Method for determining signal injection parameters for ground fault of 10kV distribution line

Abstract

The present application relates to the technical field of power maintenance, and discloses a signal injection parameter determination method for 10kV distribution line grounding fault. The present application obtains the no-load spectrum characteristic data by injecting wideband sweep frequency pulse into the line and setting the sweep frequency step according to the line length; calculates the correlation between the candidate frequency points in the available frequency band, and selects multiple characteristic frequency points with correlation lower than the threshold; distributes the injection energy according to the frequency band where each characteristic frequency point is located, so that the energy is inversely proportional to the attenuation coefficient; collects the feedback signal to extract multiple response characteristics to form the fault characteristic data; compares the fault characteristic data with the standard fault characteristic library for similarity, to obtain the fault type and matching degree; if the matching degree is lower than the threshold, the sampling is encrypted and the energy is increased in the sensitive frequency band, and then the extraction and matching are repeated; and the fault distance is calculated by using the phase offset difference value of at least two characteristic frequency points. The adaptive determination of the injection parameter is realized, and the detection success rate and positioning accuracy of the concealed fault are improved.

Description

Technical Field

[0001] This invention relates to the field of power maintenance technology, and in particular to a method for determining signal injection parameters for grounding faults in 10kV distribution lines. Background Technology

[0002] Signal injection is a commonly used technique for locating grounding faults in distribution networks. Its basic principle is to inject a specific frequency detection signal into the line after a fault occurs, and then locate the fault by tracing the signal path or detecting signal characteristics. This method has advantages such as being unaffected by line distributed capacitance and having an intuitive principle, and is widely used in practical operation and maintenance.

[0003] However, traditional signal injection methods typically employ fixed-frequency and fixed-energy injection strategies, such as the commonly used 220Hz injection signal. Due to the complex topology and numerous branches of 10kV distribution lines, and the dynamic changes in line impedance characteristics with length, load, and environmental factors, fixed-parameter injection signals are difficult to adapt to the actual operating conditions of different lines. When the injection frequency falls at the line's resonant point or in a high-attenuation band, the signal may be too strong and damage equipment, or too weak to be effectively detected. Furthermore, when facing concealed faults such as high-resistance grounding and insulator breakdown, fixed-energy signals often fail to penetrate the fault point, and the feedback signal is easily drowned out by background noise. Existing technologies lack a dynamic adjustment mechanism for injection parameters, resulting in low fault detection sensitivity and poor location accuracy, making it difficult to meet the maintenance needs under complex operating conditions. Summary of the Invention

[0004] The technical problem to be solved by this invention is that the existing technology has low sensitivity to fault detection and poor accuracy in fault location, which makes it difficult to meet the operation and maintenance needs under complex working conditions. To this end, we propose a method for determining the signal injection parameters of 10kV distribution line grounding faults.

[0005] To achieve the above objectives, this application adopts the following technical solution: a method for determining signal injection parameters for grounding faults in 10kV distribution lines, comprising the following steps:

[0006] S1. Line spectrum mapping: Inject broadband sweep pulses into the target line, adaptively set the sweep step according to the line length, and collect feedback signals to obtain no-load spectrum characteristic data including line input impedance, attenuation coefficient and background noise;

[0007] S2. Feature frequency point selection: Within the available frequency bands selected based on the attenuation coefficient and background noise, calculate the correlation between any two candidate frequency points, and select multiple frequency points with a correlation lower than a set threshold as feature frequency points.

[0008] S3. Energy Allocation: For each characteristic frequency point, energy is allocated and injected according to its frequency band, so that the energy is inversely proportional to the attenuation coefficient of that frequency point, forming signals with different frequency and energy combinations and injecting them into the line in sequence.

