Dry-type transformer ignition risk early warning method and system
By acquiring transformer temperature data to calculate a risk index and using an assessment model for early warning, the problem of early fault identification in dry-type transformers is solved, the risk of ignition is reduced, and equipment safety is ensured.
Patent Information
- Application Number
- CN202511757273.5
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-11-27
- Publication Date
- 2026-03-03
AI Technical Summary
Existing dry-type transformer body temperature control systems cannot reliably detect local thermal faults and heat dissipation system failures in the early stages, resulting in an inability to effectively prevent the risk of ignition.
By acquiring winding and ambient temperature data of the transformer body, calculating temperature gradient and comprehensive characteristic temperature data, using the ignition risk assessment model to evaluate the ignition risk index, and initiating an early warning.
It enables early, stable, and reliable identification and warning of localized thermal faults and heat dissipation system failures in dry-type transformers, reducing the risk of ignition and ensuring the safety of dry-type transformers and power distribution systems.
Smart Images
Figure CN121598154A_ABST
Abstract
Description
Technical Field
[0001] This application relates to the field of dry-type transformer ignition risk prevention and control technology, and in particular to a method and system for early warning of dry-type transformer ignition risk. Background Technology
[0002] Dry-type transformers are widely used in power distribution networks due to their simple structure, convenient maintenance, and good fire resistance. Currently, a dry-type transformer consists of the transformer body, a body temperature control system, and a heat dissipation system. The transformer body is typically equipped with a body temperature control system based on pre-embedded temperature sensing elements in the windings, such as the BWDK3206 series body temperature control system using Pt100 platinum resistance thermometers as the sensing element. This system monitors the winding temperature and controls the start and stop of the cooling fan, triggering an alarm and even tripping the circuit breaker in case of overheating. The transformer body is usually housed in a cabinet with ventilation channels. The cabinet with ventilation channels and the cooling fan together constitute the heat dissipation system of the dry-type transformer, used for active cooling of the transformer through forced air cooling.
[0003] However, existing body temperature control systems cannot reliably detect localized thermal faults in the dry-type transformer body at an early stage, that is, before they develop into open flames, nor can they detect heat dissipation faults in the heat dissipation system at an early stage. Consequently, they cannot eliminate localized thermal faults and heat dissipation faults at an early stage, leaving dry-type transformers still at risk of ignition.
[0004] Therefore, there is a need for a method for early warning of fire risk in dry-type transformers, which can reliably identify and detect local thermal faults in the transformer body and heat dissipation faults in the heat dissipation system at an early stage. Summary of the Invention
[0005] This application provides a method for early warning of fire risk in dry-type transformers, which can reliably identify and detect local thermal faults in the transformer body and heat dissipation faults in the heat dissipation system at an early stage.
[0006] In a first aspect, this application provides a method for early warning of ignition risk in a dry-type transformer, wherein the dry-type transformer includes a transformer body and an environmental zone for dissipating heat from the transformer body through heat exchange, and the method includes: Obtain the winding temperature data of the transformer body; based on multiple temperature measuring points distributed along the first and second directions in the environmental range, obtain the environmental temperature dataset of the transformer body, wherein the first direction and the second direction are set at an angle; Based on the ambient temperature dataset, the temperature gradient in the first direction and the temperature gradient in the second direction are calculated; using the ambient temperature dataset of the transformer body, the ambient temperature gradient in the first direction, and the ambient temperature gradient in the second direction, comprehensive characteristic temperature data are calculated. By using historical winding temperature data, historical comprehensive characteristic temperature data, corresponding ignition risk causes, and corresponding ignition risk indices, a classification model is trained to obtain an ignition risk assessment model. Based on real-time winding temperature data and real-time comprehensive characteristic temperature data, an ignition risk index is obtained through the aforementioned ignition risk assessment model; based on the ignition risk index, an ignition risk warning is initiated.
[0007] Secondly, this application provides a fire ignition risk warning system for dry-type transformers, wherein the dry-type transformer includes a transformer body and an environmental zone for dissipating heat from the transformer body through heat exchange, and the system includes: The temperature data acquisition module is used to acquire the winding temperature data of the transformer body; based on multiple temperature measuring points distributed along the first and second directions in the environmental range, the module acquires the environmental temperature dataset of the transformer body, wherein the first direction and the second direction are set at an angle; The comprehensive characteristic temperature data calculation module is used to calculate the temperature gradient in the first direction and the temperature gradient in the second direction based on the ambient temperature dataset; and to calculate the comprehensive characteristic temperature data using the ambient temperature dataset of the transformer body, the ambient temperature gradient in the first direction, and the ambient temperature gradient in the second direction. The ignition risk assessment model training module is used to train a classification model using historical winding temperature data, historical comprehensive characteristic temperature data, corresponding ignition risk causes, and corresponding ignition risk indices to obtain the ignition risk assessment model. The ignition risk warning module is used to obtain an ignition risk index based on real-time winding temperature data and real-time comprehensive characteristic temperature data through the ignition risk assessment model; and to activate an ignition risk warning based on the ignition risk index.
[0008] Thirdly, this application provides an electronic device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps of the dry-type transformer ignition risk warning method provided in the first aspect.
[0009] Fourthly, this application provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the steps of the dry-type transformer ignition risk warning method provided in the first aspect.
