.

[0071] Specifically, first perform a fast Fourier transform (FFT) on the original signal to identify the main energy - aggregation frequency band; then divide the signal into three sub - frequency bands according to the energy distribution: low - frequency (0.1 - 1 MHz), medium - frequency (1 - 5 MHz), and high - frequency (5 - 10 MHz); then independently perform CEEMDAN decomposition on each sub - frequency band to avoid cross - frequency - band mode aliasing; finally, calculate the Pearson correlation coefficient between each mode component and the original signal, and eliminate the invalid modes with a correlation coefficient less than the coefficient threshold (such as 0.3) to retain the key components.

[0072] In some examples, an energy - convergence criterion and a maximum number of iterations can also be used for dual constraints to ensure that the single - channel decomposition time is less than or equal to the set time threshold.

[0073] For example, define the modal energy ratio of the m - th iteration When |E m -E m-1Terminate the decomposition when |< 0.05, where IMF m is the modal component of the m-th iteration, and IMF m-1 is the modal component of the (m - 1)-th iteration.

[0074] For another example, set the maximum number of iterations to 8 to ensure that the single-channel decomposition time ≤ 1 ms.

[0075] Based on the CEEMDAN method, the present invention improves the feature extraction ability of cable transient signals through noise dynamic management and multi-scale decomposition mechanism, and can provide a high-precision and high-reliability signal processing basis for fault location in complex scenarios.

[0076] In step S2, considering that the traditional wave velocity model uses a fixed value or a single parameter (such as temperature) for correction, without considering the coupling effects of multiple parameters such as humidity, salinity, and pressure; and not modeling the temperature coefficient differences and long-term aging of materials such as XLPE and oil-paper insulation; and the transient changes of wave velocity in high-resistance faults and multi-branch scenarios cannot be tracked in real time.

[0077] Therefore, the present invention presets a reference wave velocity v0 according to the cable type (XLPE / oil-paper), and updates it online through historical fault data. The update formula is:

[0078]

[0079] where represents the updated preset reference wave velocity at the next moment; represents the updated preset reference wave velocity at the current moment; η represents the learning rate; v s represents the measured wave velocity.

[0080] For different fault types, one or more compensation factors can be triggered. For example, high-resistance grounding can trigger temperature compensation factors, humidity compensation factors, and aging compensation factors. For another example, cable faults can trigger temperature compensation factors and pressure compensation factors.

[0081] In some examples, summing up all the triggered compensation factors can obtain the final compensation factor, and then directly calculating the dynamic correction wave velocity by multiplying the final compensation factor by the preset reference wave velocity.

[0082] In some examples, the weight coefficient of the compensation factor can also be adjusted according to the environmental parameter change rate. The specific formula is:

[0083]

[0084] where w i represents the weight coefficient of the i-th compensation factor; ΔP irepresents the dimensionless value of the change rate of the environmental parameter corresponding to the i-th compensation factor; ΔP j represents the dimensionless value of the change rate of the environmental parameter corresponding to the j-th compensation factor; Q represents the number of compensation factors. It should be noted that the absolute value in the above formula refers to taking the absolute value of the change rate of the environmental parameter and then normalizing the absolute value.

[0085] Finally, combining the preset reference wave velocity, compensation factors, and weight coefficients, the dynamic correction wave velocity is determined.

[0086] Through multi-factor coupling compensation, the present invention can solve the problem of dynamic correction of wave velocity in complex environments, providing a core parameter guarantee for accurate cable fault location.

[0087] In step S3, the detection method of the initial traveling wave head can be implemented by methods such as Hilbert transform and Teager energy operator.

[0088] In some examples, determining the first arrival time of the initial traveling wave head according to the instantaneous frequency mutation point of the α-mode component includes: performing adaptive STFT-wavelet fusion analysis on the α-mode component to generate a joint time-frequency distribution; performing time-frequency ridge line tracking on the joint time-frequency distribution to determine the optimal ridge line; performing mutation point enhancement detection on the optimal ridge line, and using the time corresponding to the detected mutation point as the first arrival time of the initial traveling wave head.

[0089] Specifically, performing adaptive STFT-wavelet fusion analysis on the α-mode component includes: performing short-time Fourier transform on the α-mode component to obtain a first energy matrix; performing wavelet transform on the α-mode component to obtain a second energy matrix; performing weighted fusion on the first energy matrix and the second energy matrix to obtain a joint time-frequency distribution.

