Power grid cable temperature monitoring and alarming method, device, equipment and storage medium

Abstract

The invention provides a power grid cable temperature monitoring and alarming method, device and equipment and a storage medium. The method comprises the following steps: acquiring cable outer sheath temperature data through a temperature sensor and acquiring cable current load data and health condition indexes; calculating the temperature of the conductor according to the temperature data of the outer sheath, the current load data and the environmental parameter data by using a superposition principle calculation model based on time attenuation coefficient correction; according to the cable health condition index, calculating a grading early warning threshold value, and comparing the conductor temperature with the threshold value to determine an early warning grade; and when the early warning level reaches a preset condition, matching the sheath temperature data with a template in an abnormal mode library, identifying an abnormal reason through multiple algorithms, and generating a disposal suggestion in combination with geographic information system data. According to the invention, the conductor temperature can be quickly and accurately calculated, the early warning threshold value is dynamically adjusted, the abnormal reason is intelligently identified, and the accuracy and effectiveness of cable temperature monitoring and early warning are greatly improved.

Description

Technical Field

[0001] The present invention relates to the technical field of power grid monitoring, and particularly to a method, device, equipment and storage medium for monitoring and alarming the temperature of power grid cables. Background Art

[0002] With the acceleration of the urbanization process, the application proportion of cables in the power grid has increased year by year. Due to the excellent electrical properties of cross-linked polyethylene (XLPE), XLPE cables have become the main choice for distribution cables. However, the long-term operation of cables at high temperatures will significantly accelerate the aging process of XLPE insulation. Therefore, during the operation and maintenance stage, the current load of the cables must be appropriately restricted to prevent the conductor temperature from exceeding the maximum allowable temperature.

[0003] Existing cable temperature monitoring systems mainly calculate the conductor temperature based on the finite element method or a simplified thermal circuit model. However, these methods have problems such as high computational complexity and poor real-time performance. Especially in the case of rapid fluctuations in cable load, they cannot meet the requirements of real-time monitoring, making it difficult to timely detect potential cable overheating hazards and affecting the safe and stable operation of the power grid. Summary of the Invention

[0004] The main purpose of the present invention is to solve the technical problems of high computational complexity and poor real-time performance in existing cable temperature monitoring systems; The first aspect of the present invention provides a method for monitoring and alarming the temperature of power grid cables, and the method for monitoring and alarming the temperature of power grid cables includes: Collecting the sheath temperature data of the cable outer sheath through temperature sensors arranged at key positions of the cable, and obtaining the cable current load data and cable health status index of the cable; Calculating the conductor temperature of the cable by using a superposition principle calculation model corrected based on a time decay coefficient according to the temperature data of the cable outer sheath, the cable current load data, and the environmental parameter data; Calculating the classification warning threshold of the cable according to the cable health status index, and comparing the conductor temperature with the classification warning threshold to determine the warning level; When the warning level reaches a preset condition, performing pattern matching on the sheath temperature data with the templates in the cable temperature abnormal mode library, identifying the abnormal cause through multiple matching algorithms, and generating corresponding disposal suggestions in combination with the geographic information system data.

[0005] Optionally, in the first implementation manner of the first aspect of the present invention, the superposition principle calculation model includes a thermal response function, a time decay coefficient, and an environmental impact compensation matrix; Calculating the conductor temperature of the cable according to the temperature data of the cable outer sheath, the cable current load data, and the environmental parameter data by using a superposition principle calculation model corrected based on a time decay coefficient includes: Decompose the cable current load data into multiple step current sequences according to the sampling time interval, and apply a thermal response function to each step current sequence to calculate the corresponding basic temperature rise; Extract the environmental temperature, humidity, and groundwater level data from the environmental parameter database, construct an environmental impact mapping matrix through multiple regression analysis, and use the environmental impact mapping matrix to convert to obtain an environmental impact correction term and a humidity impact correction term; Dynamically calculate a thermal time constant adjustment coefficient according to the change rate between the historical load rate and the current load rate of the cable, correct the thermal time constant in the time decay coefficient through the thermal time constant adjustment coefficient, and apply the corrected time decay coefficient to the basic temperature rise to obtain a corrected temperature rise response; Linearly superimpose the corrected temperature rise responses corresponding to all step current sequences, and combine the initial conductor temperature, the environmental impact correction term, and the humidity impact correction term to calculate the conductor temperature of the cable.

[0006] Optionally, in the second implementation manner of the first aspect of the present invention, the decomposing the cable current load data into multiple step current sequences according to the sampling time interval, and applying a thermal response function to each step current sequence to calculate the corresponding basic temperature rise includes: Sample the cable current load data at a fixed time interval to obtain time series current data; Calculate the current difference between adjacent sampling points for the time series current data, use each difference as a step current, and construct multiple step current sequences; For each step current sequence in the multiple step current sequences, apply a thermal response function that describes the response of the conductor temperature to the step current to obtain a corresponding basic temperature rise time curve; Calculate the basic temperature rise generated by each step current sequence at the current moment according to the basic temperature rise time curve and the time point when the step current sequence occurs.

[0007] Optionally, in the third implementation manner of the first aspect of the present invention, the dynamically calculating a thermal time constant adjustment coefficient according to the change rate between the historical load rate and the current load rate of the cable, correcting the thermal time constant in the time decay coefficient through the thermal time constant adjustment coefficient, and applying the corrected time decay coefficient to the basic temperature rise to obtain a corrected temperature rise response includes: Extract the cable current load data for the past 24 hours from the cable operation history database, calculate the mean value of the cable current load data for the past 24 hours, and then divide the mean value by the cable rated current value to obtain the historical load rate; Divide the current cable current load data by the cable rated current value to obtain the current current load ratio, and calculate the change rate between the historical current load ratio and the current current load ratio, where the change rate includes the change amplitude and the change rate; Input the change rate into a preset thermal time constant adjustment model, calculate the thermal time constant adjustment coefficient, and multiply the original thermal time constant by the thermal time constant adjustment coefficient to obtain the corrected thermal time constant; Use the corrected thermal time constant to update the time decay coefficient function, obtain the corrected time decay coefficient, and apply the corrected time decay coefficient to the base temperature rise to calculate the corrected temperature rise response at the current moment.

[0008] Optionally, in the fourth implementation manner of the first aspect of the present invention, the calculating the conductor temperature of the cable by linearly superposing the corrected temperature rise responses corresponding to all step current sequences and combining the initial conductor temperature, the environmental impact correction term, and the humidity impact correction term includes: Arrange the corrected temperature rise responses generated by all step current sequences in chronological order and perform linear accumulation according to the superposition principle to obtain the total temperature rise value; Perform a weighted combination of the environmental impact correction term and the humidity impact correction term to obtain an environmental comprehensive impact correction value, and calculate the decay value of the temperature rise generated by the early step current at the current moment according to the time decay characteristic; Calculate the actual temperature of the cable conductor at the current moment by using the preset initial conductor temperature, the total temperature rise value, the environmental comprehensive impact correction value, and the temperature rise decay value, and correct the calculation result through a temperature compensation algorithm to output the final cable conductor temperature value.

[0009] Optionally, in the fifth implementation manner of the first aspect of the present invention, the calculating the grading warning threshold of the cable according to the cable health condition index and comparing the conductor temperature with the grading warning threshold to determine the warning level includes: Determine a health condition adjustment factor based on the cable health condition index, and the health condition adjustment factor decreases as the cable health condition index decreases; Retrieve the installation environment type of the cable from the cable installation environment classification library to obtain an environment adjustment factor, and obtain the season adjustment factor corresponding to the current date from the season characteristic database; Perform a weighted product operation on the base warning threshold, the health condition adjustment factor, the environment adjustment factor, and the season adjustment factor to calculate the grading warning threshold respectively; Compare the conductor temperature with the hierarchical warning threshold, and determine the warning level of the cable according to the comparison result.

