CN112834876B - Cable state detection method, device, equipment and computer readable storage medium - Google Patents

Cable state detection method, device, equipment and computer readable storage medium Download PDF

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CN112834876B
CN112834876B CN202011640335.1A CN202011640335A CN112834876B CN 112834876 B CN112834876 B CN 112834876B CN 202011640335 A CN202011640335 A CN 202011640335A CN 112834876 B CN112834876 B CN 112834876B
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fuzzy
fuzzy inference
partial discharge
cable
state
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CN112834876A (en
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黄雪莜
熊俊
梁倩仪
张宇
谢运华
王良
林创
余伟洲
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Guangzhou Power Supply Bureau of Guangdong Power Grid Co Ltd
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Guangzhou Power Supply Bureau of Guangdong Power Grid Co Ltd
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    • G—PHYSICS
    • 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
    • G01R31/1227—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 of components, parts or materials
    • G01R31/1263—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 of components, parts or materials of solid or fluid materials, e.g. insulation films, bulk material; of semiconductors or LV electronic components or parts; of cable, line or wire insulation
    • G01R31/1272—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 of components, parts or materials of solid or fluid materials, e.g. insulation films, bulk material; of semiconductors or LV electronic components or parts; of cable, line or wire insulation of cable, line or wire insulation, e.g. using partial discharge measurements
    • G—PHYSICS
    • 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
    • G01R31/08—Locating faults in cables, transmission lines, or networks
    • G01R31/081—Locating faults in cables, transmission lines, or networks according to type of conductors
    • G01R31/086—Locating faults in cables, transmission lines, or networks according to type of conductors in power transmission or distribution networks, i.e. with interconnected conductors
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y04—INFORMATION OR COMMUNICATION TECHNOLOGIES HAVING AN IMPACT ON OTHER TECHNOLOGY AREAS
    • Y04S—SYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS
    • Y04S10/00—Systems supporting electrical power generation, transmission or distribution
    • Y04S10/50—Systems or methods supporting the power network operation or management, involving a certain degree of interaction with the load-side end user applications
    • Y04S10/52—Outage or fault management, e.g. fault detection or location

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  • General Physics & Mathematics (AREA)
  • Testing Relating To Insulation (AREA)

Abstract

The application relates to a cable state detection method, a cable state detection device, a cable state detection equipment and a computer readable storage medium. The method comprises the following steps: acquiring partial discharge signals collected by a plurality of sensors corresponding to a plurality of positions on a cable; extracting partial discharge characteristics in the partial discharge signal; and obtaining the state of the cable through a fuzzy inference system according to the extracted partial discharge characteristics. The method comprehensively considers the partial discharge conditions of a plurality of position points on the cable, avoids misjudgment of the cable state when partial positions of the cable are influenced by system fluctuation and the like, improves the anti-interference capability, and further improves the accuracy of cable state judgment by adopting a fuzzy reasoning system.

Description

Cable state detection method, device, equipment and computer readable storage medium
Technical Field
The present disclosure relates to the field of electrical signal detection technologies, and in particular, to a method, an apparatus, a device, and a computer-readable storage medium for detecting a cable status.
Background
The electric energy is one of the most important energy sources at present, the requirement of people on the power supply reliability is increasing day by day, and the reliability of the cable as a tool for transmitting the electric energy also influences the power supply reliability, so that timely finding and removing the cable fault is necessary to ensure the power supply reliability. The electric field concentration of the cable with potential safety hazard or failure at the defect part will cause partial discharge, so the power department often judges the state of the cable by detecting the partial discharge condition on the cable. The existing detection device only can perform simple data acquisition and analysis functions, and has the problem of low accuracy.
Disclosure of Invention
In view of the foregoing, it is desirable to provide a method, an apparatus, a device and a computer readable storage medium for detecting a cable status.
In order to achieve the purpose, the embodiment of the invention adopts the following technical scheme:
on one hand, an embodiment of the present invention provides a method for detecting a cable state, where the method includes:
acquiring partial discharge signals collected by a plurality of sensors corresponding to a plurality of positions on a cable;
extracting partial discharge characteristics in the partial discharge signal;
and obtaining the state of the cable through a fuzzy inference system according to the extracted partial discharge characteristics.
In one embodiment, the partial discharge feature comprises: the intensity of single partial discharge, the number of partial discharges in a preset time and the average interval time of the partial discharges in the preset time.
