A power distribution intelligent temperature measurement alarm method and system

Abstract

The application provides a power distribution intelligent temperature measurement alarm method and system, real-time monitoring of temperature and multi-source data is performed at each node of a cable joint, it is ensured that the temperature and the multi-source data correspond in the same time period, in order to improve the measurement accuracy, the collected temperature data is checked to reduce the measurement error, when the required data is obtained, the out-of-limit regional data is recorded as first data when the multi-source data is abnormal, the out-of-limit regional data is recorded as second data when the multi-source data is normal, the node corresponding to the first data is an emergency abnormal node, when the node corresponding to the second data is still out of limit in the regional data obtained by next edge calculation, the node corresponding to the second data is a general abnormal node, the data of the emergency abnormal node and the general abnormal node is encapsulated into alarm information, alarm is performed, and an operator checks and solves the abnormal node.

Description

Technical Field

[0001] This invention relates to the field of power distribution temperature measurement, specifically to a method and system for intelligent power distribution temperature measurement and alarm. Background Technology

[0002] With the advancement of urban power distribution network upgrades, more and more power distribution networks are adopting cable lines. Correspondingly, the number of distribution equipment such as medium-voltage switchgear, ring main units, and cable distribution boxes is increasing. Power supply departments are facing more and more cable faults, among which overheating problems caused by poor cable insulation and contact account for a significant proportion of these faults. Cables are a crucial medium in power transmission; they generate heat during power transmission. In spaces with densely packed cables and poor heat dissipation, excessive cable temperature can easily lead to fires.

[0003] Important equipment in power systems, such as switchgear, ring main units, high-voltage cable joints, and disconnect switches, may experience aging, surface oxidation and corrosion, and loosening of fastening bolts during long-term operation. Furthermore, many power devices operate under high loads for extended periods, which can lead to abnormal temperatures. If these issues are not detected and addressed promptly, they may cause safety accidents such as melting or combustion.

[0004] During operation, cable joints in distribution cabinets may overheat due to abnormal contact, insulation, or heavy loads. If not addressed promptly, this can lead to joint damage, grounding, short circuits, and other accidents. In recent years, there have been numerous incidents of cable joint overheating causing cable joint explosions, damage to gas chamber seals, SF6 gas leaks, and cabinet failure.

[0005] In recent years, due to increased electricity load, cable joint overheating faults have shown an increasing trend. Furthermore, distribution cabinets cannot be opened while in operation, making it impossible to monitor the actual temperature of the joints inside, thus hindering the detection and handling of actual faults. As the proportion of cables increases, the workload for cable maintenance personnel is becoming increasingly heavy, making it difficult to promptly detect cable operational defects.

[0006] In existing technologies, the monitoring accuracy of cable joint data is low and the analysis capability is insufficient. When the cable temperature is abnormal, the temperature of the abnormal node will affect the temperature of surrounding nodes and even surrounding distribution cabinets, making it impossible to give an accurate location of the abnormal cable joint temperature node. This requires staff to inspect and maintain all distribution cabinets during maintenance, which is time-consuming, labor-intensive, and increases the cost of operation and maintenance. Summary of the Invention

[0007] To address the aforementioned issues, this invention proposes a method and system for intelligent temperature measurement and alarm in power distribution, which enables precise location of abnormal cable joint temperatures, facilitating accurate identification of abnormal locations by staff and reducing maintenance costs.

[0008] To achieve the above objectives, the present invention adopts the following technical solution:

[0009] A method for intelligent temperature monitoring and alarm in power distribution includes,

[0010] Acquire temperature data and multi-source data for all nodes of the cable joints in the distribution cabinet, wherein the multi-source data includes electrical quantity data;

[0011] The temperature data is subjected to a double-layer verification to obtain temperature verification data;

[0012] Edge computing is performed on the temperature verification data of all nodes to obtain the regional data of all nodes;

[0013] Each region's data is compared with a set threshold. If any region's data exceeds the limit, a pre-defined anomaly cause relationship model is used as the standard to determine whether the multi-source data corresponding to the region's data that exceeds the limit is abnormal. When the multi-source data is abnormal, the region's data that exceeds the limit is recorded as the first data. When the multi-source data is normal, the region's data that exceeds the limit is recorded as the second data. The node corresponding to the first data is designated as an emergency abnormal node. The temperature data and multi-source data of the node corresponding to the second data are updated to obtain the updated region's data. When the updated region's data exceeds the limit, the node corresponding to the second data is designated as a general abnormal node.

[0014] The emergency abnormal node is encapsulated to obtain emergency alarm information; the general abnormal node is encapsulated to obtain general alarm information.

[0015] The alarm and fault handling are carried out based on the emergency alarm information and general alarm information.

[0016] Preferably, the process of constructing the preset abnormal cause relationship model includes,

[0017] In the single-node evaluation process, the temperature verification data of a single node is randomly selected for edge computing to obtain evaluation data, and the node corresponding to the evaluation data is the evaluation node;

[0018] The evaluation data is compared with the set thresholds. If the evaluation data exceeds the limit, the cause of the anomaly is determined by analyzing multi-source data from the evaluation nodes.

