A visual monitoring system and device for high-voltage metal-enclosed switch cabinet faults

Abstract

The present invention relates to the technical field of switch cabinet fault monitoring, and specifically to a high-voltage metal-enclosed switch cabinet fault visualization monitoring system and device. The system discloses a fault component monitoring module, a fault visual analysis module, and a fault visual display module. The fault component monitoring module and the fault visual analysis module are provided. Through the operation data monitoring of each component, regular fault monitoring is performed on each component. The fault component existing in the high-voltage metal-enclosed switch cabinet is accurately determined in combination with deep learning technology. After the fault component is determined, the possibility of the fault type of the fault component is fully analyzed through the switch cabinet simulation model, and the complexity of the fault type of each fault component is accurately determined. A fault visual display module is provided, and the fault components in the high-voltage metal-enclosed switch cabinet are three-dimensionally visualized through the difference in color depth, so that monitoring personnel can intuitively understand the processing priority of the fault component.

Description

Technical Field

[0001] The present invention relates to the technical field of switch cabinet fault monitoring, and more specifically, to a high-voltage metal-enclosed switch cabinet fault visualization monitoring system and device. Background Art

[0002] In the power system, high-voltage metal-enclosed switchgear is one of the key equipment, which undertakes the important tasks of power distribution, control and protection. However, due to the complexity of the internal structure of the switchgear and the particularity of the operating environment, its fault monitoring and diagnosis has always been a technical problem. Traditional fault monitoring methods mainly rely on manual inspections and regular maintenance. This method is not only inefficient, but also difficult to detect and handle potential faults in a timely manner, which poses a serious threat to the safe and stable operation of the power system.

[0003] With the advancement of science and technology, especially the rapid development of the Internet of Things, big data and artificial intelligence technology, new technical means have been provided for switchgear fault monitoring. In recent years, some fault monitoring systems based on sensor networks and data analysis have begun to be applied to the field of switchgear. These systems can detect abnormalities and issue early warnings in a timely manner by monitoring the operating data of various components inside the switchgear in real time, thereby improving the accuracy and timeliness of fault monitoring.

[0004] However, the existing switch cabinet fault monitoring system still has some shortcomings. On the one hand, although it can monitor the occurrence of faults, it is often not accurate enough in determining the fault type and analyzing the fault complexity, making it difficult for operation and maintenance personnel to quickly and accurately locate and handle the fault. On the other hand, the display method of fault information is relatively simple, lacking in intuitiveness and interactivity, which is not conducive to the operation and maintenance personnel to fully understand the fault situation and make reasonable decisions.

[0005] In view of the above problems, the present invention proposes a high-voltage metal-enclosed switchgear fault visualization monitoring system and device. The system integrates a fault component monitoring module, a fault visualization analysis module and a fault visualization display module, accurately determines the fault component and the complexity of the fault type through deep learning technology, and intuitively presents fault information through a three-dimensional visualization display method. Summary of the invention

[0006] In view of the deficiencies in the prior art, the object of the present invention is to provide a high-voltage metal-enclosed switchgear fault visualization monitoring system and device.

[0007] To achieve the above object, the present invention provides the following technical solutions: A high-voltage metal-enclosed switch cabinet fault visualization monitoring system, including a fault component monitoring module, a fault visualization analysis module, and a fault visualization display module; The fault component monitoring module is used to periodically determine the operation fault value of each component in the high-voltage metal-enclosed switch cabinet, and determine the faulty component in the high-voltage metal-enclosed switch cabinet according to the comparison result between the operation fault value and the operation fault threshold; The fault visual analysis module creates a switch cabinet simulation model when a faulty component appears in the high-voltage metal-enclosed switch cabinet, and then obtains the comprehensive fault visual value of each faulty component; The fault visual display module determines the color depth level of the fault component according to the comprehensive fault visual value, adds three-dimensional rendering of the corresponding color depth to each fault component in the switch cabinet simulation model based on the determined color depth, and performs three-dimensional visualization of the interior of the switch cabinet simulation model.