[0010] Optionally, in the sixth implementation manner of the first aspect of the present invention, when the warning level reaches a preset condition, perform pattern matching on the sheath temperature data with templates in the cable temperature anomaly pattern library, identify the anomaly cause through multiple matching algorithms, and generate corresponding handling suggestions in combination with geographic information system data, including: When the warning level reaches a preset condition, extract the anomaly pattern template from the cable temperature anomaly pattern library, and apply the dynamic time warping algorithm to calculate the distance matrix between the sheath temperature data and each anomaly pattern template; Apply the spatial spectrum analysis method to the sheath temperature data at different positions along the cable to generate a temperature spatial distribution feature map, and at the same time calculate the time change rate curve of the sheath temperature data and perform a correlation analysis with the standard change rate pattern; Input the distance matrix, the temperature spatial distribution feature map, and the change rate correlation analysis result into a multi-factor decision-making model to determine the corresponding anomaly cause and confidence level, and query the geographic information system for the surrounding environment information data of the cable anomaly location; According to the anomaly cause, confidence level, and surrounding environment information, query the handling strategy library to generate handling suggestions including a load adjustment plan, an equipment switching path, and an emergency handling process.

[0011] The second aspect of the present invention provides a power grid cable temperature monitoring and alarm device, and the power grid cable temperature monitoring and alarm device includes: A data acquisition module, configured to collect the sheath temperature data of the cable outer sheath through temperature sensors arranged at key positions of the cable, and obtain the cable current load data and the cable health status index of the cable; A temperature calculation module, configured to calculate the conductor temperature of the cable by using a superposition principle calculation model corrected based on a time decay coefficient according to the temperature data of the cable outer sheath, the cable current load data, and the environmental parameter data; A threshold judgment module, configured to calculate the hierarchical warning threshold of the cable according to the cable health status index, and compare the conductor temperature with the hierarchical warning threshold to determine the warning level; An anomaly diagnosis module, configured to, when the warning level reaches a preset condition, perform pattern matching on the sheath temperature data with templates in the cable temperature anomaly pattern library, identify the anomaly cause through multiple matching algorithms, and generate corresponding handling suggestions in combination with geographic information system data.

[0012] In the third aspect of the present invention, a power grid cable temperature monitoring and alarming device is provided, including: a memory and at least one processor, wherein instructions are stored in the memory, and the memory and the at least one processor are interconnected through a line; the at least one processor calls the instructions in the memory to enable the power grid cable temperature monitoring and alarming device to execute the steps of the above-mentioned power grid cable temperature monitoring and alarming method.

[0013] In the fourth aspect of the present invention, a computer-readable storage medium is provided, in which instructions are stored. When it runs on a computer, it enables the computer to execute the steps of the above-mentioned power grid cable temperature monitoring and alarming method.

[0014] For the above-mentioned power grid cable temperature monitoring and alarming method, device, equipment and storage medium, temperature data of the cable outer sheath is collected through a temperature sensor, and cable current load data and health status index are obtained; a calculation model based on the superposition principle corrected by a time decay coefficient is used to calculate the conductor temperature according to the outer sheath temperature data, current load data and environmental parameter data; a hierarchical early warning threshold is calculated according to the cable health status index, and the conductor temperature is compared with the threshold to determine the early warning level; when the early warning level reaches a preset condition, the sheath temperature data is matched with templates in the abnormal mode library, the abnormal cause is identified through multiple algorithms, and a disposal suggestion is generated in combination with geographic information system data. The present invention can quickly and accurately calculate the conductor temperature, dynamically adjust the early warning threshold, and intelligently identify the abnormal cause, greatly improving the accuracy and effectiveness of cable temperature monitoring and early warning.

[0015] Other features and advantages of the present invention will be described in the subsequent specification, and, in part, will be obvious from the specification, or will be understood by implementing the present invention. The objectives and other advantages of the present invention are achieved and obtained by the structures specifically pointed out in the specification, claims and drawings.

[0016] To make the above-mentioned objectives, features and advantages of the present invention more obvious and understandable, the following specific preferred embodiments are given, and in conjunction with the accompanying drawings, the detailed description is as follows. Brief Description of the Drawings

[0017] Figure 1 It is a schematic diagram of the first embodiment of the power grid cable temperature monitoring and alarming method in the embodiment of the present invention; Figure 2 It is a schematic diagram of an embodiment of the power grid cable temperature monitoring and alarming device in the embodiment of the present invention; Figure 3 It is a schematic diagram of an embodiment of the power grid cable temperature monitoring and alarming equipment in the embodiment of the present invention. Detailed Embodiments

[0018] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings. Apparently, the described embodiments are some, but not all, of the embodiments of the present invention. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the scope of protection of the present invention.

[0019] As used in the embodiments of the present invention, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or device that includes a series of steps or units is not limited to the listed steps or units, but optionally further includes other steps or units not listed, or optionally further includes other steps or units inherent to these processes, methods, products, or devices.

[0020] To facilitate the understanding of this embodiment, a method for monitoring and alarming the temperature of a power grid cable disclosed in the embodiments of the present invention will be introduced in detail first. As Figure 1 shown, this method includes the following steps: 101. Collect the sheath temperature data of the cable outer sheath through temperature sensors arranged at key positions of the cable, and obtain the cable current load data and cable health status index of the cable; In an embodiment of the present invention, the critical positions of the cable are first determined, including areas prone to temperature anomalies such as cable joints, intersections, bends, and positions close to heat sources. Then, resistance temperature sensors (RTSs) are arranged according to the cable path. Such sensors have the characteristics of high measurement accuracy and good stability. When arranging the resistance temperature sensors, a differential density strategy is adopted, that is, the sensor arrangement density is determined by calculating the environmental heat interference coefficient, which is comprehensively calculated based on factors such as the cable environment type (such as cable trench, concrete pipe), the surrounding heat source situation (such as other power equipment, steam pipe), and seasonal ground temperature changes. In actual arrangement, for areas with a high environmental heat interference coefficient, the sensor spacing is set to 5 - 10 meters, while in areas with a stable environment, the sensor spacing can be expanded to 20 - 30 meters. These temperature sensors transmit the collected cable outer sheath temperature data to the nearest temperature measurement host through narrowband Internet of Things technologies such as NB-IoT or LoRa. The temperature measurement host has edge computing capabilities and can detect and preliminarily process the data for outliers. The processed data is then transmitted to the central data platform through the 4G network. At the same time, the system obtains the real-time current load data of the cable from the SCADA interface of the power grid monitoring system, including information such as current amplitude, phase, and harmonic content. In addition, the cable health status index is obtained from the cable asset management system. This index is comprehensively calculated based on parameters such as the cable operation years (such as a 10kV XLPE cable that has been in operation for 8 years), the historical highest temperature (such as a record of reaching 85°C), the temperature fluctuation frequency (such as the number of times the temperature changes by more than 20°C within a day), the load rate fluctuation range (such as the situation where the load rate suddenly increases from 30% to 90%), and the degree of insulation aging (known through the measurement of the dielectric loss factor). The cable health status index is represented by a value from 0 to 100, and the higher the value, the better the cable condition. For example, a brand-new cable has an index of 100, while a cable that has been in operation for 15 years and has been overloaded multiple times may have an index as low as 40.

[0021] 102. According to the temperature data of the cable outer sheath, the cable current load data, and the environmental parameter data, use the superposition principle calculation model corrected by the time decay coefficient to calculate the conductor temperature of the cable; In an embodiment of the present invention, the superposition principle calculation model includes a thermal response function, a time decay coefficient, and an environmental impact compensation matrix; calculating the conductor temperature of the cable by using the superposition principle calculation model corrected based on the time decay coefficient according to the temperature data of the cable outer sheath, the cable current load data, and the environmental parameter data includes: decomposing the cable current load data into a plurality of step current sequences according to the sampling time interval, and applying the thermal response function to each step current sequence to calculate the corresponding basic temperature rise; extracting environmental temperature, humidity, and groundwater level data from the environmental parameter database, constructing an environmental impact mapping matrix through multiple regression analysis, and obtaining an environmental impact correction term and a humidity impact correction term by converting with the environmental impact mapping matrix; dynamically calculating a thermal time constant adjustment coefficient according to the change rate between the historical load rate and the current load rate of the cable, correcting the thermal time constant in the time decay coefficient by the thermal time constant adjustment coefficient, and applying the corrected time decay coefficient to the basic temperature rise to obtain a corrected temperature rise response; linearly superimposing the corrected temperature rise responses corresponding to all step current sequences, and combining the initial conductor temperature, the environmental impact correction term, and the humidity impact correction term to calculate the conductor temperature of the cable.