[0019] Repeat the evaluation process for a single node to obtain multiple anomaly cause relationships, and summarize all anomaly cause relationships into an anomaly cause relationship model;

[0020] When the multi-source data of the evaluation node is normal, the evaluation node is not abnormal. The temperature abnormality is caused by the influence of temperature transmission from surrounding nodes. When the multi-source data of the evaluation node is abnormal, the evaluation node is abnormal. The temperature abnormality is caused by the influence of abnormal multi-source data.

[0021] Preferably, the temperature data includes the location, number, and temperature of the corresponding single node; the electrical quantity data includes the voltage and current transient steady-state signals, high-frequency signals, and ultra-high-frequency signals of the distribution cabinet; the multi-source data also includes environmental data, which includes the noise data of the distribution cabinet, the smoke detection data inside the distribution cabinet, and the ambient humidity inside and outside the distribution cabinet.

[0022] Preferably, the temperature data is subjected to double-layer verification to obtain temperature verification data. Specifically, this includes measuring the temperature data of a single node twice within a time period, which are recorded as the first temperature data and the second temperature data. The standard deviation of the first temperature data and the second temperature data is calculated. When the standard deviation is not within the set error range, the temperature data of the node is measured again until the standard deviation of the two temperature data is within the set error range, thus obtaining the temperature verification data.

[0023] Preferably, the evaluation data is obtained by edge computing based on the temperature verification data of a single node, and is calculated according to the following formula:

[0024]

[0025]

[0026] In the formula, T represents a period, x represents the x-th node, and within one period, the temperature of this node is measured in f values, namely T1, T2, ..., T3. f σ represents the root mean square value of the x-th node of Tem_x within this period. 2 em_x represents the variance of the x-th node within that period.

[0027] Preferably, the region data is obtained by edge calculation based on the verification data of all nodes within the region, and calculated according to the following formula:

[0028]

[0029]

[0030] In the formula, T is one period, x is the xth node, and T em x Let T be the root mean square value of the x-th node within this period. emμ This represents the mean value within the period, where N is the total number of nodes, and σ is the mean value. 2 em_x Let σ be the variance of the x-th node within this period. em_x Let x be the standard deviation of the x-th node within this period.

[0031] Preferably, the emergency abnormal node is encapsulated to obtain emergency alarm information; the general abnormal node is encapsulated to obtain general alarm information.

[0032] Specifically, this includes encapsulating emergency abnormal nodes, along with related temperature information and multi-source data, into an emergency alarm message; and encapsulating general abnormal nodes, along with related temperature information and multi-source data, into a general alarm message.

[0033] A power distribution intelligent temperature measurement and alarm system,

[0034] The data acquisition module is used to acquire temperature data and multi-source data of all nodes of the cable joint in the distribution cabinet. The temperature data is then double-checked to obtain verification data.

[0035] The edge computing module compares the data of each region with a set threshold. If any region's data exceeds the limit, a preset anomaly cause relationship model is used as the standard to determine whether the multi-source data corresponding to the region's data that exceeds the limit is abnormal. When the multi-source data is abnormal, the region's data that exceeds the limit is recorded as the first data; when the multi-source data is normal, the region's data that exceeds the limit is recorded as the second data. The node corresponding to the first data is designated as an emergency abnormal node. The temperature data and multi-source data of the node corresponding to the second data are updated to obtain the updated region data. When the updated region data exceeds the limit, the node corresponding to the second data is designated as a general abnormal node.

[0036] The encapsulation module is used to encapsulate the emergency abnormal node to obtain emergency alarm information; and to encapsulate the general abnormal node to obtain general alarm information.

[0037] The application module is used to trigger alarms and handle defects based on the emergency alarm information and general alarm information.

[0038] Preferably, it also includes a transmission module, which is used to transmit emergency abnormal nodes and general abnormal nodes of the edge computing module to the encapsulation module.

[0039] Preferably, the acquisition module includes a temperature sensing module, which is installed inside the plug of the cable plug. The plug and the plug are connected by a component. The temperature sensing module adopts a passive capacitor voltage divider power extraction mode to obtain the first temperature data, and obtains the second temperature data by measuring the temperature through a binding temperature measuring ring.

[0040] It is also equipped with a variety of sensors, including humidity sensors, ultrasonic sensors, voltage and current sensors, to monitor the environmental status of a single node and obtain multi-source data.

[0041] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0042] This invention provides a method for intelligent temperature measurement and alarm in power distribution. Real-time monitoring of temperature and multi-source data is performed at each node of the cable joint to ensure that the temperature and multi-source data correspond within the same time period. To improve measurement accuracy, the collected temperature data is calibrated to reduce measurement errors. When the required data is obtained, if the multi-source data is abnormal, the data exceeding the limit is recorded as the first data; if the multi-source data is normal, the data exceeding the limit is recorded as the second data. The node corresponding to the first data is designated as an emergency abnormal node. If the node corresponding to the second data still exceeds the limit in the next edge calculation, the node corresponding to the second data is designated as a general abnormal node. The data from the emergency abnormal node and the general abnormal node are encapsulated into alarm information for alarm activation. Operators then investigate and address the abnormal nodes.

