CN110766264A - Transmission cable health state monitoring and evaluating method - Google Patents
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Abstract
The invention discloses a transmission cable health state monitoring and evaluating method, which utilizes a power line communication modem and a combined time-frequency domain reflection measurement method to extract a time-frequency signal characteristic spectrogram reflecting the channel characteristics of a transmission cable, further utilizes the relation between a transmission cable degradation model and the channel characteristics of the transmission cable to represent cable degradation parameters, then utilizes the time-frequency signal characteristic spectrogram generated under different loads and cable degradation conditions to train a machine model, and then utilizes the trained machine model to evaluate and predict the degradation grade of the transmission cable. The invention has the characteristics of automatically, remotely and online realizing the health state evaluation, fault diagnosis and positioning of the transmission cable, and can avoid fault diagnosis missing detection and false alarm caused by load change.
Description
Technical Field
The invention belongs to the technical field of communication transmission, and particularly relates to a method for monitoring and evaluating the health state of a transmission cable.
Background
Monitoring and assessing the health of transmission cables is a fundamental requirement to ensure reliable, safe and stable operation of communication systems. Degradation of the health of the operating transmission cable affects the performance of the communication transmission and, in the event of a failure, necessarily results in an interruption of the communication, and a solution to avoid a failure of the operating transmission cable is to identify potential problems and predict the failure in a non-destructive manner. The existing transmission cable health state monitoring and fault diagnosis method needs to be equipped with special equipment for off-line testing and analysis and evaluation, or needs to design characteristic engineering, namely, special personnel analyze and measure the signal (current and/or voltage) characteristics, and then evaluate the health state of the transmission cable, and has high cost and low speed. Furthermore, different load conditions can result in signal characteristic changes similar to those caused by transmission cable degradation, rendering the fault diagnosis susceptible to missed detection and false alarm.
Disclosure of Invention
The invention mainly aims to provide a transmission cable health state monitoring and evaluating method, and aims to solve the technical problems in the existing method.
In order to achieve the above object, the present invention provides a method for monitoring and evaluating the health status of a transmission cable, comprising the following steps:
s1, extracting a time-frequency signal characteristic spectrogram reflecting the channel characteristics of the transmission cable by using a power line communication modem and a joint time-frequency domain reflection measurement method;
s2, representing the degradation parameter of the transmission cable according to the relation between the degradation model of the transmission cable and the channel characteristic of the transmission cable;
s3, training a machine model by using the time-frequency signal characteristic spectrogram generated under different loads and transmission cable degradation conditions;
and S4, evaluating and predicting the degradation level of the transmission cable by using the trained machine model.
As a further improvement of the above method for monitoring and evaluating the health status of the transmission cable, the degradation model of the transmission cable in step S2 specifically includes:
in the total thickness y of the insulating layerinsulWithin the range of forming a thickness of yhomoAnd is degraded at a length of lWTWithin a range of forming a thickness of ylocalLocal WT degradation.
As a further improvement of the method for monitoring and evaluating the health state of the transmission cable, the WT degradation thickness y of the transmission cable is measureddegExpressed as:
wherein, α0Denotes the diffusion constant, v, of water in the medium0Denotes the free volume void size, f0Indicating the operating frequency, epsilon, of the transmission cable0Denotes the absolute dielectric constant, F denotes the operating electric field strength of the transmission cable, ∈wDenotes the relative dielectric constant, t, of watersrRepresenting cable working time, Y representing dielectric mechanical yield strength, Y for uniform WT degradationhomo＝ydegFor local WT degeneration, ylocal＝ydeg。
As a further improvement of the above method for monitoring and evaluating the health status of the transmission cable, in the step S3, when training the machine model, classification and regression are respectively performed by using a support vector machine algorithm based on a radial basis function and an AdaBoost algorithm, and | H is selectedfM-order moment of |, hf∠ H, peak position and amplitudefM order moment of (h)JTFDRIs characterized by the peak position and amplitude of HfRepresents the channel frequency response, | H, obtained by the power line communication modemfI denotes the amplitude of the frequency response, ∠ HfRepresenting the phase of the frequency response, hfRepresents HfChannel impulse response h obtained by Fourier inverse transformationJTFDRRepresenting the waveform obtained by the joint time-frequency domain reflectometry method.