[0022] Specifically, the superposition principle calculation model is a method for calculating the conductor temperature of a cable based on thermal principles. This model includes three core components: a thermal response function, a time decay coefficient, and an environmental impact compensation matrix. The thermal response function describes the response characteristics of the conductor temperature to step current changes. This function usually includes parameters such as steady-state temperature rise, initial temperature rise, temperature rise coefficient, and thermal time constant. These parameters are obtained through cable thermal characteristic experiments. For example, for a typical 10kV XLPE three-core cable, the thermal time constants in its thermal response function are 9041 seconds, 57930 seconds, and 487400 seconds respectively. These different time constants reflect the thermal response speeds of different parts of the cable. The smaller time constant corresponds to the rapid thermal response of the conductor and the internal insulation layer, the medium time constant corresponds to the thermal response of the outer sheath, and the largest time constant represents the heat exchange process between the cable and the surrounding environment. The time decay coefficient characterizes the decay characteristic of temperature over time, usually in exponential form, and is used to calculate the influence degree of historical step current on the current temperature. For a specific cable type, the time decay coefficient needs to be customized according to its structure, material, and installation method. The environmental impact compensation matrix is used to quantify the influence of environmental factors on the cable temperature, including the weight coefficients of environmental factors such as environmental temperature, humidity, and groundwater level. This matrix is obtained through regression analysis of a large amount of historical operation data and can accurately reflect the influence degree of each environmental factor on the cable temperature and their interaction relationship.

[0023] Specifically, in the process of calculating the cable conductor temperature, the cable current load data is first sampled at fixed time intervals (such as 15 minutes), and the current difference between adjacent sampling points is calculated. Each difference is regarded as a step current sequence. For example, when the current increases from 200 A to 300 A, a step current sequence with an amplitude of 100 A is generated; if the current then drops from 300 A to 250 A, a step current sequence with an amplitude of -50 A is generated. This decomposition method simplifies the complex current change curve into a superposition of a series of step currents, greatly reducing the computational complexity. Then, the thermal response function is applied to each step current sequence to calculate its basic temperature rise. Specifically, the system substitutes the step current amplitude into the thermal response function, combines the occurrence time and the current calculation time, and obtains the basic temperature rise generated by this step current at the current moment. For a step current with an amplitude of 100 A, if 2 hours have passed since its occurrence, a basic temperature rise of 5.7 °C may be calculated through the thermal response function; for a step current that has just occurred, its basic temperature rise may be smaller, only the value in the initial heating stage. This calculation method conforms to the physical characteristics of cable thermology, can accurately reflect the dynamic impact of current changes on the conductor temperature, and is particularly suitable for actual operating scenarios where the current load fluctuates frequently.

[0024] Specifically, during the calculation process, the system extracts data such as the ambient temperature (such as 20 °C at 1 meter below the ground surface), humidity (such as 60%), and groundwater level (such as 1.5 meters below the ground surface) near the installation location from the environmental parameter database. These environmental parameters are collected and stored in real time through an environmental sensor network arranged around the cable. The sensor types include soil temperature sensors, soil humidity sensors, and groundwater level sensors, etc. Considering the complexity of the urban environment, the system also combines meteorological station data and topographic and geomorphic information in the geographic information system to reasonably interpolate the missing environmental parameters. The system uses the multiple regression analysis method to construct an environmental impact mapping matrix based on historical data. This matrix contains multiple regression coefficients, reflecting the influence degree of each environmental factor on the cable temperature. For example, the influence coefficient of the groundwater level on the temperature of directly buried cables may be as high as 0.8, while for cables in concrete pipelines, this coefficient may be only 0.2. By substituting the real-time environmental parameters into this mapping matrix, the system calculates the environmental impact correction term and the humidity impact correction term. The environmental impact correction term mainly reflects the direct impact of the ambient temperature on the conductor temperature. Generally, for every 10 °C increase in the ambient temperature, the conductor temperature may rise by 5 - 8 °C; while the humidity impact correction term considers the change in the cable heat dissipation conditions caused by humidity. An increase in humidity will increase the thermal conductivity of the soil, thereby enhancing the cable's heat dissipation effect. In some cases, a high-humidity environment can reduce the conductor temperature by 2 - 3 °C.

[0025] Specifically, during the calculation process, the system extracts the current load data for the past 24 hours from the cable operation history database, calculates the historical average load rate, and obtains the current load rate by dividing the current current load data by the cable rated current value. By comparing the historical load rate and the current load rate, the rate of change is calculated. For example, when the load rate suddenly increases from 40% to 80%, the rate of change is 100%; while when it slowly decreases from 90% to 85%, the rate of change is -5.6%. Based on this rate of change, the system queries the corresponding thermal time constant adjustment coefficient from the preset adjustment model. This adjustment model is a functional mapping relationship constructed based on experimental data and theoretical analysis, taking into account the differences in the thermal response characteristics of the cable under different load conditions. When the rate of change exceeds 50%, the adjustment coefficient may be set to 0.8; when the rate of change is between 20% and 50%, the adjustment coefficient may be 0.9; while when the rate of change is less than 20%, the adjustment coefficient approaches 1.0. This adjustment coefficient is used to correct the thermal time constant in the time decay coefficient, thereby adjusting the dynamic characteristics of the temperature response. When the load increases significantly, the thermal time constant will decrease accordingly, reflecting the characteristic that the cable heats up faster under high loads; conversely, when the load decreases, the thermal time constant increases, indicating that the cooling process is relatively slow. The system applies the corrected time decay coefficient to the basic temperature rise calculation to obtain the corrected temperature rise response considering the influence of load dynamic changes.

[0026] Specifically, the system arranges the corrected temperature rise responses corresponding to all step current sequences in chronological order and linearly accumulates them according to the superposition principle to obtain the total temperature rise value. This process is based on the linear system theory, that is, it is considered that the temperature rises generated by each step current are independent of each other and can be superimposed. In actual calculations, the system usually only considers the step currents within the time range of the past three times the maximum thermal time constant. For a 10kV XLPE cable, this time range is approximately 17 days. The influence of current changes beyond this time range on the current temperature can be ignored. This truncation process not only ensures the calculation accuracy but also improves the calculation efficiency. Then, the total temperature rise value is added to the initial conductor temperature obtained from the temperature measurement system (such as 40°C during steady-state operation), and the environmental impact correction term (such as +5°C) and humidity impact correction term (such as -2°C) are added. Finally, the actual temperature of the cable conductor is calculated. In some special cases, the system will also apply a temperature compensation algorithm to fine-tune the calculation results to reduce the system error caused by model simplification. For example, for particularly drastic load changes, the system may introduce a non-linear correction term; for the scenario of multiple cables installed in parallel, the thermal coupling effect between the cables will be considered. This calculation method based on the superposition principle has a fast calculation speed, usually completing the calculation within 1 second, and the calculation error is less than 1°C, meeting the requirements of the cable temperature monitoring system for real-time and accuracy.

[0027] Further, the steps of decomposing the cable current load data into multiple step current sequences according to the sampling time interval and applying the thermal response function to each step current sequence to calculate the corresponding basic temperature rise include: sampling the cable current load data at fixed time intervals to obtain time series current data; calculating the current difference between adjacent sampling points for the time series current data, taking each difference as a step current, and constructing multiple step current sequences; for each step current sequence in the multiple step current sequences, applying the thermal response function that describes the response of the conductor temperature to the step current to obtain the corresponding basic temperature rise time curve; and calculating the basic temperature rise generated by each step current sequence at the current moment according to the basic temperature rise time curve and the time point when the step current sequence occurs.

