A power distribution cable fault locating method and system based on impedance analysis

Abstract

The present application relates to the technical field of electric signal measurement analysis, and particularly relates to a power distribution cable fault positioning method and system based on impedance analysis. When a fault occurs in a power distribution cable, multiple traveling wave tests are performed, and multiple fault distances and multiple impedance reflection amplitudes are obtained. According to the multiple fault distances, multiple suspected fault points are determined, and a fault cable section is constructed, multiple fault cable intervals and multiple basic interval fault rates are divided. Multiple cable interval characteristics of the multiple fault cable intervals are obtained respectively, cable fault type prediction is performed, and multiple interval fault type distributions are obtained. According to the multiple impedance reflection amplitudes, fault type clustering is performed, test fault type distribution is obtained, combined with the multiple interval fault type distributions, multiple matching interval fault rates are obtained, combined with the multiple basic interval fault rates, multiple interval fault rates are calculated, and fault positioning results are output. The present application can greatly improve the accuracy, reliability and efficiency of cable fault positioning.

Description

Technical Field

[0001] This invention relates to the field of electrical signal measurement and analysis technology, and in particular to a method and system for locating faults in power distribution cables based on impedance analysis. Background Technology

[0002] Traditional methods for locating faults in power distribution cables often employ single-traveling wave methods, impedance methods, or manual inspection methods. These methods calculate the fault distance through a single test signal, measure impedance changes, or manually check each fault one by one. However, these methods have significant drawbacks: they are susceptible to environmental and signal interference, resulting in low location accuracy; they lack simultaneous identification of fault types, leading to low efficiency; manual inspections are time-consuming and costly, and it is difficult to discover hidden faults; and the lack of interval division and probability analysis results in a high degree of blindness in the troubleshooting process. These problems prevent the efficient and rapid location of power distribution cable faults. Summary of the Invention

[0003] This invention addresses the problem of inefficient and rapid fault location in existing technologies for power distribution cables by providing a method and system for fault location based on impedance analysis.

[0004] The technical solution of the present invention to solve the above-mentioned technical problems is as follows:

[0005] In a first aspect, the present invention provides a method for fault location of power distribution cables based on impedance analysis, comprising: when a fault occurs in a power distribution cable, performing multiple traveling wave tests to obtain multiple fault distances and multiple impedance reflection amplitudes; determining multiple suspected fault points based on the multiple fault distances, constructing faulty cable segments, and dividing multiple faulty cable intervals and multiple basic interval fault rates; acquiring multiple cable interval characteristics of the multiple faulty cable intervals respectively, performing cable fault type prediction, and obtaining multiple interval fault type distributions; performing fault type clustering based on the multiple impedance reflection amplitudes to obtain a test fault type distribution; combining the multiple interval fault type distributions to obtain multiple matching interval fault rates; combining the multiple basic interval fault rates to calculate multiple interval fault rates, and outputting the fault location result.

[0006] Optionally, when a fault occurs in the power distribution cable, multiple traveling wave tests are performed to obtain multiple fault distances and multiple impedance reflection amplitudes. This includes: when a fault occurs in the power distribution cable, performing multiple traveling wave tests, transmitting multiple identical test traveling wave signals in chronological order, and receiving multiple reflected traveling wave signals; obtaining the traveling wave velocity within the power distribution cable, calculating multiple fault distances based on the transmission time and reflection reception time of the multiple test traveling wave signals and multiple reflected traveling wave signals; and calculating multiple impedance reflection amplitudes based on the multiple reflected traveling wave signals.

[0007] The process involves calculating multiple impedance reflection amplitudes based on multiple reflected traveling wave signals, including: acquiring multiple incident wave voltages of multiple test traveling wave signals and acquiring multiple reflected wave voltages of multiple reflected traveling wave signals; and calculating multiple impedance reflection amplitudes based on the multiple reflected wave voltages and the multiple incident wave voltages.

[0008] Optionally, based on multiple fault distances, multiple suspected fault points are identified, and faulty cable segments are constructed. Multiple faulty cable intervals and multiple basic interval failure rates are divided, including: selecting multiple suspected fault points within the distribution cable based on multiple fault distances and test points for traveling wave testing; constructing faulty cable segments based on the suspected fault points at the endpoints within the multiple suspected fault points; dividing the faulty cable segments according to a preset cable interval length to obtain multiple faulty cable intervals; and analyzing and obtaining multiple basic interval failure rates for the multiple faulty cable intervals.

[0009] The analysis and acquisition of multiple basic section failure rates for the multiple faulty cable sections includes:

[0010] Obtain the number of suspected fault points within multiple faulty cable sections to obtain the number of fault points in multiple sections; calculate the ratio of the number of fault points in each section to the total number of fault points in multiple suspected fault points to obtain the fault rate of multiple basic sections.

[0011] Optionally, multiple cable section features of multiple faulty cable sections are obtained respectively, and cable fault type prediction is performed to obtain multiple interval fault type distributions. This includes: obtaining multiple cable section features of multiple faulty cable sections respectively; inputting the multiple cable section features into a cable section fault predictor respectively, and outputting multiple interval fault type distributions, wherein each interval fault type distribution includes multiple interval fault types and multiple interval fault type probabilities. The cable section fault predictor is constructed based on machine learning and is trained to convergence using a sample set of cable section features and a sample set of interval fault type distributions.

[0012] The process of clustering fault types based on multiple impedance reflection amplitudes to obtain a test fault type distribution includes: obtaining multiple impedance reflection amplitude ranges corresponding to multiple fault types; mapping multiple test fault types based on the impedance reflection amplitude ranges into which the multiple impedance reflection amplitudes fall, and calculating the occurrence rate of each group of test fault types to obtain multiple test fault type probabilities; and generating a test fault type distribution based on the multiple test fault types and multiple test fault type probabilities.

