Intelligent cable detection method and system

Abstract

The invention discloses an intelligent cable detection method and system, and relates to the technical field of cable detection, and the method comprises the steps: installing multi-frequency electromagnetic wave sensors at the key positions of a connector and a middle section of a cable, and carrying out the position marking after determining that the cable has a fault preliminarily; detecting external conditions, including environmental conditions, of the cable at the marked position, combining historical data of the cable, and inputting the detected external environmental data and historical data as information into a cable insulation material degradation prediction model; and through calculation of the prediction model, whether the insulation material of the cable has an aging phenomenon or not and the degradation degree are predicted. A closed-loop decision support system is provided, multi-frequency signal analysis and degradation prediction are combined, operation and maintenance personnel can be helped to carry out scientific maintenance plans and early warning in advance, the probability of occurrence of cable faults is reduced, and the service life of the cable is prolonged.

Description

Technical Field

[0001] The present invention relates to the technical field of cable detection, and in particular to an intelligent cable detection method and system thereof. Background Art

[0002] With the continuous development of power facilities and the continuous expansion of the power grid, the cable lines in the power system are taking on more and more important tasks of power transmission. In the long-term use of cables, affected by environmental changes, load fluctuations, aging and other factors, they are prone to insulation material degradation, loose joints, metal corrosion and other faults. Traditional cable detection methods mainly rely on manual inspections and regular inspections, but these methods often have shortcomings such as long detection cycles, low efficiency, and inability to monitor in real time, making it difficult to detect hidden dangers in cables in a timely manner.

[0003] At present, there are also some cable detection technologies at home and abroad, such as electromagnetic wave detection method, infrared imaging detection method, laser scanning detection method, etc. Although these technologies can improve the detection accuracy of cable faults to a certain extent, they still face some limitations. For example, traditional electromagnetic wave detection technology cannot accurately identify different types of cable defects, and it is difficult to monitor the aging degree of cables in real time; although infrared imaging and laser scanning technologies can effectively detect temperature and surface defects, they cannot deeply understand the fault details and degradation trends inside the cable.

[0004] Therefore, in order to solve the problems of efficient and accurate detection and aging prediction of cables, a new detection method is urgently needed that can accurately identify various defects of cables in a shorter time, obtain changes in the environment around the cables in real time, and make accurate aging predictions based on historical data, thereby improving the intelligence level of cable detection. Summary of the invention

[0005] The purpose of this section is to summarize some aspects of embodiments of the present invention and briefly introduce some preferred embodiments. Some simplifications or omissions may be made in this section and the specification abstract and the invention title of this application to avoid blurring the purpose of this section, the specification abstract and the invention title, and such simplifications or omissions cannot be used to limit the scope of the present invention.

[0006] In view of the above problems existing in the existing intelligent cable detection method and system, the present invention is proposed.

[0007] In order to solve the above technical problems, the present invention provides the following technical solutions: an intelligent cable detection method, comprising the following steps:

[0008] Step 1: Install multi-frequency electromagnetic wave sensors at the joints and key locations in the middle of the cable, and use electromagnetic waves of different frequencies of the sensor to scan the cable. When the electromagnetic waves encounter defects inside the cable, they will be reflected, and the sensor will receive these reflected signals;

[0009] Step 2: The received reflected signal will be sent by the sensor to the background system for analysis. By processing signals of different frequencies, it is determined that the cable is initially faulty and the position is marked;

[0010] Step 3: Detect the external conditions of the cable at the marked position, including the environmental conditions, and combine the historical data of the cable, including previous inspection records, maintenance history, and service life, and input the detected external environmental data and historical data as information into the cable insulation material degradation prediction model;

[0011] Through the calculation of the prediction model, it is predicted whether the insulation material of the cable is aging and the degree of deterioration.

[0012] As a preferred solution of the intelligent cable detection method described in the present invention, in the step 2, the integrated signal response value P of the defect position is identified. i To determine the specific defect location i, the larger the value, the more significant the defect signal here;

[0013] Among them, the comprehensive signal response value P i =S i ·A i ;

[0014] S i Used to define the signal characteristic function, A i Used to define the signal attenuation function, the expressions are:

[0015]

[0016] Among them, f i represents the signal frequency, x represents the environmental variable factor, α, β represent the model calibration parameters, which are used to control the weight of the characteristic function, and R i The reflected signal intensity at defect position i, μ represents the average value of the signal intensity, and σ represents the historical fluctuation standard deviation of defect position i.