[0028] Specifically, in the cable temperature monitoring system, it is first necessary to sample the cable current load data at fixed time intervals to obtain time series current data. This system obtains the cable current data in real time from the SCADA interface of the power grid monitoring system. The sampling interval is usually set to 15 minutes, which not only meets the time scale requirements of cable temperature changes but also effectively reduces data storage and calculation amounts. For example, for a certain 10kV XLPE cable line, the system collects the current values of 96 sampling points at 15-minute intervals within 24 hours, forming time series current data such as [230A, 245A, 258A, 270A, 285A, 275A,...]. These data are stored in the system database according to the time stamps, retaining the complete historical record of the cable load changes. In addition, the system also preprocesses the original sampling data, including outlier detection and data smoothing, to ensure the accuracy of the sampling data. The outlier detection uses the sliding window median filtering method, which can effectively identify and correct the abnormal data points caused by measurement interference or communication errors, thereby improving the accuracy of subsequent calculations.

[0029] Specifically, after obtaining the time - series current data, the system calculates the current difference between adjacent sampling points for these data. Each difference is regarded as a step current, and multiple step - current sequences are constructed. In specific implementation, the system traverses each pair of adjacent sampling points in the time - series current data, calculates the difference between the current value at the later moment minus the current value at the previous moment, and obtains a series of current change amounts. For example, for the current values of 230A and 245A at adjacent moments, the calculated current difference is 15A; for 245A and 258A, the difference is 13A; and so on. These current differences are regarded as step currents occurring at the corresponding time points. Each step current has two key attributes: its amplitude (which can be positive or negative) and the occurrence time. A positive step current indicates an increase in load, and a negative step current indicates a decrease in load. The system organizes these step currents and their time information into step - current sequences, such as [(t1, 15A), (t2, 13A), (t3, 12A), (t4, 15A), (t5, - 10A),...], where t1, t2, etc. represent the time points at which the step currents occur. This decomposition method transforms the complex load curve into a series of simple step - like changes, which conforms to the superposition characteristic of the cable thermal response and lays a foundation for subsequent temperature calculations.

[0030] Specifically, for each step - current sequence constructed by the system, the system applies a thermal response function that describes the response of the conductor temperature to the step current to obtain the corresponding basic temperature - rise time curve. The thermal response function is a function that describes the variation law of the cable conductor temperature with time under the input of a unit step current, and is usually obtained based on the cable thermophysical model and experimental measurement results. For a 10kV XLPE three - core cable, its thermal response function contains multiple exponential terms, and each exponential term corresponds to a thermal time constant and a temperature - rise coefficient, respectively representing the response characteristics of different heat - capacity components. The system substitutes the amplitude of each step current into the thermal response function to generate the temperature - response curve caused by this step current. For example, for a step current with an amplitude of 15A, the generated temperature - rise time curve may be a curve that starts from 0, gradually rises, and finally tends to a stable value (about 0.8°C); while for a step current with an amplitude of - 10A, a curve that starts from 0 and gradually drops to a stable negative value (about - 0.5°C) will be generated. These basic temperature - rise time curves accurately depict the independent contribution of each step current to the conductor temperature and lay a foundation for superposition calculations. The system has pre - established a thermal response function library for different types and specifications of cables to ensure that the correct thermal response characteristics can be matched during the calculation process.

[0031] Specifically, the system calculates the basic temperature rise generated by each step current sequence at the current moment according to the basic temperature rise time curve and the time points when the step current sequences occur. For each step current, the system calculates the time difference between the occurrence time and the current moment, and queries the temperature rise value on the corresponding basic temperature rise time curve according to this time difference. For example, for a step current with an amplitude of 15 A that occurred 2 hours ago, if the value of the basic temperature rise time curve at the 2-hour point is 0.6 °C, then the temperature rise contribution of this step current at the current moment is 0.6 °C. The system performs similar calculations for all step currents to obtain a series of basic temperature rise values. These basic temperature rise values represent the independent contributions of current changes at different times in history to the current temperature and are an important part of the subsequent conductor temperature calculation. In practical applications, considering the calculation efficiency and temperature response characteristics, the system usually only considers the step currents within the time range of three times the maximum thermal time constant in the past. For 10 kV XLPE cables, this time range is approximately 17 days. The influence of current changes beyond this time range on the current temperature has attenuated to a negligible level, so it is no longer included in the calculation. This processing method not only ensures the calculation accuracy but also improves the system operation efficiency. In addition, for step currents with small amplitudes (such as less than 1% of the cable rated current), the system will also perform screening to reduce the calculation amount without significantly affecting the accuracy.

[0032] Further, the dynamic calculation of the thermal time constant adjustment coefficient according to the change rate between the historical load rate and the current load rate of the cable, the correction of the thermal time constant in the time decay coefficient by the thermal time constant adjustment coefficient, and the application of the corrected time decay coefficient to the basic temperature rise to obtain the corrected temperature rise response include: extracting the cable current load data in the past 24 hours from the cable operation history database, calculating the average value of the cable current load data in the past 24 hours, and then dividing the average value by the cable rated current value to obtain the historical load rate; dividing the current cable current load data by the cable rated current value to obtain the current current load ratio, and calculating the change rate between the historical current load ratio and the current current load ratio, where the change rate includes the change amplitude and the change rate; inputting the change rate into a preset thermal time constant adjustment model to calculate the thermal time constant adjustment coefficient, and multiplying the original thermal time constant by the thermal time constant adjustment coefficient to obtain the corrected thermal time constant; using the corrected thermal time constant to update the time decay coefficient function to obtain the corrected time decay coefficient, and applying the corrected time decay coefficient to the basic temperature rise to calculate the corrected temperature rise response at the current moment.

[0033] Specifically, in the cable temperature monitoring system, it is first necessary to extract the cable current load data for the past 24 hours from the cable operation historical database, calculate the mean value of the cable current load data for the past 24 hours, and then divide the mean value by the cable rated current value to obtain the historical load rate. The system extracts the current records for the most recent 24 hours from the database through an SQL query statement. These records are usually at 15-minute intervals and include timestamps and corresponding current values. For example, for a certain 10kV XLPE cable (rated current of 400A), the extracted data may be in the form of [(2023-05-12 08:00:00, 220A), (2023-05-12 08:15:00, 235A),..., (2023-05-13 07:45:00, 250A)]. The system performs an arithmetic average on the current values of these 96 sampling points to obtain the average current value for the past 24 hours, such as 232A. Then, this average value is divided by the cable rated current of 400A to obtain a historical load rate of 0.58 or 58%. This historical load rate reflects the recent load level of the cable and is a benchmark for judging the current load change situation. For different cable lines, the system adjusts the extraction duration of historical data according to their operating characteristics. For distribution cables with faster load changes, 12-hour data may be used; while for main lines with more stable loads, 48-hour data may be used to obtain a more representative historical load level.

[0034] Specifically, while obtaining the historical load rate, the system divides the current cable current load data by the cable rated current value to obtain the current current load ratio, and calculates the change rate between the historical current load ratio and the current current load ratio. Among them, the change rate includes the change amplitude and the change rate. Specifically, the system obtains the latest current measurement value, such as 320A, from the current monitoring device, and then divides it by the rated current of 400A to obtain the current load ratio of 0.8 or 80%. Then, the difference between the current load ratio and the historical load rate (0.8 - 0.58 = 0.22) is calculated as the change amplitude, indicating that the load has increased by 22 percentage points. At the same time, the system also calculates the change rate, that is, the change amplitude divided by the time of change, expressed as a percentage change per hour. Assuming that the current load ratio has risen from the historical level to 80% in the most recent 2 hours, the change rate is 0.22 / 2 = 0.11 or 11% per hour. These two parameters together constitute the change rate, comprehensively describing the state of cable load change. The change amplitude reflects the degree to which the load deviates from the historical level, while the change rate reflects the severity of this change. A larger change amplitude and a higher change rate usually mean that the thermal state of the cable may change significantly and more adjustments to the thermal time constant are required.