[0010] Fifthly, this application provides a computer program product, including a computer program that, when executed by a processor, implements the steps of the dry-type transformer ignition risk warning method provided in the first aspect.
[0011] The fire ignition risk warning method for dry-type transformers provided in this application uses real-time winding temperature data... Real-time comprehensive characteristic temperature data is input into the trained ignition risk assessment model to obtain an ignition risk index, and an ignition risk warning is initiated based on the obtained ignition risk index. This enables stable and reliable identification and detection of local thermal faults in the dry-type transformer body and heat dissipation faults in the heat dissipation system at an early stage, that is, before it develops into an open flame. This is conducive to eliminating local thermal faults and heat dissipation faults at an early stage, thereby eliminating the ignition risk of dry-type transformers, realizing preventive maintenance of dry-type transformers, and further ensuring the safety of dry-type transformers and power distribution systems. Attached Figure Description
[0012] To more clearly illustrate the technical solutions in the embodiments of this application, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.
[0013] To gain a more complete understanding of this application and its beneficial effects, the following description will be provided in conjunction with the accompanying drawings, wherein the same reference numerals in the following description denote the same parts.
[0014] Figure 1 This is a flowchart illustrating a method for early warning of fire risk in a dry-type transformer, as provided in an embodiment of this application.
[0015] Figure 2 This is a schematic diagram of the temperature measurement point distribution method provided in the embodiments of this application.
[0016] Figure 3 This is a flowchart illustrating step 120 provided in the embodiments of this application.
[0017] Figure 4 This is a schematic diagram of the ignition risk assessment model in step 130 provided in the embodiments of this application; Figure 5 This is a first flowchart illustrating step 140 provided in the embodiments of this application.
[0018] Figure 6 This is a schematic diagram of the second process of step 140 provided in the embodiments of this application.
[0019] Figure 7 This is a schematic diagram of the third process of step 140 provided in the embodiments of this application.
[0020] Figure 8 This is a schematic diagram of the structure of the dry-type transformer ignition risk warning device provided in the embodiments of this application.
[0021] Figure 9This is a schematic diagram of the structure of the electronic device provided in the embodiments of this application. Detailed Implementation
[0022] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of them. All other embodiments obtained by those skilled in the art based on the embodiments of this application without creative effort are within the protection scope of this application.
[0023] Figure 1 This is a flowchart illustrating a method for early warning of ignition risk in a dry-type transformer, as provided in an embodiment of this application. Please refer to... Figure 1 The present application provides a method for early warning of fire risk of dry-type transformer. The dry-type transformer includes a transformer body and an environmental zone for dissipating heat from the transformer body through heat exchange. The method includes steps 110 to 140, which will be described in detail below.
[0024] Step 110: Obtain the winding temperature data of the transformer body; based on multiple temperature measurement points distributed along the first and second directions in the environmental range, obtain the environmental temperature dataset of the transformer body, wherein the first direction and the second direction are set at an angle.
[0025] Step 120: Based on the ambient temperature dataset, calculate the temperature gradient in the first direction and the temperature gradient in the second direction; using the ambient temperature dataset of the transformer body, the ambient temperature gradient in the first direction, and the ambient temperature gradient in the second direction, calculate the comprehensive characteristic temperature data.
[0026] Step 130: Train the classification model using historical winding temperature data, historical comprehensive characteristic temperature data, corresponding ignition risk causes, and corresponding ignition risk index to obtain the ignition risk assessment model.
[0027] Step 140: Based on real-time winding temperature data and real-time comprehensive characteristic temperature data, obtain the ignition risk index through the ignition risk assessment model; based on the ignition risk index, initiate an ignition risk warning.
[0028] It is understandable that winding temperature data and comprehensive characteristic temperature data can be used to characterize the thermal field of the transformer body within its environmental range, hereinafter referred to as the thermal field. Ignition of a dry-type transformer mainly refers to ignition of the transformer body itself.
[0029] The first direction and the second direction are set at an angle, which means that the first direction and the second direction are set at an angle greater than 0 degrees and less than 180 degrees.
[0030] In this embodiment, the causes of ignition risk include localized thermal failure and heat dissipation failure; the localized thermal failure includes current-carrying overheating and localized arc discharge; the heat dissipation failure includes cooling fan failure. Specifically, current-carrying overheating refers to overheating caused by poor contact at the connection points of the dry-type transformer body; localized arc discharge refers to localized insulation breakdown caused by uneven electric field distribution or insulation defects within the dry-type transformer body.
[0031] It is understood that the corresponding ignition risk causes refer to the ignition risk causes corresponding to historical winding temperature data and historical comprehensive characteristic temperature data, that is, the ignition risk causes of the thermal field represented by historical winding temperature data and historical comprehensive characteristic temperature data.
[0032] The ignition risk index is used to characterize the ignition risk of dry-type transformers. For example, the ignition risk index can take a value from 0 to 100. The higher the ignition risk index, the greater the ignition risk of the dry-type transformer.