In one embodiment, the fuzzy inference system includes a first fuzzy inference device and a second fuzzy inference device, and the step of obtaining the state of the cable through the fuzzy inference system based on the extracted partial discharge characteristics specifically includes:
fuzzifying the extracted partial discharge characteristics;
generating a first fuzzy inference result corresponding to each position through the first fuzzy inference device according to the local discharge characteristics after fuzzification processing;
generating a second fuzzy inference result related to the cable state through the second fuzzy inference device according to the first fuzzy inference result corresponding to each position;
defuzzification processing is carried out on the second fuzzy reasoning result to obtain the state of the cable;
the fuzzified partial discharge features comprise a first fuzzy state set corresponding to each partial discharge feature, the first fuzzy inference result corresponding to each position comprises a second fuzzy state set corresponding to each first fuzzy inference result, the second fuzzy inference result comprises a third fuzzy state set, and the first fuzzy state set, the second fuzzy state set and the third fuzzy state set comprise a plurality of fuzzy states.
In one embodiment, the first fuzzy reasoner further includes a first fuzzy inference rule base, where the first fuzzy inference rule base includes a plurality of fuzzy inference rules for generating the first fuzzy inference result, and the plurality of fuzzy inference rules in the first fuzzy inference rule base are set according to the following expression:
R 1k : if it is
Figure GDA0003722834140000021
Is that
Figure GDA0003722834140000022
Figure GDA0003722834140000023
Is that
Figure GDA0003722834140000024
And is
Figure GDA0003722834140000025
Is that
Figure GDA0003722834140000026
Then p is i Is B ik ；
Wherein R is 1k Represented as the k-th fuzzy inference rule in said first fuzzy inference rule base,
Figure GDA0003722834140000027
representing the intensity of a single partial discharge at the ith location on the cable,
Figure GDA0003722834140000028
indicating the cableThe j (th) fuzzy state in the first fuzzy state set corresponding to the intensity of the single partial discharge at the ith position 1 The state of each of the fuzzy states is,
Figure GDA0003722834140000029
representing the number of partial discharges within said preset time at the ith position on said cable,
Figure GDA00037228341400000210
representing the j (th) fuzzy state in the first fuzzy state set corresponding to the partial discharge times in the preset time of the ith position on the cable 2 The state of each of the fuzzy states is,
Figure GDA0003722834140000031
represents an average interval time of partial discharge within the preset time of the ith position on the cable,
Figure GDA0003722834140000032
representing the j (th) fuzzy state in the first fuzzy state set corresponding to the average interval time of partial discharge in the preset time of the ith position on the cable 3 A fuzzy state, p i Representing said first fuzzy inference result, B, corresponding to the ith position on said cable ik Fuzzy states corresponding to the k fuzzy inference rule in the second fuzzy state set corresponding to the first fuzzy inference result representing the ith position of the cable.
In one embodiment, the second fuzzy inference rule base includes a plurality of fuzzy inference rules for generating the second fuzzy inference result, and the plurality of fuzzy inference rules in the second fuzzy inference rule base are set according to the following expression:
R 2k : if p is 1 Is that
Figure GDA0003722834140000033
p 2 Is that
Figure GDA0003722834140000034
.., and p I Is that
Figure GDA0003722834140000035
S is C k ；
Wherein R is 2k Expressed as the k-th fuzzy inference rule, p, in said second fuzzy inference rule base h A first fuzzy inference result representing an h-th location on the cable,
Figure GDA0003722834140000036
the mth fuzzy inference result of the second fuzzy state set corresponding to the first fuzzy inference result representing the h position on the cable h Fuzzy state, h is from 1 to I, I represents the total number of a plurality of selected positions on the cable, s represents the second fuzzy inference result, C k Representing fuzzy states within said third set of fuzzy states corresponding to said k-th fuzzy inference rule.
On the other hand, an embodiment of the present invention further provides a cable state detection apparatus, including:
the data acquisition module is used for acquiring partial discharge signals acquired by a plurality of sensors corresponding to a plurality of positions on the cable;
and the fuzzy inference module is used for extracting the partial discharge characteristics in the partial discharge signals and obtaining the state of the cable according to the extracted partial discharge characteristics.
In another aspect, an embodiment of the present invention further provides a computer device, which includes a memory and a processor, where the memory stores a computer program, and the processor implements the steps of the method described in the foregoing embodiment when executing the computer program.
In another aspect, an embodiment of the present invention further provides a device for detecting a cable status, including: a plurality of sensors and a data processing device;
the sensors are connected with the data processing device and used for acquiring partial discharge signals of a plurality of positions of the cable;
the data processing apparatus comprises a memory and a processor, the memory storing a computer program, the processor implementing the following steps when executing the computer program: and extracting the partial discharge characteristics in the partial discharge signals, and obtaining the state of the cable through a fuzzy inference system according to the partial discharge characteristics.
In one embodiment, the plurality of sensors includes a high frequency current sensor.
In still another aspect, the present invention further provides a computer-readable storage medium, on which a computer program is stored, where the computer program, when executed by a processor, implements the steps of the method described in the foregoing embodiments.
