A method and apparatus for locating defects in a cable

Abstract

The application discloses a cable online defect positioning method and equipment, belongs to the technical field of cable defect positioning, and is used for solving the technical problems that the current cable online detection technology has a positioning blind area, signal attenuation causes end positioning difficulty, and positioning effect still needs to be improved. The cable online defect positioning method comprises the following steps: determining an original positioning curve of a test cable according to an incident signal input into the test cable and a reflected signal collected; performing normalization processing on the original positioning curve to obtain a first positioning curve; performing average energy operator optimization on the first positioning curve to obtain a second positioning curve; and determining a defect position of the test cable according to the second positioning curve. The application utilizes the average energy operator to reduce noise interference caused by the normalization algorithm, improve the blind area of the first end signal oscillation, and greatly improve the positioning amplitude of the original positioning curve.

Description

Technical Field

[0001] This invention relates to the field of cable defect location technology, and in particular to a method and equipment for online cable defect location. Background Technology

[0002] With the increasing demands of social development and the expansion of power distribution networks, people's requirements for power quality are rising. Because cables are buried underground, they offer advantages over overhead lines, such as not affecting the aesthetics of the city and avoiding external environmental interference, and are now widely used in urban power distribution networks. However, due to improper operation during manufacturing and laying, as well as the complex operating conditions of cables, their insulation performance gradually deteriorates, leading to minor localized defects. As the cable's operating time increases, these localized defects can further develop into faults under the influence of electric fields. To ensure urban power supply security and extend the service life of cables, manual fault inspection alone is far from sufficient; effective cable defect location and detection should be conducted to ensure a continuous and reliable power supply.

[0003] Current cable inspection technologies primarily rely on offline methods, including frequency domain reflectometry and time-frequency domain reflectometry. However, limitations in frequency and processing speed make them difficult to apply in online inspections, necessitating regional power outages to pinpoint fault locations. Power outages inevitably lead to significant economic losses. Among existing online monitoring technologies, Spread Spectral Time Domain Reflectometry (SSTDR) offers good immunity to signal noise and high location accuracy. This method inputs a waveform modulated with a PN sequence and a sinusoidal signal into the cable. Defect detection is based on traveling wave reflection and transmission characteristics. After signal input, the defect location causes impedance mismatch, resulting in signal reflection. The location is determined by acquiring the reflected signal and calculating the time delay between the incident and reflected signals. However, current SSTDR techniques still suffer from blind spots and low peak amplitude due to signal attenuation, making it difficult to locate defects at cable ends, and the accuracy of the location still needs improvement. Summary of the Invention

[0004] This invention provides a method and apparatus for online cable defect location, which addresses the following technical problems: current online cable detection technologies suffer from blind spots, signal attenuation makes end-point location difficult, and the location effect still needs improvement.

[0005] The present invention adopts the following technical solution:

[0006] On one hand, the present invention provides a method for online defect location in cables, the method comprising:

[0007] The original positioning curve of the test cable is determined based on the incident signal input into the test cable and the collected reflected signal.

[0008] The original positioning curve is normalized to obtain the first positioning curve;

[0009] The first positioning curve is optimized using the average energy operator to obtain the second positioning curve;

[0010] The location of the defect in the test cable is determined based on the second positioning curve.

[0011] Furthermore, before determining the original positioning curve of the test cable based on the incident signal input into the test cable and the collected reflected signal, the method further includes:

[0012] Connect the output port of the signal generator to the oscilloscope and the test cable respectively using T-connectors;

[0013] Start the signal generator and inject the SSTDR incident signal into the test cable;

[0014] The oscilloscope is used to acquire the incident signal of the SSTDR and the reflected signal obtained from the test cable, and the acquired incident and reflected signals are saved in the computer.

[0015] Furthermore, based on the incident signal input into the test cable and the collected reflected signal, the original positioning curve of the test cable is determined, specifically including:

[0016] The incident signal and the reflected signal are acquired in the computer;

[0017] The incident signal and the reflected signal are cross-correlated, and the results of the cross-correlation are displayed as a curve to obtain the original positioning curve of the test cable.

[0018] Furthermore, a cross-correlation operation is performed on the incident signal and the reflected signal, and the result of the cross-correlation operation is displayed in the form of a curve to obtain the original positioning curve of the test cable, specifically including:

[0019] according to Perform cross-correlation calculation on the incident signal and the reflected signal;

[0020] Where R(t) is the result of the cross-correlation operation, s(t) is the incident signal, s(t-τ) is the reflected signal, τ is the signal reflection delay time, and t represents the time t.