[0008] Furthermore, the operation fault value of a component is obtained through the following steps: collecting various types of operation data of a component in the current cycle, performing feature extraction on each type of operation data and combining them into a component operation feature set in the form of a feature set, obtaining an operation fault analysis model of the component, using the component operation feature set as input data of the operation fault analysis model, and obtaining the operation fault value of the component through the output of the operation fault analysis model.

[0009] Furthermore, the comprehensive fault visibility value of the faulty component is obtained by the following steps: determine a faulty component, obtain the independent fault visibility value Vtgs of the faulty component and compare the number of prominent times Dswt, and use the formula The comprehensive fault visibility value Cwed of the faulty component is obtained, where c1 is the independent fault visibility coefficient and c2 is the contrast prominence coefficient.

[0010] Furthermore, the comparison highlight times of the faulty component are obtained through the following steps: determine a faulty component, mark the remaining faulty components as comparison faulty components, compare the faulty component with all comparison faulty components in pairs, and when the operating fault value of the faulty component in the comparison is greater than the operating fault value of the comparison faulty component, increase the comparison highlight times by one, and mark the comparison highlight times as Dswt.

[0011] Furthermore, the process of creating a switchgear simulation model is as follows: obtain the basic data of the high-voltage metal-enclosed switchgear, simultaneously obtain the basic parameters of each component in the high-voltage metal-enclosed switchgear, select simulation software, and create a high-voltage metal-enclosed switchgear model in the simulation software based on the basic data of the high-voltage metal-enclosed switchgear, add corresponding component entities to the high-voltage metal-enclosed switchgear model based on the basic parameters of each component, and add corresponding parameters to the corresponding component entities, thereby constructing a switchgear simulation model. The switchgear simulation model can simulate the operation of each component in the high-voltage metal-enclosed switchgear.

[0012] Furthermore, the independent fault visual value of a faulty component is obtained as follows: determine all fault types of a faulty component, and then obtain the fault fitting value of the component in each fault type, sum up the fault fitting values ​​of all fault types and take the average to obtain the fault fitting mean Pksz, set the fault fitting limit, and when the fault fitting value of the fault type is greater than the fault fitting limit, mark the fault type as a possible fault type, mark the total number of possible fault types as Bnce, and use the formula The independent fault visible value Vtgs of the faulty component is obtained, where k1 is the fault fitting coefficient and k2 is the possible fault coefficient.

[0013] Furthermore, the fault fitting value of a component in a fault type is obtained by the following steps: obtaining fault parameters involved in a fault type, inputting the fault parameters involved in the fault type into a switch cabinet simulation model, the switch cabinet simulation model simulates and runs for one cycle, and then collecting various types of operating data of the component in the switch cabinet simulation model, performing feature extraction on each type of operating data and combining them into a simulation operating feature set in the form of a feature set, combining the component operating feature set of the component and the simulation operating feature set into an operating fault comparison group, obtaining a fault fitting analysis model for the component, using the operating fault comparison group as input data of the fault fitting analysis model, and the fault fitting analysis model outputs the fault fitting value.

[0014] Further, the color depth level of each faulty component is determined according to the comprehensive fault visual value, specifically: obtain the comprehensive fault visual value Cwed of the faulty component, d=1, 2, …, d-1, d; Cwe1<Cwe2<…<Cwed-1<Cwed, there are d color depth levels in total, namely color depth level 1, color depth level 2, …, color depth level d-1, color depth level d, set the comprehensive fault visual value in each range to correspond to a color depth level, the range of the comprehensive fault visual value includes (0, Cwe1], (Cwe1, Cwe2], …, (Cwed-1, Cwed], if the comprehensive fault visual value ∈ (Cwe1, Cwe2], it is determined to be the thermal runaway secondary warning level 2, the color depth of color depth level 1 < the color depth of color depth level 2 < … < the color depth of color depth level d-1 < the color depth of color depth level d.