[0043] This invention performs a two-layer verification on the collected temperature data. Temperature data for a single node is measured twice within a single time period, recorded as the first temperature data and the second temperature data. The standard deviation of the first and second temperature data is calculated. If the standard deviation is outside the set error range, the temperature data for that node is measured again. This further improves the accuracy of node temperature monitoring.

[0044] This invention provides a power distribution intelligent temperature measurement and alarm system, including a data acquisition module, an edge computing module, an encapsulation module, an application module, and a transmission module. It is set up based on the Internet of Things architecture and has the characteristics of strong communication penetration and long transmission distance.

[0045] The temperature sensing module of this invention is installed inside the plug of the plug to monitor the real-time temperature of nearby nodes and ensure the accuracy of the measurement. Attached Figure Description

[0046] Figure 1 This is a flowchart of a power distribution intelligent temperature measurement method and system disclosed in this invention.

[0047] Figure 2 This is a schematic diagram of multi-source data sensing for a power distribution intelligent temperature measurement method and system disclosed in this invention.

[0048] Figure 3 This is a schematic diagram of edge computing for a power distribution intelligent temperature measurement method and system disclosed in this invention.

[0049] Figure 4 This is a schematic diagram of a power distribution intelligent temperature measurement method and system disclosed in this invention.

[0050] Figure 5 This is a schematic diagram of the temperature sensing module of the power distribution node in a power distribution intelligent temperature measurement method and system disclosed in this invention.

[0051] Figure 6 This is a circuit diagram of a passive capacitor voltage divider for power supply in a power distribution intelligent temperature measurement method and system disclosed in this invention.

[0052] Among them, 1. plug; 2. sub-component; 3. plug. Detailed Implementation

[0053] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0054] It should be noted that, unless otherwise specified, the embodiments and features described in this application can be combined with each other.

[0055] This invention can be described in the general context of computer-executable instructions, such as program modules, that are executed by a computer. Generally, program modules include routines, programs, objects, elements, data structures, etc., that perform a specific task or implement a specific abstract data type. This invention can also be practiced in distributed computing environments where tasks are performed by remote processing devices connected via a communication network. In distributed computing environments, program modules can reside in local and remote computer storage media, including storage devices.

[0056] In this invention, terms such as "module," "device," and "system" refer to relevant entities applied to a computer, such as hardware, combinations of hardware and software, software, or software in execution. More specifically, for example, an element can be, but is not limited to, a process running on a processor, a processor, an object, an executable element, an execution thread, a program, and / or a computer. Furthermore, an application program or script running on a server, and the server itself, can also be an element. One or more elements may be in an execution process and / or thread, and elements may be localized on a single computer and / or distributed across two or more computers, and may be run on various computer-readable media. Elements can also communicate via local and / or remote processes based on signals having one or more data packets, for example, signals from data interacting with another element in a local system, a distributed system, and / or interacting with other systems via signals over a network of the Internet.

[0057] Finally, it should be noted that in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising" or "including" include not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Unless otherwise specified, an element defined by the phrase "comprising..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0058] The present invention will be further described in detail below with reference to specific embodiments. These descriptions are for explanation purposes only and are not intended to limit the scope of the invention.

[0059] This invention proposes a method for intelligent temperature measurement and alarm in power distribution systems. Please refer to [link / reference]. Figure 1 Acquire temperature data and multi-source data for all nodes of cable connectors in the distribution cabinet, wherein the multi-source data includes electrical quantity data;

[0060] The temperature data is subjected to a double-layer verification to obtain temperature verification data;

[0061] Edge computing is performed on the temperature verification data of all nodes to obtain the regional data of all nodes;

[0062] Each region's data is compared with a set threshold. If any region's data exceeds the limit, a pre-defined anomaly cause relationship model is used as the standard to determine whether the multi-source data corresponding to the region's data that exceeds the limit is abnormal. When the multi-source data is abnormal, the region's data that exceeds the limit is recorded as the first data. When the multi-source data is normal, the region's data that exceeds the limit is recorded as the second data. The node corresponding to the first data is designated as an emergency abnormal node. The temperature data and multi-source data of the node corresponding to the second data are updated to obtain the updated region's data. When the updated region's data exceeds the limit, the node corresponding to the second data is designated as a general abnormal node.

[0063] The emergency abnormal node is encapsulated to obtain emergency alarm information; the general abnormal node is encapsulated to obtain general alarm information.

[0064] The alarm and fault handling are carried out based on the emergency alarm information and general alarm information.

