A cable status assessment and early warning method and device

Abstract

The present invention discloses a cable status evaluation and early warning method and device, which analyzes the cable historical temperature data in advance, determines the acquisition period in combination with the time window value of the cable abnormality; obtains the cable operation status data within the acquisition period; pre-processes and calculates the cable operation status data according to a preset cable status evaluation model, and gives the cable status evaluation data; and gives the cable status evaluation result based on the comparison between the cable status evaluation data and the preset conditions. The evaluation and early warning method and device of the present invention can effectively monitor the cable operation status, and perform cable evaluation and early warning.

Description

Technical Field

[0001] The present invention belongs to the technical field of power equipment status monitoring, and in particular relates to a cable status assessment and early warning method and device. Background Art

[0002] Power cables are common and critical power equipment in power systems. As the use time of cables increases, the insulation of the cables will inevitably age to a certain extent. The dielectric constant of the aged cables will decrease to a certain extent, which will significantly reduce the insulation performance of the cables and cause the heat dissipation performance of the cables to deteriorate. As the core temperature of the cables continues to rise, it may even exceed the maximum allowable temperature of the cables for long-term operation, which seriously damages the service life of the cables. At the same time, since the service life of the cables is inversely proportional to the internal field strength of the cables, the aging of the cables will increase the internal electric field strength of the cables, which will accelerate the reduction of the service life of the cables and even cause safety risks. In order to solve the above problems, the existing monitoring and early warning of cables is limited to the real-time acquisition of parameters, and then independent judgment is made based on each parameter to achieve early warning. It is unable to comprehensively consider and determine the cable loss. For example, CN106017560A discloses a cable well status comprehensive monitoring and early warning system, the system includes: an intelligent monitoring terminal, a Zigbee wireless communication module and a background monitoring host for early warning analysis. The on-site intelligent monitoring terminal communicates with the background monitoring host for early warning analysis through the Zigbee wireless communication module, and the data summarized by the on-site intelligent monitoring terminal is transmitted to the background monitoring host for early warning analysis. The monitoring terminal includes: a sensor acquisition module, a cable current sampling module, a power extraction module and an intelligent monitoring host. By monitoring the real-time operating status of the cable trench and its internal cables, it realizes comprehensive monitoring of the actual operating status and operating environment of the cable trench, and timely warning of potential hidden dangers and warnings.

[0003] Therefore, how to accurately and effectively realize cable status monitoring, evaluation and early warning has become a problem that needs to be solved urgently by technical personnel in this field. Summary of the invention

[0004] In view of the defects existing in the prior art, the present invention provides a cable status assessment and early warning method and device, which establishes a cable core temperature measurement model, accurately obtains the surface temperature, and infers the cable core temperature rise, realizes the cable conductor temperature measurement, and evaluates the safety risk and cable service life based on the temperature measurement results. The present invention adopts the following technical solutions:

[0005] In a first aspect, the present invention provides a cable status assessment and early warning method, the method comprising the following steps:

[0006] Step S1, analyzing the historical temperature data of the cable in advance, and determining the collection period in combination with the time window value of the cable abnormality;

[0007] Step S2, obtaining the cable operation status data within the acquisition period;

[0008] Step S3: preprocessing and calculating the cable operation status data according to a preset cable status assessment model to provide cable status assessment data;

[0009] Step S4: Based on the comparison between the cable status evaluation data and the preset conditions, a cable status evaluation result is given.

[0010] Furthermore, in step S1, the cable operation status data includes cable surface temperature, current load size F , Workplace temperature TH and workplace humidity RH .

[0011] Furthermore, in step S2, the cable operation status data is calculated, including calculating the cable core temperature, the cable surface temperature change rate and the local temperature difference on the cable surface according to the cable surface temperature.

[0012] Further, step S1 includes:

[0013] Obtain the number of cables corresponding to the historical temperature data, and combine the cable core temperature when the cable is abnormal to give the determination coefficient of the collection period;

[0014] Determine the time from when the cable core temperature rises from the cable core temperature threshold to when a fault occurs based on historical temperature data, and screen out the maximum time from when the cable core temperature rises from the cable core temperature threshold to when a fault occurs;

[0015] The collection period for obtaining the cable operation status data is determined based on the determination coefficient of the collection period and the maximum time for the cable core temperature to rise from the cable core temperature threshold to the occurrence of a fault.

[0016] Furthermore, the calculation formula of the acquisition period is as follows:

[0017] ;

[0018] in, represents the adjustment factor; represents the coefficient of determination; m is the number of cables; n is the number of cables with different core temperatures when all cables are abnormal; Indicates the maximum time from when the cable core temperature exceeds the cable core temperature threshold to when an abnormality occurs.

[0019] Further, step S3 includes:

[0020] The cable operation status data is processed in turn by average interpolation, smoothing filtering, deviation standardization and variance maximization to obtain the processed cable operation status data;

[0021] Analyze and process the processed cable operation status data to provide the cable core temperature, cable surface temperature change rate and local temperature difference on the cable surface;

[0022] The threshold values ​​corresponding to the cable core temperature, the cable surface temperature change rate and the local temperature difference on the cable surface are obtained, and the cable status assessment data is given by combining the cable core temperature, the cable surface temperature change rate and the local temperature difference on the cable surface.

[0023] Furthermore, the cable status assessment data satisfies the following relationship:

[0024] ;

[0025] in, Indicates cable status assessment data; , , , They are the cable core temperature, the rate of change of cable surface temperature, the local temperature difference on the cable surface, and the current load; , , , They represent the cable core temperature threshold, the cable surface temperature change rate threshold, the cable surface local temperature difference threshold, and the current load magnitude threshold respectively; , , , Represents the weight parameter.