[0035] Specifically, after calculating the change amplitude and change rate, the system inputs these two parameters into a preset thermal time constant adjustment model to calculate the thermal time constant adjustment coefficient, and multiplies the original thermal time constant by the thermal time constant adjustment coefficient to obtain the corrected thermal time constant. The thermal time constant adjustment model is a predefined function mapping relationship used to convert the load change characteristics into the adjustment coefficient of the thermal time constant. This model is established based on cable thermal experiments and theoretical analysis and usually takes the form of a piecewise function. For example, when the change amplitude is less than 10% and the change rate is less than 5% / hour, the adjustment coefficient is close to 1.0, indicating that almost no adjustment is required; when the change amplitude is between 10% - 30% or the change rate is between 5% - 15% / hour, the adjustment coefficient may be 0.9, indicating a slight adjustment; when the change amplitude is greater than 30% or the change rate is greater than 15% / hour, the adjustment coefficient may drop to 0.8, indicating a large adjustment is needed. In practical applications, the system usually maps the change amplitude and change rate to a point on a two-dimensional plane, and then obtains the corresponding adjustment coefficient through table lookup or interpolation calculation. For the above example, with a change amplitude of 22% and a change rate of 11% / hour, the adjustment coefficient obtained through model calculation is 0.85. The system applies this adjustment coefficient to the original thermal time constant. For a 10kV XLPE cable, its three thermal time constants (9041 seconds, 57930 seconds, and 487400 seconds) are respectively multiplied by 0.85 to obtain the corrected thermal time constants (7685 seconds, 49241 seconds, and 414290 seconds). This adjustment reflects the physical characteristic that the cable thermal response accelerates when the load increases significantly.

[0036] Specifically, the system updates the time decay coefficient function using the corrected thermal time constant to obtain the corrected time decay coefficient, and applies the corrected time decay coefficient to the base temperature rise to calculate the corrected temperature rise response at the current moment. The time decay coefficient function usually takes an exponential form and includes the thermal time constant as a key parameter. For example, for a cable with three thermal time constants, its time decay coefficient function may include three exponential terms, each corresponding to a thermal time constant and a temperature rise coefficient. The system substitutes the corrected thermal time constant into the function expression to recalculate the time decay coefficient. These corrected time decay coefficients can more accurately reflect the thermal dynamic characteristics of the cable under the current load change conditions. Subsequently, the system applies the corrected time decay coefficient to the previously calculated base temperature rise. Specifically, the base temperature rise of each step current is adjusted according to the time difference between its occurrence time and the current time through the corrected time decay coefficient function. For example, for a 15A step current that occurred 2 hours ago, its original base temperature rise was 0.6°C. After applying the corrected time decay coefficient, it may be adjusted to 0.65°C, indicating that under the condition of increasing load, the influence of historical current changes on the current temperature will increase slightly. The system makes similar adjustments to the base temperature rises of all step currents to obtain a series of corrected temperature rise responses. This method of adjusting the thermal time constant based on the load dynamic characteristics significantly improves the accuracy of transient temperature calculation. Especially in the case of frequent fluctuations in cable load, the calculation error can usually be controlled within 0.5°C, meeting the requirements of practical engineering applications.

[0037] 103. Calculate the hierarchical warning threshold of the cable according to the cable health status index, and compare the conductor temperature with the hierarchical warning threshold to determine the warning level; In an embodiment of the present invention, the calculating the hierarchical warning threshold of the cable according to the cable health status index and comparing the conductor temperature with the hierarchical warning threshold to determine the warning level includes: determining a health status adjustment factor based on the cable health status index, and the health status adjustment factor decreases as the cable health status index decreases; retrieving the installation environment type of the cable from the cable installation environment classification library to obtain an environment adjustment factor, and obtaining the season adjustment factor corresponding to the current date from the season feature database; performing a weighted product operation on the base warning threshold, the health status adjustment factor, the environment adjustment factor, and the season adjustment factor to calculate the hierarchical warning threshold respectively; comparing the size of the conductor temperature with the hierarchical warning threshold, and determining the warning level of the cable according to the comparison result.

[0038] Specifically, in the cable temperature monitoring system, first, a health status adjustment factor is determined based on the cable health status index, and this adjustment factor decreases as the cable health status index decreases. The cable health status index is a value ranging from 0 to 100 and is calculated by comprehensively considering factors such as the cable operation years, historical maximum temperature, temperature fluctuation frequency, load rate fluctuation amplitude, and insulation aging degree. For example, for a 10kV XLPE cable that has been in operation for 8 years, with a historical maximum temperature reaching 85°C, the number of times the average weekly temperature change exceeds 20°C being 3 times, the maximum load rate fluctuation amplitude being 60%, and the insulation aging degree being medium as shown by the measurement of the dielectric loss factor, its health status index may be calculated as 75. The system converts the health status index into a health status adjustment factor according to a preset mapping relationship, and this mapping relationship usually adopts a piecewise linear or nonlinear function. In practical applications, the adjustment factor corresponding to the health status index in the range of 90 - 100 is 1.0, indicating that the cable is in good condition and can operate according to the standard warning threshold; the adjustment factor corresponding to the health status index in the range of 70 - 90 is 0.95, indicating that the cable has slight aging; the adjustment factor corresponding to the health status index in the range of 50 - 70 is 0.9, indicating that the cable has obvious aging; when the health status index is below 50, the adjustment factor may drop to 0.85 or lower, indicating that the cable is in a highly aged state and requires a more conservative warning threshold. For the aforementioned cable example, the health status adjustment factor corresponding to the health status index of 75 is 0.95, which means that the warning threshold of this cable will be slightly lower than that of a brand-new cable to increase the safety margin.

[0039] Specifically, while determining the health status adjustment factor, the system retrieves the installation environment type of the cable from the cable installation environment classification library to obtain the environmental adjustment factor, and obtains the seasonal adjustment factor corresponding to the current date from the seasonal feature database. The cable installation environment classification library contains various common installation methods, such as direct burial, cable trench, concrete pipe, tunnel, etc. Each installation method corresponds to a preset environmental adjustment factor. These adjustment factors are determined based on the differences in the heat dissipation conditions of the cable in different installation environments. For example, the adjustment factor corresponding to the well-ventilated cable tunnel installation environment is 1.0, indicating ideal heat dissipation conditions; the adjustment factor for the cable trench installation environment is 0.98, indicating good heat dissipation conditions; the adjustment factor for the concrete pipe installation is 0.95, indicating limited heat dissipation; while the adjustment factor for direct burial in dry soil may be as low as 0.93, indicating poor heat dissipation conditions. The system learns that the installation method of this cable is concrete pipe through querying the cable asset management system, so the environmental adjustment factor is 0.95. At the same time, the system also obtains the seasonal adjustment factor corresponding to the current date from the seasonal feature database. The seasonal adjustment factor reflects the influence of different seasons on the operating temperature of the cable. Generally, the seasonal adjustment factor is lower in summer and higher in winter. For example, in the northern region, the seasonal adjustment factor in midsummer (July - August) is 0.95, indicating high environmental temperature and the need for a more conservative warning threshold; the adjustment factor in spring and autumn (April - May and September - October) is 1.0, indicating moderate environmental temperature; while the adjustment factor in winter (December - February) is 1.05, indicating low environmental temperature and good heat dissipation conditions for the cable. Assuming the current date is May 15th, the system queries the seasonal feature database and obtains the corresponding seasonal adjustment factor of 1.0.