[0065] Real-time monitoring of temperature and multi-source data is performed at each node of the cable joint to ensure that the temperature and multi-source data correspond within the same time period. In order to improve the accuracy of the measurement, the collected temperature data is calibrated to reduce measurement error.

[0066] The process involves determining whether regional data exceeds limits. When regional data does exceed limits, the data for the region exceeding the limits is obtained. Based on the anomaly cause relationship model, the data for the region exceeding the limits is analyzed to determine whether the multi-source data corresponding to the data exceeding the limits is abnormal. Since abnormal cable joint temperatures may be caused by operational overvoltage (i.e., a transient process) or by harmonic voltage caused by steady-state harmonic current due to nonlinear loads, when multi-source data is abnormal, the data for the region exceeding the limits is recorded as the first data. Based on the abnormal multi-source data, the scope of the regional data anomaly can be narrowed down, ultimately identifying one or more nodes as emergency anomaly nodes.

[0067] Meanwhile, to avoid safety risks, when multi-source data is normal, data in areas exceeding the limit is recorded as the second data. The node corresponding to the second data needs to be further monitored. The data collection, temperature verification, edge computing, and other steps are repeated for the node corresponding to the second data to obtain updated regional data. If the temperature of the node corresponding to the second data cannot return to normal and the updated regional data exceeds the limit again, the node is uploaded as a general abnormal node. If it returns to normal and the updated regional data does not exceed the limit, it is not uploaded. This can prevent safety accidents from happening. Through classification and grading, more accurate early warning can be given, avoiding a lot of maintenance work for staff.

[0068] In a specific embodiment of the present invention, the process of constructing the preset abnormal cause relationship model includes,

[0069] In the single-node evaluation process, the temperature verification data of a single node is randomly selected for edge computing to obtain evaluation data, and the node corresponding to the evaluation data is the evaluation node;

[0070] The evaluation data is compared with the set threshold. If the evaluation data exceeds the limit, the relationship between the causes of the anomaly is obtained by analyzing the multi-source data of the evaluation node. When the multi-source data of the evaluation node is normal, the evaluation node is not abnormal and the temperature anomaly is caused by the influence of temperature transmission from surrounding nodes. When the multi-source data of the evaluation node is abnormal, the evaluation node is abnormal and the temperature anomaly is caused by the influence of abnormal multi-source data.

[0071] Repeat the evaluation process for a single node to obtain multiple anomaly cause relationships, and summarize all anomaly cause relationships into an anomaly cause relationship model.

[0072] By analyzing the correspondence between the evaluation data and the multi-source data of the evaluation nodes, an anomaly cause relationship model of the multi-source data is obtained. For example, when the anomaly cause result is that the evaluation data is abnormal, whether the electrical quantity data in the multi-source data of the evaluation node exceeds the set threshold. If it exceeds the threshold, the multi-source data anomaly of the evaluation node is caused by the electrical anomaly in the distribution cabinet, that is, the cause of the evaluation data anomaly is the electrical anomaly. If it does not exceed the threshold, the cause of the evaluation data anomaly is that it is affected by the surrounding abnormal nodes.

[0073] To enable forwarding, data from both emergency and general abnormal nodes are encapsulated into alarm information. Alarms are then triggered and faults are addressed based on this alarm information.

[0074] In a specific embodiment of the present invention, please refer to Figure 2 The temperature data includes the location, number, and temperature of a single node. The multi-source data consists of electrical and non-electrical quantity data of the temperature measurement data nodes. The electrical quantity data includes voltage and current transient steady-state signals, high-frequency signals, and ultra-high-frequency signals of the distribution cabinet. The multi-source data also includes environmental data, which includes noise data of the distribution cabinet, smoke detection data inside the distribution cabinet, and ambient humidity inside and outside the distribution cabinet.

[0075] Relying on the power quality monitoring system in the power grid and the application of intelligent online status monitoring equipment such as temperature and humidity monitoring devices for ring network equipment, the electrical and non-electrical quantities of the power distribution system are situationally sensed. New IoT communication technologies are employed to ensure stable sensor data transmission, enabling multiple points to be monitored with only one gateway, and collecting the necessary multi-source data. The periodic electrical quantities involved can be preliminarily analyzed using local FFT decomposition to separate the spectral components of various currents and voltages, supporting the analysis of the causes of abnormal cable node temperatures.

[0076] In a specific embodiment of the present invention, the temperature data is subjected to double-layer verification to obtain temperature verification data. Specifically, this includes measuring the temperature data of a single node within a time period twice, which are recorded as the first temperature data and the second temperature data. The standard deviation of the first temperature data and the second temperature data is calculated. When the standard deviation is not within the set error range, the temperature data of the node is measured again.

[0077] The temperature field of a cable joint indirectly reflects the degree of influence of the conductor on nearby points. Therefore, at some important points, a binding temperature measuring ring can be added. The temperature measured by the ring deviates from that measured by the temperature sensing module. By calculating the standard deviation of the two temperature measurements within a certain time range at the key points, the accuracy of the temperature measurement at the key points can be evaluated, and the temperature measurement results of the sensing module can be further verified.