[0026] Further, step S4 includes:

[0027] Compare and analyze the cable status assessment data with the warning threshold and give the result of whether to issue an alarm;

[0028] Obtain the processed cable operation status data corresponding to the alarm and perform loss analysis to provide loss results;

[0029] The loss result is compared with the loss threshold, and the loss level of the cable corresponding to the alarm is given.

[0030] Furthermore, the processed cable operation status data corresponding to the alarm is obtained and loss analysis is performed to give the loss results, including: calculating the cumulative loss of the cable based on the cable operation status number, the working life of the cable, and combined with the historical abnormal conditions, construction methods, work site conditions and load conditions during the cable working process.

[0031] Furthermore, the processed cable operation status data corresponding to the alarm is obtained and loss analysis is performed to give a loss result that satisfies the following relationship:

[0032] ;

[0033] in, is the cumulative loss of the cable, is the working age of the cable; is the factor of cable construction mode; is the workplace condition factor; , Cable work to the 1. The historical abnormal effect factor of 20 years and the effect factor of working years; For cable work to The current load size factor of the year; V is the reference rate of thermal aging of the cable; K is a constant, representing the total working time of the cable, t is the working time of the cable; a' and b' are constants related to the activation energy of the cable insulation material; is the cable core temperature.

[0034] Furthermore, after step S4, the method further comprises displaying the cable operation status data in real time on the monitoring interface, and recording the safety risk level assessment result and / or the service life assessment result in a database.

[0035] In a second aspect, the present invention provides a cable status assessment and early warning device, which adopts a cable status assessment and early warning method, and the device includes:

[0036] A cycle determination module is used to analyze the historical temperature data of the cable in advance and determine the collection cycle in combination with the time window value of the cable anomaly;

[0037] A data acquisition module, which is used to acquire the cable operation status data within the acquisition period;

[0038] A data evaluation module, which is used to pre-process and calculate the cable operation status data according to a preset cable status evaluation model, and provide cable status evaluation data;

[0039] The cable evaluation module is used to provide a cable status evaluation result based on the comparison between the cable status evaluation data and the preset conditions.

[0040] In a third aspect, the present invention provides a cable status assessment and early warning device, comprising a memory and a processor, wherein a computer program is stored in the memory, and when the computer program is executed by the processor, the above method is implemented.

[0041] In a fourth aspect, the present invention further provides an electronic device, the electronic device comprising a memory and one or more processors;

[0042] One or more application programs are stored in the memory, and the one or more application programs are suitable for being executed by the one or more processors to implement the above method.

[0043] The present invention provides a cable status assessment and early warning method and device, which at least have the following beneficial effects: effectively monitoring the cable operation status, and performing cable assessment and early warning. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] Figure 1 It is a schematic diagram of the structure of the cable and the RFID data collection tag;

[0045] Figure 2 A schematic diagram of a process of cable status assessment and early warning method;

[0046] Figure 3 A schematic diagram of a cable status assessment and early warning device;

[0047] Figure 4 A schematic diagram of the composition of hierarchical security risk level assessment factors.

[0048] Figure numerals: 1-electromagnetic shielding, 2-foam film, 3-antenna, 4-temperature sensor, 5-memory chip, 6-cable conductor, 7-conductor shielding, 8-insulation, 9-insulation shielding, 10-copper tape shielding, 11-filler, 12-isolation sleeve, 13-armor, 14-RFID data collection tag, 15-sheath. DETAILED DESCRIPTION

[0049] In order to better understand the above technical solution, the above technical solution will be described in detail below in conjunction with the accompanying drawings and specific implementation methods. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0050] The terms used in the embodiments of the present invention are only for the purpose of describing specific embodiments, and are not intended to limit the present invention. The singular forms "a", "said" and "the" used in the embodiments of the present invention and the appended claims are also intended to include plural forms, unless the context clearly indicates other meanings, and "multiple" generally includes at least two.

[0051] It should also be noted that the term "includes", "comprising" or any other variation thereof is intended to cover non-exclusive inclusion, so that a commodity or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such commodity or device. In the absence of more restrictions, the elements defined by the sentence "comprising a ..." do not exclude the existence of other identical elements in the commodity or device including the elements.

[0052] Radio Frequency Identification (RFID) is an automatic identification technology that uses radio frequency for non-invasive two-way communication. Due to the advantages of small device size, fast identification speed, high security and good environmental adaptability, RFID is currently used in the quality control of power equipment in the power grid, information management, ledger storage and operation status monitoring. In the technical solution of the invention, by installing an RFID data collection tag between the outer sheath and the armor layer of the cable, the real-time data of the cable collected can be summarized, such as Figure 1 As shown, the cable is a three-phase power cable including a sheath, an RFID data acquisition tag, armor, an isolation sleeve, a cable conductor, a conductor shield, insulation, an insulation shield, a copper tape shield, and a filler, wherein a non-invasive RFID data acquisition tag composed of an electromagnetic shield, a foam film, an antenna, a sensor, a storage chip, etc. is attached to the outside of the armor layer and inside the sheath layer of the above-mentioned cable to form a label layer. The RFID data acquisition tag is used to collect the cable status data. Due to its small size and the fact that it does not affect the cable performance indicators, it greatly facilitates practical applications. By using this tag, the subsequent maintenance cost can be reduced, and the chip has the characteristics of long identification distance and fast identification speed, which greatly improves the convenience and efficiency of monitoring.