One of the above technical solutions has the following advantages and beneficial effects:
according to the method, the device, the equipment and the computer-readable storage medium for detecting the cable state, the partial discharge characteristics in the partial discharge signals are extracted based on the partial discharge signals acquired by the multi-sensor, the state of the cable is acquired through the fuzzy inference system, the partial discharge conditions of a plurality of position points on the cable are comprehensively considered, the misjudgment of the cable state when partial positions of the cable are influenced by system fluctuation and the like is avoided, the anti-interference capability is improved, and the accuracy of the cable state judgment is further improved by adopting the fuzzy inference system.
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In order to more clearly illustrate the technical solutions in the embodiments or the conventional technologies of the present application, the drawings used in the descriptions of the embodiments or the conventional technologies will be briefly introduced below, it is obvious that the drawings in the following descriptions are only some embodiments of the present application, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
FIG. 1 is a schematic flow chart of a cable condition detection method according to one embodiment;
FIG. 2 is a schematic diagram of a cable status acquisition process according to one embodiment;
FIG. 3 is a block diagram of a cable detection device according to an embodiment;
FIG. 4 is a block diagram of a fuzzy inference module in the cable detection apparatus according to an embodiment;
fig. 5 is a schematic hardware configuration diagram of the cable detection apparatus in one embodiment.
Detailed Description
To facilitate an understanding of the present application, the present application will now be described more fully with reference to the accompanying drawings. Embodiments of the present application are set forth in the accompanying drawings. This application may, however, be embodied in many different forms and should not be construed as limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete.
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 application belongs. The terminology used herein in the description of the present application is for the purpose of describing particular embodiments only and is not intended to be limiting of the application.
It will be understood that when an element is referred to as being "connected" to another element, it can be directly connected to the other element or be connected to the other element through intervening elements. Further, "connection" in the following embodiments is understood to mean "electrical connection", "communication connection", or the like, if there is a transfer of electrical signals or data between the connected objects.
As used herein, the singular forms "a", "an" and "the" may include the plural forms as well, unless the context clearly indicates otherwise. It will be further understood that the terms "comprises/comprising," "includes" or "including," etc., specify the presence of stated features, integers, steps, operations, components, parts, or combinations thereof, but do not preclude the presence or addition of one or more other features, integers, steps, operations, components, parts, or combinations thereof. Also, as used in this specification, the term "and/or" includes any and all combinations of the associated listed items.
In one embodiment, as shown in fig. 1, there is provided a method for detecting a cable status, including the following steps S12 to S16:
and S12, acquiring partial discharge signals collected by a plurality of sensors corresponding to a plurality of positions on the cable.
It can be understood that the partial discharge phenomenon is most obvious at the cable fault, and the most common cable fault is a cable joint fault, so the sensors can be, but are not limited to, arranged at the cable joint, and can transmit the detected partial discharge signal to a server for on-line monitoring of the power department, and can also be arranged on live detection equipment of the cable, and a worker can detect the partial discharge signal on the cable by operating the live detection equipment. In addition, partial discharge does not occur continuously on the cable, and when partial discharge does not occur, signals collected by the plurality of sensors do not contain effective signals related to the partial discharge, so that the signals transmitted by the sensors need to be preprocessed, and the preprocessing process can include a process of filtering noise such as wavelet transformation and the like, and signals related to the partial discharge with high signal-to-noise ratio are obtained from the signals.
And S14, extracting the partial discharge characteristics in the partial discharge signal.
It is understood that the signal collected by the sensor is a continuous signal composed of infinite points, and some physical quantities which can represent the characteristics of the partial discharge are most valuable for analyzing the partial discharge, and can be extracted to facilitate subsequent data analysis and other steps, and these physical quantities with analytical value are referred to as the partial discharge characteristics, including but not limited to the discharge intensity, phase, power, frequency, average interval and the like of the partial discharge.
And S16, obtaining the state of the cable through a fuzzy inference system according to the extracted partial discharge characteristics.
It can be understood that the fuzzy inference system is different from the traditional logic system in the concept of judging the input states, in the traditional logic, an input in a certain state is deterministic, in the fuzzy inference system, the state of the input is non-deterministic, the fuzzy inference system considers all possible states of the input, a membership function is adopted to define the membership of the input in each state, the value range of the membership is [0,1], the closer the membership of a certain state is to 1, the greater the degree of the input belonging to the state is proved, and the fuzzy inference system judges the input states based on the membership of the input in each state. The membership functions include, but are not limited to, triangular membership functions, trapezoidal membership functions, and gaussian membership functions.
The method and the device have the advantages that the partial discharge signals acquired by the multiple sensors are used for extracting the partial discharge characteristics in the partial discharge signals, the state of the cable is acquired through the fuzzy inference system, the partial discharge conditions of multiple position points on the cable are comprehensively considered, misjudgment of the state of the cable when partial positions of the cable are affected by system fluctuation and the like is avoided, the anti-interference capacity is improved, the cable state is judged deterministically based on a traditional logic system in combination with an actual engineering scene, the boundary between a fault state and a normal state of the cable is difficult to judge, the fuzzy inference system is used for judging, and the judgment accuracy can be further improved.
