A method and system for waveform adaptive optimization of cable fault detection

Abstract

The present invention discloses a method and system for waveform adaptive optimization of cable fault detection. The method includes the following steps: obtaining a diagnostic waveform of a cable to be fault-detected, and performing one-layer wavelet decomposition to obtain the first-layer detail coefficients; selecting the wavelet basis with the largest peak-to-effect ratio as the optimal wavelet basis; respectively performing K-layer wavelet decomposition on the diagnostic waveform; calculating the detail energy based on the detail coefficients of the Kth layer, dividing the detail energy of the Kth layer by the detail energy of the (K-1)th layer, and taking the largest quotient value as the critical layer number; comparing the detail energy of the layer before or the two layers before the critical layer number, and taking the larger value as the optimal wavelet layer number; performing wavelet threshold denoising processing on the diagnostic waveform to obtain an adaptive optimization result. The method of the present invention can solve the technical problem that it is not easy to distinguish multiple fault points caused by interference points.

Description

Technical Field

[0001] The present invention belongs to the technical field of cable fault detection, specifically relates to the field of cable fault detection waveform optimization, and particularly relates to a method and system for adaptively optimizing cable fault detection waveforms. Background Art

[0002] Cables play a very crucial role in the power transmission of power systems; during operation, once a cable fault occurs, it will cause large electrical systems to shut down or even get out of control, resulting in serious economic losses and social impacts. Therefore, it is very important to diagnose local defects of cables, which can timely eliminate local defects of cables and avoid permanent faults induced by local latent defects.

[0003] Currently, the main methods for cable fault detection include: directly measuring the physical and chemical properties of cables for detection, measuring relevant electrical quantities of cables, etc.; however, the above existing methods can only evaluate the overall operating state or locate the position of permanent faults, and there are still some bottleneck problems in the evaluation of latent defects.

[0004] Currently, the evaluation of latent defects is mainly carried out through methods such as partial discharge detection, cable broadband impedance spectroscopy, and time domain reflectometry. Specifically, in the existing cable broadband impedance spectroscopy technology of FDR (Frequency Domain Reflectometry), a low-voltage variable-frequency sine signal source is used to measure the curve of the input impedance at the head end of the cable changing with frequency, and the cable operating state information is obtained according to the curve characteristics. Then, through integral transformation, the cable impedance spectrum is transformed into an impedance spectrum function in the pseudo-frequency domain, that is, the diagnostic waveform of the cable characteristic parameters changing with position is obtained. Based on the relationship between the local defect characteristic parameters of the cable and the diagnostic waveform, the location of local defects and aging is realized. The existing technical defects of the above methods include that when there are multiple fault points, due to the mutation peak generated by local aging being a gradual change type, when the distance between fault points is relatively close, the multiple reflections and refractions of signals will cause mutual interference, making it difficult to distinguish fault points and bringing difficulties to fault detection. Summary of the Invention

[0005] The purpose of the present invention is to provide a method and system for adaptively optimizing cable fault detection waveforms to solve one or more of the above existing technical problems. The method of the present invention uses wavelet transform to perform adaptive optimization processing on diagnostic waveform data, and can solve the technical problem that it is not easy to distinguish multiple fault points caused by interference points.

[0006] To achieve the above object, the present invention adopts the following technical solutions:

[0007] An adaptive optimization method for waveforms used in cable fault detection provided by the present invention includes the following steps:

[0008] Obtain the diagnostic waveform of the cable to be fault-detected;

[0009] Perform one-layer wavelet decomposition on the obtained diagnostic waveform to obtain the first-layer detail coefficients; based on the obtained first-layer detail coefficients, calculate the peak-to-effectiveness ratio, and select the wavelet basis with the largest peak-to-effectiveness ratio as the optimal wavelet basis;

[0010] Based on the obtained optimal wavelet basis, perform K-layer wavelet decomposition on the diagnostic waveform respectively; calculate the detail energy according to the detail coefficients of the Kth layer, divide the detail energy of the Kth layer by the detail energy of the (K - 1)th layer, and take the largest quotient value as the critical layer number; compare the detail energies of the layer before or the two layers before the critical layer number, and take the larger value as the optimal wavelet layer number;

[0011] Based on the obtained diagnostic waveform, obtain the wavelet transform threshold;

[0012] Based on the obtained optimal wavelet basis, the optimal wavelet layer number, and the wavelet transform threshold, perform wavelet threshold denoising processing on the diagnostic waveform to obtain the adaptive optimization result.

[0013] A further improvement of the method of the present invention is that when obtaining the diagnostic waveform of the cable to be fault-detected, the method used is FDR.