[0009] S4. Fault Feature Extraction: Collect feedback signals for each frequency and energy combination, and extract multiple response features to form fault feature data of the current line status;

[0010] S5. Fault Matching and Identification: Compare the current fault feature data with the pre-stored standard fault feature library to obtain the fault type and its matching degree;

[0011] S6. Iterative optimization: If the matching degree is lower than the set threshold, the frequency sampling points are encrypted in the sensitive frequency band corresponding to the initially determined fault type, and the fault feature extraction step and fault matching and identification step are repeated after increasing the injected energy according to the degree of matching degree loss.

[0012] S7. Fault location: Calculate the fault distance using the phase offset difference between at least two characteristic frequency points.

[0013] Preferably, in the line spectrum mapping step, the sweep frequency step is set as follows: the step frequency is determined according to the total length of the line and the signal propagation speed, so that the step frequency is inversely proportional to the line length and directly proportional to the signal propagation speed, so as to ensure that the spatial resolution of the sweep frequency signal can cover the entire line.

[0014] Preferably, in the characteristic frequency selection step, the correlation between two candidate frequency points is measured in the following way: the line input impedance functions corresponding to the two frequency points are bandpass filtered respectively, the absolute value of the inner product of the two functions after filtering is calculated, and then divided by the geometric mean of their respective energies. The resulting ratio is the correlation coefficient. When the correlation coefficient is less than 0.3, the two frequency points are determined to be linearly unrelated and are retained. When the correlation coefficient is greater than or equal to 0.3, the two frequency points are determined to be redundant, and only the one with the smaller attenuation coefficient is retained.

[0015] Preferably, in the characteristic frequency selection step: the usable frequency band refers to the frequency range where the attenuation coefficient is less than 3dB / km and the background noise is less than 1.5 times the average power of the background noise.

[0016] Preferably, the energy distribution step is as follows: for low-frequency bands greater than or equal to 20Hz and less than 500Hz, the injected energy is: the preset reference energy multiplied by the ratio of the reference attenuation coefficient to the measured attenuation coefficient, and then multiplied by the product of the low-frequency band energy coefficient and the low-frequency weighting coefficient.

[0017] For mid-frequency bands greater than or equal to 500Hz and less than 1500Hz, the injected energy is: the preset reference energy multiplied by the ratio of the reference attenuation coefficient to the measured attenuation coefficient, and then multiplied by the product of the mid-frequency band energy coefficient and the mid-frequency weighting coefficient.

[0018] For high-frequency bands greater than or equal to 1500Hz and less than or equal to 20kHz, the injected energy is: the preset reference energy multiplied by the ratio of the reference attenuation coefficient to the measured attenuation coefficient, and then multiplied by the product of the high-frequency band energy coefficient and the high-frequency weighting coefficient.

[0019] Among them, the energy coefficient of the low-frequency band is greater than that of the mid-frequency band, which is greater than that of the high-frequency band. The weighting coefficients of the low-frequency band, mid-frequency band, and high-frequency band decrease in that order.

[0020] Preferably, the multiple response features extracted in the fault feature extraction step include at least:

[0021] Amplitude response, which is the ratio of the amplitude of the feedback signal to the amplitude of the injected signal;

[0022] Phase shift, which is the difference between the phase of the feedback signal and the phase of the injected signal;

[0023] Harmonic distortion rate is the ratio of the square root of the sum of the squares of the amplitudes of all harmonic components in the feedback signal to the amplitude of the fundamental frequency.

[0024] The decay time constant is the time required for the impulse response envelope to decrease to its initial value of 1 / e.

[0025] The degree of polarization is the difference between the amplitude of the feedback component parallel to the polarization direction of the injected signal and the amplitude of the vertical component, divided by the sum of the two.

[0026] Preferably, in the fault matching and identification step, the similarity comparison considers both the amplitude difference and morphological similarity between the fault feature data and the standard feature data; wherein the amplitude difference is measured by weighted Euclidean distance, and the weight is set according to the background noise level of each frequency point; the morphological similarity is measured by dynamic time warping distance; the two distances are weighted and fused and then subjected to exponential transformation to obtain the similarity value, and the fault type with the highest similarity is taken as the identification result.