[0021] The result R(t) of the cross-correlation operation is converted from a discrete signal into a continuous signal to obtain the original positioning curve R(n); where n is a point on the curve.

[0022] Further, the original positioning curve is normalized to obtain the first positioning curve, specifically including:

[0023] The original positioning curve is normalized using a custom normalization positioning function, and the result of the normalization is displayed as a curve to obtain the first positioning curve.

[0024] Furthermore, the original positioning curve is normalized using a custom normalization positioning function, and the result of the normalization is displayed as a curve to obtain the first positioning curve, specifically including:

[0025] Based on a custom normalized positioning function: The original positioning curve is normalized.

[0026] Where C(t) is the result of normalization, R(t) is the expression for the original positioning curve, τ is the signal reflection delay time, and T s The period of the incident signal;

[0027] The result C(t) of the normalization process is converted from a discrete signal into a continuous signal to obtain the first positioning curve C(n); where n is a point on the curve.

[0028] Furthermore, the first positioning curve is optimized using the average energy operator to obtain the second positioning curve, specifically including:

[0029] The first positioning curve is optimized using the average energy operator to enhance the energy signal of the positioning point.

[0030] Based on the optimization results and the original positioning curve, the second positioning curve is determined.

[0031] Furthermore, the first positioning curve is optimized using an average energy operator; based on the optimization result and the original positioning curve, the second positioning curve is determined, specifically including:

[0032] According to ψ[C(n)]=(C 2 (n)-C(n+1)C(n-1))*sign(R(n)), and perform average energy operator optimization on the first positioning curve;

[0033] Wherein, ψ[C(n)] is the result of the average energy operator optimization, R(n) is the original positioning curve; C(n) is the first positioning curve at time n, C(n+1) is the first positioning curve at time n+1, and C(n-1) is the first positioning curve at time n-1.

[0034] according to The second positioning curve F(n) is obtained.

[0035] Further, based on the second positioning curve, the location of the defect in the test cable is determined, specifically including:

[0036] The second positioning curve of the test cable is analyzed, and the peak value of the second positioning curve is read.

[0037] Among all the peak values ​​read, target peak values ​​greater than a preset threshold are selected, and the position of the target peak value in the test cable is determined as the defect location of the test cable.

[0038] On the other hand, the present invention also provides an online cable defect location device, comprising: at least one processor; and,

[0039] A memory communicatively connected to the at least one processor; wherein,

[0040] The memory stores instructions that can be executed by the at least one processor to enable the at least one processor to perform a cable online defect location method according to any of the above embodiments.

[0041] Compared with the prior art, the cable online defect location method and equipment provided by the present invention have the following beneficial effects:

[0042] This invention utilizes a custom normalization algorithm to solve the problem of difficulty in identifying positioning peaks caused by signal attenuation at the end of the cable. It amplifies the positioning signals at faults and the end of the cable based on traditional positioning functions, achieving higher positioning accuracy and effectiveness, thus ensuring positioning of long-distance cables. However, the normalization process also amplifies the characteristics of noise signals. This invention employs an average energy operator to further enhance the energy signal of the positioning point while suppressing noise interference caused by side lobes of the positioning peak after normalization, thereby further improving the positioning effect. This invention uses the average energy operator to reduce noise interference caused by the normalization algorithm while improving the blind zone of the initial signal oscillation, and also significantly improves the positioning amplitude compared to the original positioning curve. Attached Figure Description

[0043] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort. In the drawings:

[0044] Figure 1 A flowchart of an online cable defect location method provided in an embodiment of the present invention;

[0045] Figure 2 A comparison chart of positioning improvement curves before and after the present invention is provided in an embodiment of the present invention;

[0046] Figure 3 This is a partial enlarged view of the first segment of the positioning curve provided in an embodiment of the present invention;

[0047] Figure 4 This is a schematic diagram of an online cable defect location algorithm provided in an embodiment of the present invention;

[0048] Figure 5 This is a structural schematic diagram of an online cable defect location device provided in an embodiment of the present invention. Detailed Implementation

[0049] To enable those skilled in the art to better understand the technical solutions of this invention, the technical solutions of the embodiments of this invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this invention, and not all embodiments. Based on the embodiments of this specification, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of this invention.

[0050] This invention provides a method for online defect location in cables, such as... Figure 1 As shown, the online defect location method for cables specifically includes steps S101-S104:

[0051] S101. Determine the original positioning curve of the test cable based on the incident signal input into the test cable and the collected reflected signal.