[0053] In a first aspect, the present invention provides a cable status assessment and early warning method, such as Figure 2 As shown, the method comprises the following steps:

[0054] Step S1, analyzing the historical temperature data of the cable in advance, and determining the collection period in combination with the time window value of the cable abnormality;

[0055] The present invention adopts RFID data acquisition tags, and "integrates" them outside the armor layer and inside the sheath layer of the cable through non-invasive means. The cable operation status data obtained by the RFID tag is uploaded to the receiving antenna of the terminal device through wireless transmission. The terminal device processes the collected data into data signal quantity and transmits it to the evaluation system through the communication line. The terminal device is used to realize cable operation status data acquisition, data processing and data transmission. In combination with the specification requirements in the national standard, a high-reliability sensor with a frequency of 429 to 438 Hz is selected, and its temperature measurement range is -55 to 180 ° C, the accuracy is ± 1 ° C, and the temperature measurement distance is 2m. In addition, the cable assembly rack is equipped with a temperature sensor and a humidity sensor to record the temperature and humidity parameters in the cable working area. In another embodiment, the collection of cable operation status data can be completed by the main station, the substation and the terminal device. The data transmission between the collection devices adopts the wireless network + wired network method to realize barrier-free remote data transmission, which ensures the stability and reliability of communication, improves the security of the system, and also reduces the operation and maintenance costs.

[0056] In step S1, the preset acquisition period is obtained by statistically analyzing the historical temperature data. Specifically, the following steps are performed:

[0057] Obtain the number of cables corresponding to the historical temperature data, and combine the cable core temperature when the cable is abnormal to give the determination coefficient of the collection period;

[0058] Determine the time from when the cable core temperature rises from the cable core temperature threshold to when a fault occurs based on historical temperature data, and screen out the maximum time from when the cable core temperature rises from the cable core temperature threshold to when a fault occurs;

[0059] The collection period for obtaining the cable operation status data is determined based on the determination coefficient of the collection period and the maximum time for the cable core temperature to rise from the cable core temperature threshold to the occurrence of a fault.

[0060] Assume that the maximum time from the cable core temperature exceeding the cable core temperature threshold to the occurrence of a fault is , statistics of all Within the range, the cable core temperature rises rapidly from the cable core temperature threshold to the time window value of the fault. The calculation formula of the acquisition period is determined by using the descriptive statistical analysis method as follows:

[0061] ;

[0062] in, represents the adjustment factor; represents the coefficient of determination; m is the number of cables; n is the number of cable core temperatures that are different when all cables are abnormal. The value range is [0.05,0.08].

[0063] Step S2, obtaining the cable operation status data within the acquisition period;

[0064] The cable operation status data includes cable surface temperature, current load magnitude F, workplace temperature TH and workplace humidity RH.

[0065] Step S3, preprocessing and calculating the cable operation status data according to a preset cable status assessment model to provide cable status assessment data; specifically comprising:

[0066] The cable operation status data is processed in turn by average interpolation, smoothing filtering, deviation standardization and variance maximization to obtain the processed cable operation status data;

[0067] Analyze and process the processed cable operation status data to provide the cable core temperature, cable surface temperature change rate and local temperature difference on the cable surface;

[0068] The threshold values ​​corresponding to the cable core temperature, the cable surface temperature change rate and the local temperature difference on the cable surface are obtained, and the cable status assessment data is given by combining the cable core temperature, the cable surface temperature change rate and the local temperature difference on the cable surface.

[0069] In step S3, the cable status evaluation data is calculated as follows:

[0070] ;

[0071] in, Indicates cable status assessment data; , , , They are the cable core temperature, the rate of change of cable surface temperature, the local temperature difference on the cable surface, and the current load; , , , They represent the cable core temperature threshold, the cable surface temperature change rate threshold, the cable surface local temperature difference threshold, and the current load magnitude threshold respectively; , , , represents the weight parameter, , , , ;

[0072] In the above calculation formula, if the cable core temperature is greater than the cable core temperature threshold, then ; If the cable surface temperature change rate is greater than the cable surface temperature change rate threshold, then ; If the local temperature difference on the cable surface is greater than the local temperature difference threshold on the cable surface, then ; If the current load magnitude is greater than the current load magnitude threshold, then .

[0073] In step S3, the cable operation status data is preprocessed to improve the accuracy of subsequent data calculation, evaluation and judgment, and to overcome the problems of fluctuation, instability, duplication and redundancy caused by objective factors in the monitoring data. The data missing problem is solved by the average interpolation algorithm, and the data denoising is realized by the smoothing filter function. The processed data is normalized by deviation standardization, and then the core data in the monitoring data is extracted in turn by the variance maximization principle. While meeting the data volume requirements for analysis and processing, the complexity of data processing and the resources consumed are reduced as much as possible.

[0074] In step S3, the cable operation status data is calculated, including calculating the cable core temperature, the cable surface temperature change rate and the local temperature difference on the cable surface according to the cable surface temperature.