[0017] As a preferred solution of the intelligent cable detection method described in the present invention, in step 3, the cable surrounding environmental conditions include: temperature T i 、Humidity i And the current load L i , after normalization, we get the environmental variable factor x i , expressed as:

[0018]

[0019] Among them, T max , H max , Lmax The maximized reference value of the variable represented by .

[0020] As a preferred solution of the intelligent cable detection method described in the present invention, in step 3, the historical data of the cable includes: the number of maintenance times N repair and service life T age , define the historical data degradation factor E i , the expression is:

[0021]

[0022] Among them, γ1 represents the influence weight of the number of maintenance times on the degradation factor, γ2 represents the influence weight of the service life on the degradation factor, and γ3 represents the basic balance weight of the degradation factor.

[0023] As a preferred solution of the intelligent cable detection method described in the present invention, wherein: combining the environmental factor x i and historical data factor E i Together as the input of the degradation model: H i =x i ln(1+t)+E i ;

[0024] Among them, t is time, and ln(1+t) represents the time cumulative effect of environmental degradation.

[0025] As a preferred solution of the intelligent cable detection method described in the present invention, in step 3, a comprehensive degradation prediction formula D(t) is defined, and its expression is:

[0026]

[0027] Where T represents the total length of the detection time, w i It represents the defect location weight. The closer D(t) is to 0, the milder the insulation degradation is. The closer D(t) is to 1, the more serious the insulation degradation is.

[0028] As a preferred solution of the intelligent cable detection method described in the present invention, wherein: if the comprehensive comprehensive prediction value D(t) exceeds a threshold value indicating that the insulation material is deteriorating to a high degree, the location is recorded as a high-risk area;

[0029] If the comprehensive comprehensive prediction value D(t) is lower than the threshold, indicating that the insulation material is less degraded, but the detection signal has abnormal fluctuations, then return to the preliminary fault location stage and recalibrate the reflection signal characteristics;

[0030] If the comprehensive comprehensive prediction value D(t) completely matches the historical data and environmental conditions, the prediction result is confirmed to be reliable, and the threshold range is adjusted for subsequent similar data to improve the prediction efficiency.

[0031] The detection system of the intelligent cable detection method as described above includes: a cable signal acquisition module, which scans the cable joints and key positions in the middle section through electromagnetic waves of different frequencies; a signal processing and defect identification module, which is used to receive the reflected signal from the cable signal acquisition module and perform digital processing; a cable environment monitoring module, which monitors the environmental conditions around the cable, including temperature, humidity, and current parameters, so as to provide more accurate diagnosis;

[0032] The cable historical data analysis module integrates the cable's historical inspection records, maintenance history, and service life information to provide historical data support for subsequent degradation prediction;

[0033] The cable degradation prediction module uses the trained degradation prediction model to predict whether the insulation material of the cable is aging based on the cable historical data and current environmental conditions;

[0034] As well as the fault location and diagnosis report generation module, it generates a cable fault diagnosis report based on the signal processing, defect identification results and the output of the degradation prediction model.

[0035] The present invention also discloses a computer device, comprising a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of the above-mentioned intelligent cable detection method when executing the computer program.

[0036] The present invention also discloses a computer-readable storage medium on which a computer program is stored, characterized in that when the computer program is executed by a processor, the steps of the above-mentioned intelligent cable detection method are implemented.

[0037] Beneficial effects of the present invention:

[0038] 1. The present invention installs multi-frequency electromagnetic wave sensors at key locations of the cable and uses electromagnetic waves of different frequencies to scan the cable, which can effectively identify different types of defects inside the cable and avoid the limitations of traditional single-frequency technology. Through accurate analysis of the reflected signal, the location of the cable defect can be obtained in a relatively short time.

[0039] 2. The present invention combines the environmental condition data around the cable (such as temperature, humidity, etc.) and the historical usage data of the cable (such as maintenance records, service life, etc.), and inputs them into the cable insulation material degradation prediction model to comprehensively evaluate the aging of the cable. Through the calculation of the prediction model, not only can the current degree of insulation material degradation be predicted, but also data support can be provided for subsequent operation and maintenance and repair decisions.

[0040] 3. The present invention provides a closed-loop decision support system, which combines multi-frequency signal analysis and degradation prediction to help operation and maintenance personnel make scientific maintenance plans and early warnings, reduce the probability of cable failures, and extend the service life of cables. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for describing the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work. Among them:

[0042] Figure 1 This is a schematic diagram of the overall process framework of an intelligent cable detection method proposed in the present invention. DETAILED DESCRIPTION

[0043] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the accompanying drawings.

[0044] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein, and those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0045] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The term "in one embodiment" that appears in different places in this specification does not necessarily refer to the same embodiment, nor does it refer to a separate or selective embodiment that is mutually exclusive with other embodiments.