[0052] Specifically, first connect the output port of the signal generator to the oscilloscope and the test cable respectively using a T-connector; then start the signal generator and inject the SSTDR incident signal into the test cable.

[0053] Furthermore, the input SSTDR incident signal is acquired using an oscilloscope, and the reflected signal is acquired from the test cable. Finally, the acquired incident and reflected signals are saved in the computer.

[0054] Furthermore, the incident signal and the reflected signal are acquired in the computer; then, the incident signal and the reflected signal are cross-correlated, and the result of the cross-correlation operation is displayed in the form of a curve to obtain the original positioning curve of the test cable.

[0055] As a possible implementation method, according to A cross-correlation operation is performed on the incident and reflected signals. Here, R(t) is the result of the cross-correlation operation, s(t) is the incident signal, s(t-τ) is the reflected signal, τ is the signal reflection delay time, and t represents time t. Then, the result R(t) of the cross-correlation operation is converted from a discrete signal to a continuous signal, yielding the original positioning curve R(n); n represents a point on the curve.

[0056] S102. Normalize the original positioning curve to obtain the first positioning curve.

[0057] Specifically, the incident and reflected signals attenuate as the cable length increases, affecting the judgment of the positioning results. Therefore, the obtained end-positioning waveform is often weak and difficult to achieve a good positioning effect. This invention normalizes the obtained original positioning curve using a custom normalized positioning function and displays the result of the normalization process as a curve to obtain the first positioning curve.

[0058] As one possible implementation method, based on a custom normalized positioning function: The original positioning curve is normalized. Here, C(t) represents the result of the normalization process, R(t) is the expression for the original positioning curve, τ is the signal reflection delay time, and T... s Let n be the period of the incident signal. Finally, the normalized result C(t) is converted from a discrete signal into a continuous signal to obtain the first positioning curve C(n); where n is a point on the curve.

[0059] This invention defines a normalized positioning function, which amplifies the positioning signals of the fault point and the end of the cable based on the traditional positioning function, thereby achieving higher positioning accuracy and effect.

[0060] S103. Optimize the first positioning curve using the average energy operator to obtain the second positioning curve.

[0061] Specifically, the first positioning curve can amplify the positioning effect at a distance and improve positioning sensitivity. However, the normalization process amplifies the characteristics of noise signals. This invention uses an average energy operator to enhance the energy signal of the positioning point while suppressing interference caused by the side lobes of the positioning peak, so as to further improve the positioning effect.

[0062] The first positioning curve is optimized using the average energy operator to enhance the energy signal of the positioning point; then, based on the optimization results and the original positioning curve, the second positioning curve is determined.

[0063] As a feasible implementation method, according to ψ[C(n)]=(C 2 The algorithm `(n)-C(n+1)C(n-1))*sign(R(n))` performs average energy operator optimization on the first positioning curve; where `ψ[C(n)]` is the result of the average energy operator optimization, `R(n)` is the original positioning curve, `C(n)` is the first positioning curve at time n, `C(n+1)` is the first positioning curve at time n+1, and `C(n-1)` is the first positioning curve at time n-1. To further determine the polarity of the fault, the obtained optimization result is multiplied by the polarity of the original positioning waveform to obtain the second positioning curve. The calculation formula for the second positioning curve is:

[0064] Figure 2 The curve comparison chart before and after positioning improvement provided in the embodiments of the present invention is as follows: Figure 2 As shown, the fault point and end in the original waveform are difficult to identify due to severe attenuation. However, the second positioning curve obtained after normalization and average energy operator optimization significantly improves the positioning amplitude of the original positioning curve, making the fluctuation of the fault point more obvious and greatly improving the positioning effect.

[0065] Figure 3 This is a partial enlarged view of the first segment of the positioning curve provided in an embodiment of the present invention, such as... Figure 3 As shown, the first positioning curve after normalization improves positioning accuracy compared to the original waveform, but also introduces significant signal oscillations. The second positioning curve, processed by the average energy operator, resolves the issue of significant signal oscillations and also shows a substantial improvement in positioning amplitude compared to the original curve. The positioning results reveal that due to signal oscillations, the original waveform contains high-amplitude misjudgment points (circled in the figure). The second positioning curve obtained using the method of this invention suppresses these misjudgment points, reducing their occurrence.

[0066] S104. Determine the location of the defect in the test cable based on the second positioning curve.