[0075] In the above steps, the cable core temperature, the cable surface temperature change rate and the local temperature difference on the cable surface are calculated according to the cable surface temperature, as follows:

[0076] (1) Calculate the cable core temperature

[0077] In the equivalent temperature rise model of three-core cable, , are lumped parameters, representing the equivalent thermal resistance and heat capacity of the cable, respectively. W represents the cable loss. The heat transfer equation established by the equivalent temperature rise model is as follows:

[0078]

[0079] In the above formula, Indicates the amount of heat absorbed when the cable core temperature increases over time; The heat transfer power consumption caused by the longitudinal temperature difference from the cable surface to the core is expressed by transforming the formula to obtain the following formula:

[0080] ;

[0081] Assume that the initial temperature of the cable core is ,but , then the heat transfer equation is transformed into the following form:

[0082] ;

[0083] From the above calculation formula, it can be determined that if the time When it approaches infinity, the cable core temperature will tend to be stable, and the stable value ;in , is the single-core cable loss, and its value is , according to the current load size , Conductor resistance of cable calculate; Dielectric loss of the insulation layer of single-core cable; is the equivalent thermal resistance of the cable; is the equivalent heat capacity of the cable, and the above heat transfer equation is further organized as:

[0084] ;

[0085] According to the concentrated parameters of the cable , , and the current load size and cable surface temperature collected within the preset collection period are used to estimate the cable core temperature;

[0086] Assumptions The cable core temperature at this moment is , the cable surface temperature is ,through After time, the cable core temperature is , the cable surface temperature is And so on, the temperature of the cable core at this time is as follows:

[0087] ;

[0088] in, , is the single-core cable loss, and its value is , according to the current load size , Conductor resistance of cable calculate; It is the dielectric loss of the insulation layer of the single-core cable; is the equivalent thermal resistance of the cable; is the equivalent thermal capacity of the cable; is the surface temperature of the cable at this time.

[0089] When the cable is in normal working condition, the grounding current of the cable sheath is equal to zero. At this time, the conductor loss of the cable is the main source of the cable core temperature rise. By calculating the cable core temperature It can reflect the working status of the monitored cable in real time.

[0090] When verifying the above calculation formula, the cable temperature rise current carrying capacity calculation and analysis software CYMCAP was used to simulate the cable's equivalent thermal resistance, heat capacity, cable loss, cable insulation layer dielectric loss, current load size, cable conductor resistance and other parameters, and compare and analyze the measured and calculated values ​​of the cable to verify the validity of the above formula. When the currents of 210A, 150A, 130A, 160A, 150A, 220A, 200A, 150A, 170A, and 150A are input into the cable respectively, the cable core loss is They are 4.22W, 2.16W, 1.63W, 3.92W, 2.18W, 4.66W, 3.85W, 2.18W, 2.84W, and 2.2W respectively. The sheath loss can be ignored. Comparing the measured and calculated cable core temperature data, as shown in Table 1:

[0091] Table 1 Cable core temperature data comparison table

[0092]

[0093] From the above data, it can be seen that the maximum absolute deviation between the actual measured and calculated temperature is 0.322 1 ℃. The deviation value meets the actual measurement requirements and is within a reasonable range.

[0094] In order to improve the reliability of condition monitoring, the method of taking average of multiple groups can be adopted. That is, multiple monitoring data (for example, 10 times or 20 times) are used as a group, and the average of the cable core temperature of this group is calculated. The average is used as the cable core temperature and compared with the cable core temperature threshold. If it is greater than, it is considered that an abnormality has occurred and the preset abnormal alarm condition is met.

[0095] (2) Calculate the rate of change of cable surface temperature

[0096] The cable surface temperature change rate , which is used to characterize the speed at which the cable surface temperature changes over time. The cable surface temperature change rate is calculated using the following formula:

[0097]

[0098] in, , Respectively represent the time points, , Respectively , Cable surface temperature at a given time point;

[0099] The cable surface temperature change rate can be accurately calculated based on the results of two consecutive measurements. Since the reaction time of cable abnormalities is usually short, the cable surface temperature change rate increases significantly when an abnormality occurs. In order to improve the reliability of status monitoring, a multi-group maximum value method can be adopted, that is, multiple monitoring data (for example, 10 times or 20 times) are used as a group, and the maximum change rate of the group is calculated. The maximum change rate is used as the cable surface temperature change rate and compared with the cable surface temperature change rate threshold. If it is greater than, it is considered that an abnormality has occurred and the preset abnormal alarm condition is met.

[0100] (3) Calculate the local temperature difference on the cable surface

[0101] The local temperature difference on the cable surface is used to characterize the difference between the temperature of the monitoring point on the cable surface and the temperature of the surrounding parts. The calculation formula for the local temperature difference on the cable surface within a meter range is as follows:

[0102] ;

[0103] When the cable is in normal working condition, the local temperature difference on the cable surface is small, but when the working condition is abnormal, a large temperature difference will occur. In order to improve the reliability of condition monitoring, the normalized mean value of multiple groups can be adopted, that is, multiple monitoring data (for example, 10 or 20 times) are used as a group, and the normalized mean value of the local temperature difference on the cable surface of this group is calculated. The normalized mean value is used as the local temperature difference on the cable surface and compared with the local temperature difference threshold on the cable surface. If it is greater than, it is considered that an abnormality has occurred and the preset abnormal alarm condition is met.

[0104] In addition, the real-time load of the cable during operation is determined based on the current load size obtained through monitoring, and the current load size is compared with the current load size threshold. If it is greater than, it is considered that an abnormality has occurred and the preset abnormal alarm condition is met.

[0105] Step S4: Based on the comparison between the cable status evaluation data and the preset conditions, a cable status evaluation result is given; specifically including:

[0106] Compare and analyze the cable status assessment data with the warning threshold and give the result of whether to issue an alarm;

[0107] Obtain the processed cable operation status data corresponding to the alarm and perform loss analysis to provide loss results;

[0108] The loss result is compared with the loss threshold, and the loss level of the cable corresponding to the alarm is given.

