CN114019329A - XLPE cable insulation early deterioration multidimensional evaluation system and method based on machine learning - Google Patents
XLPE cable insulation early deterioration multidimensional evaluation system and method based on machine learning Download PDFInfo
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- 229920003020 cross-linked polyethylene Polymers 0.000 title claims abstract description 108
- 239000004703 cross-linked polyethylene Substances 0.000 title claims abstract description 108
- 238000011156 evaluation Methods 0.000 title claims abstract description 45
- 238000009413 insulation Methods 0.000 title claims abstract description 35
- 238000010801 machine learning Methods 0.000 title claims abstract description 27
- 230000006866 deterioration Effects 0.000 title claims abstract description 20
- 238000000034 method Methods 0.000 title claims description 10
- 230000015556 catabolic process Effects 0.000 claims abstract description 41
- 238000006731 degradation reaction Methods 0.000 claims abstract description 39
- 238000012544 monitoring process Methods 0.000 claims abstract description 20
- 238000012360 testing method Methods 0.000 claims abstract description 18
- 238000009825 accumulation Methods 0.000 claims abstract description 16
- 238000013210 evaluation model Methods 0.000 claims abstract description 16
- 239000003990 capacitor Substances 0.000 claims description 23
- 230000032683 aging Effects 0.000 claims description 10
- 238000005259 measurement Methods 0.000 claims description 5
- 240000005572 Syzygium cordatum Species 0.000 claims description 3
- 235000006650 Syzygium cordatum Nutrition 0.000 claims description 3
- 238000004590 computer program Methods 0.000 claims description 3
- 238000013480 data collection Methods 0.000 claims description 3
- 239000002184 metal Substances 0.000 claims description 3
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- 238000004458 analytical method Methods 0.000 claims description 2
- 238000001514 detection method Methods 0.000 description 9
- 238000012549 training Methods 0.000 description 5
- 230000005540 biological transmission Effects 0.000 description 3
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- 238000010586 diagram Methods 0.000 description 3
- 238000005516 engineering process Methods 0.000 description 3
- 238000002347 injection Methods 0.000 description 3
- 239000007924 injection Substances 0.000 description 3
- 230000010354 integration Effects 0.000 description 3
- 230000005684 electric field Effects 0.000 description 2
- 230000007774 longterm Effects 0.000 description 2
- 239000000463 material Substances 0.000 description 2
- 238000012986 modification Methods 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000008878 coupling Effects 0.000 description 1
- 238000010168 coupling process Methods 0.000 description 1
- 238000005859 coupling reaction Methods 0.000 description 1
- 238000007405 data analysis Methods 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 210000001787 dendrite Anatomy 0.000 description 1
- 230000001066 destructive effect Effects 0.000 description 1
- 238000013399 early diagnosis Methods 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 230000002349 favourable effect Effects 0.000 description 1
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- 239000011810 insulating material Substances 0.000 description 1
- 238000010606 normalization Methods 0.000 description 1
- 229920000642 polymer Polymers 0.000 description 1
- 238000007781 pre-processing Methods 0.000 description 1
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- G01—MEASURING; TESTING
- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
- G01R31/12—Testing dielectric strength or breakdown voltage ; Testing or monitoring effectiveness or level of insulation, e.g. of a cable or of an apparatus, for example using partial discharge measurements; Electrostatic testing
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Abstract
The utility model provides a XLPE cable insulation early deterioration multidimensional evaluation method based on machine learning, which comprises the following steps: integrating the current to construct an XLPE cable alternating current charge test system to obtain the space charge accumulation of the XLPE cable to be tested; constructing an XLPE cable partial discharge monitoring system based on a high-frequency CT sensor to obtain basic discharge parameters of the XLPE cable in a plurality of power frequency periods, integrating the basic discharge parameters in the plurality of power frequency periods into the same power frequency period, and extracting the characteristics of key parameters; and combining the trained characteristic of the space charge accumulation amount and the key parameter of the XLPE cable with the trained multidimensional evaluation model of the early degradation of the XLPE cable to obtain a degradation evaluation result.