[0046] Example 1, reference Figure 1 , as an embodiment of the present invention, provides an intelligent cable detection method and system thereof, the method comprising the following steps:

[0047] Step 1: Install multi-frequency electromagnetic wave sensors at the joints and key locations in the middle of the cable, and use electromagnetic waves of different frequencies to scan the cable. When the electromagnetic waves encounter defects inside the cable, they will be reflected. The sensor receives these reflected signals. By installing multi-frequency electromagnetic wave sensors at key locations of the cable and scanning the cable with electromagnetic waves of different frequencies, different types of defects inside the cable can be effectively identified, avoiding the limitations of traditional single-frequency technology. Through accurate analysis of the reflected signals, the location of the cable defects can be obtained in a relatively short time.

[0048] Step 2: The received reflected signal will be sent by the sensor to the background system for analysis. By processing signals of different frequencies, it is initially determined that the cable has a fault and the position is marked.

[0049] Specifically, by identifying the integrated signal response value P of the defect location i To determine the specific defect location i, the larger the value, the more significant the defect signal here;

[0050] Among them, the comprehensive signal response value P i =S i ·A i ;

[0051] S i Used to define the signal characteristic function, A i Used to define the signal attenuation function, the expressions are:

[0052]

[0053] Among them, f i represents the signal frequency, x i represents the environmental variable factor, α and β represent the model calibration parameters, which are used to control the weight of the characteristic function, and R i The reflected signal intensity at defect position i, μ represents the average value of the signal intensity, and σ represents the historical fluctuation standard deviation of defect position i.

[0054] Step 3: Check the external conditions of the cable at the marked position, including the environmental conditions. The cable surrounding environmental conditions include: temperature T i 、Humidity i And the current load L i , after normalization, we get the environmental variable factor x i , expressed as:

[0055]

[0056] Among them, T max , H max , L max The maximum reference value of the variable represented by

[0057] Combined with the historical data of the cable, including previous inspection records, maintenance history and service life, the historical data of the cable includes: maintenance times N repair and service life T age , define the historical data degradation factor E i , the expression is:

[0058]

[0059] Among them, γ1 represents the influence weight of the number of maintenance times on the degradation factor, γ2 represents the influence weight of the service life on the degradation factor, and γ3 represents the basic balance weight of the degradation factor.

[0060] Combined with environmental factors x i Together with the historical data factor Ei, it is used as the input of the degradation model: H i =x i ln(1+t)+E i ; Where t is time, ln(1+t) represents the time cumulative effect of environmental degradation. Through the calculation of the prediction model, it is predicted whether the insulation material of the cable is aging and the degree of degradation.

[0061] Specifically, the comprehensive degradation prediction formula D(t) is defined, and its expression is:

[0062]

[0063] Where T represents the total length of the detection time, w i It represents the defect location weight. The closer D(t) is to 0, the milder the insulation degradation is. The closer D(t) is to 1, the more serious the insulation degradation is.

[0064] If the comprehensive comprehensive prediction value D(t) exceeds the threshold, indicating that the insulation material is deteriorating to a high degree, the location is recorded as a high-risk area; if the comprehensive comprehensive prediction value D(t) is lower than the threshold, indicating that the insulation material is deteriorating to a low degree, but the detection signal has abnormal fluctuations, the system returns to the preliminary fault location stage and recalibrates the reflection signal characteristics;

[0065] If the comprehensive comprehensive prediction value D(t) completely matches the historical data and environmental conditions, the prediction result is confirmed to be reliable, and the threshold range is adjusted for subsequent similar data to improve the prediction efficiency.

[0066] In summary, by combining the environmental condition data around the cable (such as temperature, humidity, etc.) and the historical usage data of the cable (such as maintenance records, service life, etc.), and inputting them into the cable insulation material degradation prediction model, the aging of the cable can be comprehensively evaluated. Through the calculation of the prediction model, not only can the current degree of insulation material degradation be predicted, but also data support can be provided for subsequent operation and maintenance and repair decisions.

[0067] This embodiment also discloses an intelligent cable detection system, which is applied to the above detection method. The system includes: a cable signal acquisition module, which scans key positions such as cable joints and middle sections through electromagnetic waves of different frequencies; a signal processing and defect identification module, which is used to receive the reflected signal from the cable signal acquisition module and perform digital processing; a cable environment monitoring module, which monitors the environmental conditions around the cable, including temperature, humidity, and current parameters, so as to provide more accurate diagnosis; a cable history data analysis module, which integrates the historical detection records, maintenance history, and service life information of the cable, and provides historical data support for subsequent degradation prediction;

[0068] The cable degradation prediction module uses the trained degradation prediction model to predict whether the insulation material of the cable is aging based on the cable historical data and current environmental conditions; and the fault location and diagnosis report generation module generates a cable fault diagnosis report based on signal processing, defect identification results and the output of the degradation prediction model.