[0109] In step S4, the warning threshold , the calculated cable condition assessment data and warning threshold Make a comparison.

[0110] In practical applications, the warning threshold , weight parameter , , , The design and adjustment of the preset conditions have an important impact on the judgment of the preset conditions and the evaluation and early warning effect of the entire device. The weight parameters need to be reasonably designed and adjusted according to the specific tasks and data so that the model can better adapt to the actual scenario. In different application scenarios, different parameter settings and adjustment methods may be required to achieve the best results. The weight parameters can be designed and adjusted through deep learning and training. During the training process, the weight matrix parameters need to be dynamically adjusted so that the preset condition judgment model composed of them can better adapt to the cable status assessment and early warning needs. Usually, methods such as learning rate decay, regularization, and gradient clipping can be used to adjust the parameters to improve the convergence speed and generalization ability of the model. Transfer learning, model fusion and other methods can also be used to further improve the performance and effect of the preset condition judgment model.

[0111] Obtain the processed cable operation status data corresponding to the alarm and perform loss analysis to provide loss results, including:

[0112] According to the service life of the cable, combined with the historical abnormal conditions, construction methods, working site conditions and load conditions during the cable operation, the initial cumulative loss of the cable is calculated. The calculation formula is as follows:

[0113] ;

[0114] in, is the initial working life of the cable; is the factor of cable construction mode; is the workplace condition factor; , Cable work to the 1. The historical abnormal effect factor of 20 years and the effect factor of working years; For cable work to The annual current load magnitude factor; is the thermal aging reference rate, which is 1.6´10 -6 ; is a constant, indicating the total working time of the cable, and its value is , t is the cable working time;

[0115] By analogy, the cumulative loss of the cable is calculated as follows:

[0116] ;

[0117] Where a' and b' are constants related to the activation energy of the cable insulation material, for example, for polyethylene insulation, , ; Cable core temperature;

[0118] According to the cumulative loss of the cable , the service life of the cable is divided into long-term, medium-term, short-term and replacement. These four states correspond to the four cumulative loss levels of the cable: good, mild, moderate and severe. The division method is as follows:

[0119]

[0120] In the above-mentioned cable operation process, the maintenance personnel will take different measures in the face of different cable loss levels. For cables with a loss level of "good", it means that they can be used for a long time, so that they can continue to operate normally; cables with a loss level of "mild" will be further evaluated during regular power outage inspections; cables with a loss level of "moderate" can only be used in the short term, and it is recommended to take samples for verification in the near future; cables with a loss level of "serious" indicate that the cable may need to be replaced, so it is necessary to stop operation and take samples for test analysis to determine whether the cable needs to be replaced.

[0121] (1) Historical abnormal factors :It is used to characterize the relationship between the number of cable working abnormalities and the cable insulation aging. The cable working abnormalities are triggered by the cable line itself rather than external influences. If the cable frequently works abnormally in the past, it means that the insulation level of the line where the cable is located has dropped significantly and is seriously aged. Moreover, every time the cable works abnormally, it will damage the current cable insulation performance and accelerate the cable insulation aging to a certain extent.

[0122] The historical abnormality factor can be determined by the recorded historical abnormality times of the cable , the values ​​are as follows:

[0123]

[0124] in, It indicates the number of historical abnormalities of the cable working up to the i-th year; when the number of historical abnormalities is less than or equal to 1, it indicates that the number of abnormalities in history is relatively small, which may be caused by quality defects in the cable production process or quality problems in the construction process; when the number of historical abnormalities is greater than or equal to 2 times and less than or equal to 3 times, it indicates that the cable has experienced a certain degree of insulation aging, resulting in an increase in the number of abnormalities; when the number of historical abnormalities is greater than or equal to 4 times, it indicates that the cable working abnormalities occur frequently and the cable insulation aging has reached the level of degradation.

[0125] (2) Factors affecting construction methods :The cable construction methods include tunnel construction, cable trench construction, buried construction and pipeline construction. Different construction methods will affect the heat dissipation performance of the cable, and the heat dissipation performance is proportional to the speed of cable insulation aging. The relationship between the heat dissipation performance of the above four construction methods is from large to small: tunnel construction, cable trench construction, buried construction, and pipeline construction. Therefore, the construction method has an impact on the heat dissipation performance of the cable. , the values ​​are as follows:

[0126]

[0127] (3) Factors affecting workplace conditions :The working conditions of the cable have a great influence on the aging of the cable insulation. If the cable works in a high temperature or humid environment for a long time, the aging of the cable insulation will be accelerated. Therefore, the working conditions factor , the values ​​are as follows:

[0128]

[0129] in, Indicates the temperature of the workplace; Indicates the humidity of the workplace.

[0130] (4) Factors affecting years of work experience :The working condition of the cable is inversely proportional to its working life, and the changes in the working condition and working life of the cable satisfy the exponential expression. Working life effect factor The calculation formula is as follows:

[0131]

[0132] in, Represents the amplitude coefficient, usually taken as 0.95; Represents the loss coefficient, usually taken as 0.02; Indicates the number of years the cable has been in service up to the i-th year.

[0133] (5) Current load size factor :

[0134] In addition to the above factors, the current load of the cable has a significant impact on the insulation aging of the cable. The greater the current load, the higher the core temperature of the cable during operation, and the faster the cable insulation aging. , the values ​​are as follows:

[0135]

[0136] In the formula, Fi represents the attenuation ratio of the current load size of the cable in the i-th year relative to the current load size when the cable initially works, that is, the ratio of the difference between the current load size when the cable initially works and the current load size when the cable works in the i-th year to the current load size when the cable initially works.