In one embodiment, the partial discharge characteristic mentioned in the above method includes: the intensity of single partial discharge, the number of partial discharges in a preset time and the average interval time of the partial discharges in the preset time.
It is understood that the intensity of a single partial discharge includes, but is not limited to, the maximum current amplitude of the partial discharge pulse. If a local discharge phenomenon with high intensity frequently occurs at a certain position on the cable, the probability of the position having a fault is high, and the state of the detected position point can be well evaluated by taking the intensity of single local discharge, the number of times of local discharge within the preset time and the average interval time of the local discharge within the preset time as the input of the fuzzy inference system.
In one embodiment, the fuzzy inference system includes a first fuzzy reasoner, a second fuzzy reasoner.
As shown in fig. 2, the step S16 can be specifically realized by the following steps:
and S162, blurring the extracted partial discharge characteristics.
It can be understood that the fuzzification processing can obtain the membership of each fuzzy state in a preset fuzzy state set of the partial discharge characteristics by inputting the value of the partial discharge characteristics into a preset membership function, and the fuzzy state set is a set of the states input after fuzzification. For example, the fuzzy state set of each partial discharge characteristic includes three fuzzy states of high, medium and low, and the intensity of the single partial discharge is input into a preset membership function, so that the input intensity of the single partial discharge belonging to the high, medium and low degrees can be obtained. The setting of the related parameters in the membership function can be continuously updated through a neural network or deep learning and other modes according to historical data, so that the subjectivity caused by manually setting the parameters is avoided.
And S164, generating a first fuzzy inference result corresponding to each position through a first fuzzy inference device according to the local discharge characteristics after the fuzzification processing.
It can be understood that the first fuzzy inference device synthesizes each partial discharge characteristic of each position of a plurality of positions on the cable, carries out inference judgment on the state of a single position, generates a first fuzzy inference result, and obtains the fuzzy state set of each position and the membership corresponding to each fuzzy state in the fuzzy state set. For example, the first fuzzy inference result is defined as the significance of the partial discharge, the fuzzy state set of the first fuzzy inference result is set to include three fuzzy states of strong, medium and weak, and when the feature of the partial discharge at a certain position after fuzzification processing is input into the first fuzzy inference device, the significance of the partial discharge at the certain position can be generated to belong to the degrees of strong, medium and weak.
And S166, generating a second fuzzy inference result related to the cable state through a second fuzzy inference device according to the first fuzzy inference result corresponding to each position.
It can be understood that after the first fuzzy inference results of a plurality of positions are obtained respectively, in order to avoid that a strong partial discharge signal happens when a part of positions are interfered and a cable fault is misjudged, the state of the cable in the detected section needs to be judged by combining the results of the plurality of positions, so that the purpose is achieved by arranging a second-layer fuzzy inference device.
And S168, defuzzifying the second fuzzy inference result to obtain the state of the cable.
It can be understood that the fuzzy reasoning result includes the fuzzy state set and the membership degree corresponding to each fuzzy state in the fuzzy state set, and an objective evaluation on the cable state can be obtained only by adopting a defuzzification processing method. For example, the defuzzification processing is performed by using a maximum membership method, and when the fuzzy state set of the cable state is set to include three fuzzy states of health, hidden danger and fault, the fuzzy state with the maximum membership is selected as the current state of the detected cable. Methods of defuzzification also include, but are not limited to, maximum mean, area average, and center of gravity.
The fuzzy partial discharge features comprise a first fuzzy state set corresponding to each partial discharge feature, the first fuzzy inference result corresponding to each position comprises a second fuzzy state set corresponding to the first fuzzy inference result, the second fuzzy inference result comprises a third fuzzy state set, and the first fuzzy state set, the second fuzzy state set and the third fuzzy state set comprise a plurality of fuzzy states.
In the embodiment, the fuzzy inference system is divided into at least two layers to perform a fuzzy inference process, partial discharge signals obtained by a plurality of sensors are integrated to obtain the state of each position of a plurality of positions of the cable, and the state of each position is integrated to evaluate the state of the cable, so that the reliability of fuzzy inference is improved.