[0027] Preferably, in the iterative optimization step, the degree of missing matching is 1 minus the current degree of matching, the energy amplification factor is set to the sum of 1 and the degree of missing matching, the energy amplification factor is a dimensionless quantity with no physical unit, used to characterize the amplification ratio of injected energy relative to the reference energy, the degree of matching is a dimensionless quantity with no physical unit, and the value range is 0-1, used to characterize the degree of matching between the measured fault characteristics and the standard fault characteristics; when encrypting frequency sampling, the original frequency sweep step is reduced to one-quarter, and new frequency sampling points are generated in the sensitive frequency band.

[0028] Preferably, in the fault location step, the fault distance is calculated as follows: the phase offset difference between two characteristic frequency points is selected, multiplied by the signal propagation speed, divided by 4π and multiplied by the absolute value of the frequency difference between the two frequency points; the average value of the calculation results of multiple sets of different frequency point pairs is taken as the final fault distance.

[0029] Preferably, the standard fault feature library is pre-established in the following way: in a laboratory environment, steps S1-S7 are repeated under different line conditions for various typical fault types, including high-resistance grounding, insulator breakdown, and surge arrester damage, to obtain standard feature data for each fault type; at the same time, the response intensity distribution of each fault type at different frequency points is statistically analyzed, and the continuous frequency band with the highest response intensity is determined as the sensitive frequency band of the fault type and stored in association with the standard feature data.

[0030] The technical effects and advantages of this invention are as follows:

[0031] This invention adaptively sets the frequency sweep step through line spectrum mapping to obtain the true impedance, attenuation, and noise characteristics of the line, solving the problem of mismatch between traditional fixed parameter injection and line operating conditions. Within the available frequency band, redundant frequency points are eliminated based on correlation coefficients, and linearly independent characteristic frequency points are selected to cover the most comprehensive fault information with the fewest frequency points. Injected energy is allocated inversely according to the measured attenuation coefficient of each frequency point, ensuring a basically balanced signal-to-noise ratio and significantly enhancing the penetration capability for hidden faults such as high-impedance grounding. Five-dimensional features (amplitude, phase, harmonic distortion rate, attenuation time constant, and polarization degree) are extracted from the feedback signal to construct a fault fingerprint, which is then matched with a standard feature library for similarity, upgrading fault identification from a single criterion to multi-dimensional pattern recognition. When the matching confidence is insufficient, sampling is encrypted and energy is amplified in sensitive frequency bands, achieving adaptive evolution of the diagnostic strategy. Finally, the fault distance is calculated using the phase difference of multiple frequency points, eliminating single-frequency interference and taking the average value, significantly improving positioning accuracy. Attached Figure Description

[0032] The disclosure of this invention is illustrated with reference to the accompanying drawings. It should be understood that the drawings are for illustrative purposes only and are not intended to limit the scope of protection of this invention. In the drawings, the same reference numerals are used to refer to the same parts:

[0033] Figure 1 This is a flowchart of the method of the present invention;

[0034] Figure 2 This is a schematic diagram of energy distribution and signal injection according to the present invention;

[0035] Figure 3 This is a schematic diagram illustrating the fault location principle of the present invention. Detailed Implementation

[0036] It is readily understood that, based on the technical solution of this invention, those skilled in the art can propose various interchangeable structural methods and implementations without altering the essential spirit of the invention. Therefore, the following detailed embodiments and accompanying drawings are merely illustrative examples of the technical solution of this invention and should not be considered as the entirety of the invention or as limitations or restrictions on the technical solution of this invention.