[0090] For example, performing short-time Fourier transform (STFT) on the α-mode component, with the window length τ = min(0.1 / f max , 10) μs and the step size being 0.1τ; simultaneously performing wavelet transform (Morlet basis, scale [0.1, 10] μs), extracting the time-frequency energy matrix E(t, f); then fusing the high-frequency resolution of STFT and the low-frequency stability of wavelet to generate the joint time-frequency distribution: E fuse (t, f) = w STFT E STFT + w Wa E Wa ; where E STFT is the first energy matrix, w STFT is the weight of the first energy matrix, E Wa is the second energy matrix, w Wa is the weight of the second energy matrix, and the weights of weighted fusion are dynamically adjusted according to the frequency band.

[0091] Specifically, performing time-frequency ridge tracking on the joint time-frequency distribution includes: searching for energy extreme points from the joint time-frequency distribution to construct an initial ridge candidate set; and optimizing the ridge continuity based on dynamic programming to determine the optimal ridge from the initial ridge candidate set.

[0092] The initial ridge candidate set R = {(t i , f i ) | E(t i , f i ) > δ·max(E)}, where E(t i , f i ) is the energy of the joint time-frequency distribution corresponding to the time point t i and the frequency point f i ; δ is the energy threshold coefficient, and its value range is [0, 1]; max(E) is the global maximum time-frequency energy.

[0093] The cost function for optimizing the ridge continuity based on dynamic programming is:

[0094]

[0095] where C(t k , f k ) represents the optimal cumulative cost at the current time t k at the current frequency f k ; C(t k-1 , f k-1 ) represents the optimal cumulative cost at the previous time t k-1 at the previous frequency f k-1 ; λ represents the frequency jump penalty factor; E(t k , f k ) represents the energy value at the current time t k at the current frequency f k .

[0096] Specifically, performing mutation point enhancement detection on the optimal ridge includes: calculating the instantaneous frequency gradient along the optimal ridge and detecting the extreme points; repeating the ridge tracking in different frequency bands of 0.1 - 1 MHz, 1 - 5 MHz, and 5 - 10 MHz, and performing weighted processing on the extreme points obtained from all ridge tracking to obtain the mutation points.

[0097] For example, the instantaneous frequency gradient

[0098] The time of the extreme point

[0099] The present invention uses the dynamic ridge tracking method for instantaneous frequency mutation point detection, which can reduce the misjudgment of instantaneous frequency mutation points caused by high-frequency noise in complex scenarios and at the same time keep the recognition of the wavefront polarity clear.

[0100] In some examples, the expression for screening the second arrival time of the first reflected wave by using the amplitude gradient of the zero-mode component is as follows:

[0101]

[0102] where ▽A represents the amplitude gradient of the zero-mode component; S0(t2±Δt) represents the signal amplitude of the zero-mode component within the time window corresponding to the second arrival time t2; Δt represents the half-width of the time window; ▽A base represents the reference gradient value, which is generally determined by statistical analysis of historical fault data. The present invention can accurately identify the first reflected wave in the case of multiple reflected waves being aliased due to a multi-branch network.

[0103] In step S4, the impedance matrix is called to iteratively correct the initial fault distance, including: locating the candidate fault interval according to the initial fault distance, and calling the topology database to obtain all branch parameters (such as impedance and inductance) within the candidate fault interval; intercepting the reflected wave period through reflected wave spectrum matching, and extracting spectrum features; calculating the theoretical reflected wave by introducing an equivalent reflected path synthesis model; dynamically correcting the branch parameters by minimizing the difference in the second norm between the spectrum features and the theoretical reflected wave; and recalculating the initial fault distance based on the corrected branch parameters.

[0104] In some examples, the topology database stores structure data, electrical parameters, and environmental parameters. The structure data includes but is not limited to node coordinates, branch lengths, cable types (XLPE / oil paper), and joint models; the electrical parameters include but are not limited to impedance per unit length, admittance, and historical fault wave velocity; and the environmental parameters include but are not limited to real-time temperature, humidity, and aging coefficient.

[0105] In some examples, the calculation formula for the theoretical reflected wave is as follows:

[0106] S mo (f) = ∑A eq S src (f)·e -θ·2πfD

[0107]

[0108] where A eq is the equivalent reflected wave weight, which is calculated from the branch reflection coefficient Γ x and the propagation attenuation to invert the multi-path superposition effect; S src (f) is the source signal spectrum of the fault type, including the frequency characteristics of the initial type, such as the high-frequency components of local pulses; e -θ·2πfD is the amplitude attenuation term of the traveling wave propagation, which is related to the fault distance D and the attenuation coefficient θ; γ is the propagation constant, fc is the cut-off frequency of the cable.