[0015] Furthermore, a high-voltage metal-enclosed switch cabinet fault visualization monitoring device is provided, and the monitoring device is used to store a high-voltage metal-enclosed switch cabinet fault visualization monitoring system.

[0016] Compared with the prior art, the present invention has the following beneficial effects: 1. Set up a fault component monitoring module and a fault visual analysis module. Through the operation data monitoring of each component, perform regular fault monitoring on each component. Combined with deep learning technology, accurately determine the faulty components in the high-voltage metal-enclosed switchgear. After determining the faulty components, use the switchgear simulation model to fully analyze the possibility of the fault type of the faulty component and accurately determine the complexity of the fault type of each faulty component. 2. Set up a fault visual display module, combine the fault complexity and fault severity, conduct a two-dimensional analysis of the faulty components, and perform a three-dimensional visual display of the faulty components in the high-voltage metal-enclosed switchgear through the difference in color depth, so that monitoring personnel can intuitively understand the processing priority of the faulty components. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 It is a system operation flow chart of a high-voltage metal-enclosed switchgear fault visualization monitoring system; Figure 2 This is a system module block diagram of a high-voltage metal-enclosed switchgear fault visualization monitoring system; Figure 3 The flowchart for obtaining the operating fault value of a component. DETAILED DESCRIPTION

[0018] Example 1: Reference Figure 1-Figure 3 A high-voltage metal-enclosed switchgear fault visualization monitoring system includes a fault component monitoring module, a fault visual analysis module, and a fault visual display module.

[0019] Fault component monitoring module: periodically determine the operating fault value of each component in the high-voltage metal-enclosed switchgear (the components in the high-voltage metal-enclosed switchgear only include core components, including busbars, circuit breakers, transformers, etc.), set the operating fault threshold (the operating fault threshold is the system preset threshold), when the operating fault value of the component is greater than the operating fault threshold, the component is marked as a faulty component, when the operating fault value of the component is less than or equal to the operating fault threshold, no processing is performed.

[0020] The operation fault value of a component is obtained through the following steps: collecting various types of operation data of a component in the current cycle (the type of operation data that needs to be collected for each component is different. Taking the bus as an example, the collected operation data types include bus temperature, bus current, bus voltage, bus leakage current, etc.; taking the circuit breaker as an example, the collected operation data types include closing and opening coil current, closing and opening coil voltage, circuit breaker moving contact stroke, circuit breaker contact speed, circuit breaker noise, etc.), extracting features of various types of operation data and combining them into a component operation feature set in the form of a feature set (feature extraction methods include statistical analysis, spectrum analysis, etc.), obtaining an operation fault analysis model for the component, using the component operation feature set as input data of the operation fault analysis model, and outputting the operation fault analysis model to obtain the operation fault value of the component.

[0021] Each component corresponds to an operation fault analysis model (for example, a busbar and a circuit breaker each correspond to an operation fault analysis model), and each operation fault analysis model is constructed based on deep learning. In this embodiment, the operation fault analysis models of two components are disclosed as examples. First, the construction process of the operation fault analysis model of the busbar is disclosed in detail, and then the construction process of the operation fault analysis model of the circuit breaker is briefly disclosed.

[0022] The process of building the busbar operation fault analysis model is as follows: collect the component operation feature sets of u buses, build a deep learning model, use the elevator operation features as the training data of the deep learning model, assign an operation fault value to each training data, and the value range of the operation fault value is (1.0~8.0). The larger the value of the operation fault value, the greater the probability of busbar operation fault, and the smaller the value of the operation fault value, the smaller the probability of busbar operation fault. The training data is divided into training set, verification set and verification set according to the set ratio of 5:2:1, and the training set, verification set and verification set are trained. After the training is completed, the construction of the busbar operation fault analysis model is completed.