In one embodiment, the first fuzzy inference engine further includes a first fuzzy inference rule base, the first fuzzy inference rule base includes a plurality of fuzzy inference rules for generating the first fuzzy inference result, and the plurality of fuzzy inference rules in the first fuzzy inference rule base are set according to the following expression:
R 1k : if it is
Figure GDA0003722834140000091
Is that
Figure GDA0003722834140000092
Is that
Figure GDA0003722834140000093
And is
Figure GDA0003722834140000094
Is that
Figure GDA0003722834140000095
Then p is i Is B ik ；
Wherein R is 1k Represented as the k-th fuzzy inference rule in said first fuzzy inference rule base,
Figure GDA0003722834140000096
representing the intensity of a single partial discharge at the ith location on the cable,
Figure GDA0003722834140000097
representing the j (th) fuzzy state in the first fuzzy state set corresponding to the intensity of the single partial discharge at the i (th) position on the cable 1 The state of each of the fuzzy states is,
Figure GDA0003722834140000098
indicating the number of partial discharges within a preset time at the ith position on the cable,
Figure GDA0003722834140000099
representing the j-th fuzzy state in the first fuzzy state set corresponding to the number of partial discharges in the preset time of the ith position on the cable 2 The state of each of the fuzzy states is,
Figure GDA00037228341400000910
represents the average interval time of partial discharge in the preset time of the ith position on the cable,
Figure GDA00037228341400000911
a second position on the cable corresponding to the average interval time of partial discharge within a preset timeJ in a fuzzy state set 3 A fuzzy state, p i Representing said first fuzzy inference result, B, corresponding to the ith position on said cable ik Fuzzy states corresponding to the k-th fuzzy inference rule within the second set of fuzzy states representing the first fuzzy inference result corresponding to the ith position of the cable.
Specifically, in one embodiment, the partial discharge characteristics are selected as a single partial discharge intensity, a number of partial discharges within a preset time, and an average interval of partial discharges within a preset time, and, for the ith position,
Figure GDA0003722834140000101
the intensity of a single partial discharge is indicated,
Figure GDA0003722834140000102
indicating the number of partial discharges within a preset time,
Figure GDA0003722834140000103
the first fuzzy state sets corresponding to the intensity of single partial discharge, the frequency of partial discharge in the preset time and the average interval time of partial discharge in the preset time respectively comprise two fuzzy states of high H and low L, and p i The first fuzzy inference result corresponding to the ith position is represented, a second fuzzy state set of the first fuzzy inference result comprises three fuzzy states of strong S, medium M and weak W, and the fuzzy inference rule in the first fuzzy rule base comprises the following expressions:
(1) if (a)
Figure GDA0003722834140000104
Is H) and (
Figure GDA0003722834140000105
Is L) and
Figure GDA0003722834140000106
is L) then (p) i Is W)
(2) If (a)
Figure GDA0003722834140000107
Is H) and (
Figure GDA0003722834140000108
Is L) and
Figure GDA0003722834140000109
is H) then (p) i Is W)
(3) If (a)
Figure GDA00037228341400001010
Is H) and (
Figure GDA00037228341400001011
Is H) and (
Figure GDA00037228341400001012
Is L) then (p) i Is S)
(4) If (a)
Figure GDA00037228341400001013
Is H) and (
Figure GDA00037228341400001014
Is H) and (
Figure GDA00037228341400001015
Is H) then (p) i Is S)
(5) If (a)
Figure GDA00037228341400001016
Is L) and
Figure GDA00037228341400001017
is L) and
Figure GDA00037228341400001018
is L) then (p) i Is M)
(6) If (a)
Figure GDA00037228341400001019
Is L) and
Figure GDA00037228341400001020
is L) and
Figure GDA00037228341400001021
is H) then (p) i Is W)
(7) If (a)
Figure GDA00037228341400001022
Is L) and
Figure GDA00037228341400001023
is H) and (
Figure GDA00037228341400001024
Is L) then (p) i Is M)
(8) If (a)
Figure GDA00037228341400001025
Is L) and
Figure GDA00037228341400001026
is H) and (
Figure GDA00037228341400001027
Is H) then (p) i Is W)
In one embodiment, the second fuzzy inference engine further includes a second fuzzy inference rule base, the second fuzzy inference rule base includes a plurality of fuzzy inference rules for generating the second fuzzy inference result, and the plurality of fuzzy inference rules in the second fuzzy inference rule base are set according to the following expression:
R 2k : if p is 1 Is that
Figure GDA0003722834140000111
p 2 Is that
Figure GDA0003722834140000112
.., and p I Is that
Figure GDA0003722834140000113
S is C k ；
Wherein R is 2k Expressed as the k-th fuzzy inference rule, p, in said second fuzzy inference rule base h A first fuzzy inference result representing an h-th location on the cable,
Figure GDA0003722834140000114
the mth fuzzy inference result of the second fuzzy state set corresponding to the first fuzzy inference result representing the h position on the cable h Fuzzy state, h is from 1 to I, I represents the total number of a plurality of selected positions on the cable, s represents the second fuzzy inference result, C k Representing fuzzy states within said third set of fuzzy states corresponding to said k-th fuzzy inference rule.