Description
Technical Field
The disclosure relates to the technical field of cable insulation diagnosis, in particular to a XLPE cable insulation early deterioration multidimensional evaluator system and method based on machine learning.
Background
The statements in this section merely provide background information related to the present disclosure and may not necessarily constitute prior art.
Crosslinked polyethylene (XLPE) cables are applied in large scale in the power transmission process of a power system, and faults and hidden dangers caused by insulation aging problems and lack of early diagnosis and detection technology of the faults and hidden dangers become bottlenecks which disturb safe, stable and reliable operation of the power system. Moreover, cable lines put into operation at an early stage enter the end of the service life in a successive manner, and due to the shortage of diagnosis and state detection theories and technologies, the cables can continue to have insufficient safe and stable operation criteria, the risk of insulation aging failure is increased day by day, the potential safety hazard of long-term operation is buried, and the economic loss and social influence caused by faults are huge.
Therefore, the state of the cable can be reflected in time in the evaluation of the early insulation deterioration of the cable, and measures are taken to intervene in time according to the evaluation result, but most of the evaluation of the early insulation deterioration of the cable in the prior art is based on the local discharge or the space charge evaluation alone, the early insulation deterioration result of the cable cannot be accurately evaluated, and the actual state of the cable has deviation, so that the multidimensional evaluation method comprehensively considering the space charge and the local discharge has great significance for the fault diagnosis of the XLPE cable.
Disclosure of Invention
The embodiment of the specification aims to provide an XLPE cable insulation early deterioration multidimensional evaluation method and system based on machine learning, space charge and partial discharge characteristics are comprehensively considered, cable state reflection is efficiently achieved based on a machine learning algorithm, an early deterioration result of actual states of a cable and a cable can be accurately evaluated, and the evaluation result is consistent with the actual state of the cable.
The invention provides a multidimensional evaluation system for early insulation degradation of XLPE cables based on machine learning, which is realized by the following technical scheme:
a XLPE cable insulation early deterioration multidimensional evaluation system based on machine learning comprises: the system comprises an XLPE cable space charge testing module, an XLPE cable partial discharge monitoring module and an XLPE cable early deterioration multidimensional evaluation module;
the XLPE cable alternating current charge measurement module is used for measuring the space charge accumulation of the XLPE cable;
the XLPE cable partial discharge monitoring module is used for extracting the characteristics of key parameters by monitoring basic discharge parameters of the XLPE cable in a plurality of power frequency periods;
the XLPE cable early degradation multidimensional evaluation model is used for obtaining a degradation evaluation result according to space charge accumulation and the characteristics of key parameters.
According to the technical scheme, the XLPE cable alternating current charge measuring module is packaged in the metal shielding shell, one end of the XLPE cable alternating current charge measuring module is connected with the high-voltage terminal, the other end of the XLPE cable alternating current charge measuring module is connected with the testing terminal, the high-voltage terminal is connected with the high-voltage power supply, the testing terminal is connected with the equipment to be tested, the XLPE cable alternating current charge measuring module comprises an integrating capacitor, the integrating capacitor is arranged between the high-voltage terminal and the testing terminal, and the integrating capacitor is used for integrating current flowing through the integrating capacitor to obtain space accumulated charge.
According to a further technical scheme, the XLPE cable alternating current charge measurement module further comprises an impedance converter and an analog-to-digital converter, the impedance converter is connected with an integrating capacitor and used for converting accumulated charges obtained by the integrating capacitor into stable voltage, the impedance converter is connected with the analog-to-digital converter, and the analog-to-digital converter is used for converting analog voltage output by the impedance converter into digital voltage.
According to the technical scheme, the XLPE cable partial discharge monitoring module comprises a high-frequency current transformer, a wireless synchronization module and a host, wherein the high-frequency current transformer obtains a partial discharge signal by detecting induced voltage caused by partial current in partial discharge, and the partial discharge signal is uploaded to the host through the wireless synchronization module to be subjected to data collection, processing and analysis.