[0013] Optionally, by combining multiple interval fault type distributions, multiple matching interval fault rates are obtained; by combining the multiple basic interval fault rates, multiple interval fault rates are calculated, and fault location results are output. This includes: calculating the similarity between the test fault type distribution and the multiple interval fault type distributions to obtain multiple matching interval fault rates; calculating multiple interval fault rates based on the multiple matching interval fault rates and the multiple basic interval fault rates; and selecting the fault cable interval with the highest interval fault rate and the test fault type with the highest probability of test fault type based on the multiple interval fault rates as the fault location results.

[0014] The process of calculating the similarity between the test fault type distribution and multiple interval fault type distributions to obtain multiple matching interval fault rates includes: calculating the similarity between the probability of multiple test fault types of multiple test fault types within the test fault type distribution and the probability of multiple interval fault types of multiple interval fault types within each interval fault type distribution, and calculating the mean to obtain multiple matching interval fault rates.

[0015] Secondly, the present invention provides a power distribution cable fault location system based on impedance analysis, comprising:

[0016] The traveling wave test module is used to perform multiple traveling wave tests when a fault occurs in the power distribution cable, and process the data to obtain multiple fault distances and multiple impedance reflection amplitudes.

[0017] The suspected fault point investigation module is used to identify multiple suspected fault points based on multiple fault distances, construct faulty cable segments, and divide multiple faulty cable intervals and multiple basic intervals into fault rates.

[0018] The fault type prediction module is used to acquire the characteristics of multiple faulty cable sections, predict the cable fault type, and obtain the fault type distribution of multiple sections.

[0019] The fault location module is used to cluster fault types based on multiple impedance reflection amplitudes to obtain the test fault type distribution, combine multiple interval fault type distributions to obtain multiple matching interval fault rates, combine the multiple basic interval fault rates to calculate multiple interval fault rates, and output the fault location result.

[0020] By implementing this invention, when a power distribution cable fails, multiple traveling wave tests can be performed to obtain multiple fault distances and multiple impedance reflection amplitudes. Multiple tests avoid the random errors of a single test and simultaneously acquire two core data types, distance and impedance, providing dual basis for subsequent location and fault type determination.

[0021] By implementing this invention, multiple suspected fault points can be identified based on multiple fault distances, and faulty cable segments can be constructed. Multiple faulty cable intervals and multiple basic interval fault rates can be divided, narrowing the scope of fault investigation. The entire cable segment can be focused on a specific interval, and the basic interval fault rate provides an initial reference for subsequent priority judgment, thereby improving the positioning efficiency.

[0022] By implementing this invention, it is possible to obtain multiple cable section features of multiple faulty cable sections, predict cable fault types, obtain multiple section fault type distributions, and accurately predict fault types by combining section characteristics, providing a reference for type dimensions for subsequent matching and reducing the probability of fault type misjudgment.

[0023] By implementing this invention, it is possible to cluster fault types based on multiple impedance reflection amplitudes to obtain the test fault type distribution, combine multiple interval fault type distributions to obtain multiple matching interval fault rates, combine the multiple basic interval fault rates to calculate multiple interval fault rates, output fault location results, fuse multi-dimensional data of distance, impedance, and interval features, and ensure the accuracy and reliability of the location results through similarity matching and probability calculation, thereby clarifying the fault interval and type.

[0024] In summary, by implementing this invention, dual precise location of fault range and fault type in power distribution cables can be achieved, greatly improving the accuracy, reliability and efficiency of fault location, and reducing the blindness of fault investigation. Attached Figure Description

[0025] Figure 1 A flowchart illustrating a method for locating faults in power distribution cables based on impedance analysis, provided by this invention.

[0026] Figure 2 This is a schematic diagram of a power distribution cable fault location system based on impedance analysis provided by the present invention.

[0027] In the attached diagram, the components represented by each number are as follows:

[0028] Traveling wave testing module 11, suspected fault point investigation module 12, fault type prediction module 13, fault location module 14. Detailed Implementation

[0029] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0030] In the description of this invention, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of indicated technical features. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more features. In the description of this invention, "a plurality of" means two or more, unless otherwise explicitly specified.

[0031] In the description of this invention, the term "for example" is used to mean "used as an example, illustration, or description." Any embodiment described as "for example" in this invention is not necessarily to be construed as being more preferred or advantageous than other embodiments. The following description is provided to enable any person skilled in the art to make and use the invention. Details are set forth in the following description for purposes of explanation. It should be understood that those skilled in the art will recognize that the invention can be made without using these specific details. In other instances, well-known structures and processes will not be described in detail to avoid obscuring the description of the invention with unnecessary detail. Therefore, the invention is not intended to be limited to the embodiments shown, but is consistent with the broadest scope of the principles and features disclosed herein.

[0032] Example 1, as Figure 1 As shown, this embodiment of the invention provides a method for fault location in power distribution cables based on impedance analysis, including:

[0033] S100: When a fault occurs in the power distribution cable, multiple traveling wave tests are performed to obtain multiple fault distances and multiple impedance reflection amplitudes;

[0034] S200: Based on multiple fault distances, identify multiple suspected fault points, construct faulty cable segments, and divide multiple faulty cable sections and multiple basic section fault rates;

[0035] S300: Obtain the characteristics of multiple cable sections with multiple faults, predict the cable fault type, and obtain the fault type distribution of multiple sections.