[0014] A further improvement of the method of the present invention is that the step of performing one-layer wavelet decomposition on the obtained diagnostic waveform to obtain the first-layer detail coefficients includes:

[0015] Perform one-layer wavelet decomposition on the obtained diagnostic waveform by selecting three types of wavelet bases, namely symN, dbN, and coifN, to obtain the first-layer detail coefficients; where N represents the order of the wavelet.

[0016] A further improvement of the method of the present invention is that the value range of N is an integer from 1 to 8.

[0017] A further improvement of the method of the present invention is that in the step of calculating the peak-to-effectiveness ratio based on the obtained first-layer detail coefficients and selecting the wavelet basis with the largest peak-to-effectiveness ratio as the optimal wavelet basis, the calculation expression of the peak-to-effectiveness ratio is,

[0018]

[0019] In the formula, P 1 represents the first-layer peak-to-effectiveness ratio, D 1 represents the first-layer detail coefficients, N represents the order of the wavelet, M represents the total amount of detail coefficients, and D i represents the ith first-layer detail coefficient.

[0020] A further improvement of the method of the present invention lies in that the calculation expression of the detailed energy is

[0021]

[0022] In the formula, E j represents the detailed energy of the j-th layer, C represents the total amount of detailed coefficients of the j-th layer, D jk represents the detailed coefficient of the j-th layer when the total number of layers is K layers, and E represents the quotient of the detailed energy of the j-th layer and the (j - 1)-th layer.

[0023] A further improvement of the method of the present invention lies in that the value range of K is an integer from 1 to 15.

[0024] A further improvement of the method of the present invention lies in that the step of obtaining the wavelet transform threshold based on the obtained diagnostic waveform includes:

[0025] Based on the obtained diagnostic waveform, use the ddencmp function to obtain the wavelet transform threshold.

[0026] An adaptive optimization system for cable fault detection waveforms provided by the present invention includes:

[0027] A diagnostic waveform acquisition module for acquiring the diagnostic waveform of the cable to be fault-detected;

[0028] An optimal wavelet basis acquisition module for performing one-layer wavelet decomposition on the obtained diagnostic waveform to obtain the first-layer detailed coefficients; based on the obtained first-layer detailed coefficients, calculating the peak-to-effect ratio, and selecting the wavelet basis with the largest peak-to-effect ratio as the optimal wavelet basis;

[0029] An optimal wavelet layer acquisition module for performing K-layer wavelet decomposition on the diagnostic waveform respectively based on the obtained optimal wavelet basis; calculating the detailed energy according to the detailed coefficients of the K-th layer, taking the quotient of the detailed energy of the K-th layer and the detailed energy of the (K - 1)-th layer, and taking the largest quotient as the critical layer number; comparing the detailed energies of the layer before the critical layer number and the two layers before, and taking the larger value as the optimal wavelet layer number;

[0030] A wavelet transform threshold acquisition module for obtaining the wavelet transform threshold based on the obtained diagnostic waveform;

[0031] An optimization result acquisition module for performing wavelet threshold denoising processing on the diagnostic waveform based on the obtained optimal wavelet basis, the optimal wavelet layer number, and the wavelet transform threshold to obtain an adaptive optimization result.

[0032] Compared with the prior art, the present invention has the following beneficial effects:

[0033] Different from the prior art which processes waveforms through methods such as signal-to-noise ratio or neural networks, the method of the present invention does not require obtaining an ideal processed signal in advance and has no great demand for the amount of data. The method of the present invention directly uses approximation coefficients to select wavelet coefficients to achieve a filtering effect, which is more in line with the actual application. It can adaptively calculate, select and apply wavelet transform for processing without modifying values or performing manual operations for different signals. Specifically and explanatorily, the method of the present invention uses wavelet transform to process diagnostic waveforms, adaptively obtains wavelet transform coefficients and performs filtering processing by calculating the energy and peak efficiency ratio of different coefficients and comparing them, so as to achieve the result of determining the location of the fault point. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following briefly introduces the drawings required for the description of the embodiments or the prior art; obviously, the drawings in the following description are some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0035] Figure 1 is a schematic flow chart of a method for adaptively optimizing waveforms for cable fault detection according to an embodiment of the present invention;

[0036] Figure 2 is a schematic diagram for comparing fault location in an embodiment of the present invention; wherein, Figure 2 in (a) is a schematic diagram of fault location obtained by the existing traditional FDR, Figure 2 in (b) is a schematic diagram of fault location obtained by the method of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0037] In order to enable those skilled in the art to better understand the solution of the present invention, the following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0038] It should be noted that the terms "first", "second", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0039] The present invention is further described in detail below in conjunction with the accompanying drawings:

[0040] See also Figure 1 , an adaptive optimization method for cable fault detection waveform according to an embodiment of the present invention comprises the following steps:

[0041] Obtain a diagnostic waveform (i.e., a diagnostic function) of the cable to be detected for fault; illustratively, when obtaining the diagnostic waveform, the method adopted may be FDR (Frequency Domain Reflectometer), TFDR (Time Frequency Domain Reflectometer), or TDR (Time Domain Reflectometer); further explanatory, when using FDR, specifically including using integral transformation to transform the cable frequency domain impedance spectrum to obtain a diagnostic waveform of the cable characteristic parameters varying with position; the diagnostic waveform contains interference noise, which refers to the interference noise caused by multiple refraction and reflection of the signal when there are multiple fault points and the distance between the fault points is close, or the interference caused by the complex cable laying environment, and its existence will bring certain difficulties to the location of the fault point;

[0042] The obtained diagnostic waveform is selected to perform a layer of wavelet decomposition using three types of wavelet bases: symN (Symlets wavelet, approximately symmetrical compact support orthogonal wavelet), dbN (Daubechies wavelet, compact support orthogonal wavelet), and coifN (Coiflets wavelet) to obtain the first layer of detail coefficients.

[0043] Wherein, N=1-8, N represents the category of wavelet basis;

[0044] Based on the obtained first-layer detail coefficients, calculate the peak efficiency ratio, and select the wavelet basis with the largest peak efficiency ratio as the optimal wavelet basis. Explanatorily, since the amplitude of the effective signal is larger than that of the noise signal, the peak ratio is higher, and it is easier to obtain a better denoising effect. Therefore, select the wavelet basis with the maximum peak ratio as the optimal wavelet basis;

[0045] Among them, the calculation expression of the peak efficiency ratio is

[0046]

[0047] In the formula, P 1 represents the first-layer peak efficiency ratio, D 1 represents the first-layer detail coefficients, N represents the order of the wavelet, M represents the total amount of detail coefficients, and D i represents the i-th first-layer detail coefficient;

[0048] Based on the obtained optimal wavelet basis, perform wavelet decomposition on the diagnostic waveform for K layers (exemplarily optional, K = 1 - 15). Calculate the detail energy according to the detail coefficients of the K-th layer, divide the detail energy of this layer by that of the K - 1 layer, and take the maximum quotient value as the critical layer number. Compare the detail energies of the layer before and two layers before the critical layer number, and take the larger value as the optimal wavelet layer number. Explanatorily, the detail energy of the wavelet refers to the square of the detail coefficients of each layer, which can reflect the energy distribution of each layer of wavelet coefficients to judge the concentration situation. Calculate the quotient of the detail energy of each layer of the wavelet and the previous layer. Within a certain range, when the quotient value is the largest, it can be considered that the wavelet coefficients are distributed in the entire time domain. At this time, the low-frequency component is decomposed into wavelet coefficients, and it is not easy to separate the noise signal from the effective signal. Compare the energy values of the two layers before the maximum layer. Therefore, select the layer with the larger energy value as the optimal decomposition layer number;

[0049] Among them, the calculation expression of the detail energy is

[0050]

[0051] In the formula, E j represents the detail energy of the j-th layer, C represents the total amount of detail coefficients of the j-th layer, D jk represents the detail coefficient of the j-th layer when the total number of layers is K layers, and E represents the quotient of the detail energy of the j-th layer and the j - 1 layer;

[0052] Based on the diagnostic waveform, obtain the wavelet transform threshold; Exemplarily, the ddencmp function can be used;

[0053] Based on the obtained optimal wavelet basis, optimal wavelet layer number, and wavelet transform threshold, perform wavelet threshold denoising on the diagnostic waveform to obtain the adaptive optimization result.

[0054] In a further optional embodiment of the present invention, the step of obtaining the wavelet transform threshold based on the diagnostic waveform specifically includes:

[0055] Use the ddencmp function in MATLAB to obtain the threshold of the signal as the selected optimal threshold.

[0056] Implement and combine the above three parts of the waveform filtering algorithm through MATLAB, so as to adaptively select the wavelet basis, threshold and decomposition level according to the characteristics of the diagnostic waveform itself, and finally obtain the waveform diagram.