In a further technical solution, the key parameter characteristics include amplitude, phase and number of parameters.
According to a further technical scheme, the XLPE cable partial discharge monitoring module is used for generating a PRPD spectrogram according to discharge basic parameters in a plurality of power frequency periods, and the PRPD spectrogram is used for integrating the discharge basic parameters in the plurality of power frequency periods into the same power frequency period.
The second aspect of the invention provides a multidimensional evaluation method for the early insulation degradation of an XLPE cable based on machine learning, which is realized by the following technical scheme:
measuring the space charge accumulation amount of the XLPE cable;
monitoring basic discharge parameters of the XLPE cable in a plurality of power frequency periods, integrating the basic discharge parameters in the power frequency periods into the same power frequency period, and extracting the characteristics of key parameters;
and combining the trained characteristic of the space charge accumulation amount and the key parameter of the XLPE cable with the trained multidimensional evaluation model of the early degradation of the XLPE cable to obtain a degradation evaluation result.
According to a further technical scheme, an SVM model is adopted in the XLPE cable early-stage degradation multi-dimensional evaluation model.
According to a further technical scheme, the degradation evaluation result comprises electric aging, thermal aging, water tree aging and a normal state.
A third aspect of the present invention provides a computer device, comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor executes the program to implement the steps of any one of the above-mentioned methods for multidimensional evaluation of early insulation degradation of XLPE cable based on machine learning.
Compared with the prior art, the beneficial effect of this disclosure is:
the method comprehensively considers space charge and partial discharge characteristics, efficiently realizes the cable state reflection based on a machine learning algorithm, and has great application value for promoting the safe and reliable operation of the cable. The evaluation of space charge adopts a charge integration method, and has good effect on shallow and deep trapped charges; the hand-held high-frequency current transformer is adopted for partial discharge, so that the detection is convenient, rapid and accurate; the technology can find and troubleshoot faults in the early stage of the faults, is favorable for improving the power supply reliability of the power transmission line, and greatly reduces the occurrence of power transmission network accidents. Space charge and partial discharge factors are comprehensively considered, an evaluation system is constructed based on an SVM algorithm, the accuracy is greatly improved, and the effective evaluation of the early insulation state of the cable is realized.
Drawings
The accompanying drawings, which are included to provide a further understanding of the disclosure, illustrate embodiments of the disclosure and together with the description serve to explain the disclosure and are not to limit the disclosure.
FIG. 1 is a schematic diagram of an XLPE cable space charge test system according to an embodiment of the present invention;
FIG. 2 is a schematic diagram of an XLPE cable space charge test system according to an embodiment of the present invention;
FIG. 3 is an external view of a high frequency current sensor according to an embodiment of the present invention;
FIG. 4 is a wireless synchronization module appearance of an embodiment of the present invention;
FIG. 5 is an external view of a host of an embodiment of the present invention;
fig. 6 is a multidimensional evaluation model of early degradation of XLPE cables according to an embodiment of the present invention.
Detailed Description
It should be noted that the following detailed description is exemplary and is intended to provide further explanation of the disclosure. Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this disclosure belongs.
It is noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of example embodiments according to the present disclosure. As used herein, the singular forms "a", "an" and "the" are intended to include the plural forms as well, and it should be understood that when the terms "comprises" and/or "comprising" are used in this specification, they specify the presence of stated features, steps, operations, devices, components, and/or combinations thereof, unless the context clearly indicates otherwise.
Example one
The embodiment discloses a multi-dimensional evaluation method for the early insulation degradation of an XLPE cable based on machine learning, and as introduced in the background art, the evaluation of the early insulation degradation of the XLPE cable in the prior art is relatively single, so that the multi-dimensional evaluation method for the early insulation degradation of the XLPE cable based on machine learning is provided, space charge and partial discharge characteristics are comprehensively considered, and the multi-dimensional state reflection of the early insulation degradation of the cable is efficiently realized based on a machine learning algorithm.