[0069] Example 2, in order to verify the effectiveness of the intelligent cable detection method proposed in the present invention, a specific example is given below. In the example, a cable in operation of a certain power company is used for detection. By installing a multi-frequency electromagnetic wave sensor and combining the environmental data and historical data of the cable for comprehensive analysis, we can identify different defects inside the cable and predict the degradation of the cable insulation material. The example reflects the beneficial effects of the present technical solution through a series of real experimental data.

[0070] Test object: 10KV high-voltage cable used by a power company, with a total line length of about 10 kilometers and a running time of 10 years. Test object: 10KV high-voltage cable used by a power company, with a total line length of about 10 kilometers and a running time of 10 years. Environmental conditions: The cable is located in a high humidity and high temperature environment with high electromagnetic interference around it.

[0071] Experimental procedures

[0072] 1. Install multi-frequency electromagnetic wave sensors: Install multi-frequency electromagnetic wave sensors at the cable joints and key locations (such as junction boxes, joint parts, etc.).

[0073] 2. Signal scanning and collection: The sensor uses electromagnetic waves of different frequencies to scan the cable, collect the cable reflection signal and send it to the background system for analysis.

[0074] 3. Signal analysis and fault location: By processing the frequency components of the reflected signal, the defect type inside the cable (such as loose joints, insulation aging, conductor breakage, etc.) can be identified.

[0075] 4. External environment and historical data analysis: Combine the historical usage data of the cable (such as maintenance records, service life) and the current external environment data (such as temperature and humidity) into the cable insulation material degradation prediction model to predict cable aging.

[0076] The experimental data demonstrates the fault identification and degradation prediction results at different cable locations, combining comprehensive analysis of sensor feedback, environmental conditions, and historical data.

[0077] Table 1 Experimental data comparison table

[0078]

[0079]

[0080] It can be seen from the experimental data that the reflected signals collected by the multi-frequency electromagnetic wave sensor can more accurately identify different types of cable faults, such as loose joints, insulation aging, conductor breakage, etc. Specifically, the change in the intensity of the reflected signal of the sensor in different frequency ranges reflects the characteristics of different defects inside the cable, thus providing a basis for fault location.

[0081] Combined with the environmental data (temperature, humidity, etc.) and historical data (maintenance records, service life, etc.) of the cable, the prediction model of the present invention can further calculate the degree of aging of the cable insulation material. For example, at joint position 1, the reflected signal intensity is high, and the historical data shows that the position has been repaired. Combined with the temperature and humidity data, the prediction model outputs a result of slight aging with a confidence level of 90%, and proposes a repair suggestion of replacing the joint.

[0082] At the joint position 2, although some aging was detected, the number of historical repairs was small and the service life was long. The confidence of the prediction model was 85%, and it was recommended to continue monitoring and extend the service life. At the middle position 1, the conductor was detected to be broken, and the temperature and humidity conditions were relatively poor. The confidence of the prediction model was as high as 92%, and it was recommended to replace the cable segment as soon as possible.

[0083] This embodiment also provides a computer device, which is applicable to an intelligent cable detection method and system thereof, including: a memory and a processor; the memory is used to store computer executable instructions, and the processor is used to execute computer executable instructions to implement an intelligent cable detection method and system thereof as proposed in the above embodiment.

[0084] The computer device may be a terminal, and the computer device includes a processor, a memory, a communication interface, a display screen and an input device connected via a system bus. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The communication interface of the computer device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner can be achieved through WIFI, an operator network, NFC (near field communication) or other technologies. The display screen of the computer device may be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device may be a touch layer covering the display screen, or a key, trackball or touchpad provided on the housing of the computer device, or an external keyboard, touchpad or mouse, etc.

[0085] This embodiment also provides a storage medium on which a computer program is stored. When the program is executed by a processor, an intelligent cable detection method and system thereof are implemented as proposed in the above embodiment; the storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (Static Random Access Memory, referred to as SRAM), electrically erasable programmable read-only memory (Electrically Erasable Programmable Read-Only Memory, referred to as EEPROM), erasable programmable read-only memory (Erasable Programmable Read Only Memory, referred to as EPROM), programmable read-only memory (Programmable Red-Only Memory, referred to as PROM), read-only memory (Read-Only Memory, referred to as ROM), magnetic storage, flash memory, magnetic disk or optical disk.