[0078] In a specific embodiment of the present invention, please refer to Figure 3 The evaluation data is obtained by edge computing based on the temperature verification data of a single node, and is calculated according to the following formula:

[0079]

[0080]

[0081] In the formula, T represents one period, x represents the x-th node, and within one period, the temperature of this node is measured in f values, namely T1, T2...T f σ represents the root mean square value of the x-th node of Tem_x within this period. 2 em_x represents the variance value of the x-th node within that period;

[0082] The root mean square (RMS) processing and variance processing are performed on the temperature measurement samples taken from node x of a certain distribution cabinet within a time period to assess the degree of abnormal deviation of the temperature measurement point within a period. At the same time, a temperature change curve of a single cable joint can be plotted.

[0083] If the variance value continuously exceeds the threshold set within the period, it is initially determined that the temperature of node x is abnormal. The coordinates of the node with the abnormal temperature, including the distribution box number and node number, the abnormal temperature, and the abnormal time, are used as evaluation data and uploaded to the edge computing module via wireless communication. The multi-node temperature measurement data of other distribution boxes are similar. It should be noted that all the distribution cabinet node temperature data uploaded to the edge computing module are data from the same time segment.

[0084] A multi-distribution module cable node linkage analysis was performed at a single-dimensional level, namely node temperature. The measured temperatures were processed for variance and standard deviation to assess the dispersion of cable joint temperature changes across multiple nodes in the region. Preliminary diagnosis and location of node temperature points were then conducted.

[0085] In a specific embodiment of the present invention, please refer to Figure 3 The regional data is obtained by edge calculation based on the temperature verification data of all nodes in the region, and is calculated according to the following formula:

[0086]

[0087]

[0088] In the formula, T is one period, x is the xth node, and T em x The average value of the x-th node during this period Root value, T emμ This represents the mean value within the period, where N is the total number of nodes, and σ is the mean value. 2 em x Let σ be the variance of the x-th node within this period. em_x Let x be the standard deviation of the x-th node within this period;

[0089] By performing root mean square and variance processing through edge computing, firstly, taking multiple nodes of the distribution cabinet as objects, the root mean square value of the temperature of all nodes in the distribution cabinet within the same period is calculated. Then, taking multiple distribution cabinets as objects, the variance of the root mean square values ​​in multiple distribution cabinets within a time period is calculated to evaluate the dispersion of the temperature change of cable joints in multiple nodes in this area.

[0090] Determine whether the multi-source data of the node corresponding to the out-of-limit area data is abnormal. When the multi-source data is abnormal, the out-of-limit area data is recorded as the first data. When the multi-source data is normal, the out-of-limit area data is recorded as the second data.

[0091] At multiple dimensions, such as voltage, current, high frequency, and infrared signals, the system performs in-depth data fusion and linkage analysis on cable nodes of multiple power distribution modules. It evaluates and analyzes power distribution modules, diagnoses and locates weak links and abnormal points in equipment and nodes, and provides preliminary causes such as reduced insulation, overheating, and overload. At the same time, it can also determine the underlying causes of abnormalities, such as hardware processes, transient overvoltage, or nonlinear loads, thereby reducing potential safety risks.

[0092] Based on the temperature measurement results of multiple distribution cabinets in the area, and cross-checked with the results of multi-source data linkage analysis. Taking voltage, infrared and other data as examples, if the coordinates of abnormal nodes match the conclusions obtained from edge calculations, it can be considered that overvoltage is the cause; the linkage analysis of multi-source data will effectively improve accuracy. This enables the assessment and analysis of the health of power distribution modules, as well as the diagnosis and location of weak links and abnormal nodes in the equipment.

[0093] In a specific embodiment of the present invention, the emergency abnormal node is encapsulated to obtain emergency alarm information; the general abnormal node is encapsulated to obtain general alarm information.

[0094] Specifically, this includes encapsulating emergency abnormal nodes, along with related temperature information and multi-source data, into an emergency alarm message; and encapsulating general abnormal nodes, along with related temperature information and multi-source data, into a general alarm message.

[0095] The sensor provides text data. To facilitate information transmission and processing, the data is processed into a MessagePack format, and a protocol identifier header, such as MQTT, is added to it. The resulting MQTT messages are transmitted sequentially as a data stream over an established TCP long connection. Upon receiving the data stream, TCP divides it into small data blocks. Each small block, along with its added TCP header, forms a TCP packet. These packets are then sent via the transmission module, which follows the IP protocol. Upon receiving a packet transmission request, the transmission module encapsulates the packet into an IP datagram, fills in the header, and sends the encapsulated alarm information. The encapsulation module, upon receiving the data request, enters the transmission module to parse the data, verify the packet order and integrity, extracts the data from the data blocks to obtain the MQTT message, and then hands it over to the application module for processing.