As the above transmission cable healthIn a further improvement of the state monitoring and evaluating method, the waveform hJTFDRThe processing procedure of the peak position and amplitude is specifically as follows:
generating a gaussian envelope chirp signal sgc(t) as an incident signal required for the joint time-frequency domain reflectometry method;
envelope of Gaussian chirp signal sgc(t) reflected channel impulse response h obtained with power line communication modemref(t) convolution to obtain a reflected signal
For Gaussian envelope chirp signal sgc(t) and reflected signals
Performing a correlation operation to obtain a correlation signal
Absolute value processing is carried out on the related signal u (t) to obtain a waveform hJTFDR＝|u(t)|。
As a further improvement of the above method for monitoring and evaluating the health status of the transmission cable, the step S4 of evaluating and predicting the degradation level of the transmission cable by using the trained machine model specifically includes:
performing transmission cable aging classification including local WT degradation and uniform WT degradation using the trained machine model;
performing a transmission cable aging severity assessment; when uniform WT degradation occurs, the degradation severity is evaluated to a first level; when the severity of degradation equals zero, assessing a healthy state;
local WT degradation positioning is performed and subdivided into several stages according to different degradation severity.
As a further improvement of the transmission cable health state monitoring and evaluating method, when the deterioration severity of the uniform WT is evaluated, the deterioration severity is predicted by using an AdaBoost algorithm, and different equivalent life values are adopted to represent the severity of different uniform deterioration.
As a further improvement of the above-mentioned transmission cable health status monitoring and evaluation method, the WT degradation thickness y of the transmission cable is utilizeddegEstimating the equivalent life of the transmission cable, expressed as
Wherein, teqRepresenting the equivalent life of the transmission cable.
As a further improvement of the method for monitoring and evaluating the health state of the transmission cable, when the severity of local WT degradation is evaluated, an AdaBoost algorithm regressor is trained, the AdaBoost algorithm is executed by using a node closest to the degradation to predict the severity of the degradation, and the severity of the degradation of the transmission cable is represented by using the relative thickness of an insulating material affected by the local WT degradation.
As a further improvement of the method for monitoring and evaluating the health state of the transmission cable, when local WT degradation is positioned, the position of the starting point of the local WT degradation is predicted by adopting a support vector machine algorithm based on a radial basis function, and then the position of the starting point of the local WT degradation is correctedWT×γlocalPerforming a high accuracy prediction using the local WT degradation relative thickness gammalocalDetermining the degradation length lWTAccording to the length of degradation lWTDetermining a local WT degradation endpoint location, wherein gammalocal＝ylocal/yinsulIndicating the relative thickness of the local WT degradation.
The invention has the beneficial effects that: the invention utilizes a power line communication modem and a joint time-frequency domain reflection measurement method to extract a time-frequency signal characteristic spectrogram reflecting the channel characteristics of a transmission cable, further utilizes the relation between a transmission cable degradation model and the channel characteristics of the transmission cable to represent degradation parameters of the transmission cable, then utilizes the time-frequency signal characteristic spectrogram generated under different loads and transmission cable degradation conditions to train a machine model, and then utilizes the trained machine model to evaluate and predict the degradation grade of the transmission cable. The invention has the characteristics of automatically, remotely and online realizing the health state evaluation, fault diagnosis and positioning of the transmission cable, and can avoid fault diagnosis missing detection and false alarm caused by load change.