[0023] The construction process of the operation fault analysis model of the circuit breaker is as follows: collect the component operation feature sets of u circuit breakers, the larger the value of the operation fault value, the greater the probability of the circuit breaker operation fault, the smaller the value of the operation fault value, the smaller the probability of the circuit breaker operation fault, and the rest is consistent with the construction process of the busbar operation fault analysis model.

[0024] Fault visual analysis module: When a faulty component appears in a high-voltage metal-enclosed switchgear, a switchgear simulation model is created to obtain the comprehensive fault visual values ​​of each faulty component.

[0025] The comprehensive fault visibility value of the faulty component is obtained by the following steps: determine a faulty component, obtain the independent fault visibility value Vtgs of the faulty component and compare the number of prominent times Dswt, and use the formula The comprehensive fault visibility value Cwed of the faulty component is obtained, where c1 is the independent fault visibility coefficient, c2 is the contrast prominence coefficient, the value of c1 is 0.74, and the value of c2 is 0.71.

[0026] The comparison highlight times of the faulty component are obtained through the following steps: determine a faulty component, mark the remaining faulty components (except the determined faulty component) as comparison faulty components, compare the faulty component with all comparison faulty components in pairs, and when the operating fault value of the faulty component in the comparison is greater than the operating fault value of the comparison faulty component, increase the comparison highlight times by one, and when the operating fault value of the faulty component in the comparison is less than or equal to the operating fault value of the comparison faulty component, do not process it, and mark the comparison highlight times as Dswt.

[0027] The process of creating a switchgear simulation model is as follows: obtain the basic data of the high-voltage metal-enclosed switchgear (the basic data includes the cabinet size, internal layout, etc.), and simultaneously obtain the basic parameters of each component in the high-voltage metal-enclosed switchgear (the basic parameters of the busbar include rated voltage, rated current, insulation level, etc., and the basic parameters of the circuit breaker include rated voltage, rated current, breaking capacity, etc. The basic parameters are the component parameters of each component in the initial and normal state), select ANSYS simulation software, and create a high-voltage metal-enclosed switchgear model in the simulation software based on the basic data of the high-voltage metal-enclosed switchgear. Based on the basic parameters of each component, add corresponding component entities in the high-voltage metal-enclosed switchgear model, including busbar entities, circuit breaker entities, etc., and add corresponding parameters to the corresponding component entities, and then construct a switchgear simulation model. The switchgear simulation model can simulate the operation of each component in the high-voltage metal-enclosed switchgear.

[0028] The independent fault visible value of a faulty component is obtained as follows: determine all fault types of a faulty component (that is, determine all possible fault types of a faulty component. Taking the busbar as an example, possible fault types include overheating fault, short circuit fault, insulation fault, overload fault, etc. Taking the circuit breaker as an example, possible fault types include refusal to operate fault, breaking fault, current carrying fault, etc.), and then obtain the fault fitting value of the component in each fault type, sum and average the fault fitting values ​​of all fault types, and obtain the fault fitting mean Pksz, set the fault fitting limit (the fault fitting limit is the system setting threshold), when the fault fitting value of the fault type is greater than the fault fitting limit, mark the fault type as a possible fault type, when the fault fitting value of the fault type is less than or equal to the fault fitting limit, do not process it, mark the total number of possible fault types as Bnce, and use the formula The independent fault visible value Vtgs of the faulty component is obtained, where k1 is the fault fitting coefficient, k2 is the possible fault coefficient, the value of k1 is 0.42, and the value of k2 is 0.89.

[0029] The fault fitting value of a component in a fault type is obtained by the following steps: obtaining fault parameters involved in a fault type (each fault type includes corresponding fault parameters. Taking the opening fault of a circuit breaker as an example, the fault parameters include arc extinguishing chamber state parameters, contact state parameters, etc.), inputting the fault parameters involved in the fault type into the switch cabinet simulation model, and the switch cabinet simulation model simulates and runs for one cycle (the operation cycle is consistent with the duration of the collection cycle of the operation fault values ​​of each component in the high-voltage metal-enclosed switch cabinet), and then collecting various types of operation data of the component in the switch cabinet simulation model, extracting features of each type of operation data and combining them into a simulation operation feature set in the form of a feature set, combining the component operation feature set of the component and the simulation operation feature set into an operation fault comparison group, obtaining the fault fitting analysis model of the component, using the operation fault comparison group as input data of the fault fitting analysis model, and the fault fitting analysis model outputs the fault fitting value.