[0037] Example 1: As Figures 1-3 As shown, the present invention provides a technical solution: a method for determining signal injection parameters for grounding faults in 10kV distribution lines, the specific steps of which are as follows:

[0038] Step S1. Line Spectrum Mapping: During ground faults or routine inspections, a wideband sweep pulse generator is used to inject wideband sweep pulses into the target 10kV distribution line; to ensure clear identification of the spectral characteristics of the entire line, the sweep pulse step is... A suitable selection is necessary: ​​too large a value may miss critical impedance resonant points, while too small a value will prolong the testing time; therefore, this invention employs an adaptive frequency sweep strategy, based on the total line length. and signal propagation speed Determine the step frequency:

[0039] ;

[0040] in Frequency sweep step, unit: Hz;

[0041] The speed at which the signal propagates in the line is determined by the line type and ranges from 2.95 × 10⁻⁶. 8 -3.0×10 8 ;

[0042] The total length of the line is in meters (m).

[0043] This formula, derived from transmission line theory, ensures that the spatial resolution of the swept frequency signal is sufficient to cover the entire line, satisfying the constraint of spatial sampling frequency. .

[0044] After the pulse is injected, the data acquisition unit acquires the feedback signal to obtain the line input impedance. attenuation coefficient and background noise This constitutes the unloaded spectrum characteristic data. Among them:

[0045] Frequency, in Hz;

[0046] For frequency The line input impedance, in Ω;

[0047] For frequency Signal attenuation coefficient under [condition], in dB / km;

[0048] For frequency Background noise power spectral density.

[0049] Step S2. Feature Frequency Selection: Analyze the collected spectrum data and first select suitable frequency bands for signal transmission; usually, the attenuation coefficient is retained. Below 3dB / km and background noise The frequency range is 1.5 times lower than the average power of the background noise, thus eliminating frequency bands with excessive transmission loss or severe interference.

[0050] Average power of background noise Calculate using the following formula:

[0051] ;

[0052] In the formula:

[0053] , These are the lower and upper sweep frequencies, respectively, in Hz;

[0054] This represents the average power of the background noise.

[0055] Within the available frequency band, the correlation between any two candidate frequency points is further calculated to avoid selecting frequency points with redundant information; for any two candidate frequency points and Correlation coefficient Calculate using the following formula:

[0056] ;

[0057] In the formula:

[0058] For frequency point and The correlation coefficient is dimensionless.

[0059] This is the line input impedance function;

[0060] For The bandpass filter function is the one for the center frequency;

[0061] ,in For bandwidth parameters;

[0062] For The bandpass filter function is the one for the center frequency;

[0063] The denominator is the geometric mean of the impedance spectrum energies corresponding to the two frequency points.

[0064] When the correlation coefficient is less than 0.3, the two frequency points are considered linearly independent and can be retained simultaneously. When the correlation coefficient is greater than or equal to 0.3, the two are considered to have redundant information, and only the one with the smaller attenuation coefficient is retained. In this way, a set of independent and distinctive feature frequency points is finally selected, denoted as... ,in This represents the total number of characteristic frequency points.

[0065] Step S3. Energy Allocation: Due to the significant differences in attenuation characteristics of signals from different frequency bands in power distribution lines, to ensure that the feedback signals at all characteristic frequency points have sufficient signal-to-noise ratios, it is necessary to allocate injected energy according to the characteristics of the frequency band at each frequency point. Energy allocation follows the "inverse attenuation" principle, that is, the energy is inversely proportional to the attenuation coefficient of that frequency point. In specific implementation, the frequency band is divided into:

[0066] Low frequency band (greater than or equal to 20Hz and less than 500Hz);

[0067] Mid-frequency band (greater than or equal to 500Hz and less than 1500Hz);

[0068] High frequency band (greater than or equal to 1500Hz and less than or equal to 20kHz);

[0069] Injected energy at low frequency bands = Preset reference energy × (Reference attenuation coefficient ÷ Measured attenuation coefficient) × Low frequency band energy coefficient × Low frequency weighting coefficient;