Specifically, in one embodiment, there are 3 sensors, the partial discharge characteristics are selected as the intensity of a single partial discharge, the number of partial discharges within a preset time, and the average interval of the partial discharges within a preset time, and for the ith position,
Figure GDA0003722834140000115
which indicates the intensity of a single partial discharge,
Figure GDA0003722834140000116
indicating the number of partial discharges within a preset time,
Figure GDA0003722834140000117
the first fuzzy state sets corresponding to the intensity of single partial discharge, the frequency of partial discharge in the preset time and the average interval time of partial discharge in the preset time respectively comprise two fuzzy states of high H and low L, and p 1 Representing a first fuzzy inference result, p, corresponding to the 1 st sensor 2 Representing a first fuzzy inference result, p, corresponding to the 2 nd sensor 3 Representing a first fuzzy inference result corresponding to the 3 rd sensor, the first fuzzy inferenceThe second fuzzy state set of the rational result comprises three fuzzy states of strong S, medium M and weak W, wherein S represents the second fuzzy reasoning result of the detected cable, and the third fuzzy state set of the second fuzzy reasoning result comprises three fuzzy states of health FH, hidden danger FM and fault FL.
(1) If (p) 1 Is W) and (p) 2 Is W) and (p) 3 Is W) then (s is FH)
(2) If (p) 1 Is W) and (p) 2 Is W) and (p) 3 Is M) then (s is FM)
(3) If (p) 1 Is W) and (p) 2 Is M) and (p) 3 Is W) then (s is FM)
(4) If (p) 1 Is W) and (p) 2 Is M) and (p) 3 Is M) then (s is FL)
(5) If (p) 1 Is M) and (p) 2 Is W) and (p) 3 Is W) then (s is FM)
(6) If (p) 1 Is M) and (p) 2 Is W) and (p) 3 Is M) then (s is FL)
(7) If (p) 1 Is M) and (p) 2 Is M) and (p) 3 Is W) then (s is FM)
(8) If (p) 1 Is M) and (p) 2 Is M) and (p) 3 Is M) then (s is FL)
The above embodiments are merely illustrative and not restrictive, and the number of the fuzzy rules in the first fuzzy rule base and the second fuzzy rule base and the conditions and results of the fuzzy rules may be adjusted according to actual engineering.
It should be understood that although the various steps in the flowcharts of fig. 1-2 are shown in order as indicated by the arrows, the steps are not necessarily performed in order as indicated by the arrows. The steps are not performed in the exact order shown and described, and may be performed in other orders, unless explicitly stated otherwise. Moreover, at least some of the steps in fig. 1-2 may include multiple steps or multiple stages, which are not necessarily performed at the same time, but may be performed at different times, which are not necessarily performed in sequence, but may be performed in turn or alternately with other steps or at least some of the other steps or stages.
In one embodiment, as shown in fig. 3, there is also provided a cable status detecting apparatus 100, including: a data acquisition module 110 and a fuzzy inference module 120, wherein:
the data acquisition module 110 is configured to acquire partial discharge signals collected by a plurality of sensors corresponding to a plurality of positions on the cable. The fuzzy inference module 120 is configured to extract a partial discharge feature in the partial discharge signal, and obtain a state of the cable according to the extracted partial discharge feature.
In one embodiment, as shown in fig. 4, the fuzzy inference module 120 further comprises: a fuzzification submodule 120A, a first fuzzy inference submodule 120B, a second fuzzy inference submodule 120C, and a defuzzification submodule 120D.
The blurring sub-module 120A is configured to perform blurring processing on the extracted partial discharge characteristics. The first fuzzy inference submodule 120B is configured to generate a first fuzzy inference result corresponding to each position according to the local discharge feature after the fuzzification processing. The second fuzzy inference submodule 120C is configured to generate a second fuzzy inference result about the cable status according to the first fuzzy inference result corresponding to each location. The defuzzification submodule 120D is configured to perform defuzzification processing on the second fuzzy inference result to obtain a state of the cable.
For specific limitations of the cable status detection device, reference may be made to the above limitations of the cable status detection method, which are not described herein again. The modules in the cable state detection device can be wholly or partially realized by software, hardware and a combination thereof. The modules can be embedded in a hardware form or independent from a processor in the computer device, and can also be stored in a memory in the computer device in a software form, so that the processor can call and execute operations corresponding to the modules. It should be noted that, in the embodiment of the present application, the division of the module is schematic, and is only one logic function division, and there may be another division manner in actual implementation.
In an embodiment, there is also provided a computer device comprising a memory and a processor, the memory having stored therein a computer program, the processor implementing the steps of the method of any of the above embodiments when executing the computer program.
In one embodiment, as shown in fig. 5, there is also provided a cable status detection apparatus 300 comprising a plurality of sensors 30 and a data processing device 50.