[0057] The method of the embodiment of the present invention selects the corresponding wavelet basis and wavelet decomposition level according to the detail coefficients after wavelet transform of the function to achieve the effect of adaptive filtering; it completely adapts to transform the waveform according to the differences of the diagnostic waveforms without manual adjustment of coefficients, that is, without knowing the required effect after waveform filtering in advance, calculate the wavelet transform coefficients required for specific diagnostic waveforms in advance, and through this technology, the resolution and determination effect of multiple cable fault points can be optimized.

[0058] Please refer to Figure 2 , by establishing a simulation model, simulate the situation of two-point faults on the cable, and perform wavelet threshold filtering according to the above process of the embodiment of the present invention. According to the characteristics of the simulated fault waveform, the best wavelet basis is sym8, and the best wavelet decomposition level is 2 layers. The waveform curves before and after filtering are as Figure 2 shown. From Figure 2 it can be concluded that the interference of the image before wavelet transform is relatively serious, and there is certain interference around the defect point, which may cause misjudgment; after adaptive wavelet transform, the interference signals near the fault point are filtered out, and there are obviously two peak positions on the waveform, which are the positions of the fault points, reducing the possibility of misjudgment.

[0059] In summary, the method provided by the embodiment of the present invention is an adaptive optimization filtering method for the waveform data of the frequency domain reflection method fault location, which can be used to reduce the influence of interference noise during fault analysis. Specifically, for the characteristic that the ideal function cannot be judged in advance for the diagnostic waveform, the methods of using the signal-to-noise ratio to compare and obtain the best wavelet transform coefficients and the method of training through neural networks do not meet the actual requirements; in the method of the present invention, the detail coefficients are used to compare data such as the peak efficiency ratio and energy ratio when selecting different wavelet bases and layers, and the most suitable wavelet coefficients are selected; when there are multiple fault points, the positions of each fault point can be obtained more clearly by filtering out the interference signals; the wavelet transform is used to completely adaptively select coefficients according to the characteristics of different diagnostic waveforms, which is convenient for popularization and application in practice.

[0060] The following is an apparatus embodiment of the present invention, which can be used to implement the method embodiment of the present invention. For details not disclosed in the apparatus embodiment, please refer to the method embodiment of the present invention.

[0061] Another embodiment of the present invention provides an adaptive optimization system for cable fault detection waveforms, including:

[0062] A diagnostic waveform acquisition module, configured to acquire the diagnostic waveform of the cable to be fault-detected;

[0063] An optimal wavelet basis acquisition module, configured to perform one-layer wavelet decomposition on the acquired diagnostic waveform to obtain the first-layer detail coefficients; based on the acquired first-layer detail coefficients, calculate the peak-to-effect ratio, and select the wavelet basis with the largest peak-to-effect ratio as the optimal wavelet basis;

[0064] An optimal wavelet layer acquisition module, configured to perform K-layer wavelet decomposition on the diagnostic waveform respectively based on the acquired optimal wavelet basis; calculate the detail energy based on the detail coefficients of the Kth layer, divide the detail energy of the Kth layer by the detail energy of the (K - 1)th layer, and take the largest quotient as the critical layer number; compare the detail energies of the layer before the critical layer number and the two layers before it, and take the larger value as the optimal wavelet layer number;

[0065] A wavelet transform threshold acquisition module, configured to obtain the wavelet transform threshold based on the acquired diagnostic waveform;

[0066] An optimization result acquisition module, configured to perform wavelet threshold denoising processing on the diagnostic waveform based on the acquired optimal wavelet basis, the optimal wavelet layer number, and the wavelet transform threshold, and obtain an adaptive optimization result.

[0067] Those skilled in the art should understand that the embodiments of the present application can be provided as a method, a system, or a computer program product. Therefore, the present application can adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present application can adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk memories, CD-ROMs, optical memories, etc.) containing computer-usable program codes.

[0068] The present application is described with reference to the flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or block in the flowchart and / or block diagram, and the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to generate a machine, such that the instructions executed by the processor of the computer or other programmable data processing devices generate for implementing the processFigure 1 one process or multiple processes and / or blocks Figure 1 a device for the functions specified in one block or multiple blocks.

[0069] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer-readable memory produce a manufactured article including an instruction device, and the instruction device implements the process Figure 1 one process or multiple processes and / or blocks Figure 1 the functions specified in one block or multiple blocks.

[0070] These computer program instructions can also be loaded onto a computer or other programmable data processing device, so that a series of operation steps are performed on the computer or other programmable device to produce a computer-implemented process. Therefore, the instructions executed on the computer or other programmable device provide for implementing the process Figure 1 one process or multiple processes and / or blocks Figure 1 the steps of the functions specified in one block or multiple blocks.