[0033] The corresponding ignition risk index refers to the ignition risk index corresponding to historical winding temperature data and historical comprehensive characteristic temperature data, that is, the magnitude of the ignition risk of the dry-type transformer under the thermal field represented by historical winding temperature data and historical comprehensive characteristic temperature data. The dry-type transformer ignition risk early warning method provided in this application uses real-time winding temperature data... Real-time comprehensive characteristic temperature data is input into the trained ignition risk assessment model to obtain an ignition risk index, and an ignition risk warning is initiated based on the obtained ignition risk index. This enables stable and reliable identification and detection of local thermal faults in the dry-type transformer body and heat dissipation faults in the heat dissipation system at an early stage, that is, before it develops into an open flame. This is conducive to eliminating local thermal faults and heat dissipation faults at an early stage, thereby eliminating the ignition risk of dry-type transformers, realizing preventive maintenance of dry-type transformers, and further ensuring the safety of dry-type transformers and power distribution systems.
[0034] The following description of the dry-type transformer, including the transformer body and the environmental zone for dissipating heat from the transformer body through heat exchange, will be further illustrated by an embodiment.
[0035] In some embodiments, the dry-type transformer includes a transformer body with windings, a body temperature control system with a temperature sensing element, and a heat dissipation system with a cabinet and a cooling fan; the temperature sensing element is disposed on the windings; the transformer body is disposed in the cabinet, the cabinet having a vent for cold air circulation and an environmental zone for heat exchange between the cold air and the transformer body, and the cooling fan is used to deliver cold air into the cabinet through the vent; The step of obtaining the winding temperature data of the transformer body includes: obtaining the winding temperature data of the transformer body through the body temperature measurement system.
[0036] In this embodiment, the causes of ignition risk include localized thermal failures and heat dissipation failures; the localized thermal failures include overheating during current flow and localized arc discharge; the heat dissipation failures include cooling fan failure and blocked ventilation openings. In this embodiment, the environmental zone is the space enclosed by the inner wall of the cabinet and the outer wall of the transformer body.
[0037] In some embodiments, the cabinet is further provided with a cabinet door. In this embodiment, the causes of the ignition risk include local thermal failure and heat dissipation failure; the local thermal failure includes overheating of the current flow and local arc discharge; the heat dissipation failure includes cooling fan failure, vent blockage, and abnormal opening of the cabinet door.
[0038] The following description Figure 1 Further optional specific embodiments for each step in the process of handling the risk of fire ignition in dry-type transformers, as described in the example.
[0039] Step 110: Obtain the winding temperature data of the transformer body; based on multiple temperature measurement points distributed along the first and second directions in the environmental range, obtain the environmental temperature dataset of the transformer body, wherein the first direction and the second direction are set at an angle.
[0040] In some embodiments, the plurality of temperature measuring points distributed in the environmental interval along the first and second directions include at least a first group of temperature measuring points arranged along the first direction and a second group of temperature measuring points arranged along the first direction, wherein the first group of temperature measuring points and the second group of temperature measuring points are respectively located in the environmental intervals on opposite sides of the dry-type transformer body. Each temperature measuring point in the first group and one temperature measuring point in the second group are arranged along the second direction; The first set of temperature measurement points yields the first set of ambient temperature data; the second set of temperature measurement points yields the second set of ambient temperature data.
[0041] In some embodiments, the first direction is a vertical direction and the second direction is a horizontal direction.
[0042] In some embodiments, the distribution of multiple temperature measuring points can be such that the first group of temperature measuring points is distributed in the environmental area on the left side of the dry-type transformer body, and the second group of temperature measuring points is distributed in the environmental area on the right side of the dry-type transformer body. Alternatively, the distribution of multiple temperature measuring points can be such that the first group of temperature measuring points is distributed in the environmental area on the front side of the dry-type transformer body, and the second group of temperature measuring points is distributed in the environmental area on the rear side of the dry-type transformer body.
[0043] In some embodiments, the first set of temperature measuring points can be That is, the first temperature measurement point, the second temperature measurement point, The m-th temperature measurement point, the second group of temperature measurement points can be That is, the (m+1)th temperature measuring point and the (m+2)th temperature measuring point. The 2m temperature measurement point, among which, It is an integer greater than or equal to 2. and Arranged along the second direction, and Arranged along the second direction, ... and Arranged along the second direction. Through the first set of temperature measuring points... The first set of corresponding ambient temperature data was obtained. ; through the second set of temperature measuring points The corresponding second set of ambient temperature data was obtained. .
[0044] For example, please refer to Figure 2 , Figure 2 This is a schematic diagram of a temperature measurement point distribution method provided in an embodiment of this application. The dry-type transformer includes a transformer body 10 and an environmental zone 20 for dissipating heat from the transformer body 10 through heat exchange. In this embodiment, four temperature measurement points are provided, including a first group of temperature measurement points arranged along a first direction. and The second set of temperature measuring points arranged along the first direction and The first group of temperature measuring points and The second group of temperature measuring points and The environmental zones located on opposite sides of the dry-type transformer body; the first group of temperature measuring points With one of the temperature measuring points in the second group Arranged along the second direction, the first group of temperature measuring points With one of the temperature measuring points in the second group Arranged along the second direction; through the first group of temperature measuring points and The first set of corresponding ambient temperature data was obtained. and ; through the second set of temperature measuring points and The corresponding second set of ambient temperature data was obtained. and .
[0045] Step 120: Based on the ambient temperature dataset, calculate the temperature gradient in the first direction and the temperature gradient in the second direction; using the ambient temperature dataset of the transformer body, the ambient temperature gradient in the first direction, and the ambient temperature gradient in the second direction, calculate the comprehensive characteristic temperature data.