Drawings
FIG. 1 is a schematic flow chart of a transmission cable health monitoring and evaluating method of the present invention;
FIG. 2 is a longitudinal cross-sectional view of aged insulation of a transmission cable in an embodiment of the present invention;
FIG. 3 shows an embodiment h of the present inventionJTFDRSchematic diagram of the treatment process of (1);
FIG. 4 shows an embodiment h of the present inventionJTFDRA waveform schematic diagram;
FIG. 5 is a schematic diagram of a machine learning-based transmission cable health monitoring framework in an embodiment of the present invention;
FIG. 6 is a schematic view of a multi-stage health monitoring of a transmission cable in an embodiment of the present invention;
FIG. 7 is a schematic diagram of a T-type topology in an embodiment of the present invention;
FIG. 8 is a graphical representation of local degradation identification performance as a function of degradation severity for an embodiment of the present invention;
FIG. 9 is a schematic diagram of the detection performance of a locally degraded branch location in an embodiment of the present invention;
FIG. 10 is a schematic illustration of the severity of degradation of a transmission cable in an embodiment of the present invention; wherein FIG. 10a is uniform degradation and FIG. 10b is partial degradation;
FIG. 11 is a graphical illustration of a localized degradation positioning result in an embodiment of the invention; where fig. 11a is the starting position and fig. 11b is the degenerate length.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
The main solution of the embodiment of the invention is as follows:
according to the difference of insulating materials used for producing and processing the transmission cable, the transmission cable is divided into a lamination type and an extrusion type. Laminated transmission cables are mainly affected by thermal degradation, while extruded transmission cables are mainly affected by electrical aging, i.e. the formation of water-trees (WT) and electrical-trees (ET). In view of the fact that WT degradation is one of the prominent reasons for the failure of a crush cable, the invention provides a transmission cable health state monitoring and fault diagnosis method aiming at the transmission cable aging caused by WT degradation.
As shown in fig. 1, a method for monitoring and evaluating the health status of a transmission cable includes the following steps:
s1, extracting a time-frequency signal characteristic spectrogram reflecting the channel characteristics of the transmission cable by using a Power Line Communication Modem (PLCM) and Joint Time Frequency Domain Reflection (JTFDR) measurement method;
s2, representing the degradation parameter of the transmission cable according to the relation between the degradation model of the transmission cable and the channel characteristic of the transmission cable;
s3, training a machine model by using the time-frequency signal characteristic spectrogram generated under different loads and transmission cable degradation conditions;
and S4, evaluating and predicting the degradation level of the transmission cable by using the trained machine model.
In an optional embodiment of the present invention, in step S1, a time-frequency signal feature spectrogram reflecting channel characteristics of a transmission cable is extracted by using a Power Line Communication Modem (PLCM) and a joint time-frequency domain reflectometry (JTFDR) measurement method.
In an alternative embodiment of the present invention, the above step S2 analyzes the WT degradation mechanism of the transmission cable insulation medium, and under normal operating conditions, approximately uniform WT degradation occurs along the transmission cable with increasing service life, and water ingress or local defects may lead to significant local WT degradation.
In order to accurately simulate the degradation characteristics of a transmission cable, the present invention models the aging of the transmission cable along the line, a schematic diagram of which is shown in fig. 2.
The cable degradation model specifically comprises the following steps:
in the total thickness y of the insulating layerinsulWithin the range of forming a thickness of yhomoAnd is degraded at a length of lWTWithin a range of forming a thickness of ylocalPart ofThe WT degrades.
Transmission cable WT degradation thickness ydegIncrease and cable operating time tsrAbout, expressed as:
wherein, α0Denotes the diffusion constant, v, of water in the medium0Denotes the free volume void size, f0Indicating the operating frequency, epsilon, of the transmission cable0Denotes the absolute dielectric constant, F denotes the operating electric field strength of the transmission cable, ∈wDenotes the relative dielectric constant, ε, of waterw＝81-jσw/2πf0ε0，σwDenotes the conductivity of the water, tsrRepresenting cable working time, Y representing dielectric mechanical yield strength, Y for uniform WT degradationhomo＝ydegFor local WT degeneration, ylocal＝ydeg。
After the transmission cable insulation is degraded by WT, i.e. the black area in fig. 2, the white area represents the insulation integrity, and the relative dielectric constant of the transmission cable insulation after being degraded by WT is represented as:
wherein epsilonPERepresenting the relative dielectric constant, q, of the non-degraded insulating materialwDenotes the absolute water content of the WT region and D denotes the depolarization factor.
For the insulating material of a section of cable with WT degradation and non-degradation, the equivalent relative dielectric constant is calculated by using a series dielectric model and is expressed as:
wherein, ydegIndicating WT degradation thickness.