Space charge is an important factor causing dielectric aging and even breakdown, the space charge causes local electric field distortion, polymer molecular structure damage is accelerated, defects in cable insulation are increased under the long-term action of an electric field and temperature, accumulated space charge reacts on the dielectric, and the insulation aging speed is accelerated.
The occurrence of partial discharge is a precursor to a destructive failure such as electrical dendrite or breakdown, and is one of the important characteristics of an insulating material of a power cable that undergoes early deterioration.
As shown in fig. 1, the present application provides a multidimensional evaluation system for early insulation degradation of XLPE cable based on machine learning, comprising: the system comprises an XLPE cable space charge testing module, an XLPE cable partial discharge monitoring module and an XLPE cable early deterioration multidimensional evaluation model;
the XLPE cable alternating current charge measurement module is used for measuring the space charge accumulation of the XLPE cable;
the XLPE cable partial discharge monitoring module is used for extracting the characteristics of key parameters by monitoring basic discharge parameters in a plurality of power frequency periods of the XLPE cable;
the XLPE cable early degradation multidimensional evaluation model is used for obtaining a degradation evaluation result according to the space charge accumulation and the characteristics of key parameters.
Fig. 2 is a schematic diagram of an XLPE cable space charge testing system, as shown in fig. 2, the XLPE cable ac charge measuring module is packaged in a metal shielding case, one end of the XLPE cable ac charge measuring module is connected to a high-voltage terminal, the other end of the XLPE cable ac charge measuring module is connected to a testing terminal, a high-voltage power supply is connected to the high-voltage terminal, a device to be tested is connected to the testing terminal, the XLPE cable ac charge measuring module includes an integrating capacitor, an impedance converter, an analog-to-digital converter and a transmitter, the integrating capacitor is disposed between the high-voltage terminal and the testing terminal, and the integrating capacitor is used for integrating current flowing through the integrating capacitor to obtain space accumulated charge. The integration capacitor is used for integrating the current flowing through the integration capacitor to obtain space accumulated charge. The integrating capacitor is connected with an impedance converter, the impedance converter is used for converting accumulated charges Q (t) obtained by the integrating capacitor into stable voltage VQ (t) which is not influenced by external noise, the impedance converter is connected with an analog-to-digital (AD) converter, and the AD converter is used for converting the output analog voltage VQ (t) of the impedance converter into digital voltage VD (t); the transmitter (4) is used for transmitting the digital voltage VD (t) signal to the user terminal through the antenna.
The invention inserts the integrating capacitor between the high-voltage terminal and the detection target, and the current flowing through the integrating capacitor is integrated to further measure the charge accumulation amount of the detection target.
As shown in fig. 3-5, the XLPE cable partial discharge monitoring system includes a High Frequency Current Transformer (HFCT), a wireless synchronization module and a host, where the high frequency current transformer is shown in fig. 3, and it is a method for detecting partial discharge signals of an ac XLPE cable on line by using a ribbon coil as a sensor, when partial discharge occurs inside the ac XLPE cable, the partial current generated by the partial discharge can be decomposed into two components, namely a tangential component along a cable surface and an axial component along the cable, when flowing in a grounding spiral shielding layer of the ac XLPE cable, where the axial component can generate induced voltage on the ribbon sensor surrounding the surface of the XLPE cable, so that the partial discharge detection system can detect the partial discharge signals inside the ac XLPE cable through the induced voltage signals on the ribbon sensor.
Meanwhile, the strip-shaped sensor of the device has a wide signal coupling frequency band bandwidth. The detected partial discharge signal is uploaded to the host computer shown in fig. 5 through the wireless synchronization module shown in fig. 4, and further data collection and analysis processing are performed.
The XLPE cable partial discharge monitoring module is used for generating a PRPD spectrogram according to discharge basic parameters in a plurality of power frequency periods, and the PRPD spectrogram is used for integrating the discharge basic parameters in the plurality of power frequency periods into the same power frequency period.