[0040] Specifically, the system performs a weighted product operation on the basic warning threshold, the health condition adjustment factor, the environmental adjustment factor, and the seasonal adjustment factor to calculate the warning thresholds for the four levels of blue warning, yellow warning, orange warning, and red warning respectively. The basic warning threshold is a standard temperature threshold preset based on the cable type and rated parameters. For 10kV XLPE cables, its basic warning thresholds are usually set as follows: blue warning (attention level) 70°C, yellow warning (caution level) 80°C, orange warning (warning level) 85°C, red warning (danger level) 90°C. These basic thresholds are determined based on cable design specifications and long-term operation experience, reflecting the cable insulation aging rate and safety margin at different temperatures. The system multiplies these basic thresholds by the aforementioned three adjustment factors respectively to obtain the graded warning thresholds for specific cables under specific conditions. The calculation formula is: Warning Threshold = Basic Warning Threshold × Health Condition Adjustment Factor × Environmental Adjustment Factor × Seasonal Adjustment Factor. For the aforementioned example, the health condition adjustment factor is 0.95, the environmental adjustment factor is 0.95, and the seasonal adjustment factor is 1.0. Then the warning thresholds for the four levels are respectively: Blue Warning Threshold = 70°C × 0.95 × 0.95 × 1.0 = 63.2°C, Yellow Warning Threshold = 80°C × 0.95 × 0.95 × 1.0 = 72.2°C, Orange Warning Threshold = 85°C × 0.95 × 0.95 × 1.0 = 76.7°C, Red Warning Threshold = 90°C × 0.95 × 0.95 × 1.0 = 81.2°C. This dynamic adjustment mechanism enables the warning threshold to adapt to changes in cable status and environmental conditions, improving the pertinence and effectiveness of warnings.

[0041] Specifically, the system compares the calculated conductor temperature with the hierarchical warning thresholds, and determines the warning level of the cable according to the comparison results. The system reads the currently calculated cable conductor temperature, such as 65°C, and then compares it with the warning thresholds of four levels (63.2°C, 72.2°C, 76.7°C, and 81.2°C) in turn. Since 65°C is greater than the blue warning threshold of 63.2°C but less than the yellow warning threshold of 72.2°C, the system determines that the current cable is in the blue warning (attention level) state. Different warning levels correspond to different disposal strategies: the blue warning indicates that the cable operation status needs to be monitored, and the monitoring frequency should be increased; the yellow warning indicates that the cable temperature is approaching the warning line, and it is necessary to closely monitor and consider load adjustment; the orange warning indicates that the cable temperature has reached the dangerous area, and it is necessary to immediately adjust the load or take other cooling measures; the red warning indicates that the cable temperature is extremely high, there are serious safety hazards, and it is necessary to take emergency measures, and even consider outage. The system notifies relevant personnel of the determined warning level through various methods, including interface display, SMS notification, email alert, etc., and triggers the corresponding disposal process according to the preset rules. This temperature monitoring and alarm mechanism based on multi-level warning thresholds can timely detect the risk of cable overheating, prevent the accelerated aging of insulation and faults caused by too high temperature, and significantly improve the safety and reliability of power grid operation.

[0042] 104. When the warning level reaches the preset condition, the sheath temperature data is matched with the templates in the cable temperature anomaly pattern library, the anomaly causes are identified through various matching algorithms, and corresponding disposal suggestions are generated in combination with the geographical information system data.

[0043] In an embodiment of the present invention, the step of when the warning level reaches the preset condition, matching the sheath temperature data with the templates in the cable temperature anomaly pattern library, identifying the anomaly causes through various matching algorithms, and generating corresponding disposal suggestions in combination with the geographical information system data includes: when the warning level reaches the preset condition, extracting the anomaly pattern templates from the cable temperature anomaly pattern library, and applying the dynamic time warping algorithm to calculate the distance matrix between the sheath temperature data and each anomaly pattern template; applying the spatial spectrum analysis method to the sheath temperature data at different positions along the cable to generate a temperature spatial distribution feature map, and at the same time calculating the time change rate curve of the sheath temperature data and performing a correlation analysis with the standard change rate pattern; inputting the distance matrix, the temperature spatial distribution feature map, and the change rate correlation analysis results into a multi-factor decision-making model to determine the corresponding anomaly causes and confidence levels, and querying the geographical information system for the surrounding environment information data of the cable anomaly location; according to the anomaly causes, confidence levels, and surrounding environment information, querying the disposal strategy library to generate disposal suggestions including load adjustment plans, equipment switching paths, and emergency disposal procedures.

[0044] Specifically, in the cable temperature monitoring system, when the warning level reaches the preset condition, the system immediately extracts the abnormal mode template from the cable temperature abnormal mode library and applies the dynamic time warping algorithm to calculate the distance matrix between the sheath temperature data and each abnormal mode template. The preset condition is usually a yellow warning or above, indicating that the cable temperature has exceeded the normal operating range and further diagnosis of the abnormal cause is required. The cable temperature abnormal mode library is a built-in knowledge base of the system, which contains various typical temperature abnormal modes, such as overload, insulation aging, joint heating, external heat source interference, etc. Each abnormal mode has its characteristic temperature curve, which is generated through historical fault case analysis and theoretical simulation, and describes the time evolution characteristics of the cable temperature under different abnormal conditions. For example, the overload mode is characterized by a uniform increase in temperature and a positive correlation with the current load; the insulation aging mode is characterized by a local abnormal increase in temperature under normal load; the joint heating mode is characterized by a significantly higher temperature at the joint than at other positions; while the external heat source interference mode is characterized by uneven temperature distribution and low correlation with the current load. The system extracts the time series of the cable sheath temperature data for the most recent 24 hours from the data platform and applies the dynamic time warping (DTW) algorithm to these data to calculate their similarity with each abnormal mode template. The DTW algorithm can handle the nonlinear stretching and alignment problems of time series and is particularly suitable for comparing temperature curves with inconsistent time scales. The calculation result forms a distance matrix, and each element in the matrix represents the similarity distance between the measured temperature data and a certain abnormal mode template. The smaller the distance, the higher the similarity.

[0045] In addition to time series analysis, the system also applies spatial spectrum analysis to sheath temperature data at different locations along the cable to generate a temperature spatial distribution feature map. It also calculates a time-rate-of-change curve for the sheath temperature data and performs correlation analysis with a standard rate-of-change pattern. Spatial spectrum analysis focuses on the spatial distribution of temperature. By analyzing data from temperature sensors placed at different locations along the cable, it generates a feature map reflecting the spatial distribution of temperature. This feature map, with the cable path as the horizontal axis and the temperature value as the vertical axis, clearly displays the temperature distribution along the entire cable length. Under normal circumstances, the temperature distribution should be relatively uniform along the cable length, or exhibit a gradual change consistent with the load distribution. However, under abnormal circumstances, the feature map may exhibit localized abrupt changes or abnormal peaks, which often indicate the location of a fault. For example, if the feature map shows a significant high temperature peak at a joint (e.g., at the 300-meter mark), this typically indicates an overheating problem at that joint. The system also calculates a time-rate-of-change curve for the sheath temperature data—the rate of temperature change over time. This curve reflects the dynamic nature of temperature changes, with different types of abnormalities typically exhibiting different rate-of-change characteristics. Temperature rises caused by overload typically show a slowly rising rate of change curve; sudden external heat source interference shows a steep rise; and temperature rises caused by insulation aging may show a more volatile rate of change curve. The system correlates the calculated rate of change curve with the standard rate of change patterns in the abnormal pattern library, calculating the correlation coefficient between the two to further assist in determining the type of abnormality.

[0046] Next, the system inputs the distance matrix, the temperature spatial distribution feature map, and the change rate correlation analysis results into a multi-factor decision-making model to determine the corresponding abnormal causes and confidence levels, and queries the geographical information system for the surrounding environmental information data of the cable abnormal location. The multi-factor decision-making model is a decision-making mechanism that comprehensively considers various factors and is usually implemented based on fuzzy logic or Bayesian networks. This model takes the above three analysis results as inputs, comprehensively evaluates various possible abnormal causes, and calculates the confidence level of each cause. The model first standardizes each analysis result to make the indicators at different scales comparable. Then, it applies a weighted algorithm to calculate the comprehensive score, and the weight coefficients are determined based on historical fault case statistics and expert experience. For example, for joint fault judgment, the weight of the spatial distribution feature is relatively high; while for overload judgment, the weights of time series matching and change rate analysis are relatively high. Finally, the model outputs a list of abnormal causes and their corresponding confidence levels, such as: joint overheating (confidence level 85%), insulation aging (confidence level 15%). At the same time, the system sends the determined abnormal location coordinates (such as 300 meters on the cable path) to the geographical information system to query the surrounding environmental information of this location, including topography, building facilities, underground pipeline crossing conditions, etc. For example, the system may query that there is a municipal heating pipeline construction in progress near this location, or the cable passes through the cable gallery of a busy road. These environmental information provide important background materials for abnormal cause analysis and disposal plan formulation, and help to judge whether the abnormality is caused by internal faults or external interference.