The plurality of sensors 30 are used to collect partial discharge signals at a plurality of locations on the cable. The data processing device 50 comprises a memory 50A in which a computer program is stored and a processor 50B which, when executing the computer program, implements the steps of:
and extracting the partial discharge characteristics in the partial discharge signals, and obtaining the state of the cable through a fuzzy inference system according to the partial discharge characteristics.
In one embodiment, the plurality of sensors 30 includes high frequency current sensors.
In an embodiment, a computer-readable storage medium is also provided, on which a computer program is stored, which, when being executed by a processor, carries out the steps of the method of any of the above embodiments.
It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above can be implemented by hardware instructions of a computer program, which can be stored in a non-volatile computer-readable storage medium, and when executed, can include the processes of the embodiments of the methods described above. Any reference to memory, storage, database or other medium used in the embodiments provided herein can include at least one of non-volatile and volatile memory. Non-volatile Memory may include Read-Only Memory (ROM), magnetic tape, floppy disk, flash Memory, optical storage, or the like. Volatile Memory can include Random Access Memory (RAM) or external cache Memory. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM), for example.
In the description herein, references to the description of "some embodiments," "other embodiments," "desired embodiments," etc., 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 invention. In this specification, a schematic description of the above terminology may not necessarily refer to the same embodiment or example.
The technical features of the above embodiments can be arbitrarily combined, and for the sake of brevity, all possible combinations of the technical features in the above embodiments are not described, but should be considered as the scope of the present specification as long as there is no contradiction between the combinations of the technical features.
The above-mentioned embodiments only express several embodiments of the present application, and the description thereof is more specific and detailed, but not construed as limiting the scope of the invention. It should be noted that, for a person skilled in the art, several variations and modifications can be made without departing from the concept of the present application, which falls within the scope of protection of the present application. Therefore, the protection scope of the present patent shall be subject to the appended claims.

Claims (8)

1. A method of detecting a condition of a cable, the method comprising:
acquiring partial discharge signals collected by a plurality of sensors corresponding to a plurality of positions on a cable;
extracting partial discharge characteristics in the partial discharge signal; the partial discharge characteristics comprise the intensity of single partial discharge, the frequency of partial discharge within preset time and the average interval time of partial discharge within the preset time;
obtaining the state of the cable through a fuzzy reasoning system according to the extracted partial discharge characteristics; the fuzzy inference system comprises a first fuzzy inference device and a second fuzzy inference device, and the step of obtaining the state of the cable through the fuzzy inference system according to the extracted partial discharge characteristics specifically comprises the following steps:
fuzzifying the extracted partial discharge characteristics;
generating a first fuzzy inference result corresponding to each position through the first fuzzy inference device according to the local discharge characteristics after the fuzzification processing;
generating a second fuzzy inference result related to the cable state through the second fuzzy inference device according to the first fuzzy inference result corresponding to each position;
defuzzification processing is carried out on the second fuzzy reasoning result to obtain the state of the cable;
the fuzzified partial discharge features comprise a first fuzzy state set corresponding to each partial discharge feature, the first fuzzy inference result corresponding to each position comprises a second fuzzy state set corresponding to each first fuzzy inference result, the second fuzzy inference result comprises a third fuzzy state set, and the first fuzzy state set, the second fuzzy state set and the third fuzzy state set comprise a plurality of fuzzy states.
2. The method for detecting the cable status according to claim 1, wherein the first fuzzy inference engine further comprises a first fuzzy inference rule base, the first fuzzy inference rule base comprises a plurality of fuzzy inference rules for generating the first fuzzy inference result, and the plurality of fuzzy inference rules in the first fuzzy inference rule base are set according to the following expression:
R 1k : if it is
Figure FDA0003722834130000021
Is that
Figure FDA0003722834130000022
Figure FDA0003722834130000023
Is that
Figure FDA0003722834130000024
And is
Figure FDA0003722834130000025
Is that
Figure FDA0003722834130000026
Then p is i Is B ik ；
Wherein R is 1k Represented as the k-th fuzzy inference rule in said first fuzzy inference rule base,
Figure FDA0003722834130000027
representing the intensity of a single partial discharge at the ith location on the cable,
Figure FDA0003722834130000028
representing the j (th) fuzzy state in the first fuzzy state set corresponding to the intensity of the single partial discharge at the i (th) position on the cable 1 The state of each of the fuzzy states is,
Figure FDA0003722834130000029
representing the number of partial discharges within said preset time at the ith position on said cable,
Figure FDA00037228341300000210
representing the j (th) fuzzy state in the first fuzzy state set corresponding to the partial discharge times in the preset time of the ith position on the cable 2 The state of each of the fuzzy states is,
Figure FDA00037228341300000211
represents an average interval time of partial discharge within the preset time of the ith position on the cable,
Figure FDA00037228341300000212
representing the j (th) fuzzy state in the first fuzzy state set corresponding to the average interval time of partial discharge in the preset time of the ith position on the cable 3 A fuzzy state, p i Representing said first fuzzy inference result, B, corresponding to the ith position on said cable ik Fuzzy states corresponding to the k fuzzy inference rule in the second fuzzy state set corresponding to the first fuzzy inference result representing the ith position of the cable.