[0036] S400: Based on multiple impedance reflection amplitudes, cluster fault types to obtain the test fault type distribution. Combine the multiple interval fault type distributions to obtain multiple matching interval fault rates. Combine the multiple basic interval fault rates to calculate multiple interval fault rates and output the fault location result.

[0037] In step S100 of this application embodiment, when a fault occurs in the power distribution cable, multiple traveling wave tests are performed, and multiple fault distances and multiple impedance reflection amplitudes are obtained through processing, including:

[0038] When a fault occurs in the power distribution cable, multiple traveling wave tests are performed. The same multiple test traveling wave signals are transmitted in chronological order, and the reflected multiple traveling wave signals are received.

[0039] The traveling wave velocity within the power distribution cable is obtained, and multiple fault distances are calculated based on the transmission and reflection / reception times of multiple test traveling wave signals and multiple reflected traveling wave signals.

[0040] Multiple impedance reflection amplitudes are calculated based on multiple reflected traveling wave signals.

[0041] In this embodiment of the application, the purpose of step S100 is to obtain multiple sets of fault distances and impedance reflection amplitudes through multiple traveling wave tests, so as to provide accurate and reliable basic data for subsequent suspected fault point locking and fault type analysis. It is the core data input link of the entire fault location process.

[0042] To achieve the above objectives, it is necessary to first perform multiple traveling wave tests when a fault occurs in the power distribution cable, transmitting the same multiple test traveling wave signals in chronological order, and receiving the multiple reflected traveling wave signals.

[0043] The principle is that a test signal with fixed parameters is sent to the faulty cable through a traveling wave test device. The signal will be reflected when it encounters the fault point. The reflected signal is received and key time points are recorded.

[0044] Specifically, traveling wave signals with the same parameters can be continuously transmitted at fixed time intervals to ensure signal consistency in each test and avoid data deviation due to differences in the signals themselves.

[0045] For example, a square wave test signal with a frequency of 2MHz is selected, and the transmission interval is set to 0.05 seconds, with a total of 15 transmissions. The first transmission time is t0=0.002 seconds, and the received reflected signal time is t1=0.006 seconds; the second transmission time is t0=0.007 seconds, and the received time is t1=0.011 seconds, and so on, to complete 15 tests and obtain 15 sets of transmission-reception time data.

[0046] Next, it is necessary to obtain the traveling wave velocity within the power distribution cable, and calculate multiple fault distances based on the transmission and reflection reception times of multiple test traveling wave signals and multiple reflected traveling wave signals.

[0047] The traveling wave velocity (v) is determined by the cable material and structure. The common range for power distribution cables is 1.5 × 10⁸ m / s to 2.5 × 10⁸ m / s; a typical value of v = 2.2 × 10⁸ m / s is selected here. The transmission time (t_transmit) is the specific time for each transmission of the test signal, read from the timing system of the test equipment. The reception time (t_receive) is the specific time for each reception of the reflected signal, using the same timing standard as the transmission time.

[0048] The time it takes for the traveling wave to travel from the test point to the fault point and back once is Δt = t_receive - t_transmit. The fault distance (L) = (traveling wave speed × round-trip time) / 2, that is, L = v × (t_receive - t_transmit) / 2.

[0049] For example, in the first set of data: t_send = 0.002 seconds, t_receive = 0.006 seconds, Δt = 0.004 seconds, L1 = 2.2 × 10^8 m / s × 0.004 s / 2 = 4400 m.

[0050] Data set 2: t_transmit = 0.007 seconds, t_receive = 0.011 seconds, Δt = 0.004 seconds, L2 = 2.2 × 10^8 m / s × 0.004 s / 2 = 4400 m.

[0051] Data set 3: t_transmit = 0.012 seconds, t_receive = 0.015 seconds, Δt = 0.003 seconds, L3 = 2.2 × 10^8 m / s × 0.003 s / 2 = 3300 m.

[0052] Data set 4: t_transmit = 0.017 seconds, t_receive = 0.021 seconds, Δt = 0.004 seconds, L4 = 2.2 × 10^8 m / s × 0.004 s / 2 = 4400 m.

[0053] Ultimately, 15 sets of fault distance data were obtained, such as 4400m, 3300m, 4380m, etc.

[0054] Then, based on multiple reflected traveling wave signals, multiple impedance reflection amplitudes are calculated.

[0055] In step S100 of this application embodiment, multiple impedance reflection amplitudes are calculated based on multiple reflected traveling wave signals, including:

[0056] Acquire multiple incident wave voltages of multiple test traveling wave signals, and acquire multiple reflected wave voltages of multiple reflected traveling wave signals;

[0057] Multiple impedance reflection amplitudes are calculated based on multiple reflected wave voltages and multiple incident wave voltages.

[0058] The incident wave voltage (Uin) is the voltage value of the traveling wave signal propagating in the cable, which is set by the test equipment. Here, it is set to 8V for example.

[0059] The reflected voltage (Ureflected) is the signal voltage value reflected back to the test equipment from the fault point. It is obtained through equipment detection and is affected by the impedance of the fault point.

[0060] The core formula for the reflection coefficient (ρ) is ρ=(Zf-Zc) / (Zf+Zc), where Zf is the fault point impedance and Zc is the characteristic impedance of the cable. Common power distribution cables have Zc=50Ω, 75Ω, etc., and Zc=50Ω is selected here.

[0061] Since the impedance reflection amplitude is directly related to the reflection coefficient and the incident wave voltage, it can be derived in practical engineering by combining Ureflected / Uinjected with ρ. When simplifying the calculation, the impedance reflection amplitude can be expressed as ≈|Ureflected / Uinjected|×|ρ|. The final result is expressed as a dimensionless value with a range of 0-1.