Calculating the voltage V from an approximately cylindrical geometryoLower distanceFrom the center r of the conductorcondThe electric field at which the electric field strength is greatest forms the WT degradation onset, which is expressed as:
in an alternative embodiment of the present invention, in the step S3, classification and regression are performed by using a radial basis function-based support vector machine (RBF-SVM) algorithm and an AdaBoost algorithm, respectively, when training the machine model.
The RBF-SVM algorithm is a classic machine learning algorithm, a subset of a training data set is used for constructing a hyperplane support vector for prediction, and the characteristics of sparsity and large interval of the support vector enable a Support Vector Machine (SVM) to provide accurate prediction for unknown data samples, and different kernel functions are used for exploring a high-dimensional space.
The AdaBoost algorithm is a meta-machine learning algorithm, a plurality of weak learners are weighted and combined into a strong learner in sequence, and the calculation is simple; and a forward addition model is adopted to be robust to overfitting.
The invention selects machine learning characteristics through analyzing related characteristics, and specifically comprises the following steps:
the channel frequency response H obtained by the power line communication modem is caused by higher dielectric loss caused by cable degradationfThe attenuation in the whole frequency band is increased and its amplitude H is selectedfThe m-order moment m of | ═ 1, 2, 3, 4 as a feature;
the channel impulse response h is selected such that the cable's wave propagation speed is reduced in the degraded area due to cable degradationfPeak position, amplitude and HfPhase ∠ H offThe m-order moment m of (1, 2, 3, 4) as a characteristic;
in order to extract clearer degradation identification and positioning characteristics, a waveform h obtained by a combined time-frequency domain reflection measurement method is selectedJTFDRThe peak position and amplitude of (c) as a feature.
As shown in fig. 3, the waveform hJTFDRThe processing procedure of the peak position and amplitude is specifically as follows:
generating a gaussian envelope chirp signal sgc(t) g (t) × c (t), g (t) and c (t) are gaussian-shaped waveforms and chirp signals, respectively, as incident signals required for the joint time-frequency domain reflectometry method;
for implementation in a power line communication modem, a pre-generated and pre-stored gaussian envelope chirp signal s is usedgc(t) reflected channel impulse response h estimated by modem for power line communicationref(t) convolution to obtain a reflected signal
Expressed as:
wherein,
representing a linear convolution operation;
to obtainThe size and the position of the peak value, the degradation detection and the positioning of the transmission cable are realized, and the Gaussian envelope chirp signal s is processedgc(t) and reflected signalsPerforming a cross-correlation operation to obtain a cross-correlation signal u (t), denoted as:
absolute value processing is carried out on the cross-correlation signal u (t) to obtain a waveform hJTFDRAs shown in fig. 4 (A, B, C, D in the figure represents the four peaks of the waveform).
As shown in fig. 5, the present invention identifies and locates the degradation curve type as a supervised class, and assesses the degradation location and the degradation severity as a regression. For each supervised class, using the presence/absence of a local regression as a training label; for theEach supervised regression using the predicted value γhomo，lWTAs a training label.
In an alternative embodiment of the present invention, the above step S4 of estimating and predicting the degradation level of the transmission cable by using the trained machine model specifically includes three stages, as shown in fig. 6;
the first stage utilizes the trained machine model to execute transmission cable aging classification, including local WT degradation and uniform WT degradation;
a second stage of performing transmission cable aging severity assessment; when uniform WT degradation occurs, the degradation severity is evaluated as first order, i.e., I order; when the severity of degradation equals zero, assessing a healthy state;
the third stage performs local WT degeneration localization and is subdivided into several stages, i.e., II-V stages, according to different degeneration severity.
In the present embodiment, a classic T-type topology is taken as an example, as shown in fig. 7. It is assumed that all six branches of the topology have the degenerate profile shown in fig. 1. Considering a symmetrical topology, the length of the transmission cable between each PLCM and the Branching Point (BP) is 500 meters, a 500 meter length of transmission cable is also placed between each PLCM and any Branch Extension (BE), and the load impedance U (0, 50) + jU (-50, 50), U (a, b) represents a uniform random distribution between a, b. Setting up gammahomo～U(0，0.05)，γlocal～U(0.1，1)，lWTU (100, 300) m, the local degradation center is located within 100m from the branch center.