[0070] Injected energy at mid-frequency points = preset reference energy × (reference attenuation coefficient ÷ measured attenuation coefficient) × mid-frequency energy coefficient × mid-frequency weighting coefficient;

[0071] Injected energy at high frequency points = preset reference energy × (reference attenuation coefficient ÷ measured attenuation coefficient) × high frequency energy coefficient × high frequency weighting coefficient;

[0072] The energy coefficients of the low-frequency band, mid-frequency band, and high-frequency band decrease sequentially, as do the weighting coefficients of the low-frequency band, mid-frequency band, and high-frequency band, to reflect the characteristic that low-frequency signals are more suitable for long-distance transmission. The signal synthesis and control unit generates signals with different frequencies and energy combinations according to the allocated energy and injects them into the line sequentially.

[0073] Step S4. Fault Feature Extraction: After signal injection, the feedback signal is collected for each frequency and energy combination; to comprehensively characterize the fault state, the following five response features are extracted to form fault feature data:

[0074] Amplitude response The ratio of the feedback signal amplitude to the injected signal amplitude is given by the formula:

[0075] ,in For frequency point amplitude response, For frequency point Injected signal amplitude; For frequency point Feedback signal amplitude;

[0076] Phase shift The difference between the phase of the feedback signal and the phase of the injected signal; the formula is:

[0077] ,in For frequency point Phase offset, in rad; For the phase of the injected signal, The feedback signal phase;

[0078] Harmonic distortion rate The ratio of the square root of the sum of the squares of the amplitudes of the 2nd to 5th harmonic components in the feedback signal to the amplitude of the fundamental frequency; the formula is: In the formula For frequency point Harmonic distortion rate; For the first The amplitude of the second harmonic component, ;

[0079] decay time constant : Defined as the time required for the impulse response envelope to decrease to 1 / e of its initial value, in seconds, where It is a natural constant;

[0080] polarization degree The difference between the amplitude of the feedback component parallel to the polarization direction of the injected signal and the amplitude of the vertical component, divided by the sum of the two; ,in For frequency point degree of polarization The amplitude of the feedback component is parallel to the polarization direction of the injected signal. The amplitude of the feedback component is perpendicular to the polarization direction of the injected signal.

[0081] Will The features mentioned above at each frequency point are arranged in frequency order to form... Fault feature data matrix with 5 rows and 5 columns .

[0082] Step S5. Fault Matching and Identification: The extracted fault feature data is compared with a pre-established standard fault feature library for similarity. The similarity comparison considers both amplitude difference and morphological similarity: amplitude difference is measured by weighted Euclidean distance, with weights set according to the background noise level at each frequency (the higher the noise, the smaller the weight); morphological similarity is measured by dynamic time warping (DTW) distance. The two distances are weighted and fused, and then exponentially transformed to obtain the similarity value. The fault type with the highest similarity is taken as the identification result, and its matching degree (confidence) is output.

[0083] Step S6. Iterative Optimization: If the matching degree is lower than the preset threshold (e.g., 0.8), it indicates that the current feature data is insufficient to accurately identify the fault. At this time, the iterative optimization process is started:

[0084] First, based on the initially determined fault type, find its corresponding sensitive frequency band (i.e., the frequency band with the strongest response to that fault type); within this sensitive frequency band, reduce the original frequency sweep step to one-quarter and increase the density of frequency sampling points to obtain more refined spectral characteristics;

[0085] Secondly, the injected energy is increased according to the degree of mismatch; the energy amplification factor is set to 1 plus the degree of mismatch, the lower the mismatch, the greater the energy increase;

[0086] Then repeat steps S4 and S5 until the matching degree meets the requirements or the maximum number of iterations is reached.