[0062] For example, for the first set of data: Uin = 8V, Ureverse = 2.4V, assuming the fault point Zf = 100Ω, first calculate ρ = (100-50) / (100+50) = 50 / 150 ≈ 0.333, and the impedance reflection amplitude ≈ |2.4 / 8| × 0.333 ≈ 0.3 × 0.333 ≈ 0.0999.

[0063] Data set 2: Uin = 8V, Ureverse = 3.2V, fault point Zf = 75Ω, ρ = (75-50) / (75+50) = 25 / 125 = 0.2, impedance reflection amplitude ≈ |3.2 / 8| × 0.2 = 0.4 × 0.2 = 0.08.

[0064] Group 3 data: Uin = 8V, Ureverse = 1.6V, fault point Zf = 25Ω, ρ = (25-50) / (25+50) = -25 / 75 ≈ -0.333, after taking the absolute value, the impedance reflection amplitude ≈ |1.6 / 8| × 0.333 ≈ 0.2 × 0.333 ≈ 0.0666.

[0065] Ultimately, 15 sets of impedance reflection amplitude data were obtained, such as 0.0999, 0.08, 0.0666, and 0.102.

[0066] In step S200 of this application embodiment, multiple suspected fault points are determined based on multiple fault distances, and faulty cable segments are constructed. Multiple faulty cable intervals and multiple basic interval fault rates are then defined, including:

[0067] Based on multiple fault distances and the test points for traveling wave testing, multiple suspected fault points were selected within the power distribution cable.

[0068] Based on the suspected fault points at the endpoints within multiple suspected fault points, construct the faulty cable segment;

[0069] The faulty cable segment is divided according to the preset cable interval length to obtain multiple faulty cable intervals;

[0070] The failure rates of multiple basic sections of the multiple faulty cable sections are obtained through analysis.

[0071] In this embodiment of the application, the purpose of the above steps is to narrow down the fault range from multiple sets of fault distance data, identify the key intervals for fault investigation, and calculate the fault rate of the basic intervals to provide a basis for further precise location based on the fault type.

[0072] To achieve the above objectives, it is first necessary to select multiple suspected fault points within the power distribution cable based on multiple fault distances and the test points for traveling wave testing.

[0073] For example, assume that the test point for the traveling wave test is located at the beginning of the power distribution cable, with coordinates 0m. Assume that step S100 has obtained 15 sets of fault distance data, namely 4400m, 3300m, 4380m, 4420m, 3320m, 4390m, 4410m, 3280m, 4370m, 4430m, 3310m, 4360m, 4440m, 3290m, and 4350m.

[0074] Using the test points as a baseline, the cable location corresponding to each fault distance is considered a suspected fault point. Based on the above data, the coordinates of the suspected fault points selected on the cable, sorted by distance, are 3280m, 3290m, 3300m, 3310m, 3320m, 4350m, 4360m, 4370m, 4380m, 4390m, 4400m, 4410m, 4420m, 4430m, and 4440m, totaling 15 suspected fault points.

[0075] Next, based on the suspected fault points at the endpoints within the multiple suspected fault points, it is necessary to construct the faulty cable segment.

[0076] Based on the aforementioned 15 suspected fault points, with a minimum value of 3280m and a maximum value of 4440m, these two points are designated as endpoint suspected fault points. Specifically, a faulty cable segment is constructed, starting from the minimum endpoint suspected fault point and ending at the maximum endpoint suspected fault point. In this example, the range of the faulty cable segment is from 3280m to 4440m, with a length of 4440m - 3280m = 1160m.

[0077] The faulty cable segment is divided according to the preset cable interval length to obtain multiple faulty cable intervals.

[0078] Starting from the beginning of the faulty cable segment, the cable is divided into sections according to a preset length. For example, the preset cable section length can be set to 100m. If the faulty cable segment ranges from 3280m to 4440m, then the first section is 3280m-3380m, the second section is 3380m-3480m, the third section is 3480m-3580m, and so on. Finally, 11.6 sections are obtained on the 1160m faulty cable segment, which are rounded to 12 faulty cable sections. The last section less than 100m is listed separately.

[0079] Furthermore, it is necessary to analyze and obtain the failure rates of multiple basic sections of the multiple faulty cable sections.

[0080] In step S200 of this application embodiment, analyzing and obtaining the failure rates of multiple basic sections of the multiple faulty cable sections includes:

[0081] Obtain the number of suspected fault points within multiple faulty cable sections, and obtain the number of fault points in multiple sections;

[0082] Calculate the ratio of the number of fault points in each interval to the total number of fault points in multiple suspected fault points to obtain the fault rates of multiple basic intervals.

[0083] Specifically, for example, in the above example, the total number of suspected fault points is 15. Further statistics on the number of suspected fault points within each interval are needed. In the above example: there are 5 suspected fault points in the 3280m-3380m interval (3280m, 3290m, 3300m, 3310m, 3320m); there are 10 suspected fault points in the 4350m-4450m interval (4350m, 4360m, 4370m, 4380m, 4390m, 4400m, 4410m, 4420m, 4430m, 4440m); the number of suspected fault points in the remaining intervals is 0.

[0084] Next, the basic interval failure rate needs to be calculated. Optionally, the basic interval failure rate = number of suspected fault points in the interval ÷ total number of suspected fault points. For example, the basic interval failure rate for the 4350m-4450m interval = 10 ÷ 15 ≈ 0.667; the basic interval failure rate for the 3280m-3380m interval = 5 ÷ 15 ≈ 0.333; the basic interval failure rate for intervals with no suspected fault points = 0 ÷ 15 = 0.