[0086] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. An intelligent cable detection method, characterized in that: The following steps are involved: Step 1: Install multi-frequency electromagnetic wave sensors at the joints and key locations in the middle of the cable, and use electromagnetic waves of different frequencies of the sensor to scan the cable. When the electromagnetic waves encounter defects inside the cable, they will be reflected, and the sensor will receive these reflected signals; Step 2: The received reflected signal will be sent by the sensor to the background system for analysis. By processing signals of different frequencies, it is determined that the cable is initially faulty and the position is marked; Step 3: Detect the external conditions of the cable at the marked position, including the environmental conditions, and combine the historical data of the cable, including previous inspection records, maintenance history, and service life, and input the detected external environmental data and historical data as information into the cable insulation material degradation prediction model; Through the calculation of the prediction model, it is predicted whether the insulation material of the cable is aging and the degree of deterioration.

2. An intelligent cable detection method according to claim 1, characterized in that: In step 2, the integrated signal response value P of the defect position is identified. i To determine the specific defect location i, the larger the value, the more significant the defect signal here; Among them, the comprehensive signal response value P i =S i ·A i ; S i Used to define the signal characteristic function, A i Used to define the signal attenuation function, the expressions are: Among them, f i represents the signal frequency, x i represents the environmental variable factor, α and β represent the model calibration parameters, which are used to control the weight of the characteristic function, and R i The reflected signal intensity at defect position i, μ represents the average value of the signal intensity, and σ represents the historical fluctuation standard deviation of defect position i.

3. An intelligent cable detection method according to claim 2, characterized in that: In step 3, the cable surrounding environmental conditions include: temperature T i 、Humidity i And the current load L i , after normalization, we get the environmental variable factor x i , expressed as: Among them, T max , H max , L max The maximized reference value of the variable represented by .

4. An intelligent cable detection method according to claim 3, characterized in that: In step 3, the historical data of the cable includes: the number of repairs N repair and service life T age , define the historical data degradation factor E i , the expression is: Among them, γ1 represents the influence weight of the number of maintenance times on the degradation factor, γ2 represents the influence weight of the service life on the degradation factor, and γ3 represents the basic balance weight of the degradation factor.

5. An intelligent cable detection method according to claim 4, characterized in that: Combined with environmental factors x i and historical data factor E i Together as the input of the degradation model: H i =x i ln(1+t)+E i ; Among them, t is time, and ln(1+t) represents the time cumulative effect of environmental degradation.

6. An intelligent cable detection method according to claim 5, characterized in that: In the step 3, a comprehensive degradation prediction formula D(t) is defined, and its expression is: Where T represents the total length of the detection time, w i It represents the defect location weight. The closer D(t) is to 0, the milder the insulation degradation is. The closer D(t) is to 1, the more serious the insulation degradation is.

7. An intelligent cable detection method according to claim 5, characterized in that: If the combined comprehensive prediction value D(t) exceeds the threshold value indicating a high degree of insulation material degradation, the location is recorded as a high-risk area; If the comprehensive comprehensive prediction value D(t) is lower than the threshold, indicating that the insulation material is less degraded, but the detection signal has abnormal fluctuations, then return to the preliminary fault location stage and recalibrate the reflection signal characteristics; If the comprehensive comprehensive prediction value D(t) completely matches the historical data and environmental conditions, the prediction result is confirmed to be reliable, and the threshold range is adjusted for subsequent similar data to improve the prediction efficiency.

8. The detection system of the intelligent cable detection method according to claim 7, characterized in that: The system includes: a cable signal acquisition module, which scans the cable joints and key positions in the middle section through electromagnetic waves of different frequencies; a signal processing and defect identification module, which receives the reflected signal from the cable signal acquisition module and performs digital processing; a cable environment monitoring module, which monitors the environmental conditions around the cable, including temperature, humidity, and current parameters, in order to provide more accurate diagnosis; The cable historical data analysis module integrates the cable's historical inspection records, maintenance history, and service life information to provide historical data support for subsequent degradation prediction; The cable degradation prediction module uses the trained degradation prediction model to predict whether the insulation material of the cable is aging based on the cable historical data and current environmental conditions; As well as the fault location and diagnosis report generation module, it generates a cable fault diagnosis report based on the signal processing, defect identification results and the output of the degradation prediction model.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the intelligent cable detection method and system thereof are implemented as described in any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of an intelligent cable detection method and system thereof are implemented as described in any one of claims 1 to 7.

Images