[0067] Specifically, the second positioning curve of the test cable is analyzed, and the peak value of the second positioning curve is read. Among all the peak values ​​read, target peak values ​​greater than a preset threshold are selected, and the positions corresponding to the target peak values ​​in the test cable are determined as the defect locations of the test cable.

[0068] In one embodiment, Figure 2The horizontal axis represents the distance on the test cable, in meters, and the vertical axis represents the curve amplitude. For example... Figure 2 As shown in the diagram, the second positioning curve exhibits a distinct peak value, corresponding to an abscissa of 100 meters. Therefore, the fault location of the test cable is 100 meters from its starting point. The prominence or indistinctness of the peak value can be determined using a set threshold.

[0069] As a feasible implementation method, Figure 4 This is a schematic diagram of an online cable defect location algorithm provided in an embodiment of the present invention, as shown below. Figure 4 As shown, the specific implementation process of this method is as follows:

[0070] (1) Connect the output port of the signal generator to the oscilloscope and the test cable respectively using a T-connector, and inject the SSTDR incident signal into the cable.

[0071] (2) The oscilloscope acquires the incident signal and the reflected signal from the cable and saves the signal on the computer.

[0072] (3) The computer performs cross-correlation calculations on the obtained incident and reflected waveforms to obtain the original positioning waveform.

[0073] (4) Normalize the original positioning waveform.

[0074] (5) The second positioning curve is obtained by optimizing the first positioning curve after normalization using the average energy operator.

[0075] (6) Analyze the cable within the end range, and the obvious peak point is the defect.

[0076] In addition, embodiments of the present invention also provide an online cable defect location device, such as... Figure 5 As shown, the cable online defect location equipment specifically includes:

[0077] At least one processor; and a memory communicatively connected to the at least one processor; wherein,

[0078] The memory stores instructions that can be executed by at least one processor, so that at least one processor can perform the following:

[0079] The original positioning curve of the test cable is determined based on the incident signal input into the test cable and the collected reflected signal.

[0080] The original positioning curve is normalized to obtain the first positioning curve;

[0081] The first positioning curve is optimized using the average energy operator to obtain the second positioning curve;

[0082] The location of the defect in the test cable is determined based on the second positioning curve.

[0083] This invention utilizes a custom normalization algorithm to solve the problem of difficulty in identifying positioning peaks caused by signal attenuation at the end of the cable. It amplifies the positioning signals at faults and the end of the cable based on traditional positioning functions, achieving higher positioning accuracy and effectiveness, thus ensuring positioning of long-distance cables. However, the normalization process also amplifies the characteristics of noise signals. This invention employs an average energy operator to further enhance the energy signal of the positioning point while suppressing noise interference caused by side lobes of the positioning peak after normalization, thereby further improving the positioning effect. This invention uses the average energy operator to reduce noise interference caused by the normalization algorithm while improving the blind zone of the initial signal oscillation, and also significantly improves the positioning amplitude compared to the original positioning curve.

[0084] The systems, devices, modules, or units described in the above embodiments can be implemented by computer chips or entities, or by products with certain functions. A typical implementation device is a computer. Specifically, a computer can be, for example, a personal computer, laptop computer, cellular phone, camera phone, smartphone, personal digital assistant, media player, navigation device, email device, game console, tablet computer, wearable device, or any combination of these devices.

[0085] For ease of description, the above devices are described in terms of function, divided into various units. Of course, in implementing this specification, the functions of each unit can be implemented in one or more software and / or hardware components.

[0086] Those skilled in the art will understand that the embodiments of this specification can be provided as methods, systems, or computer program products. Therefore, the embodiments of this specification can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the embodiments of this specification can take the form of a computer program product implemented 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.

[0087] This specification is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this specification. It will be understood that each block of the flowchart illustrations and / or block diagrams, and 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 processor, 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, create a machine for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0088] 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.

[0089] 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.

[0090] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0091] Memory may include non-persistent storage in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM. Memory is an example of computer-readable media.

[0092] Computer-readable media includes both permanent and non-permanent, removable and non-removable media that can store information using any method or technology. Information can be computer-readable instructions, data structures, modules of programs, or other data. Examples of computer storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, magnetic magnetic disk storage or other magnetic storage devices, or any other non-transferable medium that can be used to store information accessible by a computing device. As defined herein, computer-readable media does not include transient computer-readable media, such as modulated data signals and carrier waves.

[0093] It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0094] This specification can be described in the general context of computer-executable instructions that are executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, etc., that perform a specific task or implement a specific abstract data type. This specification can also be practiced in distributed computing environments, where tasks are performed by remote processing devices connected via a communication network. In distributed computing environments, program modules can reside in local and remote computer storage media, including storage devices.