[0085] In step S300 of this application embodiment, multiple cable section characteristics of multiple faulty cable sections are obtained respectively, cable fault type prediction is performed, and the fault type distribution of multiple sections is obtained, including:

[0086] The characteristics of multiple faulty cable sections are obtained separately.

[0087] Multiple cable section features are input into the cable section fault predictor, and multiple section fault type distributions are output. Each section fault type distribution includes multiple section fault types and multiple section fault type probabilities. The cable section fault predictor is built based on machine learning and is trained to convergence using a sample set of cable section features and a sample set of section fault type distributions.

[0088] In this embodiment of the application, the purpose of step S300 is to combine the characteristics of the fault interval to predict the possible fault type and probability of each interval, so as to provide a basis for the subsequent accurate location of the fault point by combining the basic fault rate.

[0089] To achieve the above steps, it is first necessary to obtain the characteristics of multiple cable sections for multiple faulty cable sections.

[0090] The cable section characteristics include information such as the type and number of connected electrical equipment, maintenance records, and the impedance reflection amplitude corresponding to the section.

[0091] For example, taking the 4350m-4450m fault cable section divided in step S200 as an example, the connected power equipment includes: 1 cable joint and 2 branch boxes; the maintenance record includes: no maintenance record in the past year, and repair due to insulation aging 3 years ago; impedance reflection amplitude: the reflection amplitude of the suspected fault point in this section in step S100 is 0.102 (ρ≈+0.1).

[0092] In practice, the above-mentioned characteristic information can be collected one by one for each faulty cable section to form a characteristic set for that section, such as "Equipment: 1 joint + 2 branch boxes; Maintenance: No maintenance for 1 year / Insulation repair 3 years ago; Reflection amplitude: 0.133".

[0093] Then, multiple cable section features need to be input into the cable section fault predictor to obtain multiple section fault type distributions. Each section fault type distribution includes multiple section fault types and multiple section fault type probabilities. The cable section fault predictor is built based on machine learning and is trained to convergence using a sample set of cable section features and a sample set of section fault type distributions.

[0094] Specifically, the first step is to construct a sample cable section feature set, which contains a large number of cable section features with labeled fault types, such as "Equipment: 1 joint; Maintenance: Overhauled 6 months ago; Reflection amplitude: 1.0" corresponding to open circuit faults, and a sample section fault type distribution set, such as "Open circuit fault: 95%; Low resistance fault: 5%". These samples are used to train a machine learning model, such as random forest, until convergence, to obtain a cable section fault predictor.

[0095] Then, the characteristics of the cable section, such as the characteristics of the "4350m-4450m" section, are input into the cable section fault predictor. The cable section fault predictor can then output the fault type distribution of that section, for example, "high resistance fault: 80%; open circuit fault: 15%; low resistance fault: 5%".

[0096] High-resistance faults, open-circuit faults, and low-resistance faults are common fault types in power distribution cables, with the core difference lying in the impedance characteristics at the fault point. In a low-resistance fault, the fault point impedance (Zf) is much smaller than the cable's characteristic impedance (Zc), typically indicating a metallic short circuit. In an open-circuit fault, the circuit is completely broken at the fault point, and the fault point impedance (Zf) is much larger than the cable's characteristic impedance (Zc), classifying it as an open-circuit fault. In a high-resistance fault, the fault point impedance (Zf) is greater than the cable's characteristic impedance (Zc), but not as large as in an open circuit, indicating poor contact or insulation degradation.

[0097] In step S400 of this application embodiment, fault type clustering is performed based on multiple impedance reflection amplitudes to obtain the test fault type distribution, including:

[0098] Obtain multiple impedance reflection amplitude ranges corresponding to various fault types;

[0099] Based on the impedance reflection amplitude range into which the multiple impedance reflection amplitudes fall, multiple test fault types are mapped and obtained, and the occurrence rate of each test fault type is calculated to obtain the probability of multiple test fault types.

[0100] A test failure type distribution is generated based on the multiple test failure types and the probabilities of the multiple test failure types.

[0101] In step S400 of this application embodiment, the purpose of the above step is to determine the type and probability distribution of the current fault by the actual measured impedance reflection amplitude, so as to provide actual data reference for subsequent comparison with the predicted interval fault type distribution and accurate location of the fault interval.

[0102] To achieve the above objectives, it is first necessary to obtain multiple impedance reflection amplitude ranges corresponding to various fault types. That is, based on the correspondence between fault impedance and reflection coefficient, the reflection amplitude ranges corresponding to different fault types are predefined as standards for subsequent matching.

[0103] For example, for low-resistance faults, the impedance reflection amplitude is approximately 1 (ρ≈-1), with a range of 0.9 to 1.0; for open-circuit faults, the impedance reflection amplitude is approximately 1 (ρ≈+1), with a range of 0.9 to 1.0; for high-resistance faults, the impedance reflection amplitude is smaller, with a range of 0.05 to 0.8.

[0104] Next, based on the impedance reflection amplitude range into which the multiple impedance reflection amplitudes fall, multiple test fault types need to be mapped and obtained, and the occurrence rate of each test fault type is calculated to obtain the probability of multiple test fault types; that is, the impedance reflection amplitude of each actual test is matched with the preset fault type amplitude range, the occurrence rate of each type is counted, and then divided by the total number of tests to obtain the corresponding probability.