[0046] In some embodiments, the step of calculating the temperature gradient in the first direction and the temperature gradient in the second direction based on the ambient temperature dataset includes: The ambient temperature gradient in the first direction is obtained by using multiple sets of ambient temperature data from two adjacent temperature measuring points distributed along the first direction. The step of obtaining the ambient temperature gradient in the second direction by using multiple sets of ambient temperature data from two adjacent temperature measuring points distributed along the second direction.
[0047] In some embodiments, the step of obtaining the ambient temperature gradient in the first direction by using multiple sets of ambient temperature data from two adjacent temperature measuring points distributed along the first direction includes: The temperature measurement points that are adjacent to each other in the first group of temperature measurement points are grouped together, and the temperature measurement points that are adjacent to each other in the second group of temperature measurement points are grouped together to obtain multiple sets of corresponding ambient temperature data. Calculate the difference between two ambient temperature data in each ambient temperature data group along the first direction, and calculate the arithmetic mean of the differences corresponding to all ambient temperature data groups to obtain the ambient temperature gradient in the first direction. The step of obtaining the ambient temperature gradient in the second direction by using multiple sets of ambient temperature data from two adjacent temperature measuring points distributed along the second direction includes: By taking the first group of temperature measurement points and two temperature measurement points in the second group arranged along the second direction as a group, multiple corresponding groups of ambient temperature data are obtained. Calculate the difference between two ambient temperature data points in each ambient temperature data set along the second direction, and take the arithmetic mean of the differences for all ambient temperature data sets to obtain the ambient temperature gradient in the second direction.
[0048] In some embodiments, the first set of temperature measuring points can be That is, the first temperature measurement point, the second temperature measurement point, The m-th temperature measurement point, the second group of temperature measurement points can be That is, the (m+1)th temperature measuring point and the (m+2)th temperature measuring point. The 2m temperature measurement point, among which, It is an integer greater than or equal to 2. and Arranged along the second direction, and Arranged along the second direction, ... and Arranged along the second direction. Through the first set of temperature measuring points... The first set of corresponding ambient temperature data was obtained. ; through the second set of temperature measuring points The corresponding second set of ambient temperature data was obtained. .
[0049] In some embodiments, the ambient temperature gradient in the first direction is calculated using the following formula: ; in, The ambient temperature gradient is in the first direction. The first temperature measurement point in the first group Ambient temperature data measured at each temperature measuring point; The first temperature measurement point in the first group Ambient temperature data measured at each temperature measuring point; For the second group of temperature measurement points Ambient temperature data measured at each temperature measuring point; For the second group of temperature measurement points Ambient temperature data measured at each temperature measuring point; It is an integer greater than or equal to 2.
[0050] In some embodiments, the ambient temperature gradient in the second direction is calculated using the following formula: ; in, This represents the ambient temperature gradient in the second direction. , It is an integer greater than or equal to 2; The first of the second group of temperature measurement points Ambient temperature data measured at each temperature measuring point; The first temperature measurement point in the first group Ambient temperature data measured at each temperature measuring point.
[0051] In some embodiments, in the step of calculating comprehensive characteristic temperature data using the ambient temperature dataset of the transformer body, the ambient temperature gradient in the first direction, and the ambient temperature gradient in the second direction... ; in, The comprehensive characteristic temperature data; For the middle Temperature data measured at each temperature measuring point , It is an integer greater than or equal to 2; The ambient temperature gradient in the first direction; This represents the ambient temperature gradient in the second direction. This is the first preset correction factor; This is the second preset correction coefficient.
[0052] For example, the first preset correction coefficient and the second preset correction coefficient are obtained by optimization and calibration using simulation or experimental data that includes ignition failure scenarios. It is understood that the ignition failure scenarios include localized thermal failure scenarios and heat dissipation failure scenarios.
[0053] It is understandable that in this embodiment, The ambient temperature gradient in the first direction described in the foregoing embodiments can be obtained using the formula for calculating the gradient. It can be obtained using the calculation formula for the ambient temperature gradient in the second direction described in the foregoing embodiments.
[0054] In some embodiments, please refer to Figure 3 Step 120 includes, based on the ambient temperature dataset ,in, For integers greater than or equal to 2, the calculation formula is used: ; Obtain the ambient temperature gradient in the first direction ; Calculation formula: ; Obtain the ambient temperature gradient in the second direction ; Based on ambient temperature dataset The ambient temperature gradient in the first direction The ambient temperature gradient in the second direction Through the calculation formula: ; Obtain comprehensive characteristic temperature data .
[0055] In this embodiment, the physical meaning of each mathematical symbol in the calculation formula is the same as that in the previous embodiment, and will not be repeated here.
[0056] Step 130: Use historical winding temperature data, historical comprehensive characteristic temperature data, corresponding ignition risk causes, and corresponding ignition risk index to train a classification model and obtain an ignition risk assessment model.