The system monitors partial discharge signals in XLPE cables to generate PRPD spectrogram which is used for measuring discharge basic parameters (U) in a plurality of power frequency periodsp、
Np) And integrating the parameters into the same power frequency period, and extracting the amplitude, phase and quantity of key parameters.
Example two
The embodiment of the specification provides a multidimensional evaluation method for early insulation degradation of an XLPE cable based on machine learning, which is realized by the following technical scheme:
the method comprises the following steps:
step S1, inserting an integrating Capacitor (CINT) between the high-voltage terminal and the detection target, and obtaining the space charge accumulation amount of the XLPE cable by integrating the current;
the system reflects the accumulation of space charge in the cable in Q (t) -t images by integrating the current. The ratio of the charge value at 300s to the initial time is defined as the space charge injection ratio R ═ Q (t ═ 300s)/Q0, and the space charge injection characteristics can be comprehensively reflected.
Step S2, constructing an XLPE cable partial discharge monitoring system based on the high-frequency CT sensor, and monitoring the XLPE cable to obtain discharge basic parameters (U) of the XLPE cable in a plurality of power frequency periodsp、
Np) And integrating basic discharge parameters in a plurality of power frequency periods into the same power frequency period, and extracting the characteristics of key parameters.
The key parameter characteristics include amplitude, phase and magnitude.
Step S3: and combining the trained characteristic of the space charge accumulation amount and the key parameter of the XLPE cable with the trained multidimensional evaluation model of the early degradation of the XLPE cable to obtain a degradation evaluation result.
Fig. 6 is a multidimensional evaluation model of early deterioration of XLPE cables, and as shown in fig. 6, an SVM model is adopted as the multidimensional evaluation model of early deterioration of XLPE cables.
The process for training the XLPE cable early degradation multidimensional evaluation model comprises the following steps:
inputting and preprocessing data, training a model and debugging parameters, and normally using the model.
Data input for space charge injection ratio (R) and partial discharge characteristic value (U) obtained by space charge and partial discharge test systemp、
Np) Taking the characteristic quantity, and carrying out normalization pretreatment;
and extracting a data rule by combining a specific fault label, dividing 2/3 data into a training set and taking 1/3 data as a test set, and training a model and optimizing parameters.
And finally, taking the parameter with the highest recognition rate as a final parameter for normal use of the early deterioration multi-dimensional evaluation model.
The degradation evaluation results include electrical aging, thermal aging, water tree aging and normal states, the operation state of the cable can be roughly judged according to the distribution characteristics of data under different faults, and the data characteristics are shown in the following table 1.
Table 1 summary of XLPE cable early failure performance characteristics
The multi-dimensional evaluation model for early degradation of the cable based on SVM algorithm training further refines the rule, and corresponding fault detection results can be obtained by directly inputting the trained model into space charge and partial discharge detection data.
The implementation of the specific modules in this embodiment can be referred to in the related art in the first embodiment, and will not be described in detail here.
EXAMPLE III
The embodiment of the specification provides computer equipment, which comprises a memory, a processor and a computer program stored on the memory and capable of running on the processor, wherein the processor executes the program to realize the steps of the XLPE cable insulation early degradation multidimensional evaluation method based on machine learning in the first embodiment.
It is to be understood that throughout the description of the present specification, reference to the term "one embodiment", "another embodiment", "other embodiments", or "first through nth embodiments", etc., is intended to mean that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the present invention. In this specification, the schematic representations of the terms used above do not necessarily refer to the same embodiment or example. Furthermore, the particular features, structures, or materials described may be combined in any suitable manner in any one or more embodiments or examples.
The above description is only a preferred embodiment of the present disclosure and is not intended to limit the present disclosure, and various modifications and changes may be made to the present disclosure by those skilled in the art. Any modification, equivalent replacement, improvement and the like made within the spirit and principle of the present disclosure should be included in the protection scope of the present disclosure.