[0105] For example, step S100 has obtained 15 impedance reflection amplitude data, such as: 1.0, 1.0, 0.133, 0.0999, 0.08, 0.0666, 1.0, 0.102, 0.09, 0.07, 0.085, 1.0, 0.11, 0.095, 0.08. Among them, 1.0 falls into the "low resistance / open circuit fault range", and the rest fall into the "high resistance fault range". Statistically, among the 15 data, the low resistance / open circuit fault type appears 4 times and the high resistance fault type appears 11 times. The calculated probabilities are: low resistance / open circuit fault probability = 4 ÷ 15 ≈ 26.7%, high resistance fault probability = 11 ÷ 15 ≈ 73.3%.

[0106] Based on the multiple test fault types and their probabilities, a test fault type distribution is generated. This involves integrating the matched test fault types with their corresponding probabilities to form the actual fault type probability distribution result.

[0107] For example, based on the above statistical results, the distribution of test fault types generated in this fault is as follows: "Low resistance / open circuit fault: 26.7%; High resistance fault: 73.3%".

[0108] In step S400 of this application embodiment, multiple interval fault type distributions are combined to obtain multiple matching interval fault rates, and multiple interval fault rates are calculated based on the multiple basic interval fault rates. The fault location result is then output, including:

[0109] Calculate the similarity between the test fault type distribution and the fault type distribution of multiple intervals to obtain the fault rate of multiple matching intervals;

[0110] Multiple interval failure rates are calculated based on the multiple matching interval failure rates and the multiple basic interval failure rates;

[0111] Based on the failure rates of multiple intervals, the faulty cable interval with the highest failure rate and the test fault type with the highest probability of test fault type are selected as the fault location results.

[0112] In this embodiment of the application, the purpose of the above steps in step S400 is to combine the actual test fault type distribution with the predicted interval fault type distribution to calculate the comprehensive fault rate of each fault interval, and finally lock the specific interval and fault type where the fault occurred.

[0113] To achieve the above objectives, it is first necessary to calculate the similarity between the test fault type distribution and the fault type distribution of multiple intervals, and obtain the fault rates of multiple matching intervals.

[0114] In step S400 of this application embodiment, the similarity between the test fault type distribution and the fault type distribution of multiple intervals is calculated to obtain the fault rate of multiple matching intervals, including:

[0115] Calculate the similarity between the probability of multiple test fault types within the test fault type distribution and the probability of multiple interval fault types within each interval fault type distribution, and calculate the mean to obtain multiple matching interval fault rates.

[0116] The test fault type distribution and the fault type distribution of each interval are converted into vectors, and the similarity between the vectors is calculated. This similarity is the matching interval fault rate of the corresponding interval.

[0117] For example, the distribution of test fault types is as follows: low resistance / open circuit faults 26.7%, high resistance faults 73.3%; for a certain range, such as the 4350m-4450m range, the fault type distribution is as follows: high resistance faults 80%, open circuit faults 15%, low resistance faults 5%.

[0118] Then, similarity calculation is performed. Taking cosine similarity as an example: the probabilities of the two are used as vectors. The test fault type distribution vector is (0.267, 0.733), and the interval fault type distribution vector is (0.2, 0.8). Low resistance / open circuit is merged into one category. Cosine similarity = (0.267 × 0.2 + 0.733 × 0.8) ÷ (√(0.267 × 0.8)). 2 +0.733 2 )×√(0.2 2 +0.8 2 The failure rate of the matching interval is approximately 0.98, meaning the failure rate of the matching interval in this interval is 0.98.

[0119] Next, multiple interval failure rates need to be calculated based on the multiple matching interval failure rates and multiple basic interval failure rates; that is, the matching interval failure rate of each interval is multiplied by the basic interval failure rate, or weighted according to a preset weight, to obtain the comprehensive failure probability of that interval, i.e., the interval failure rate.

[0120] For example, suppose the matching interval failure rate in the 4350m-4450m range is 0.98. The basic interval failure rate for this range is approximately 10 ÷ 15 ≈ 0.667. Then, for example, the corresponding interval failure rate = matching interval failure rate × basic interval failure rate = 0.98 × 0.667 ≈ 0.653.

[0121] Finally, based on the failure rates of multiple intervals, it is necessary to filter the faulty cable interval with the highest failure rate and the test fault type with the highest probability, as the fault location result. That is, compare the interval failure rates of all intervals and select the faulty cable interval corresponding to the maximum value; at the same time, select the type with the highest probability in the test fault type distribution, and use both as the fault location result.

[0122] For example, suppose the interval failure rate distribution is as follows: 0.653 for the 4350m-4450m interval, and 0.333 × 0.8 ≈ 0.266 for other intervals such as 3280m-3380m (assuming a matching interval failure rate of 0.8). The overall failure rate (0.653) of the 4350m-4450m interval is significantly higher than other intervals, and therefore it is identified as the specific interval where the failure occurred. Since high-resistance faults have the highest probability of occurrence among the tested fault types (73.3%), the interval with the highest failure rate is 4350m-4450m, and the highest probability of occurrence among the tested fault types is high-resistance faults.

[0123] Example 2, as Figure 2 As shown, based on the same inventive concept as the impedance analysis-based fault location method for distribution cables provided in Embodiment 1, this embodiment of the invention also provides an impedance analysis-based fault location system for distribution cables, comprising:

[0124] The traveling wave test module 11 is used to perform multiple traveling wave tests when a fault occurs in the power distribution cable, and process and obtain multiple fault distances and multiple impedance reflection amplitudes.

[0125] The suspected fault point investigation module 12 is used to identify multiple suspected fault points based on multiple fault distances, construct faulty cable segments, and divide multiple faulty cable intervals and multiple basic intervals into fault rates.