[0095] The various embodiments in this specification are described in a progressive manner. Similar or identical parts between embodiments can be referred to interchangeably. Each embodiment focuses on describing the differences from other embodiments. In particular, the embodiments for apparatus, devices, and non-volatile computer storage media are basically similar to the method embodiments, so the descriptions are relatively simple; relevant parts can be referred to the descriptions of the method embodiments.

[0096] The foregoing has described specific embodiments of this specification. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recited in the claims may be performed in a different order than that shown in the embodiments and may still achieve the desired result. Furthermore, the processes depicted in the drawings do not necessarily require the specific or sequential order shown to achieve the desired result. In some embodiments, multitasking and parallel processing are possible or may be advantageous.

[0097] The above description is merely one or more embodiments of this specification and is not intended to limit this specification. Various modifications and variations can be made to the one or more embodiments of this specification by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principle of one or more embodiments of this specification should be included within the scope of the claims of this specification.

Claims

1. A method for online defect location in cables, characterized in that, The method includes: Based on the incident signal input into the test cable and the collected reflected signal, the original positioning curve of the test cable is determined. Specifically, this includes: acquiring the incident signal and the reflected signal in a computer; performing a cross-correlation operation on the incident signal and the reflected signal, and displaying the result of the cross-correlation operation in the form of a curve to obtain the original positioning curve of the test cable. The original positioning curve is normalized to obtain a first positioning curve. Specifically, this includes: normalizing the original positioning curve using a custom normalization positioning function, and displaying the result of the normalization process as a curve to obtain the first positioning curve. The first positioning curve is optimized using an average energy operator to obtain a second positioning curve. Specifically, this includes: optimizing the first positioning curve using an average energy operator to enhance the energy signal of the positioning point; and determining the second positioning curve based on the optimization result and the original positioning curve. The defect location of the test cable is determined based on the second positioning curve, specifically including: analyzing the second positioning curve of the test cable and reading the peak value of the second positioning curve; filtering out target peak values ​​greater than a preset threshold from all the peak values ​​read, and determining the position of the target peak value in the test cable as the defect location of the test cable; The first positioning curve is optimized using an average energy operator; based on the optimization result and the original positioning curve, the second positioning curve is determined, specifically including: according to The first positioning curve is optimized using the average energy operator. Wherein, ψ[C(n)] is the result of the average energy operator optimization, R(n) is the original positioning curve; C(n) is the first positioning curve at time n, C(n+1) is the first positioning curve at time n+1, and C(n-1) is the first positioning curve at time n-1. according to This yields the second positioning curve; Wherein, F(n) is the second positioning curve.

2. The method for online defect location of cables according to claim 1, characterized in that, Before determining the original positioning curve of the test cable based on the incident signal input into the test cable and the collected reflected signal, the method further includes: Connect the output port of the signal generator to the oscilloscope and the test cable respectively using T-connectors; Start the signal generator and inject the SSTDR incident signal into the test cable; The oscilloscope is used to acquire the incident signal of the SSTDR and the reflected signal obtained from the test cable, and the acquired incident and reflected signals are saved in the computer.

3. The method for online defect location of cables according to claim 1, characterized in that, The incident signal and the reflected signal are cross-correlated, and the result of the cross-correlation operation is displayed as a curve to obtain the original positioning curve of the test cable, specifically including: according to The incident signal and the reflected signal are cross-correlated. Where R(t) is the result of the cross-correlation operation, and s(t) is the incident signal. The reflected signal, Let t be the signal reflection delay time, and t represent time t. The result R(t) of the cross-correlation operation is converted from a discrete signal into a continuous signal to obtain the original positioning curve R(n); where n is a point on the curve.

4. The method for online defect location of cables according to claim 3, characterized in that, The original positioning curve is normalized using a custom normalization positioning function, and the result of the normalization is displayed as a curve to obtain the first positioning curve, specifically including: Based on a custom normalized positioning function: The original positioning curve is then normalized. Where C(t) is the result of normalization, and T s The period of the incident signal; The result C(t) of the normalization process is converted from a discrete signal into a continuous signal to obtain the first positioning curve C(n); where n is a point on the curve.

5. An online cable defect location device, characterized in that, The device includes: At least one processor; and, A memory communicatively connected to the at least one processor; wherein, The memory stores instructions executable by the at least one processor to enable the at least one processor to perform a cable online defect location method according to any one of claims 1-4.

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