[0137] After the above step S4, the method further comprises:

[0138] Step S5, based on the hierarchical security risk level assessment factors, conduct a security risk level assessment to obtain a security risk level assessment result. Specifically, it includes:

[0139] (1) Constructing hierarchical security risk level assessment factors

[0140] like Figure 4 As shown in the figure, the safety risk level assessment factors are divided into three levels: the result level, which indicates the ultimate purpose of the safety risk level assessment and the final result that can be achieved by solving the problem; the factor level, which indicates the various factors to be considered in the safety risk level assessment; and the operation and maintenance level, which indicates the corresponding operation and maintenance disposal according to the safety risk level assessment results. The safety risk level assessment results are divided into four levels, from high to low, namely the first level (G1), the second level (G2), the third level (G3) and the fourth level (G4).

[0141] The results of the safety risk level assessment are quantified into a scoring range, and different operation and maintenance measures are set according to different assessment results: for situations where the safety risk level is "small", wait and observe without taking any action; for situations where the safety risk level is "average", it is necessary to eliminate hidden dangers, and ensure that hidden dangers are thoroughly resolved by strengthening inspections and patrols; for situations where the safety risk level is "high", it is necessary to eliminate hidden dangers in a timely manner, and implement 24-hour key monitoring of safety risk points to ensure that there are no blind spots and full coverage of safety risk points; for situations where the safety risk level is "very high", it is necessary to immediately cut off the power supply and check each item one by one to find out the root cause of the safety risk, repair or replace damaged components in a timely manner, and restore the normal operation of the system as soon as possible.

[0142] Safety risk level assessment factors include three first-order assessment sub-factors: operating status data, abnormal status data, and construction status data. Among them, operating status data includes four second-order assessment sub-factors: cable surface temperature change rate, current load size, workplace temperature, and workplace humidity; abnormal status data includes two second-order assessment sub-factors: abnormal cable core temperature and abnormal cumulative loss; construction status data includes two second-order assessment sub-factors: construction method and workplace conditions, totaling eight second-order assessment sub-factors.

[0143] (2) Establishing a comparison matrix of evaluation sub-factors

[0144] In the hierarchical safety risk level assessment factor analysis, the assessment sub-factors belonging to the same level are compared with each other, such as the cable surface temperature change rate, current load size, workplace temperature and workplace humidity. The importance of the assessment sub-factors is compared and expressed quantitatively, and expressed by establishing an assessment sub-factor comparison matrix. The calculation formula is as follows:

[0145]

[0146] in, represents the evaluation sub-factor comparison matrix; n represents the number of evaluation sub-factors; Represents the evaluation sub-factor Comparison evaluation sub-factors A quantitative representation of the importance of the evaluation sub-factor comparison matrix. Represents the evaluation sub-factor The results compared with themselves are equally important; in addition, the evaluation sub-factors and evaluation sub-factors The quantitative expression of , then evaluate the sub-factors and evaluation sub-factors The quantitative expression of ,Right now Satisfies the following relationship:

[0147]

[0148] when hour, The values ​​of are shown in Table 2:

[0149] Table 2 Quantitative values ​​of evaluation sub-factors

[0150]

[0151] In the above table, "slightly higher", "high", and "much higher" in the importance comparison results are all judged based on the comparison results with the preset interval. The value range of the preset interval can be dynamically adjusted according to different application scenarios.

[0152] (3) Perform consistency check on the comparison matrix of evaluation sub-factors and evaluate the joint weight vector of sub-factors relative to the result layer at the second order

[0153] Normalized evaluation sub-factor comparison matrix The row vector of weight vector ;

[0154]

[0155] in, Represents the weight vector The weight of

[0156] The weight vector obtained by the above calculation formula cannot be used directly, and its rationality must be verified through consistency detection. Specifically, the weight vector is checked for consistency through consistency parameters to verify its rationality;

[0157] The calculation formula of the consistency parameter is as follows:

[0158]

[0159] in, Indicates the order of the factor comparison matrix; Represents consistency parameters; is the maximum eigenvalue of the factor comparison matrix, and the calculation formula is as follows:

[0160]

[0161] The values ​​of the consistency parameters are shown in Table 3:

[0162] Table 3 Consistency parameter values ​​under different factor comparison matrix orders

[0163]

[0164] in, , it means that the factor comparison matrix passes the consistency test.

[0165] In another embodiment, by using matrix simulation software Function to calculate the weight vector Compare the largest eigenvalue of the matrix with the factors , The syntax of the function is as follows:

[0166]

[0167] in, represents an n×n matrix, is an n×n matrix whose column vectors are The characteristic vector of is an n×n matrix whose diagonal elements are The eigenvalues ​​of the eigenvalue matrix are returned by the eig function. and the eigenvector matrix , the feature vector After standardization, the weight vector can be obtained .

[0168] Taking the above consistency parameter values ​​as an example, according to the historical training data and the values ​​of quantitative representation, the first-order factor comparison matrix composed of three first-order evaluation sub-factors of operation status data, abnormal status data and construction status data in the safety risk level assessment is obtained. B 1:

[0169]

[0170] Next, the weight vector of the first-order evaluation sub-factors is calculated ;

[0171] ;

[0172] Calculate the first-order factor comparison matrix B The largest eigenvalue of 1 , then the consistency parameter of the first-order factor comparison matrix is , then the first-order factor comparison matrix B 1 Pass the consistency test, the corresponding That is, it is confirmed as the weight vector of the first-order evaluation sub-factor.