3. The method for detecting the cable status according to claim 1, wherein a plurality of fuzzy inference rules are included in the second fuzzy inference rule base for generating the second fuzzy inference result, and the plurality of fuzzy inference rules in the second fuzzy inference rule base are set according to the following expression:
R 2k : if p is 1 Is that
Figure FDA00037228341300000213
p 2 Is that
Figure FDA00037228341300000214
.., and p I Is that
Figure FDA00037228341300000215
S is C k ；
Wherein R is 2k Expressed as the k-th fuzzy inference rule, p, in said second fuzzy inference rule base h A first fuzzy inference result representing an h-th location on the cable,
Figure FDA00037228341300000216
the mth fuzzy inference result of the second fuzzy state set corresponding to the first fuzzy inference result representing the h position on the cable h Fuzzy state, h is from 1 to I, I represents the total number of a plurality of selected positions on the cable, s represents the second fuzzy inference result, C k Is indicated in the third ambiguityFuzzy states in the state set corresponding to the k-th fuzzy inference rule.
4. A cable condition detection device, comprising:
the data acquisition module is used for acquiring partial discharge signals acquired by a plurality of sensors corresponding to a plurality of positions on the cable; the partial discharge characteristics comprise the intensity of single partial discharge, the number of partial discharge within preset time and the average interval time of partial discharge within the preset time;
the fuzzy inference module is used for extracting the partial discharge characteristics in the partial discharge signals and obtaining the state of the cable through a fuzzy inference system according to the extracted partial discharge characteristics; the fuzzy inference system comprises a first fuzzy inference device and a second fuzzy inference device, and the obtaining of the state of the cable through the fuzzy inference system according to the extracted partial discharge characteristics comprises:
fuzzifying the extracted partial discharge characteristics;
generating a first fuzzy inference result corresponding to each position through the first fuzzy inference device according to the local discharge characteristics after the fuzzification processing;
generating a second fuzzy inference result related to the cable state through the second fuzzy inference device according to the first fuzzy inference result corresponding to each position;
defuzzification processing is carried out on the second fuzzy reasoning result to obtain the state of the cable;
the fuzzified partial discharge features comprise a first fuzzy state set corresponding to each partial discharge feature, the first fuzzy inference result corresponding to each position comprises a second fuzzy state set corresponding to each first fuzzy inference result, the second fuzzy inference result comprises a third fuzzy state set, and the first fuzzy state set, the second fuzzy state set and the third fuzzy state set comprise a plurality of fuzzy states.
5. A computer device comprising a memory and a processor, the memory storing a computer program, characterized in that the processor realizes the steps of the method of any one of claims 1 to 3 when executing the computer program.
6. An apparatus for detecting a condition of a cable, comprising: a plurality of sensors and a data processing device;
the sensors are connected with the data processing device and used for acquiring partial discharge signals of a plurality of positions of the cable;
the data processing apparatus comprises a memory and a processor, the memory storing a computer program, the processor implementing the following steps when executing the computer program: extracting partial discharge characteristics in the partial discharge signals, and obtaining the state of the cable through a fuzzy inference system according to the partial discharge characteristics; the partial discharge characteristics comprise the intensity of single partial discharge, the frequency of partial discharge within preset time and the average interval time of partial discharge within the preset time;
the fuzzy inference system comprises a first fuzzy inference device and a second fuzzy inference device, and the step of obtaining the state of the cable through the fuzzy inference system according to the extracted partial discharge characteristics specifically comprises the following steps:
fuzzifying the extracted partial discharge characteristics;
generating a first fuzzy inference result corresponding to each position through the first fuzzy inference device according to the local discharge characteristics after the fuzzification processing;
generating a second fuzzy inference result related to the cable state through the second fuzzy inference device according to the first fuzzy inference result corresponding to each position;
defuzzification processing is carried out on the second fuzzy reasoning result to obtain the state of the cable;
the fuzzified partial discharge features comprise a first fuzzy state set corresponding to each partial discharge feature, the first fuzzy inference result corresponding to each position comprises a second fuzzy state set corresponding to each first fuzzy inference result, the second fuzzy inference result comprises a third fuzzy state set, and the first fuzzy state set, the second fuzzy state set and the third fuzzy state set comprise a plurality of fuzzy states.
7. The detection apparatus of claim 6, wherein the plurality of sensors comprises high frequency current sensors.
8. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the steps of the method of any one of claims 1 to 3.
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