[0087] Step S7. Fault Location: After determining the fault type, calculate the fault distance using the phase offset difference between at least two characteristic frequency points. Select two characteristic frequency points. and ( Phase offset difference Fault distance for:

[0088] ;

[0089] The absolute value of the phase offset difference between two characteristic frequency points, in rad;

[0090] The speed of signal propagation, expressed in m / s;

[0091] To improve accuracy, the average of the calculation results from multiple pairs of different frequency points is taken as the final fault distance:

[0092] ;

[0093] In the formula: The final fault distance is in meters. The number of frequency point pairs selected; For the first The fault distance is calculated by frequency point pairs; a frequency point pair refers to a calculation unit composed of two characteristic frequency points with a preset frequency difference selected to realize dual-frequency phase difference fault location, and each set of frequency point pairs corresponds to a set of fault distance calculation results.

[0094] Example 2: This example further illustrates the key calculation formulas involved in the above method and their physical meaning.

[0095] 1. Adaptive setting of sweep frequency step:

[0096] In step S1, the frequency sweep step is performed. Based on transmission line theory, this ensures that spatial aliasing does not occur during frequency domain sampling; for example, for a line with a total length of L=10km, taking... ,but This means that sampling one point every 7.5kHz is sufficient to ensure that the reflected signal at the end of the line can be distinguished.

[0097] 2. Precise calculation of frequency correlation:

[0098] In step S2, the correlation coefficient The calculation formula essentially measures the similarity of the impedance spectrum shapes at two frequency points; when When the impedance spectrum shapes corresponding to the two frequency points are significantly different, they represent different physical characteristics of the line (for example, one frequency point mainly reflects the capacitance to ground, and the other mainly reflects the line inductance), therefore both should be retained; when If the two frequency points have highly overlapping information, only the one with the smaller attenuation coefficient needs to be retained.

[0099] 3. Phase difference algorithm for fault location:

[0100] In step S7, the dual-frequency phase difference method is used to eliminate the effects of the initial phase of the signal source and the phase delay along the entire length of the line; formula The derivation principle is: the signal propagates from the injection point to the fault point and then returns, with a round trip distance of... The resulting phase change is:

[0101] For two different frequencies and By subtracting the phase measurements, the common term of the initial phase of the signal source and the phase delay over the entire length of the line can be eliminated, resulting in... The above formula can be obtained by inverse solution; by calculating and averaging multiple frequency point pairs, the impact of random noise on the positioning results can be significantly reduced.

[0102] Example 3: 1. Establishment of a standard fault feature library:

[0103] To achieve accurate fault matching, a standard fault feature library needs to be established in advance. A simulated 10kV distribution line is built in a laboratory environment, and various typical fault types are set, including high-resistance grounding (grounding through resistors of different resistance values), insulator breakdown, and surge arrester damage. For each fault type, steps S1 to S7 are repeated under different line lengths and branch conditions to obtain a large amount of sample data.

[0104] For each fault type Statistics on each frequency point Average response strength :

[0105] ;

[0106] In the formula:

[0107] Fault type The number of test samples;

[0108] For the first Each sample at frequency point The eigenvector at that location;

[0109] For the first Fault type The test samples at the frequency point The vector norm of the feature vector at a given frequency point is used to characterize the frequency of that sample. The magnitude of the fault signal response amplitude (energy) is the core quantitative indicator for calculating the average response intensity.

[0110] The continuous frequency band with the highest response intensity is identified as the sensitive frequency band for this type of fault. For example, a high-impedance ground fault may exhibit high impedance response sensitivity in the low-frequency range below 500Hz; this low-frequency range is then designated as the sensitive frequency range for high-impedance ground faults. Determine using the following formula:

[0111] ;

[0112] In the formula:

[0113] Fault type Maximum response strength at all frequency points;

[0114] The response strength threshold is typically set to 0.8.

[0115] These sensitive frequency band data are stored in association with standard feature data to guide iterative optimization in step S6.