[0096] This invention proposes a power distribution intelligent temperature measurement and alarm system. Please refer to [link / reference]. Figure 4 ,

[0097] The data acquisition module is used to acquire temperature data and multi-source data of all nodes of the cable joint in the distribution cabinet. The temperature data is then double-checked to obtain verification data.

[0098] The edge computing module compares the data of each region with a set threshold. If any region's data exceeds the limit, a preset anomaly cause relationship model is used as the standard to determine whether the multi-source data corresponding to the region's data that exceeds the limit is abnormal. When the multi-source data is abnormal, the region's data that exceeds the limit is recorded as the first data; when the multi-source data is normal, the region's data that exceeds the limit is recorded as the second data. The node corresponding to the first data is designated as an emergency abnormal node. The temperature data and multi-source data of the node corresponding to the second data are updated to obtain the updated region data. When the updated region data exceeds the limit, the node corresponding to the second data is designated as a general abnormal node.

[0099] The encapsulation module is used to encapsulate the emergency abnormal node to obtain emergency alarm information; and to encapsulate the general abnormal node to obtain general alarm information.

[0100] The application module is used to trigger alarms and handle defects based on the emergency alarm information and general alarm information.

[0101] The system disclosed in this invention is based on the Internet of Things (IoT) architecture. The acquisition module includes one or more cable node temperature monitoring terminals mounted on a power distribution cabinet, various sensors monitoring multi-source data, a base station communicating with the temperature monitoring terminals, and an edge data network and remote monitoring module connected to the base station. The node temperature monitoring terminal includes a temperature sensing module and a wireless communication module. The temperature monitoring module is electrically connected to the wireless communication module. The temperature sensing module receives actual temperature information of the power distribution system nodes, and the wireless communication module transmits the cable node temperature monitoring information to the edge data network and remote monitoring module.

[0102] The data acquisition module is the foundation of the data system. It acquires analog signals and converts them into digital signals, as well as directly acquires digital signals from electronic devices such as serial port devices. Ultimately, all of these signals are forwarded to the application module by the transport layer.

[0103] Temperature monitoring terminals and various sensors serve as sensing modules for system information perception. Their main functions are twofold: firstly, to collect data such as humidity, smoke detection, and secondly, to monitor status using infrared, ultrasonic, high-frequency, and ultra-high-frequency signals. In a regional power distribution system, a single distribution cabinet can be configured with multiple cable joint temperatures; therefore, the configuration principle can be "distribution cabinet number + cabinet joint number." Each cable joint temperature measurement module can perform preliminary calculations to characterize the rate of temperature change of a single node within a single dimension over a given period.

[0104] The wireless communication module primarily transmits the read digital information to the edge data network and remote monitoring module via wireless signals. The temperatures of all cable connector temperature measurement modules in the area can also be transmitted to the edge computing network wirelessly. The wireless technology involved is the LoRa protocol, which can be optimized for power consumption and size according to actual needs.

[0105] The edge computing module is located in the edge data network and is used to perform edge computing and linkage analysis based on the node temperature information and corresponding multi-source information data to determine the temperature anomalies of the monitored nodes in the power distribution system, and to transmit the emergency abnormal nodes and general abnormal nodes to the encapsulation module. The edge data network adopts LoRa IoT communication technology to stably send the collected temperature data to the gateway. It has the characteristics of strong communication penetration, long transmission distance and low power consumption, which can ensure stable signal transmission even under weak energy conditions.

[0106] The encapsulation module, including the remote monitoring module such as the cloud platform, can encapsulate emergency and general abnormal nodes into identifiable alarm information, facilitating data transmission to the application module. In the IoT architecture, the encapsulation module acts as a bridge between the upper and lower layers, integrating the management, control, and operation of the underlying terminal devices (i.e., the devices in the acquisition module), providing application development and a unified interface for the upper-layer application modules, and constructing an end-to-end channel between devices and applications.

[0107] The application module is used to issue alarms and handle faults based on the alarm information. The application module includes software applications such as a mobile app, which, upon receiving alarm information, issues an alarm and promptly notifies maintenance personnel to handle the fault. Based on actual needs, relevant IoT applications are built on top of the encapsulated module. By analyzing transient signals of electrical quantities in multi-source integrated data, early warnings can be given regarding the deterioration of cable joint discharge, such as the gradual evolution of a single-phase ground fault into a more serious two-phase fault, and then into a three-phase short circuit. Data on latent faults can be obtained before the fault officially deteriorates, helping staff to promptly cut off the fault source and reduce operational risks.

[0108] In a specific embodiment of the present invention, a transmission module is also included. This transmission module transmits emergency and general abnormal nodes from the edge computing module to the encapsulation module. The transmission module is primarily responsible for transmitting and processing the information acquired by the acquisition module. It employs low-power, long-distance, wide-area network LoRa wireless communication technology, with a range of several kilometers to tens of kilometers.