When local degradation identification is carried out, the ith PLCM p is utilizediTo identify the presence or absence of local degradation, including local degradation at pi-BP or pi-BEiFIG. 8 shows the detection performance as a function of γlocalA curve of variation. As can be seen from the figure, use hJTFDRThe detection performance of the extracted features is good, and all gamma rayslocalThe false alarm rate of (a) is negligible and h is usedrefThe performance of the extracted features is poor no matter the RBF-SVM algorithm or the AdaBoost algorithm is adopted, especially in gammalocalAnd lower.
When in pi-BP or pi-BEiWhen the presence of local degradation is detected, reuse of pjJ ═ 1, 2, 3; j ≠ i confirms whether or not degeneration exists in piBP, the detection result is shown in FIG. 9, and the detection performance is all gammalocalThe range is high.
When the type of the degradation curve is divided into WT uniform degradation, the AdaBoost algorithm is used for predicting the severity of the aging, and different equivalent life values are used for representing the severity of different uniform degradation. The service life of the transmission cable is set to comply with t-U (0, 32.5) distribution, and the equivalent life prediction performance is shown in FIG. 10 (a). As can be seen from the graph, the estimated lifetime is very close to the actual lifetime.
WT degenerate thickness y with transmission cabledegEstimating the equivalent life of the transmission cable, expressed as
Wherein, teqRepresenting the equivalent life of the transmission cable. The equivalent lifetime intuitively reflects the severity of the WT degradation experienced by the transmission cable. Maximum expected service life t of transmission cable max30 years, the relative thickness of the insulation material of the cable degraded by WT, i.e. the maximum allowable uniform ageing severity max (γ) was estimated from thishomo) To clearly distinguish local WT degradation, γ is set at 0.05local＝0.1。
To characterize the severity of the transmission cable degradation, y is useddeg/yinsulIndicating the relative thickness of the insulating material affected by WT degradation, for local WT degradation: y ═ ylocal，γlocal＝ylocal/yinsul(ii) a For uniform WT degradation: y ═ yhomo，γhomo＝yhomo/yinsul(ii) a For no WT degradation, there is γ along the entire length of the transmission cablehomo＝yhomo/yinsul。
When local degradation severity evaluation is performed, when the classifier indicates that local degradation exists, the severity of the local degradation is further evaluated, and an AdaBoost algorithm is executed by using a node closest to the degradation, and the prediction result is shown in fig. 10 (b). ByFrom h, it can be seenJTFDRThe prediction accuracy of the extracted features in the waveform is high.
When local degradation positioning is carried out, as a final diagnosis stage, accurate positioning needs to be carried out on obvious local degradation, including determining the positions of head and tail end points of the local degradation. The starting point position is first determined and then the degradation length is estimated. The RBF-SVM algorithm is adopted to predict the position of the degradation starting point, and the prediction result is shown in figure 11(a), so that the precision is high. Due to hJTFDRThe position of the peak is clear, and the position of the degradation starting point can be accurately predicted. Next, the length of degradation is predicted to determine the end point position, first for lWT×γlocalPerforming prediction and then using γ obtained in FIG. 10(b)localDetermination of lWT. To lWT×γlocalAs shown in fig. 11(b), the prediction accuracy is high.
It will be appreciated by those of ordinary skill in the art that the embodiments described herein are intended to assist the reader in understanding the principles of the invention and are to be construed as being without limitation to such specifically recited embodiments and examples. Those skilled in the art can make various other specific changes and combinations based on the teachings of the present invention without departing from the spirit of the invention, and these changes and combinations are within the scope of the invention.
Claims (10)
1. A transmission cable health state monitoring and evaluating method is characterized by comprising the following steps:
s1, extracting a time-frequency signal characteristic spectrogram reflecting the channel characteristics of the transmission cable by using a power line communication modem and a joint time-frequency domain reflection measurement method;
s2, representing the degradation parameter of the transmission cable according to the relation between the degradation model of the transmission cable and the channel characteristic of the transmission cable;
s3, training a machine model by using the time-frequency signal characteristic spectrogram generated under different loads and transmission cable degradation conditions;
and S4, evaluating and predicting the degradation level of the transmission cable by using the trained machine model.