[0030] Each component corresponds to a fault fitting analysis model, and each fault fitting analysis model is constructed based on deep learning. The construction process of the fault fitting analysis model of the circuit breaker is as follows: collect m operating fault comparison groups of circuit breakers (if it is a fault fitting analysis model of a transformer, collect m operating fault comparison groups of transformers), build a deep learning model, use the operating fault comparison group as training data for the deep learning model, assign a fault fitting value to each training data, and the fault fitting value range is (5.0~10.0). The larger the value of the fault fitting value, the closer the component operating feature set and the simulation operating feature set in the operating fault comparison group are, and the smaller the value of the fault fitting value, the less similar the component operating feature set and the simulation operating feature set are. The training data is divided into a training set, a validation set and a validation set according to a set ratio of 3:1:1, and the training set, the validation set and the validation set are trained. After the training is completed, the fault fitting analysis model of the circuit breaker is completed.

[0031] A fault component monitoring module and a fault visual analysis module are set up to monitor the operation data of each component and conduct regular fault monitoring on each component. The deep learning technology is used to accurately determine the faulty components in the high-voltage metal-enclosed switchgear. After the faulty components are determined, the possibility of the fault type of the faulty components is fully analyzed through the switchgear simulation model to accurately determine the complexity of the fault type of each faulty component.

[0032] Fault visual display module: Determine the color depth level of the faulty component according to the comprehensive fault visual value, add three-dimensional rendering of the corresponding color depth to each faulty component in the switch cabinet simulation model based on the determined color depth, and perform a three-dimensional visual display of the interior of the switch cabinet simulation model (the interactive rendering function in the simulation software can meet the display function, allowing monitoring personnel to intuitively see the faulty components, and determine the complexity and severity of the fault according to the difference in color depth of the faulty components, so that monitoring personnel can intuitively understand the processing priority of the faulty components).

[0033] Determine the color depth level of each faulty component according to the comprehensive fault visual value, specifically: obtain the comprehensive fault visual value Cwed of the faulty component, d=1, 2, ..., d-1, d; Cwe1<Cwe2<…<Cwed-1<Cwed, there are d color depth levels in total, namely color depth level 1, color depth level 2, ..., color depth level d-1, color depth level d, set the comprehensive fault visual value in each range to correspond to a color depth level, the range of the comprehensive fault visual value includes (0, Cwe1], (Cwe1, Cwe2], ..., (Cwed-1, Cwed], if the comprehensive fault visual value ∈ (Cwe1, Cwe2], it is determined to be the thermal runaway secondary warning level 2, the color depth of color depth level 1 < the color depth of color depth level 2 < ... < the color depth of color depth level d-1 < the color depth of color depth level d.

[0034] A fault visual display module is set up to conduct a two-dimensional analysis of the fault components based on the fault complexity and fault severity. The fault components in the high-voltage metal-enclosed switchgear are displayed in three dimensions through the difference in color depth, allowing monitoring personnel to intuitively understand the processing priority of the fault components.

[0035] Embodiment 2: A visual monitoring device for faults of a high-voltage metal-enclosed switch cabinet, wherein the monitoring device is used to store a visual monitoring system for faults of a high-voltage metal-enclosed switch cabinet.

[0036] The above formulas are all dimensionless and numerical calculations. The formula is a formula for the most recent real situation obtained by collecting a large amount of data and performing software simulation. The preset parameters in the formula are set by technicians in this field according to actual conditions.