[0057] In some embodiments, the ignition risk assessment model includes a cause identification model and an index assessment model; The steps of training a classification model using historical winding temperature data, historical comprehensive characteristic temperature data, corresponding ignition risk causes, and corresponding ignition risk indices to obtain an ignition risk assessment model include: The classification model is trained under supervision by taking the feature vector composed of historical winding temperature data and historical comprehensive characteristic temperature data as input and the corresponding ignition risk cause as output, so as to obtain the cause identification model. Using historical winding temperature data, historical comprehensive characteristic temperature data, and the corresponding ignition risk causes as inputs, and the corresponding ignition risk index as outputs, the classification model is trained under supervision to obtain the index evaluation model.
[0058] Understandably, please refer to Figure 4 The ignition risk assessment model has a two-level cascaded classification model framework consisting of a cause identification model (as the first-level model of the ignition risk assessment model) and an index assessment model (as the second-level model of the ignition risk assessment model).
[0059] For ease of description, real-time winding temperature data will be used. The thermal field characterized by real-time integrated characteristic temperature data is called the first thermal field.
[0060] Understandably, this involves real-time winding temperature data. The process of inputting real-time comprehensive characteristic temperature data into the ignition risk assessment model to obtain the ignition risk index is actually the process of assessing the ignition risk index of the dry-type transformer under the first thermal field.
[0061] The steps for assessing the ignition risk of a dry-type transformer under the first thermal field using the ignition risk assessment model include: firstly, identifying the causes of ignition risk in the dry-type transformer under the first thermal field using a cause identification model; and then, using an index assessment model to evaluate the ignition risk index of the dry-type transformer under the first thermal field based on the identified causes of ignition risk. Therefore, the ignition risk assessment model, constructed using a two-level cascade classification model, obtains the ignition risk index of the dry-type transformer under the first thermal field based on the identified causes of ignition risk. This can solve the problem of different ignition risk indices for the same first thermal field caused by different ignition risk causes, and enables a more accurate assessment of the ignition risk index of the dry-type transformer under the first thermal field.
[0062] Step 140: Based on real-time winding temperature data and real-time comprehensive characteristic temperature data, obtain the ignition risk index through the ignition risk assessment model; based on the ignition risk index, initiate an ignition risk warning.
[0063] In some embodiments, please refer to Figure 5 The data based on real-time winding temperature The steps for obtaining the ignition risk index from real-time comprehensive characteristic temperature data through the aforementioned ignition risk assessment model include: Real-time winding temperature data Real-time comprehensive characteristic temperature data The constructed feature vector is input into the cause identification model to obtain the corresponding ignition risk cause; the corresponding ignition risk cause is real-time winding temperature data. Causes of ignition risk in dry-type transformers under thermal field characterized by real-time comprehensive characteristic temperature data; Real-time winding temperature data Real-time integrated characteristic temperature data The feature vector constituting the causes of ignition risk is input into the index evaluation model to obtain the corresponding ignition risk index; the corresponding ignition risk index is real-time winding temperature data. The ignition risk index of dry-type transformers under thermal field characterized by real-time comprehensive characteristic temperature data.
[0064] In some embodiments, please refer to Figure 6 The step of initiating an ignition risk warning based on the ignition risk index includes: The ignition risk index is compared with a preset index; when the ignition risk index is less than or equal to the preset index, it is determined that there is no ignition risk, and the process proceeds to the step of obtaining the winding temperature data of the transformer body; when the ignition risk index is greater than the preset index, it is determined that there is an ignition risk, and an ignition risk warning is activated.
[0065] In some embodiments, the dry-type transformer further includes a cooling fan for providing forced convection air to the ambient area to dissipate heat from the transformer body through heat exchange; Please see Figure 7 Before the step of initiating an ignition risk warning based on the ignition risk index, the method further includes: Based on the rate of change of comprehensive characteristic temperature data before and after the cooling fan starts over time, the measured rate of change and the baseline rate of change are obtained; the ratio of the measured rate of change to the baseline rate of change is calculated to obtain the efficiency ratio of the cooling fan. Please continue reading. Figure 7 The step of initiating an ignition risk warning based on the ignition risk index includes: The ignition risk index is compared with a preset index, and the efficiency ratio of the cooling fan is compared with a preset efficiency ratio. When the ignition risk index is less than or equal to the preset index, and the efficiency ratio of the cooling fan is greater than or equal to the preset efficiency ratio, it is determined that there is no ignition risk, and the process jumps to the step of obtaining the winding temperature data of the transformer body. When the ignition risk index is greater than the preset index, or the efficiency ratio of the cooling fan is less than the preset efficiency ratio, it is determined that there is an ignition risk, and an ignition risk warning is activated.
[0066] It is understandable that the rate of change of the comprehensive characteristic temperature data before and after the cooling fan starts over time is calculated using the following formula: ; in, The rate of change of comprehensive characteristic temperature data before and after the cooling fan starts over time; Before starting the cooling fan Comprehensive characteristic temperature data at any given time; To ensure stable operation of the cooling fan after startup Comprehensive characteristic temperature data at any given time.
[0067] The efficiency ratio of the cooling fan is calculated using the following formula: , in, This refers to the efficiency ratio of the cooling fan. , which is the measured rate of change, that is, the rate of change of the comprehensive characteristic temperature data before and after the cooling fan starts under actual conditions; ( , which is the baseline rate of change, that is, the rate of change of the comprehensive characteristic temperature data before and after the cooling fan starts, calibrated by experiments when the cooling fan is running normally.