[0126] The fault type prediction module 13 is used to acquire the characteristics of multiple cable sections of multiple faulty cable sections, perform cable fault type prediction, and obtain the fault type distribution of multiple sections.

[0127] The fault location module 14 is used to cluster fault types based on multiple impedance reflection amplitudes to obtain the test fault type distribution, process multiple interval fault type distributions to obtain multiple matching interval fault rates, calculate multiple interval fault rates based on the multiple basic interval fault rates, and output the fault location result.

[0128] Furthermore, the traveling wave test module 11 includes the following execution steps:

[0129] When a fault occurs in the power distribution cable, multiple traveling wave tests are performed. The same multiple test traveling wave signals are transmitted in chronological order, and the reflected multiple traveling wave signals are received.

[0130] The traveling wave velocity within the power distribution cable is obtained, and multiple fault distances are calculated based on the transmission and reflection / reception times of multiple test traveling wave signals and multiple reflected traveling wave signals.

[0131] Multiple impedance reflection amplitudes are calculated based on multiple reflected traveling wave signals.

[0132] Among them, multiple impedance reflection amplitudes are calculated based on multiple reflected traveling wave signals, including:

[0133] Acquire multiple incident wave voltages of multiple test traveling wave signals, and acquire multiple reflected wave voltages of multiple reflected traveling wave signals;

[0134] Multiple impedance reflection amplitudes are calculated based on multiple reflected wave voltages and multiple incident wave voltages.

[0135] Furthermore, the suspected fault location investigation module 12 includes the following execution steps:

[0136] Based on multiple fault distances and the test points for traveling wave testing, multiple suspected fault points were selected within the power distribution cable.

[0137] Based on the suspected fault points at the endpoints within multiple suspected fault points, construct the faulty cable segment;

[0138] The faulty cable segment is divided according to the preset cable interval length to obtain multiple faulty cable intervals;

[0139] The failure rates of multiple basic sections of the multiple faulty cable sections are obtained through analysis.

[0140] The analysis and acquisition of multiple basic section failure rates for the multiple faulty cable sections includes:

[0141] Obtain the number of suspected fault points within multiple faulty cable sections, and obtain the number of fault points in multiple sections;

[0142] Calculate the ratio of the number of fault points in each interval to the total number of fault points in multiple suspected fault points to obtain the fault rates of multiple basic intervals.

[0143] Furthermore, the fault type prediction module 13 includes the following execution steps:

[0144] The characteristics of multiple faulty cable sections are obtained separately.

[0145] Multiple cable section features are input into the cable section fault predictor, and multiple section fault type distributions are output. Each section fault type distribution includes multiple section fault types and multiple section fault type probabilities. The cable section fault predictor is built based on machine learning and is trained to convergence using a sample set of cable section features and a sample set of section fault type distributions.

[0146] Furthermore, the fault location module 14 includes the following execution steps:

[0147] Obtain multiple impedance reflection amplitude ranges corresponding to various fault types;

[0148] Based on the impedance reflection amplitude range into which the multiple impedance reflection amplitudes fall, multiple test fault types are mapped and obtained, and the occurrence rate of each test fault type is calculated to obtain the probability of multiple test fault types.

[0149] A test failure type distribution is generated based on the multiple test failure types and the probabilities of the multiple test failure types.

[0150] Calculate the similarity between the test fault type distribution and the fault type distribution of multiple intervals to obtain the fault rate of multiple matching intervals;

[0151] Multiple interval failure rates are calculated based on the multiple matching interval failure rates and the multiple basic interval failure rates;

[0152] Based on the failure rates of multiple intervals, the faulty cable interval with the highest failure rate and the test fault type with the highest probability of test fault type are selected as the fault location results.

[0153] This includes calculating the similarity between the test fault type distribution and multiple interval fault type distributions to obtain multiple matching interval fault rates, including:

[0154] Calculate the similarity between the probability of multiple test fault types within the test fault type distribution and the probability of multiple interval fault types within each interval fault type distribution, and calculate the mean to obtain multiple matching interval fault rates.

[0155] It should be noted that the descriptions of each embodiment in the above embodiments have different focuses. For parts that are not described in detail in a certain embodiment, please refer to the relevant descriptions in other embodiments.

[0156] Those skilled in the art will understand that embodiments of the present invention can provide methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0157] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, as well as combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0158] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0159] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0160] Although preferred embodiments of the invention have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Clearly, those skilled in the art can make various alterations and variations to the invention without departing from its spirit and scope. Therefore, if these modifications and variations fall within the scope of the invention and its equivalents, the invention is also intended to include these modifications and variations.

Claims

1. A method for locating a fault in a power distribution cable based on impedance analysis, characterized by, The method comprises: When a power distribution cable fails, multiple traveling wave tests are performed, and multiple fault distances and multiple impedance reflection amplitudes are processed and obtained; According to the multiple fault distances, multiple suspected fault points are determined, a fault cable section is constructed, multiple fault cable intervals and multiple basic interval fault rates are divided; Multiple cable interval characteristics of the multiple fault cable intervals are respectively obtained, cable fault type prediction is performed, and multiple interval fault type distributions are obtained; According to the multiple impedance reflection amplitudes, fault type clustering is performed to obtain a test fault type distribution, and in combination with the multiple interval fault type distributions, multiple matching interval fault rates are processed and obtained, in combination with the multiple basic interval fault rates, multiple interval fault rates are calculated and obtained, and a fault positioning result is output.