[0047] Finally, based on the cause of the anomaly, confidence level, and surrounding environment information, the system queries the disposal strategy library to generate disposal suggestions that include load adjustment plans, device switching paths, and emergency disposal procedures. The disposal strategy library is a rule-based knowledge base that contains standard disposal solutions for various abnormal situations. First, the system retrieves matching disposal templates from the strategy library according to the cause of the anomaly and the confidence level. For example, for a high-confidence joint overheating fault, the system will retrieve the disposal template for joint faults; if there are multiple possible causes and similar confidence levels, multiple disposal templates will be considered simultaneously. Then, the system customizes and adjusts the disposal templates in combination with the cable importance level, load conditions, and surrounding environment information. If the surrounding environment of the abnormal location is complex (such as dense underground pipelines or heavy traffic), the system will generate more detailed construction safety precautions; if the abnormal cable is an important power supply line, the system will give priority to maintenance plans under live conditions. The final generated disposal suggestions usually include three parts: load adjustment plans, device switching paths, and emergency disposal procedures. The load adjustment plan provides specific suggestions on how to reduce or transfer the cable load, such as "it is recommended to reduce the line load below 60% of the rated current and continuously monitor the temperature change". The device switching path lists the standby lines available for load transfer and the switch operation sequence, such as "transfer the load to the #3 standby line through switches S15 and S23". The emergency disposal procedure provides specific steps for on-site inspection and disposal, such as "arrange an infrared thermometer to check the joint at 300 meters, prepare emergency repair materials, and contact the professional repair team to standby". The system presents these disposal suggestions to the operation and maintenance personnel in the form of a report and notifies them through different channels (such as workstation interface, mobile APP, SMS, phone call) according to the urgency level.

[0048] In this embodiment, the temperature data of the cable outer sheath is collected by temperature sensors, and the cable current load data and health status index are obtained; using the superposition principle calculation model corrected based on the time decay coefficient, the conductor temperature is calculated according to the outer sheath temperature data, current load data, and environmental parameter data; the grading warning threshold is calculated according to the cable health status index, and the conductor temperature is compared with the threshold to determine the warning level; when the warning level reaches the preset condition, the sheath temperature data is matched with the templates in the abnormal mode library, the cause of the anomaly is identified through multiple algorithms, and disposal suggestions are generated in combination with the geographical information system data. The present invention can quickly and accurately calculate the conductor temperature, dynamically adjust the warning threshold, and intelligently identify the cause of the anomaly, greatly improving the accuracy and effectiveness of cable temperature monitoring and warning.

[0049] The method for monitoring and alarming the temperature of the grid cable in the embodiment of the present invention is described above. Next, the device for monitoring and alarming the temperature of the grid cable in the embodiment of the present invention will be described. Please refer to the Figure 2, an embodiment of the power grid cable temperature monitoring and alarm device in the embodiments of the present invention includes: A data acquisition module 201, configured to collect the sheath temperature data of the cable outer sheath through temperature sensors arranged at key positions of the cable, and obtain the cable current load data and the cable health status index of the cable; A temperature calculation module 202, configured to calculate the conductor temperature of the cable by using a superposition principle calculation model corrected based on a time decay coefficient according to the temperature data of the cable outer sheath, the cable current load data, and the environmental parameter data; A threshold judgment module 203, configured to calculate the hierarchical early warning threshold of the cable according to the cable health status index, and compare the conductor temperature with the hierarchical early warning threshold to determine the early warning level; An abnormal diagnosis module 204, configured to, when the early warning level reaches a preset condition, perform pattern matching on the sheath temperature data with a template in a cable temperature abnormal mode library, identify the abnormal cause through multiple matching algorithms, and generate corresponding handling suggestions in combination with the geographic information system data.

[0050] In the embodiments of the present invention, the power grid cable temperature monitoring and alarm device runs the above-mentioned power grid cable temperature monitoring and alarm method. The power grid cable temperature monitoring and alarm device collects the cable outer sheath temperature data through temperature sensors and obtains the cable current load data and the health status index; uses a superposition principle calculation model corrected based on a time decay coefficient to calculate the conductor temperature according to the outer sheath temperature data, the current load data, and the environmental parameter data; calculates the hierarchical early warning threshold according to the cable health status index, and compares the conductor temperature with the threshold to determine the early warning level; when the early warning level reaches a preset condition, performs matching on the sheath temperature data with a template in the abnormal mode library, identifies the abnormal cause through multiple algorithms, and generates handling suggestions in combination with the geographic information system data. The present invention can quickly and accurately calculate the conductor temperature, dynamically adjust the early warning threshold, and intelligently identify the abnormal cause, greatly improving the accuracy and effectiveness of cable temperature monitoring and early warning.

[0051] Above Figure 2 The power grid cable temperature monitoring and alarm device in the embodiments of the present invention is described in detail from the perspective of modular functional entities. Next, the power grid cable temperature monitoring and alarm device in the embodiments of the present invention is described in detail from the perspective of hardware processing.

[0052] Figure 3FIG. 0 is a schematic structural diagram of a power grid cable temperature monitoring and alarm device provided by an embodiment of the present invention. The power grid cable temperature monitoring and alarm device 300 may vary greatly due to different configurations or performances, and may include one or more processors (central processing units, CPUs) 310 (for example, one or more processors) and a memory 320, and one or more storage media 330 for storing application programs 333 or data 332 (for example, one or more mass storage device terminals). Among them, the memory 320 and the storage medium 330 may be transient storage or persistent storage. The program stored in the storage medium 330 may include one or more modules (not shown in the figure), and each module may include a series of instruction operations on the power grid cable temperature monitoring and alarm device 300. Further, the processor 310 may be configured to communicate with the storage medium 330 and execute a series of instruction operations in the storage medium 330 on the power grid cable temperature monitoring and alarm device 300 to implement the steps of the above-mentioned power grid cable temperature monitoring and alarm method.

[0053] The power grid cable temperature monitoring and alarm device 300 may further include one or more power supplies 340, one or more wired or wireless network interfaces 350, one or more input / output interfaces 360, and / or one or more operating systems 331, such as Windows Serve, Mac OS X, Unix, Linux, FreeBSD, and so on. Those skilled in the art can understand that Figure 3 The shown structure of the power grid cable temperature monitoring and alarm device does not limit the power grid cable temperature monitoring and alarm device provided by the present invention, and may include more or fewer components than shown in the figure, or combine certain components, or have different component arrangements.

[0054] The present invention also provides a computer-readable storage medium, which may be a non-volatile computer-readable storage medium or a volatile computer-readable storage medium. Instructions are stored in the computer-readable storage medium. When the instructions run on a computer, the computer is caused to execute the steps of the power grid cable temperature monitoring and alarm method.

[0055] Those skilled in the art can clearly understand that for the convenience and brevity of description, the specific working processes of the above-described system or device and unit can refer to the corresponding processes in the foregoing method embodiments, and will not be described herein again.

[0056] When the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of this technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in various embodiments of the present invention. The aforementioned storage medium includes: various media such as USB flash drives, mobile hard disks, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical discs that can store program codes.

[0057] As described above, the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions described in the foregoing embodiments, or perform equivalent replacements for some of the technical features; and these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of various embodiments of the present invention.