[0109] The present invention converts the impedance parameter errors (such as aging and environmental changes) and topological structure uncertainties (such as reflection coefficient errors at branch nodes) in the cable network into a mathematical optimization problem, and corrects the system model through backpropagation, achieving accurate ranging under complex topologies.

[0110] Embodiment 2: A three-core cable fault ranging and analysis system for a distribution network, which is used to implement a three-core cable fault ranging and analysis method as described in Embodiment 1, as Figure 2 shown, including a modal decomposition module, a wave velocity correction module, a time difference analysis module, and a ranging analysis module.

[0111] Among them, the modal decomposition module is used to perform empirical modal decomposition on the three-phase voltage and current signals to separate the α-mode component and the zero-mode component; the wave velocity correction module is used to generate a dynamically corrected wave velocity based on cable type, environmental temperature, and soil humidity parameters; the time difference analysis module is used to determine the first arrival time of the initial traveling wave head according to the instantaneous frequency mutation point of the α-mode component, and use the amplitude gradient of the zero-mode component to screen the second arrival time of the first reflected wave, and calculate the arrival time difference according to the difference between the second arrival time and the first arrival time; the ranging analysis module is used to calculate the initial fault distance by using the single-end ranging method based on the dynamically corrected wave velocity and the arrival time difference, and call the impedance matrix to iteratively correct the initial fault distance to obtain the final ranging result.

[0112] Working principle: The present invention breaks through the limitation of single-parameter correction by coupling dynamic wave velocity with multiple factors, constructs a dynamic compensation model of cable type - environmental parameters - historical data, and solves the problem of transient drift of wave velocity; in addition, by integrating the high-frequency resolution of STFT and the low-frequency stability of wavelets, and combining dynamic programming to optimize the ridge line continuity, the detection accuracy of traveling wave heads can be improved.

[0113] Those skilled in the art should understand that the embodiments of the present application can be provided as methods, systems, or computer program products. Therefore, the present application can take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present application can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk memories, CD-ROMs, optical memories, etc.) containing computer-usable program codes.

[0114] This application is described with reference to the flowcharts and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the present application. It should be understood that each flow and / or block in the flowcharts and / or block diagrams, and the combination of flows and / or blocks in the flowcharts and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to generate a machine, such that the instructions executed by the processor of the computer or other programmable data processing devices produce a means for implementing the functions specified in one or more flows and / or blocks Figure 1 in one or more flows and / or blocks Figure 1 or in one or more blocks.

[0115] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, such that the instructions stored in the computer-readable memory produce a manufactured article including an instruction means that implements the functions specified in one or more flows and / or blocks Figure 1 in one or more flows and / or blocks Figure 1 or in one or more blocks.

[0116] These computer program instructions can also be loaded onto a computer or other programmable data processing device, such that a series of operation steps are executed on the computer or other programmable device to generate a computer-implemented process, and thus the instructions executed on the computer or other programmable device provide steps for implementing the functions specified in one or more flows and / or blocks Figure 1 in one or more flows and / or blocks Figure 1 or in one or more blocks.

[0117] The specific embodiments described above further elaborate on the purpose, technical solutions, and beneficial effects of the present invention. It should be understood that the above description is only for the specific embodiments of the present invention and is not used to limit the protection scope of the present invention. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for fault location analysis of a three-core cable in a distribution network, characterized in that: The following steps are involved: Perform empirical mode decomposition on the three-phase voltage and current signals to separate the α-mode component and the zero-mode component; Generates dynamically corrected wave speed based on cable type, ambient temperature and soil moisture parameters; Determine the first arrival time of the initial traveling wave head according to the instantaneous frequency mutation point of the α-mode component, select the second arrival time of the first reflected wave by using the amplitude gradient of the zero-mode component, and calculate the arrival time difference according to the difference between the second arrival time and the first arrival time; Based on the dynamically corrected wave velocity and the arrival time difference, a single-ended ranging method is used to calculate the initial fault distance, and the impedance matrix is ​​called to iteratively correct the initial fault distance to obtain a final ranging result.

2. A method for fault location analysis of a three-core cable in a distribution network according to claim 1, characterized in that: The performing of empirical mode decomposition on the three-phase voltage and current signals comprises: The amplitude of white noise is dynamically adjusted through the local signal-to-noise ratio, and multi-scale modal aliasing suppression is achieved by combining wavelet packet band segmentation. And / or, a dual constraint of energy convergence criterion and maximum number of iterations is adopted to ensure that the single channel decomposition time is less than or equal to a set time threshold.