[0068] In this embodiment, both situations where the ignition risk index is greater than a preset index and situations where the efficiency ratio of the cooling fan is less than the preset efficiency ratio are considered as ignition risks. When either of these situations occurs, an ignition risk warning is activated.
[0069] Specifically, determining an ignition risk risk based on the ignition risk index being greater than a preset index is a judgment based on the thermal field itself, used to identify and detect localized thermal faults and heat dissipation system malfunctions, and to trigger an alarm. Similarly, determining an ignition risk based on the cooling fan's efficiency ratio being less than the preset efficiency ratio is a judgment based on the cooling fan's own influence on the thermal field, used to further enhance the identification and detection of heat dissipation system malfunctions, and to trigger an alarm.
[0070] For example, the localized thermal fault includes overheating of the current flow and localized arcing. The heat dissipation fault includes cooling fan failure, blocked vents, and abnormal opening of cabinet doors.
[0071] In other words, the existence of ignition risk is judged not only based on the thermal field itself, but also on the influence of the cooling fan on the thermal field, forming a complementary risk warning system that further improves the reliability, accuracy and early warning capability of ignition risk warning.
[0072] Figure 8 This is a schematic diagram of the structure of the dry-type transformer ignition risk warning device provided in the embodiments of this application.
[0073] This application provides a dry-type transformer ignition risk early warning system. The dry-type transformer includes a transformer body and an environmental zone for dissipating heat from the transformer body through heat exchange. The system includes: a temperature data acquisition module 801, a comprehensive characteristic temperature data calculation module 802, an ignition risk assessment model training module 803, and an ignition risk early warning module 804. Wherein: Temperature data acquisition module 801 is used to acquire winding temperature data of the transformer body; based on multiple temperature measuring points distributed along the first and second directions in the environmental range, it acquires an environmental temperature dataset of the transformer body, wherein the first direction and the second direction are set at an angle; The comprehensive characteristic temperature data calculation module 802 is used to calculate the temperature gradient in the first direction and the temperature gradient in the second direction based on the ambient temperature dataset; and to calculate the comprehensive characteristic temperature data using the ambient temperature dataset of the transformer body, the ambient temperature gradient in the first direction, and the ambient temperature gradient in the second direction. The ignition risk assessment model training module 803 is used to train a classification model using historical winding temperature data, historical comprehensive characteristic temperature data, corresponding ignition risk causes, and corresponding ignition risk index to obtain the ignition risk assessment model. The ignition risk warning module 804 is used to obtain an ignition risk index based on real-time winding temperature data and real-time comprehensive characteristic temperature data through the ignition risk assessment model; and to activate an ignition risk warning based on the ignition risk index.
[0074] In practical applications, the above system can be a terminal device or a chip applied to a terminal device. In this application, the system can implement the functions of multiple units through software, hardware, or a combination of software and hardware, enabling the system to execute the steps of the dry-type transformer ignition risk warning method provided in any of the above embodiments. Furthermore, the technical effects of each technical solution of this system can be referenced to the technical effects of the corresponding technical solutions in the dry-type transformer ignition risk warning method, and will not be elaborated upon further in this application.
[0075] Figure 9 This is a schematic diagram of the structure of the electronic device provided in the embodiments of this application.
[0076] Based on the hardware implementation of each unit in the above system, embodiments of this application also provide an electronic device, such as... Figure 9 As shown, the electronic device 900 includes a memory 910 and a processor 920. The memory 910 stores a computer program, and the processor 920 executes the computer program to implement the steps of the dry-type transformer ignition risk warning method provided in any of the above embodiments.
[0077] Of course, in practical applications, such as Figure 9 As shown, the various components in the electronic device 900 are coupled together via a bus system 930. It is understood that the bus system 930 is used to enable communication between these components. In addition to a data bus, the bus system 930 also includes a power bus, a control bus, and a status signal bus. However, for clarity, all buses are labeled as bus system 930 in the figure.
[0078] In practical applications, the aforementioned processor can be at least one of the following: Application-Specific Integrated Circuit (ASIC), Digital Signal Processing Device (DSPD), Programmable Logic Device (PLD), Field-Programmable Gate Array (FPGA), controller, microcontroller, and microprocessor. It is understood that, for different devices, the electronic devices used to implement the functions of the aforementioned processor can also be other types, and the embodiments of this application do not specifically limit this.
[0079] The aforementioned memory can be volatile memory, such as random-access memory (RAM); or non-volatile memory, such as read-only memory (ROM), flash memory, hard disk drive (HDD), or solid-state drive (SSD); or a combination of the above types of memory, and provides instructions and data to the processor.
[0080] The electronic devices described in the embodiments of this application can be terminal devices or chips applied to terminal devices. The terminal devices described in the embodiments of this application can include terminal devices such as computers.
[0081] In an exemplary embodiment, this application also provides a computer-readable storage medium, such as a memory including a computer program, which can be executed by a processor of an electronic device to perform the steps of the aforementioned method.
[0082] This application also provides a computer program product, including a computer program that, when executed by a processor, implements the steps of the method described in any one of the embodiments of this application.
[0083] Optionally, the computer program product can be applied to the electronic device in the embodiments of this application, and the computer program instructions cause the computer to execute the corresponding processes implemented by the electronic device in the various methods of the embodiments of this application. For the sake of brevity, they will not be described in detail here.