[0071] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit them. Although the present invention has been described in detail with reference to the above embodiments, those of ordinary skill in the art should understand that: modifications or equivalent replacements can still be made to the specific implementation manners of the present invention, and any modifications or equivalent replacements that do not depart from the spirit and scope of the present invention should be covered by the protection scope of the claims of the present invention.

Claims

1. An adaptive optimization method for cable fault detection waveforms, characterized in that, it includes the following steps: Obtain the diagnostic waveform of the cable to be fault-detected; Perform one-layer wavelet decomposition on the obtained diagnostic waveform to obtain the first-layer detail coefficients; based on the obtained first-layer detail coefficients, calculate the peak efficiency ratio, and select the wavelet basis with the largest peak efficiency ratio as the optimal wavelet basis; Based on the obtained optimal wavelet basis, perform K-layer wavelet decomposition on the diagnostic waveform respectively; calculate the detail energy according to the detail coefficients of the Kth layer, divide the detail energy of the Kth layer by the detail energy of the (K - 1)th layer, and take the largest quotient as the critical layer number; compare the detail energies of the layer before or the two layers before the critical layer number, and take the larger value as the optimal wavelet layer number; Based on the obtained diagnostic waveform, obtain the wavelet transform threshold; Based on the obtained optimal wavelet basis, the optimal wavelet layer number, and the wavelet transform threshold, perform wavelet threshold denoising processing on the diagnostic waveform to obtain the adaptive optimization result; wherein, when obtaining the diagnostic waveform of the cable to be fault-detected, the method used is FDR; the step of performing one-layer wavelet decomposition on the obtained diagnostic waveform to obtain the first-layer detail coefficients includes: selecting three types of wavelet bases, namely symN, dbN, and coifN, for one-layer wavelet decomposition on the obtained diagnostic waveform to obtain the first-layer detail coefficients; where N represents the order of the wavelet; in the calculation of the peak efficiency ratio based on the obtained first-layer detail coefficients, where the peak efficiency ratio is calculated and the wavelet basis with the largest peak efficiency ratio is selected as the optimal wavelet basis, the calculation expression of the peak efficiency ratio is, Wherein, P 1 represents the peak efficiency ratio of the first layer, D 1 represents the set of detail coefficients of the first layer, N represents the order of the wavelet, M represents the total amount of detail coefficients, and D i represents the i-th detail coefficient of the first layer; the calculation expression of the detail energy is, where E j represents the detail energy of the j-th layer, C represents the total amount of detail coefficients of the j-th layer, and D ji represents the i-th detail coefficient of the j-th layer when the total number of layers is K layers, and E represents the quotient of the detail energy of the j-th layer and the (j - 1)-th layer; the step of obtaining the wavelet transform threshold based on the obtained diagnostic waveform includes: based on the obtained diagnostic waveform, use the ddencmp function to obtain the wavelet transform threshold.

2. An adaptive optimization method for cable fault detection waveforms according to claim 1, characterized in that, the value range of N is an integer from 1 to 8.

3. An adaptive optimization method for cable fault detection waveforms according to claim 1, characterized in that, the value range of K is an integer from 1 to 15.

4. An adaptive optimization system for cable fault detection waveforms, characterized in that, it is used to implement the adaptive optimization method described in any one of claims 1 to 3, and includes: A diagnostic waveform acquisition module, used to acquire the diagnostic waveform of the cable to be fault-detected; An optimal wavelet basis acquisition module, used to perform one-layer wavelet decomposition on the acquired diagnostic waveform to obtain the first-layer detail coefficients; based on the acquired first-layer detail coefficients, calculate the peak efficiency ratio, and select the wavelet basis with the largest peak efficiency ratio as the optimal wavelet basis; An optimal wavelet layer number acquisition module, used to perform K-layer wavelet decomposition on the diagnostic waveform respectively based on the acquired optimal wavelet basis; calculate the detail energy according to the detail coefficients of the Kth layer, divide the detail energy of the Kth layer by the detail energy of the (K - 1)th layer, and take the largest quotient as the critical layer number; compare the detail energies of the layer before, the two layers before the critical layer number, and take the larger value as the optimal wavelet layer number; A wavelet transform threshold obtaining module, which is used to obtain a wavelet transform threshold based on the obtained diagnostic waveform; An optimization result obtaining module, which is used to perform wavelet threshold denoising processing on the diagnostic waveform based on the obtained optimal wavelet basis, the optimal wavelet layer number, and the wavelet transform threshold, so as to obtain an adaptive optimization result.

Images