Claims

1. A method for monitoring and alarming the temperature of power grid cables, characterized in that, The method for monitoring and alarming the temperature of power grid cables includes: Collecting the sheath temperature data of the cable outer sheath through temperature sensors arranged at key positions of the cable, and obtaining the cable current load data and cable health status index of the cable; Calculating the conductor temperature of the cable by using a superposition principle calculation model corrected based on a time decay coefficient according to the temperature data of the cable outer sheath, the cable current load data, and the environmental parameter data; Calculating the hierarchical warning threshold of the cable according to the cable health status index, and comparing the conductor temperature with the hierarchical warning threshold to determine the warning level; When the warning level reaches a preset condition, performing pattern matching on the sheath temperature data with templates in the cable temperature abnormal mode library, identifying the abnormal cause through multiple matching algorithms, and generating corresponding disposal suggestions in combination with geographic information system data.

2. The power grid cable temperature monitoring and alarming method according to claim 1, wherein The superposition principle calculation model includes a thermal response function, a time decay coefficient, and an environmental impact compensation matrix; The calculating the conductor temperature of the cable by using a superposition principle calculation model corrected based on a time decay coefficient according to the temperature data of the cable outer sheath, the cable current load data, and the environmental parameter data includes: Decomposing the cable current load data into multiple step current sequences according to the sampling time interval, and applying the thermal response function to each step current sequence to calculate the corresponding basic temperature rise; Extracting the environmental temperature, humidity, and groundwater level data from the environmental parameter database, constructing an environmental impact mapping matrix through multiple regression analysis, and using the environmental impact mapping matrix to transform to obtain an environmental impact correction term and a humidity impact correction term; Dynamically calculating a thermal time constant adjustment coefficient according to the change rate between the historical load rate and the current load rate of the cable, correcting the thermal time constant in the time decay coefficient through the thermal time constant adjustment coefficient, and applying the corrected time decay coefficient to the basic temperature rise to obtain a corrected temperature rise response; Linearly superimposing the corrected temperature rise responses corresponding to all step current sequences, and combining the initial conductor temperature, the environmental impact correction term, and the humidity impact correction term to calculate the conductor temperature of the cable.

3. The method for monitoring and alarming the temperature of the power grid cable according to claim 2, characterized in that, The decomposing the cable current load data into multiple step current sequences according to the sampling time interval, and applying the thermal response function to each step current sequence to calculate the corresponding basic temperature rise includes: Sampling the cable current load data at a fixed time interval to obtain time series current data; Calculating the current difference between adjacent sampling points for the time series current data, taking each difference as a step current, and constructing multiple step current sequences; Applying the thermal response function describing the response of the conductor temperature to the step current to each step current sequence in the multiple step current sequences to obtain the corresponding basic temperature rise time curve; Calculating the basic temperature rise generated by each step current sequence at the current moment according to the basic temperature rise time curve and the time point when the step current sequence occurs.

4. The method for monitoring and alarming the temperature of a power grid cable according to claim 2, wherein, Dynamically calculating a thermal time constant adjustment coefficient according to the change rate between the historical load rate and the current load rate of the cable, correcting the thermal time constant in the time decay coefficient through the thermal time constant adjustment coefficient, and applying the corrected time decay coefficient to the base temperature rise to obtain a corrected temperature rise response includes: Extracting the cable current load data of the past 24 hours from the cable operation history database, calculating the average value of the cable current load data of the past 24 hours, and then dividing the average value by the cable rated current value to obtain the historical load rate; Dividing the current cable current load data by the cable rated current value to obtain the current current load ratio, and calculating the change rate between the historical current load ratio and the current current load ratio, wherein the change rate includes a change amplitude and a change rate; Inputting the change rate into a preset thermal time constant adjustment model, calculating the thermal time constant adjustment coefficient, and multiplying the original thermal time constant by the thermal time constant adjustment coefficient to obtain a corrected thermal time constant; Updating the time decay coefficient function using the corrected thermal time constant to obtain a corrected time decay coefficient, and applying the corrected time decay coefficient to the base temperature rise to calculate the corrected temperature rise response at the current moment.

5. The power grid cable temperature monitoring and alarming method according to claim 2, characterized in that, Linearly superposing the corrected temperature rise responses corresponding to all step current sequences, and combining the initial conductor temperature, the environmental impact correction term, and the humidity impact correction term to calculate the conductor temperature of the cable includes: Arranging the corrected temperature rise responses generated by all step current sequences in chronological order and linearly accumulating them according to the superposition principle to obtain the total temperature rise value; Performing a weighted combination of the environmental impact correction term and the humidity impact correction term to obtain an environmental comprehensive impact correction value, and calculating the decay value of the temperature rise generated by the early step current at the current moment according to the time decay characteristic; Calculating the actual temperature of the cable conductor at the current moment based on the preset initial conductor temperature, the total temperature rise value, the environmental comprehensive impact correction value, and the temperature rise decay value, and correcting the calculation result through a temperature compensation algorithm to output the final cable conductor temperature value.

6. The method for monitoring and alarming the temperature of a power grid cable according to claim 1, wherein, Calculating the grading warning threshold of the cable according to the cable health condition index, and comparing the conductor temperature with the grading warning threshold to determine the warning level includes: Determining a health condition adjustment factor based on the cable health condition index, and the health condition adjustment factor decreases as the cable health condition index decreases; Retrieving the installation environment type of the cable from the cable installation environment classification library to obtain an environmental adjustment factor, and obtaining the season adjustment factor corresponding to the current date from the season characteristic database; Performing a weighted product operation on the base warning threshold, the health condition adjustment factor, the environmental adjustment factor, and the season adjustment factor to calculate the grading warning threshold respectively; Comparing the size of the conductor temperature with the grading warning threshold, and determining the warning level of the cable according to the comparison result.

7. The power grid cable temperature monitoring and alarming method according to claim 1, characterized in that, When the warning level reaches the preset condition, perform pattern matching on the sheath temperature data with the templates in the cable temperature anomaly pattern library, identify the anomaly cause through multiple matching algorithms, and generate corresponding handling suggestions in combination with the geographic information system data, including: When the warning level reaches the preset condition, extract the anomaly pattern templates from the cable temperature anomaly pattern library, and apply the dynamic time warping algorithm to calculate the distance matrix between the sheath temperature data and each anomaly pattern template; Apply the spatial spectrum analysis method to the sheath temperature data at different positions along the cable to generate a temperature spatial distribution feature map, and at the same time calculate the time change rate curve of the sheath temperature data and conduct a correlation analysis with the standard change rate pattern; Input the distance matrix, the temperature spatial distribution feature map, and the change rate correlation analysis result into the multi-factor decision-making model to determine the corresponding anomaly cause and confidence level, and query the geographic information system for the surrounding environment information data of the cable anomaly location; According to the anomaly cause, confidence level, and surrounding environment information, query the handling strategy library to generate handling suggestions including a load adjustment plan, an equipment switching path, and an emergency handling process.

8. A power grid cable temperature monitoring and alarm device, characterized in that, The power grid cable temperature monitoring and alarm device includes: A data acquisition module for collecting the sheath temperature data of the cable outer sheath through temperature sensors arranged at key positions of the cable, and obtaining the cable current load data and the cable health status index of the cable; A temperature calculation module for calculating the conductor temperature of the cable by using a superposition principle calculation model corrected based on a time decay coefficient according to the temperature data of the cable outer sheath, the cable current load data, and the environmental parameter data; A threshold judgment module for calculating the hierarchical warning threshold of the cable according to the cable health status index, and comparing the conductor temperature with the hierarchical warning threshold to determine the warning level; An anomaly diagnosis module for, when the warning level reaches the preset condition, performing pattern matching on the sheath temperature data with the templates in the cable temperature anomaly pattern library, identifying the anomaly cause through multiple matching algorithms, and generating corresponding handling suggestions in combination with the geographic information system data.

9. A power grid cable temperature monitoring and alarming device, characterized in that, The power grid cable temperature monitoring and alarm equipment includes: a memory and at least one processor, and instructions are stored in the memory; The at least one processor calls the instructions in the memory so that the power grid cable temperature monitoring and alarm equipment executes the steps of the power grid cable temperature monitoring and alarm method described in any one of claims 1-7.

10. A computer-readable storage medium, on which instructions are stored, characterized in that, When the instructions are executed by the processor, the steps of the power grid cable temperature monitoring and alarm method described in any one of claims 1-7 are implemented.

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