[0084] This application also provides a computer program.
[0085] Optionally, the computer program can be applied to the electronic device in the embodiments of this application. When the computer program is run on a computer, it causes the computer to execute the corresponding processes implemented by the electronic device in the various methods of the embodiments of this application. For the sake of brevity, it will not be described in detail here.
[0086] It should be understood that in the embodiments of this application, data such as user information are involved. When the embodiments of this application are applied to specific products or technologies, user permission or consent is required, and the collection, use and processing of related data must comply with the relevant laws, regulations and standards of the relevant countries and regions.
[0087] It should be understood that the terminology used in this application is for the purpose of describing particular embodiments only and is not intended to be limiting of the application. The singular forms “a,” “the,” and “the” used in this application and the appended claims are also intended to include the plural forms unless the context clearly indicates otherwise. It should also be understood that the term “and / or” as used herein refers to and includes any or all possible combinations of one or more of the associated listed items. The expressions “having,” “may have,” “comprising,” and “including,” or “may include” and “may contain” used herein may be used to indicate the presence of a corresponding feature (e.g., an element such as a number, function, operation, or component), but do not exclude the presence of additional features.
[0088] It should be understood that although the terms first, second, third, etc., may be used in this application to describe various information, this information should not be limited to these terms. These terms are only used to distinguish information of the same type from one another, and are not necessarily used to describe a specific order or sequence. For example, without departing from the scope of this invention, first information may also be referred to as second information, and similarly, second information may also be referred to as first information.
[0089] The technical solutions described in the embodiments of this application can be combined arbitrarily without conflict.
[0090] In the several embodiments provided in this application, it should be understood that the disclosed methods, systems, and devices can be implemented in other ways. The embodiments described above are merely illustrative. For example, the division of units is only a logical functional division, and in actual implementation, there may be other division methods, such as: multiple units or components can be combined, or integrated into another system, or some features can be ignored or not executed. In addition, the coupling, direct coupling, or communication connection between the various components shown or discussed can be through some interfaces, and the indirect coupling or communication connection between devices or units can be electrical, mechanical, or other forms.
[0091] The units described above as separate components may or may not be physically separate. The components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of the units may be selected to achieve the purpose of this embodiment according to actual needs.
[0092] In addition, each functional unit in the various embodiments of this application can be integrated into one processing unit, or each unit can be a separate unit, or two or more units can be integrated into one unit; the integrated unit can be implemented in hardware or in the form of hardware plus software functional units.
[0093] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any changes or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application.
Claims
1. A method for early warning of ignition risk in a dry-type transformer, wherein the dry-type transformer includes a transformer body and an environmental zone for dissipating heat from the transformer body through heat exchange, characterized in that... include: Obtain the winding temperature data of the transformer body; based on multiple temperature measuring points distributed along the first and second directions in the environmental range, obtain the environmental temperature dataset of the transformer body, wherein the first direction and the second direction are set at an angle; Based on the ambient temperature dataset, the temperature gradient in the first direction and the temperature gradient in the second direction are calculated; using the ambient temperature dataset of the transformer body, the ambient temperature gradient in the first direction, and the ambient temperature gradient in the second direction, comprehensive characteristic temperature data are calculated. By using historical winding temperature data, historical comprehensive characteristic temperature data, corresponding ignition risk causes, and corresponding ignition risk indices, a classification model is trained to obtain an ignition risk assessment model. Based on real-time winding temperature data and real-time comprehensive characteristic temperature data, the ignition risk index is obtained through the aforementioned ignition risk assessment model. Based on the aforementioned ignition risk index, an ignition risk warning is activated.
2. The method for early warning of ignition risk in dry-type transformers according to claim 1, characterized in that, The plurality of temperature measuring points distributed in the environmental interval along the first and second directions include at least a first group of temperature measuring points arranged along the first direction and a second group of temperature measuring points arranged along the first direction, wherein the first group of temperature measuring points and the second group of temperature measuring points are respectively located in the environmental intervals on opposite sides of the dry-type transformer body. Each temperature measuring point in the first group and one temperature measuring point in the second group are arranged along the second direction; The first set of temperature measurement points yields the first set of ambient temperature data; the second set of temperature measurement points yields the second set of ambient temperature data.
3. The method for early warning of ignition risk in dry-type transformers according to claim 2, characterized in that, The step of calculating the temperature gradient in the first direction and the temperature gradient in the second direction based on the ambient temperature dataset includes: The ambient temperature gradient in the first direction is obtained by using multiple sets of ambient temperature data from two adjacent temperature measuring points distributed along the first direction. The step of obtaining the ambient temperature gradient in the second direction by using multiple sets of ambient temperature data from two adjacent temperature measurement points distributed along the second direction.