[0173] Next, we continue to calculate the relevant second-order evaluation sub-factors. Based on the historical training data and the values ​​of the quantitative representation, we can obtain the second-order factor comparison matrix composed of the cable surface temperature change rate, current load size, workplace temperature and workplace humidity in the safety risk level assessment. B 11 The second-order factor comparison matrix is ​​composed of abnormal state data including abnormal cable core temperature and abnormal cumulative loss. B 12 The second-order factor comparison matrix is ​​composed of two second-order evaluation sub-factors: construction status data, including construction methods and workplace conditions. B 13 ;

[0174]

[0175] Next, the weight vector of the second-order evaluation sub-factors is calculated;

[0176]

[0177] The maximum eigenvalue and consistency parameter of each second-order factor comparison matrix are calculated respectively, and consistency test is performed. The test results show that each second-order factor comparison matrix passes the consistency test, and the corresponding weight vector That is, the weight vector of the second-order evaluation sub-factor is confirmed. According to the calculated weight vectors and the hierarchical relationship between the evaluation sub-factors, the joint weight of the evaluation sub-factors is calculated.

[0178] This can determine the joint weight vector of the second-order evaluation sub-factors relative to the result layer .

[0179] (4) Determine the influence matrix of evaluation sub-factors

[0180] The impact of the assessment sub-factor is used to characterize the impact of the assessment sub-factor on the security risk. In order to better reflect the impact of each secondary assessment sub-factor on the security risk, each secondary assessment sub-factor is divided into 4 stages, and the stage threshold corresponding to each stage is set respectively. According to the different situations of the secondary assessment sub-factor, the impact corresponding to the 4 stages is preset to be 0.25, 0.50, 0.75, and 1.00 respectively, and the stage threshold and impact of each secondary assessment sub-factor are listed.

[0181] The above four stage thresholds and the corresponding influences are obtained based on historical training data. The influence matrix can be obtained based on the above four stage thresholds and the monitoring data of each secondary evaluation sub-factor , the calculation formula is as follows:

[0182]

[0183] in, Represents the influence matrix of the secondary evaluation sub-factors; Indicates The value of the influence of the second-level evaluation sub-factor.

[0184] (5) Calculation of the scoring range for safety risk level assessment

[0185] According to the joint weight vector and influence matrix of the second-order evaluation sub-factors relative to the result layer calculated above, the scoring range of the safety risk level assessment is calculated. :

[0186]

[0187] in, represents the joint weight vector of the second-order evaluation sub-factors relative to the result layer; Represents the influence matrix of the second-order assessment sub-factors; by comparing the four levels preset in the stage of building a hierarchical safety risk level assessment factor, the scoring range can be determined The corresponding level.

[0188] The following is an explanation of the above safety risk level assessment process with specific examples. Take a section of cable line located in the old city of a prefecture-level city in a province in East China as an example. The cable model is YJV22-8.7 / 15-xxxxxxxx, the working site conditions are civil buildings within 24 meters, and the construction method is cable trench installation. According to operation and maintenance information, this section of cable line has been put into use since 2012 and has been in continuous use for more than 12 years. Only one abnormality occurred during the use period. During the use period, the cable trench environment of the cable has always remained dry and without water seepage. Query the current load records during the working period. The current load of the line was less than 50% in the first 8 years of use. With the changes in population density and urban development, the current load increased to 60% in the next 4 years.

[0189] Calculate the cumulative loss of this section of cable line , the cumulative loss is obtained by calculating Compared with the preset service life classification threshold, it can be seen that the cumulative loss of this section of cable line is abnormally in a "good" state. Comparing this test result with the result of aging test using special equipment, it can be seen that the above cumulative loss result obtained by this application is basically consistent with the test result of special equipment. Based on the above information of this section of cable line, the influence matrix of the second-order evaluation sub-factors is calculated. .

[0190] ;

[0191] Calculate the score range for security risk level assessment :

[0192] ;

[0193] By comparing the four levels preset in the stage of building a hierarchical security risk level assessment factor, the scoring range can be determined The corresponding level is the fourth level. At the same time, all operating status data of this section of cable are within a reasonable range, so no measures need to be taken.

[0194] After the above steps S4 and / or S5, the method further includes displaying the cable operation status data in real time on the monitoring interface, and recording the service life assessment results (loss assessment results) and the safety risk level assessment results in a database.

[0195] The monitoring interface includes the cable list area, cable information area, historical data area and current data area. Click the "View" button on the right side of the cable list area to view the information, historical records, current monitored status values, and safety risk level assessment results and / or service life assessment results of the cable line. The format of the data record in the database is "monitoring time + cable information + service life assessment results".

[0196] See also Figure 3 As shown, the present invention provides a cable status assessment and early warning device, which adopts the above-mentioned cable status assessment and early warning method, and the device includes:

[0197] A cycle determination module is used to analyze the historical temperature data of the cable in advance and determine the collection cycle in combination with the time window value of the cable anomaly;

[0198] A data acquisition module, which is used to acquire the cable operation status data within the acquisition period;

[0199] A data evaluation module, which is used to pre-process and calculate the cable operation status data according to a preset cable status evaluation model, and provide cable status evaluation data;

[0200] The cable evaluation module is used to provide a cable status evaluation result based on the comparison between the cable status evaluation data and the preset conditions.

[0201] The present invention provides a cable status assessment and early warning device, comprising a memory and a processor, wherein a computer program is stored in the memory, and when the computer program is executed by the processor, the above-mentioned assessment and early warning method is implemented.