[0109] In a specific embodiment of the present invention, please refer to Figure 5 and Figure 6 The acquisition module is equipped with a temperature sensing module, which is installed inside the plug 3 of the cable plug 1. The plug 1 and the plug 3 are connected by a component 2. The temperature sensing module adopts a passive capacitor voltage divider power extraction mode to obtain the first temperature data, and obtains the second temperature data by measuring the temperature through a binding temperature measuring ring.

[0110] It is also equipped with a variety of sensors, including humidity sensors, ultrasonic sensors, voltage and current sensors, to monitor the environmental status of a single node and obtain multi-source data.

[0111] As shown in Figure 5, the temperature sensing module is installed inside the plug 3 of the plug 1. The plug 1 and the plug 3 are connected by a component 2, allowing direct measurement of the temperature at the cable outlet and the cable head crimp. Due to its modular installation, it is convenient to install, provides high measurement accuracy, and has a stable signal transmission frequency. The internal circuit design of the temperature sensing module is sophisticated, adapting to mainstream insulating plugs in terms of size and installation method, enabling "interchangeability and immediate use," suitable for gas / solid insulated ring main units. The temperature sensing unit includes a temperature sensing module, a power supply module, and a communication module. The part directly connected to the conductor at the measuring point has a nut-like structure, allowing direct contact with the conductor during temperature measurement for accurate temperature readings.

[0112] The temperature measurement module uses the principle of capacitive voltage division to obtain energy, which can avoid battery safety hazards. The A / D conversion unit, memory and data processor are integrated on a chip and further integrated into the sub-components of the measurement module. The sub-components can be fully matched with the plug in terms of physical specifications and dimensions, and accurately measure and evaluate the temperature of electrical connection points in a direct contact mode. The digital signal of the measured temperature is directly fed back, as well as preliminary calculation information such as the related rate of change. This invention is not limited to this.

[0113] As shown in Figure 6, given the reality that there is only voltage and no current at the contact point between the cable cone and the cable plug, the temperature sensing module adopts a passive capacitor voltage divider power supply mode. The circuit structure shows that:

[0114]

[0115] U=(I C2 -I)Z C1 +I C2 Z C2

[0116] In practical engineering applications, the two capacitors are made of the same material, and their parameter consistency is highly required to ensure the accuracy of voltage division, while the low-voltage capacitor limits the primary voltage of the transformer. Furthermore, the transformer and power electronic converter have been optimized to reduce losses, ensuring low-power circuitry and reliable operation even under low energy conditions. Additionally, the cable core can be considered a uniform heat source; the heat conduction process due to temperature and dielectric loss can be expressed using Fourier's laws of heat conduction and energy conservation. The temperature field distribution equation for the cable joint can be expressed as:

[0117]

[0118] In the formula: λ is the thermal conductivity, t em Let S be the temperature of the object, and S be the thermal energy of the internal heat source.

[0119] The main boundary conditions involved are cable joint protection and air convection, which can be quantified as follows:

[0120]

[0121] Where t bou Let t be the boundary temperature. amb Let n be the ambient air temperature, and n be the ambient air temperature. sur denoted as the outward normal to the heat exchange surface, and h as the convective heat transfer coefficient.

[0122] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0123] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0124] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0125] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the specific implementation of the present invention. Any modifications or equivalent substitutions that do not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A method for intelligent temperature measurement and alarm in power distribution, characterized in that, include, Acquire temperature data and multi-source data for all nodes of the cable joints in the distribution cabinet, wherein the multi-source data includes electrical quantity data; The temperature data is subjected to a double-layer verification to obtain temperature verification data; Edge computing is performed on the temperature verification data of all nodes to obtain the regional data of all nodes; the regional data includes the variance and standard deviation of the temperature verification data of each node within the period; The data for each region is compared with the set threshold. If any region's data exceeds the limit, the preset abnormal cause relationship model is used as the standard to determine whether the multi-source data corresponding to the region's data that exceeds the limit is abnormal. When the multi-source data is abnormal, the region's data that exceeds the limit is recorded as the first data. When the multi-source data is normal, the region's data that exceeds the limit is recorded as the second data. The node corresponding to the first data is designated as the emergency abnormal node. Repeatedly collect temperature data and multi-source data corresponding to the second data node, and perform double-layer verification on the temperature data to obtain updated temperature verification data. Perform edge calculation on the updated temperature verification data to obtain updated regional data. When the updated regional data exceeds the limit, the node corresponding to the second data is regarded as a general abnormal node. The emergency abnormal node is encapsulated to obtain emergency alarm information; the general abnormal node is encapsulated to obtain general alarm information. The alarm and fault handling are carried out based on the emergency alarm information and general alarm information.