4. The method for early warning of ignition risk in dry-type transformers according to claim 3, characterized in that, The step of obtaining the ambient temperature gradient in the first direction by using multiple sets of ambient temperature data from two adjacent temperature measuring points distributed along the first direction includes: The temperature measurement points that are adjacent to each other in the first group of temperature measurement points are grouped together, and the temperature measurement points that are adjacent to each other in the second group of temperature measurement points are grouped together to obtain multiple sets of corresponding ambient temperature data. Calculate the difference between two ambient temperature data in each ambient temperature data group along the first direction, and calculate the arithmetic mean of the differences corresponding to all ambient temperature data groups to obtain the ambient temperature gradient in the first direction. The step of obtaining the ambient temperature gradient in the second direction by using multiple sets of ambient temperature data from two adjacent temperature measuring points distributed along the second direction includes: By taking the first group of temperature measurement points and two temperature measurement points in the second group arranged along the second direction as a group, multiple corresponding groups of ambient temperature data are obtained. Calculate the difference between two ambient temperature data points in each ambient temperature data set along the second direction, and take the arithmetic mean of the differences for all ambient temperature data sets to obtain the ambient temperature gradient in the second direction.
5. The method for early warning of ignition risk in dry-type transformers according to claim 1, characterized in that, The ignition risk assessment model includes a cause identification model and an index assessment model; The steps of training a classification model using historical winding temperature data, historical comprehensive characteristic temperature data, corresponding ignition risk causes, and corresponding ignition risk indices to obtain an ignition risk assessment model include: The classification model is trained under supervision by taking the feature vector composed of historical winding temperature data and historical comprehensive characteristic temperature data as input and the corresponding ignition risk cause as output, so as to obtain the cause identification model. Using historical winding temperature data, historical comprehensive characteristic temperature data, and the corresponding ignition risk causes as inputs, and the corresponding ignition risk index as outputs, the classification model is trained under supervision to obtain the index evaluation model.
6. The method for early warning of ignition risk in dry-type transformers according to claim 5, characterized in that, The data based on real-time winding temperature The steps for obtaining the ignition risk index from real-time comprehensive characteristic temperature data through the aforementioned ignition risk assessment model include: Real-time winding temperature data The feature vector composed of real-time integrated characteristic temperature data is input into the cause identification model to obtain the corresponding ignition risk cause; the corresponding ignition risk cause is the real-time winding temperature data. Causes of ignition risk in dry-type transformers under thermal field characterized by real-time comprehensive characteristic temperature data; Real-time winding temperature data The feature vector, composed of real-time integrated characteristic temperature data and ignition risk causes, is input into the index evaluation model to obtain the corresponding ignition risk index; the corresponding ignition risk index is the real-time winding temperature data. The ignition risk index of dry-type transformers under thermal field characterized by real-time comprehensive characteristic temperature data.
7. The method for early warning of ignition risk in dry-type transformers according to claim 5, characterized in that, The step of initiating an ignition risk warning based on the ignition risk index includes: The ignition risk index is compared with a preset index; when the ignition risk index is less than or equal to the preset index, it is determined that there is no ignition risk, and the process proceeds to the step of obtaining the winding temperature data of the transformer body; when the ignition risk index is greater than the preset index, it is determined that there is an ignition risk, and an ignition risk warning is activated.
8. The method for early warning of ignition risk in dry-type transformers according to claim 5, characterized in that, The dry-type transformer also includes a cooling fan for providing forced convection air to the environmental zone to dissipate heat from the transformer body through heat exchange; Before the step of initiating an ignition risk warning based on the ignition risk index, the method further includes: Based on the rate of change of comprehensive characteristic temperature data before and after the cooling fan starts over time, the measured rate of change and the baseline rate of change are obtained; the ratio of the measured rate of change to the baseline rate of change is calculated to obtain the efficiency ratio of the cooling fan. The step of initiating an ignition risk warning based on the ignition risk index includes: The ignition risk index is compared with a preset index, and the efficiency ratio of the cooling fan is compared with a preset efficiency ratio. When the ignition risk index is less than or equal to the preset index, and the efficiency ratio of the cooling fan is greater than or equal to the preset efficiency ratio, it is determined that there is no ignition risk, and the process jumps to the step of obtaining the winding temperature data of the transformer body. When the ignition risk index is greater than the preset index, or the efficiency ratio of the cooling fan is less than the preset efficiency ratio, it is determined that there is an ignition risk, and an ignition risk warning is activated.
9. A fire ignition risk warning system for a dry-type transformer, wherein the dry-type transformer includes a transformer body and an environmental zone for dissipating heat from the transformer body through heat exchange, characterized in that... include: The temperature data acquisition module is used to acquire the winding temperature data of the transformer body; based on multiple temperature measuring points distributed along the first and second directions in the environmental range, the module acquires the environmental temperature dataset of the transformer body, wherein the first direction and the second direction are set at an angle; The comprehensive characteristic temperature data calculation module is used to calculate the temperature gradient in the first direction and the temperature gradient in the second direction based on the ambient temperature dataset; and to calculate the comprehensive characteristic temperature data using the ambient temperature dataset of the transformer body, the ambient temperature gradient in the first direction, and the ambient temperature gradient in the second direction. The ignition risk assessment model training module is used to train a classification model using historical winding temperature data, historical comprehensive characteristic temperature data, corresponding ignition risk causes, and corresponding ignition risk indices to obtain the ignition risk assessment model. The ignition risk warning module is used to obtain the ignition risk index based on real-time winding temperature data and real-time comprehensive characteristic temperature data through the ignition risk assessment model. Based on the aforementioned ignition risk index, an ignition risk warning is activated.
10. An electronic device comprising a memory and a processor, the memory storing a computer program, the processor executing the computer program to implement the steps of the dry-type transformer ignition risk warning method provided in any one of claims 1 to 8.