2. The method for monitoring and evaluating the health status of the transmission cable according to claim 1, wherein the degradation model of the transmission cable in the step S2 is specifically:
in the total thickness y of the insulating layerinsulWithin the range of forming a thickness of yhomoAnd is degraded at a length of lWTWithin a range of forming a thickness of ylocalLocal WT degradation.
3. The transmission cable health monitoring and assessment method of claim 2, wherein the WT degradation thickness y of the transmission cable is determineddegExpressed as:
wherein, α0Denotes the diffusion constant, v, of water in the medium0Denotes the free volume void size, f0Indicating the operating frequency, epsilon, of the transmission cable0Denotes the absolute dielectric constant, F denotes the operating electric field strength of the transmission cable, ∈wDenotes the relative dielectric constant, t, of watersrRepresenting cable working time, Y representing dielectric mechanical yield strength, Y for uniform WT degradationhomo＝ydegFor local WT degeneration, ylocal＝ydeg。
4. The method for monitoring and evaluating the health status of transmission cable according to claim 3, wherein the step S3 is implemented by performing classification and regression respectively using a support vector machine algorithm and an AdaBoost algorithm based on radial basis function when training a machine model, and selecting | HfM-order moment of |, hf∠ H, peak position and amplitudefM order moment of (h)JTFDRIs characterized by the peak position and amplitude of HfRepresents the channel frequency response, | H, obtained by the power line communication modemfI denotes the amplitude of the frequency response, ∠ HfRepresenting the phase of the frequency response, hfRepresents HfObtained by inverse Fourier transformChannel impulse response, hJTFDRRepresenting the waveform obtained by the joint time-frequency domain reflectometry method.
5. The transmission cable health monitoring and assessment method of claim 4, wherein said waveform hJTFDRThe processing procedure of the peak position and amplitude is specifically as follows:
generating a gaussian envelope chirp signal sgc(t) as an incident signal required for the joint time-frequency domain reflectometry method;
envelope of Gaussian chirp signal sgc(t) reflected channel impulse response h obtained with power line communication modemref(t) convolution to obtain a reflected signal
For Gaussian envelope chirp signal sgc(t) and reflected signals
Performing a cross-correlation operation to obtain a cross-correlation signal
Absolute value processing is carried out on the cross-correlation signal u (t) to obtain a waveform hJTFDR＝|u(t)|。
6. The method for monitoring and evaluating the health status of the transmission cable according to claim 5, wherein the step S4 for evaluating and predicting the degradation level of the transmission cable by using the trained machine model specifically comprises:
performing transmission cable aging classification using the trained machine model, including local WT degradation and uniform WT degradation;
performing a transmission cable aging severity assessment; when uniform WT degradation occurs, the degradation severity is evaluated to a first level; when the severity of degradation equals zero, assessing a healthy state;
local WT degradation positioning is performed and subdivided into several stages according to different degradation severity.
7. The transmission cable health monitoring and assessment method of claim 6, wherein in assessing a uniform WT degradation severity, the AdaBoost algorithm is used to predict the degradation severity and different equivalent lifetime values are used to characterize different uniform degradation severity.
8. The transmission cable health monitoring and assessment method of claim 7, wherein the WT degradation thickness y of the transmission cable is useddegEstimating the equivalent life of the transmission cable, expressed as
Wherein, teqRepresenting the equivalent life of the transmission cable.
9. The method of claim 8, wherein in evaluating the severity of degradation of local WT, an AdaBoost algorithm regressor is trained, the AdaBoost algorithm is executed using the node closest to degradation to predict the severity of degradation, and the relative thickness of the insulation material affected by local WT degradation is used to characterize the severity of degradation of the transmission cable.
10. The transmission cable health monitoring and assessment method of claim 9, wherein in locating local WT degradation, the local WT degradation starting point position is predicted using a radial basis function based support vector machine algorithm, and then for lWT×γlocalPerforming a high accuracy prediction using the local WT degradation relative thickness gammalocalDetermining the degradation length lWTAccording to the length of degradation lWTDetermining a local WT degradation endpoint location, wherein gammalocal＝ylocal/yinsulIndicating the relative thickness of the local WT degradation.
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