3. A method for fault location analysis of a three-core cable in a distribution network according to claim 1, characterized in that: The generation process of the dynamic correction wave velocity is specifically as follows: Determine the preset reference wave speed according to the cable type and update it online through historical fault data; triggering at least one compensation factor according to the fault type, and adjusting a weight coefficient of the compensation factor according to a rate of change of an environmental parameter; The dynamically corrected wave speed is determined by combining the preset reference wave speed, the compensation factor and the weight coefficient.

4. A method for fault location analysis of a three-core cable in a distribution network according to claim 1, characterized in that: The step of determining the first arrival time of the initial traveling wave head according to the instantaneous frequency mutation point of the α-mode component comprises: Performing adaptive STFT-wavelet fusion analysis on the α-mode component to generate a joint time-frequency distribution; Tracking the time-frequency ridge line of the joint time-frequency distribution to determine the optimal ridge line; The optimal ridge line is subjected to enhanced detection of mutation points, and the time corresponding to the detected mutation points is used as the first arrival time of the initial traveling wave head.

5. A method for fault location analysis of a three-core cable in a distribution network according to claim 4, characterized in that: The step of performing adaptive STFT-wavelet fusion analysis on the α-mode component comprises: Performing short-time Fourier transform on the α-mode component to obtain a first energy matrix; Performing wavelet transform on the α modulus component to obtain a second energy matrix; The first energy matrix and the second energy matrix are weightedly fused to obtain a joint time-frequency distribution.

6. A method for fault location analysis of a three-core cable in a distribution network according to claim 4, characterized in that: The step of tracking the time-frequency ridgeline of the joint time-frequency distribution includes: Searching for energy extreme value points from the joint time-frequency distribution to construct an initial ridge candidate set; Optimizing ridge continuity based on dynamic programming to determine the optimal ridge from the initial ridge candidate set; The cost function for optimizing ridge continuity based on dynamic programming is: Among them, C(t k ,f k ) represents the current time t k At the current frequency f k The optimal cumulative cost at k-1 ,f k-1 ) represents the previous time t k-1 At the previous frequency f k-1 The optimal cumulative cost at ; λ represents the frequency jump penalty factor; E(t k ,f k ) represents the current time t k At the current frequency f k The energy value at .

7. A method for fault location analysis of a three-core cable in a distribution network according to claim 4, characterized in that: The step of performing enhanced detection of mutation points on the optimal ridge line includes: Calculating the instantaneous frequency gradient along the optimal ridge line and detecting extreme value points; The ridge line tracing was repeated in different frequency bands of 0.1-1 MHz, 1-5 MHz, and 5-10 MHz, and all extreme points obtained by ridge line tracing were weighted to obtain mutation points.

8. A method for fault location analysis of a three-core cable in a distribution network according to claim 1, characterized in that: The expression for screening the second arrival time of the first reflected wave by using the amplitude gradient of the zero mode component is: Wherein, ▽A represents the amplitude gradient of the zero-mode component; S0(t2±Δt) represents the signal amplitude of the zero-mode component in the time window corresponding to the second arrival time t2; Δt represents the half width of the time window; ▽A base Indicates the base gradient value.

9. A method for fault location analysis of a three-core cable in a distribution network according to claim 1, characterized in that: The calling impedance matrix to iteratively correct the initial fault distance includes: Locate the candidate fault interval according to the initial fault distance, and call the topology database to obtain all branch parameters in the candidate fault interval; By matching the reflected wave spectrum, the reflected wave time period is intercepted and the spectrum features are extracted; Theoretical reflection waves are calculated by introducing an equivalent reflection path synthesis model; Dynamically modifying the branch parameters by minimizing the difference between the second norm of the frequency spectrum feature and the theoretical reflected wave as an optimization target; The initial fault distance is recalculated based on the revised branch parameters.

10. A three-core cable fault distance analysis system for a distribution network, characterized in that: The system is used to implement a distribution network three-core cable fault location analysis method as described in any one of claims 1 to 9, comprising: The modal decomposition module is used to perform empirical modal decomposition on the three-phase voltage and current signals to separate the α-mode component and the zero-mode component; A wave speed correction module is used to generate a dynamic correction wave speed based on cable type, ambient temperature and soil moisture parameters; a time difference analysis module, configured to determine a first arrival time of an initial traveling wave head according to an instantaneous frequency mutation point of the α-mode component, screen a second arrival time of a first reflected wave using an amplitude gradient of the zero-mode component, and calculate an arrival time difference according to a difference between the second arrival time and the first arrival time; The ranging analysis module is used to calculate the initial fault distance based on the dynamic correction wave speed and the arrival time difference by using a single-ended ranging method, and to call the impedance matrix to iteratively correct the initial fault distance to obtain a final ranging result.

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