2. The method for intelligent temperature measurement and alarm in power distribution according to claim 1, characterized in that, The process of constructing the preset abnormal cause relationship model includes, In the single-node evaluation process, the temperature verification data of a single node is randomly selected for edge computing to obtain evaluation data, and the node corresponding to the evaluation data is the evaluation node; The evaluation data is compared with the set threshold. If the evaluation data exceeds the limit, the relationship between the causes of the anomaly is obtained by analyzing the multi-source data of the evaluation node. When the multi-source data of the evaluation node is normal, the evaluation node is not abnormal and the temperature anomaly is caused by the influence of temperature transmission from surrounding nodes. When the multi-source data of the evaluation node is abnormal, the evaluation node is abnormal and the temperature anomaly is caused by the influence of abnormal multi-source data. Repeat the evaluation process for a single node to obtain multiple anomaly cause relationships, and summarize all anomaly cause relationships into an anomaly cause relationship model.

3. The method for intelligent temperature measurement and alarm in power distribution according to claim 1, characterized in that, The temperature data includes the location, number, and temperature of the corresponding single node. The electrical quantity data includes the voltage and current transient steady-state signals, high-frequency signals, and ultra-high-frequency signals of the distribution cabinet. The multi-source data also includes environmental data, which includes the noise data of the distribution cabinet, the smoke detection data inside the distribution cabinet, and the ambient humidity inside and outside the distribution cabinet.

4. The method for intelligent temperature measurement and alarm in power distribution according to claim 1, characterized in that, The temperature data is subjected to a two-layer verification to obtain temperature verification data. Specifically, the temperature data of a single node within a time period is measured twice, and recorded as the first temperature data and the second temperature data. The standard deviation of the first temperature data and the second temperature data is calculated. When the standard deviation is not within the set error range, the temperature data of that node is measured again until the standard deviation of the two temperature data is within the set error range, and the temperature verification data is obtained.

5. The method for intelligent temperature measurement and alarm in power distribution according to claim 2, characterized in that, The evaluation data is obtained by edge computing based on the temperature verification data of a single node, and is calculated according to the following formula: In the formula, T As one cycle, x For the first x There are [number] nodes, and the temperature of that node is measured within one cycle. f There are 1 value, respectively T 1. T 2… T f express, Tem _ x No. x The root mean square value of the node within this period. σ 2 em _ x It indicates the first x The variance of the node during this period.

6. The method for intelligent temperature measurement and alarm in power distribution according to claim 1, characterized in that, The regional data is obtained by edge calculation based on the temperature verification data of all nodes within the region, and is calculated according to the following formula: In the formula, T As one cycle, x For the first x 1 node T em_x For the first x The root mean square value of each node within this period. T emμ This represents the mean value over the specified period. N The total number of nodes. σ 2 em_x For the first x The variance of each node within this period. σ em_x For the first x The standard deviation of each node within this period.

7. The method for intelligent temperature measurement and alarm in power distribution according to claim 1, characterized in that, The emergency abnormal node is encapsulated to obtain emergency alarm information; the general abnormal node is encapsulated to obtain general alarm information. Specifically, this includes encapsulating emergency abnormal nodes, related temperature information, and multi-source data into an emergency alarm message; The general abnormal nodes, along with related temperature information and multi-source data, are encapsulated into a general alarm message.

8. A temperature alarm system according to any one of claims 1-7 of the intelligent temperature measurement and alarm method for power distribution, characterized in that, The data acquisition module is used to acquire temperature data and multi-source data of all nodes of the cable joint in the distribution cabinet. The temperature data is then double-checked to obtain verification data. The edge computing module compares the data of each region with the set threshold. If the data of a region exceeds the limit, the module uses the preset abnormal cause relationship model as the standard to determine whether the multi-source data corresponding to the data of the region that exceeds the limit is abnormal. When the multi-source data is abnormal, the data of the region that exceeds the limit is recorded as the first data. When the multi-source data is normal, the data of the region that exceeds the limit is recorded as the second data. The node corresponding to the first data is designated as the emergency abnormal node. Repeatedly collect temperature data and multi-source data corresponding to the second data node, and perform double-layer verification on the temperature data to obtain updated temperature verification data. Perform edge calculation on the updated temperature verification data to obtain updated regional data. When the updated regional data exceeds the limit, the node corresponding to the second data is regarded as a general abnormal node. The encapsulation module is used to encapsulate the emergency abnormal node to obtain emergency alarm information; and to encapsulate the general abnormal node to obtain general alarm information. The application module is used to trigger alarms and handle defects based on the emergency alarm information and general alarm information.

9. A power distribution intelligent temperature measurement and alarm system according to claim 8, characterized in that, It also includes a transmission module, which is used to transmit emergency and general abnormal nodes of the edge computing module to the encapsulation module.

10. A power distribution intelligent temperature measurement and alarm system according to claim 8, characterized in that, The acquisition module is equipped with a temperature sensing module. The temperature sensing module is installed inside the plug (3) of the cable plug (1). The plug (1) and the plug (3) are connected by a component (2). The temperature sensing module adopts a passive capacitor voltage divider power extraction mode to obtain the first temperature data and obtain the second temperature data by measuring the temperature through a binding temperature measuring ring. It is also equipped with a variety of sensors, including humidity sensors, ultrasonic sensors, voltage and current sensors, to monitor the environmental status of a single node and obtain multi-source data.

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