[0116] 2. Example of iterative optimization scenario:

[0117] Suppose that during a field diagnostic, the initial matching result indicates "suspected high-resistance grounding," but the matching degree is only 0.65 (below the threshold of 0.8); at this point, the system initiates iterative optimization:

[0118] First, by calling the sensitive frequency band information of "high impedance grounding" in the standard library, it was found that its sensitive frequency band is 100Hz to 300Hz.

[0119] Then, within this frequency band, the original sweep step is reduced to one-quarter, i.e. New frequency sampling points are generated by encryption within the range of 100Hz to 300Hz.

[0120] At the same time, calculate the energy amplification factor. This will increase the injected energy to 1.35 times the original amount.

[0121] Finally, the signal was re-injected using the new parameter set, and features were extracted. Due to the denser sampling and stronger energy, the weak fault features were amplified, and the second matching degree improved to 0.92, successfully confirming a high-resistance grounding fault and providing a precise fault location.

[0122] The technical scope of this invention is not limited to the content described above. Those skilled in the art can make various modifications and variations to the above embodiments without departing from the technical concept of this invention, and all such modifications and variations should fall within the protection scope of this invention.

Claims

A method for determining signal injection parameters for grounding faults in 1.10kV distribution lines, characterized in that, Includes the following steps: S1. Line spectrum mapping: Inject broadband sweep pulses into the target line, adaptively set the sweep step according to the line length, and collect feedback signals to obtain no-load spectrum characteristic data including line input impedance, attenuation coefficient and background noise; S2. Feature frequency point selection: Within the available frequency bands selected based on the attenuation coefficient and background noise, calculate the correlation between any two candidate frequency points, and select multiple frequency points with a correlation lower than a set threshold as feature frequency points. S3. Energy Allocation: For each characteristic frequency point, energy is allocated and injected according to its frequency band, so that the energy is inversely proportional to the attenuation coefficient of that frequency point, forming signals with different frequency and energy combinations and injecting them into the line in sequence. S4. Fault Feature Extraction: Collect feedback signals for each frequency and energy combination, and extract multiple response features to form fault feature data of the current line status; S5. Fault Matching and Identification: Compare the current fault feature data with the pre-stored standard fault feature library to obtain the fault type and its matching degree; S6. Iterative optimization: If the matching degree is lower than the set threshold, the frequency sampling points are encrypted in the sensitive frequency band corresponding to the initially determined fault type, and the fault feature extraction step and fault matching and identification step are repeated after increasing the injected energy according to the degree of matching degree loss. S7. Fault location: Calculate the fault distance using the phase offset difference between at least two characteristic frequency points.

2. The method for determining signal injection parameters for grounding faults in 10kV distribution lines according to claim 1, characterized in that, In the line spectrum mapping step, the sweep frequency step is set as follows: the step frequency is determined according to the total length of the line and the signal propagation speed, so that the step frequency is inversely proportional to the line length and directly proportional to the signal propagation speed, so as to ensure that the spatial resolution of the sweep frequency signal can cover the entire line.

3. The method for determining signal injection parameters for grounding faults in 10kV distribution lines according to claim 1, characterized in that, In the characteristic frequency selection step, the correlation between two candidate frequency points is measured in the following way: the line input impedance functions corresponding to the two frequency points are bandpass filtered respectively, the absolute value of the inner product of the two functions after filtering is calculated, and then divided by the geometric mean of their respective energies. The resulting ratio is the correlation coefficient. When the correlation coefficient is less than 0.3, the two frequency points are determined to be linearly independent and are retained. When the correlation coefficient is greater than or equal to 0.3, the information of the two frequency points is considered redundant, and only the one with the smaller attenuation coefficient is retained.

4. The method for determining signal injection parameters for grounding faults in 10kV distribution lines according to claim 1, characterized in that, In the characteristic frequency selection step: the usable frequency band refers to the frequency range where the attenuation coefficient is less than 3dB / km and the background noise is less than 1.5 times the average power of the background noise.