Claims (10)
1. A XLPE cable insulation early deterioration multidimensional evaluation system based on machine learning is characterized by comprising: the system comprises an XLPE cable space charge testing module, an XLPE cable partial discharge monitoring module and an XLPE cable early deterioration multidimensional evaluation module;
the XLPE cable alternating current charge measurement module is used for measuring the space charge accumulation of the XLPE cable;
the XLPE cable partial discharge monitoring module is used for extracting the characteristics of key parameters by monitoring basic discharge parameters of the XLPE cable in a plurality of power frequency periods;
the XLPE cable early degradation multidimensional evaluation model is used for obtaining a degradation evaluation result according to space charge accumulation and the characteristics of key parameters.
2. The multi-dimensional evaluation system for the early insulation degradation of the XLPE cable based on machine learning as claimed in claim 1, wherein the XLPE ac cable charge measuring module is packaged in a metal shielding case, one end of the XLPE ac cable charge measuring module is connected to a high voltage terminal, the other end of the XLPE ac cable charge measuring module is connected to a test terminal, the high voltage terminal is connected to a high voltage power supply, the test terminal is connected to a device to be tested, the XLPE ac cable charge measuring module comprises an integrating capacitor, the integrating capacitor is disposed between the high voltage terminal and the test terminal, and the integrating capacitor is used for integrating the current flowing through the integrating capacitor to obtain the space accumulated charge.
3. The multi-dimensional evaluation system for the insulation early deterioration of the XLPE cable based on the machine learning as claimed in claim 2, wherein the AC charge measurement module of the XLPE cable further comprises an impedance converter and an analog-to-digital converter, the impedance converter is connected with an integrating capacitor, the impedance converter is used for converting the accumulated charge obtained by the integrating capacitor into a stable voltage, and the impedance converter is connected with the analog-to-digital converter, and the analog-to-digital converter is used for converting the analog voltage output by the impedance converter into a digital voltage.
4. The multi-dimensional evaluation system for the early insulation degradation of the XLPE cable based on machine learning as claimed in claim 1, wherein the XLPE cable partial discharge monitoring module comprises a high frequency current transformer, a wireless synchronization module and a host, the high frequency current transformer obtains a partial discharge signal by detecting an induced voltage caused by a partial current in the partial discharge, and the partial discharge signal is uploaded to the host through the wireless synchronization module for data collection, processing and analysis.
5. The multi-dimensional evaluation system for the early insulation degradation of XLPE cable based on machine learning as claimed in claim 1, wherein said key parameters include amplitude, phase and number of parameters.
6. The multi-dimensional evaluation system for the early insulation degradation of the XLPE cable based on machine learning of claim 1, wherein the XLPE cable partial discharge monitoring module is configured to generate a PRPD spectrogram according to discharge basic parameters in a plurality of power frequency cycles, and the PRPD spectrogram is configured to integrate the discharge basic parameters in the plurality of power frequency cycles into the same power frequency cycle.
7. A XLPE cable insulation early deterioration multidimensional evaluation method based on machine learning is characterized by comprising the following steps:
measuring the space charge accumulation amount of the XLPE cable;
monitoring basic discharge parameters of the XLPE cable in a plurality of power frequency periods, integrating the basic discharge parameters in the power frequency periods into the same power frequency period, and extracting the characteristics of key parameters;
and combining the trained characteristic of the space charge accumulation amount and the key parameter of the XLPE cable with the trained multidimensional evaluation model of the early degradation of the XLPE cable to obtain a degradation evaluation result.
8. The machine learning-based XLPE cable insulation early degradation multidimensional evaluation method of claim 7, wherein the XLPE cable early degradation multidimensional evaluation model employs an SVM model.
9. The machine learning-based XLPE cable insulation early degradation multidimensional evaluation method of claim 7, the degradation evaluation results comprising electrical aging, thermal aging, water tree aging, and normal state.
10. A computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor implements the steps of the method for multidimensional evaluation of early insulation degradation of XLPE cable based on machine learning according to any of claims 7-9 when executing the program.
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