[0202] The present invention provides an electronic device, which includes a memory and one or more processors; one or more applications are stored in the memory, and the one or more applications are suitable for being executed by the one or more processors to implement the above-mentioned evaluation and early warning method.

[0203] Various implementations of the systems and techniques described above can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various implementations may include: being implemented in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0204] The program code for implementing the method of the present disclosure may be written in any combination of one or more programming languages. These program codes may be provided to a processor or controller of a general-purpose computer, a special-purpose computer, or other programmable data processing device, so that the program code, when executed by the processor or controller, enables the functions / operations specified in the flow chart and / or block diagram to be implemented. The program code may be executed entirely on the machine, partially on the machine, partially on the machine and partially on a remote machine as a stand-alone software package, or entirely on a remote machine or server.

[0205] In the context of the present invention, a machine-readable medium may be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, device, or equipment. A machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, device, or equipment, or any suitable combination of the foregoing. A more specific example of a machine-readable storage medium may include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0206] To provide interaction with a user, the systems and techniques described herein can be implemented on a computer having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user can provide input to the computer. Other types of devices can also be used to provide interaction with the user; for example, the feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including acoustic input, voice input, or tactile input).

[0207] Finally, it should be noted that the above preferred 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 through the above preferred embodiments, those skilled in the art should understand that various changes can be made in form and details without departing from the scope defined by the claims of the present invention.

Claims

1. A cable status assessment and early warning method, characterized in that: The method comprises the following steps: Step S1, analyzing the historical temperature data of the cable in advance, and determining the collection period in combination with the time window value of the cable abnormality; Step S2, obtaining the cable operation status data within the acquisition period; Step S3: preprocessing and calculating the cable operation status data according to a preset cable status assessment model to provide cable status assessment data, including: The cable operation status data is processed by average interpolation, smoothing filtering, deviation standardization and variance maximization in turn to give the processed cable operation status data; the processed cable operation status data is analyzed and processed to give the cable core temperature, cable surface temperature change rate and local temperature difference on the cable surface; the threshold values ​​corresponding to the cable core temperature, cable surface temperature change rate and local temperature difference on the cable surface are obtained, and the cable status evaluation data is given in combination with the cable core temperature, cable surface temperature change rate and local temperature difference on the cable surface; Cable status assessment data satisfies the following relationship: ； in, Indicates cable status assessment data; , , , They are the cable core temperature, the rate of change of cable surface temperature, the local temperature difference on the cable surface, and the current load; , , , They represent the cable core temperature threshold, the cable surface temperature change rate threshold, the cable surface local temperature difference threshold, and the current load magnitude threshold respectively; represents the weight parameter; Step S4: Based on the comparison between the cable status assessment data and the preset conditions, a cable status assessment result is provided, including: The cable status assessment data is compared and analyzed with the warning threshold to determine whether an alarm should be issued; the processed cable operation status data corresponding to the alarm is obtained and loss analysis is performed to determine the loss result; the loss result is compared with the loss threshold to determine the loss level of the cable corresponding to the alarm.

2. The method according to claim 1, characterized in that In step S2, the cable operation status data includes cable surface temperature, current load size F , Workplace temperature TH and workplace humidity RH .

3. The method according to claim 1, characterized in that Step S1 includes: Obtain the number of cables corresponding to the historical temperature data, and combine the cable core temperature when the cable is abnormal to give the determination coefficient of the collection period; Determine the time from when the cable core temperature rises from the cable core temperature threshold to when a fault occurs based on historical temperature data, and screen out the maximum time from when the cable core temperature rises from the cable core temperature threshold to when a fault occurs; The collection period for obtaining the cable operation status data is determined based on the determination coefficient of the collection period and the maximum time for the cable core temperature to rise from the cable core temperature threshold to the occurrence of a fault.

4. The method according to claim 3, characterized in that The calculation formula of the acquisition period is as follows: ； in, represents the adjustment factor; represents the coefficient of determination; m is the number of cables; n is the number of cables with different core temperatures when all cables are abnormal; Indicates the maximum time from when the cable core temperature exceeds the cable core temperature threshold to when an abnormality occurs.

5. The method according to claim 1, characterized in that Obtain the processed cable operation status data corresponding to the alarm and perform loss analysis to give the loss result, satisfying the following relationship: ； in, is the cumulative loss of the cable, is the working age of the cable; is the factor of cable construction mode; is the workplace condition factor; , Cable work to the i 1. The historical abnormal effect factor of 20 years and the effect factor of working years; For cable work to i The annual current load magnitude factor; is the thermal aging reference rate of the cable; is a constant, representing the total working time of the cable, t is the working time of the cable; a' and b' are constants related to the activation energy of the cable insulation material; is the cable core temperature.

6. The method according to claim 1, characterized in that After step S4, the method further includes displaying the cable operation status data on the monitoring interface in real time and recording the loss results in a database.

7. A cable status assessment and early warning device, characterized in that: The cable status assessment and early warning method according to any one of claims 1 to 6 is adopted, and the device comprises: A cycle determination module is used to analyze the historical temperature data of the cable in advance and determine the collection cycle in combination with the time window value of the cable anomaly; A data acquisition module, which is used to acquire the cable operation status data within the acquisition period; A data evaluation module, which is used to pre-process and calculate the cable operation status data according to a preset cable status evaluation model, and provide cable status evaluation data; The cable evaluation module is used to provide a cable status evaluation result based on the comparison between the cable status evaluation data and the preset conditions.

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