2. The impedance analysis based fault location method for distribution cables as claimed in claim 1 wherein, When a power distribution cable fails, multiple traveling wave tests are performed, and multiple fault distances and multiple impedance reflection amplitudes are processed and obtained, which comprises: When a power distribution cable fails, multiple traveling wave tests are performed, and multiple test traveling wave signals are transmitted in time sequence, and multiple reflected traveling wave signals are received; The traveling wave speed in the power distribution cable is obtained, and according to the transmission time and reflection receiving time of the multiple test traveling wave signals and the multiple reflected traveling wave signals, the multiple fault distances are calculated and obtained; According to the multiple reflected traveling wave signals, the multiple impedance reflection amplitudes are calculated and obtained.

3. The impedance analysis based fault location method for distribution cables as claimed in claim 2 wherein, According to the multiple reflected traveling wave signals, the multiple impedance reflection amplitudes are calculated and obtained, which comprises: Multiple incident wave voltages of the multiple test traveling wave signals are obtained, and multiple reflected wave voltages of the multiple reflected traveling wave signals are obtained; According to the multiple reflected wave voltages and the multiple incident wave voltages, the multiple impedance reflection amplitudes are calculated and obtained.

4. The impedance analysis based fault location method for distribution cables as claimed in claim 1 wherein, According to the multiple fault distances, multiple suspected fault points are determined, a fault cable section is constructed, multiple fault cable intervals and multiple basic interval fault rates are divided, which comprises: According to the multiple fault distances and the test points for the traveling wave test, multiple suspected fault points are selected in the power distribution cable; According to the endpoint suspected fault points in the multiple suspected fault points, a fault cable section is constructed; According to a preset cable interval length, the fault cable section is divided to obtain multiple fault cable intervals; Multiple basic interval fault rates of the multiple fault cable intervals are analyzed and obtained.

5. The impedance analysis based fault location method for distribution cables as claimed in claim 4 wherein, Multiple basic interval fault rates of the multiple fault cable intervals are analyzed and obtained, which comprises: The number of suspected fault points in the multiple fault cable intervals is obtained to obtain multiple interval fault point numbers; The ratio of each interval fault point number to the total number of fault points of the multiple suspected fault points is calculated to obtain the multiple basic interval fault rates.

6. The impedance analysis based fault location method for distribution cables as claimed in claim 1 wherein, Multiple cable interval characteristics of the multiple fault cable intervals are respectively obtained, cable fault type prediction is performed, and multiple interval fault type distributions are obtained, which comprises: Multiple cable interval characteristics of the multiple fault cable intervals are respectively obtained; The multiple cable interval characteristics are respectively input into a cable interval fault predictor to output the multiple interval fault type distributions, wherein each interval fault type distribution comprises multiple interval fault types and multiple interval fault type probabilities, the cable interval fault predictor is constructed based on machine learning and is trained to convergence by using a sample cable interval characteristic set and a sample interval fault type distribution set.

7. The impedance analysis based fault location method for distribution cables as claimed in claim 6 wherein, According to the plurality of impedance reflection amplitudes, the fault type clustering is performed, a test fault type distribution is obtained, and the test fault type distribution includes: Obtain a plurality of impedance reflection amplitude ranges corresponding to a plurality of fault types; According to the impedance reflection amplitude range in which the plurality of impedance reflection amplitudes falls, a plurality of test fault types are mapped and obtained, and the proportion of the number of occurrences of each group of test fault types is calculated to obtain a plurality of test fault type probabilities; According to the plurality of test fault types and the plurality of test fault type probabilities, a test fault type distribution is generated.

8. The impedance analysis based fault location method for distribution cables as claimed in claim 1 wherein, In combination with the plurality of interval fault type distributions, a plurality of matching interval fault rates are processed and obtained, in combination with the plurality of basic interval fault rates, a plurality of interval fault rates are calculated and obtained, and a fault location result is output, including: Calculate the similarity of the test fault type distribution and the plurality of interval fault type distributions to obtain a plurality of matching interval fault rates; According to the plurality of matching interval fault rates and the plurality of basic interval fault rates, a plurality of interval fault rates are calculated and obtained; According to the plurality of interval fault rates, the interval fault rate of the fault cable interval and the test fault type probability of the test fault type with the largest test fault type probability are selected as the fault location result.

9. The impedance analysis based fault location method for distribution cables as claimed in claim 8 wherein, Calculate the similarity of the test fault type distribution and the plurality of interval fault type distributions to obtain a plurality of matching interval fault rates, including: Calculate the similarity of the plurality of test fault type probabilities of the plurality of test fault types in the test fault type distribution and the plurality of interval fault type probabilities of the plurality of interval fault types in each interval fault type distribution, and calculate the average to obtain a plurality of matching interval fault rates.

10. An impedance analysis based power distribution cable fault location system characterized by, The system is used to implement the power distribution cable fault location method based on impedance analysis as claimed in any one of claims 1-9, and the system includes: A traveling wave test module for performing a plurality of traveling wave tests when a fault occurs in a power distribution cable, and processing a plurality of fault distances and a plurality of impedance reflection amplitudes; A suspected fault point investigation module for determining a plurality of suspected fault points according to a plurality of fault distances, and constructing a fault cable segment, dividing a plurality of fault cable intervals and a plurality of basic interval fault rates; A fault type prediction module for obtaining a plurality of cable interval features of a plurality of fault cable intervals, respectively, performing cable fault type prediction, and obtaining a plurality of interval fault type distributions; A fault location module for performing fault type clustering according to a plurality of impedance reflection amplitudes, obtaining a test fault type distribution, in combination with a plurality of interval fault type distributions, processing a plurality of matching interval fault rates, in combination with the plurality of basic interval fault rates, calculating a plurality of interval fault rates, and outputting a fault location result.

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