5. The method for determining signal injection parameters for grounding faults in 10kV distribution lines according to claim 1, characterized in that, The energy allocation step is as follows: For low-frequency bands greater than or equal to 20Hz and less than 500Hz, the injected energy is: the preset reference energy multiplied by the ratio of the reference attenuation coefficient to the measured attenuation coefficient, and then multiplied by the product of the low-frequency band energy coefficient and the low-frequency weighting coefficient. For mid-frequency bands greater than or equal to 500Hz and less than 1500Hz, the injected energy is: the preset reference energy multiplied by the ratio of the reference attenuation coefficient to the measured attenuation coefficient, and then multiplied by the product of the mid-frequency band energy coefficient and the mid-frequency weighting coefficient. For high-frequency bands greater than or equal to 1500Hz and less than or equal to 20kHz, the injected energy is: the preset reference energy multiplied by the ratio of the reference attenuation coefficient to the measured attenuation coefficient, and then multiplied by the product of the high-frequency band energy coefficient and the high-frequency weighting coefficient. Among them, the energy coefficient of the low-frequency band is greater than that of the mid-frequency band, which is greater than that of the high-frequency band. The weighting coefficients of the low-frequency band, mid-frequency band, and high-frequency band decrease in that order.

6. The method for determining signal injection parameters for grounding faults in 10kV distribution lines according to claim 1, characterized in that, The multiple response features extracted in the fault feature extraction step include at least: Amplitude response, which is the ratio of the amplitude of the feedback signal to the amplitude of the injected signal; Phase shift, which is the difference between the phase of the feedback signal and the phase of the injected signal; Harmonic distortion rate is the ratio of the square root of the sum of the squares of the amplitudes of all harmonic components in the feedback signal to the amplitude of the fundamental frequency. The decay time constant is the time required for the impulse response envelope to decrease to its initial value of 1 / e. The degree of polarization is the difference between the amplitude of the feedback component parallel to the polarization direction of the injected signal and the amplitude of the vertical component, divided by the sum of the two.

7. The method for determining signal injection parameters for grounding faults in 10kV distribution lines according to claim 1, characterized in that, In the fault matching and identification step, the similarity comparison considers both the amplitude difference and morphological similarity between the fault feature data and the standard feature data. The amplitude difference is measured by weighted Euclidean distance, with the weights set according to the background noise level at each frequency point; the morphological similarity is measured by dynamic time-warped distance; the two distances are weighted and fused, and then exponentially transformed to obtain the similarity value. The fault type with the highest similarity is taken as the identification result.

8. The method for determining signal injection parameters for grounding faults in 10kV distribution lines according to claim 1, characterized in that, In the iterative optimization step, the degree of missing matching is 1 minus the current degree of matching, and the energy increase factor is set to the sum of 1 and the degree of missing matching; when encrypting frequency sampling, the original frequency sweep step is reduced to one-quarter, and new frequency sampling points are generated in the sensitive frequency band.

9. The method for determining signal injection parameters for grounding faults in 10kV distribution lines according to claim 1, characterized in that, In the fault location step, the fault distance is calculated as follows: select the phase offset difference between two characteristic frequency points, multiply it by the signal propagation speed, divide it by 4π and multiply it by the absolute value of the frequency difference between the two frequency points; take the average value of the calculation results of multiple sets of different frequency point pairs as the final fault distance.

10. The method for determining signal injection parameters for grounding faults in 10kV distribution lines according to claim 1, characterized in that, The standard fault feature library is pre-established in the following way: In a laboratory environment, steps S1-S7 are repeated under different line conditions for various typical fault types, including high-resistance grounding, insulator breakdown, and surge arrester damage, to obtain standard feature data for each fault type; at the same time, the response intensity distribution of each fault type at different frequency points is statistically analyzed, and the continuous frequency band with the highest response intensity is determined as the sensitive frequency band of the fault type and stored in association with the standard feature data.

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