[0037] The above embodiments can be implemented in whole or in part by software, hardware, firmware or any other combination. When implemented by software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, the process or function described in the embodiment of the present application is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions can be transmitted from one website site, computer, server or data center to another website site, computer, server or data center by wired or wireless (e.g., infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that contains one or more available media sets. The available medium can be a magnetic medium (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium. The semiconductor medium can be a solid-state hard disk.

[0038] It should be understood that in the various embodiments of the present application, the size of the serial numbers of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0039] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0040] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0041] In the several embodiments provided in the present application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0042] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application can be essentially or partly embodied in the form of a software product that contributes to the prior art. The computer software product is stored in a storage medium and includes several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the methods described in the various embodiments of the present application. The aforementioned storage media include: various media that can store program codes, such as USB flash drives, mobile hard disks, read-only memories (ROM), random access memories (RAM), magnetic disks or optical disks.

[0043] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. A high-voltage metal-enclosed switchgear fault visualization monitoring system, characterized in that: It includes fault component monitoring module, fault visual analysis module and fault visual display module; The fault component monitoring module is used to periodically determine the operation fault value of each component in the high-voltage metal-enclosed switch cabinet, and determine the faulty component in the high-voltage metal-enclosed switch cabinet according to the comparison result between the operation fault value and the operation fault threshold; The fault visual analysis module creates a switch cabinet simulation model when a faulty component appears in the high-voltage metal-enclosed switch cabinet, and then obtains the comprehensive fault visual value of each faulty component; The fault visual display module determines the color depth level of the fault component according to the comprehensive fault visual value, adds three-dimensional rendering of the corresponding color depth to each fault component in the switch cabinet simulation model based on the determined color depth, and performs three-dimensional visualization of the interior of the switch cabinet simulation model.

2. A high-voltage metal-enclosed switch cabinet fault visualization monitoring system according to claim 1, characterized in that: The operation fault value of a component is obtained through the following steps: collecting various types of operation data of a component in the current cycle, extracting features of various types of operation data and combining them into a component operation feature set in the form of a feature set, obtaining an operation fault analysis model of the component, using the component operation feature set as input data of the operation fault analysis model, and obtaining the operation fault value of the component through the output of the operation fault analysis model.

3. A high-voltage metal-enclosed switch cabinet fault visualization monitoring system according to claim 1, characterized in that: The comprehensive fault visibility value of the faulty component is obtained by the following steps: determine a faulty component, obtain the independent fault visibility value Vtgs of the faulty component and compare the number of prominent times Dswt, and use the formula The comprehensive fault visibility value Cwed of the faulty component is obtained, where c1 is the independent fault visibility coefficient and c2 is the contrast prominence coefficient.

4. A high-voltage metal-enclosed switch cabinet fault visualization monitoring system according to claim 3, characterized in that: The comparison highlight times of the faulty component are obtained through the following steps: determine a faulty component, mark the remaining faulty components as comparison faulty components, compare the faulty component with all comparison faulty components in pairs, and when the operating fault value of the faulty component in the comparison is greater than the operating fault value of the comparison faulty component, increase the comparison highlight times by one, and mark the comparison highlight times as Dswt.

5. A high-voltage metal-enclosed switch cabinet fault visualization monitoring system according to claim 1, characterized in that: The process of creating a switchgear simulation model is as follows: obtain the basic data of the high-voltage metal-enclosed switchgear, simultaneously obtain the basic parameters of each component in the high-voltage metal-enclosed switchgear, select the simulation software, and create a high-voltage metal-enclosed switchgear model in the simulation software based on the basic data of the high-voltage metal-enclosed switchgear. Based on the basic parameters of each component, add corresponding component entities to the high-voltage metal-enclosed switchgear model, and add corresponding parameters to the corresponding component entities, thereby constructing a switchgear simulation model. The switchgear simulation model can simulate the operation of each component in the high-voltage metal-enclosed switchgear.

6. A high-voltage metal-enclosed switch cabinet fault visualization monitoring system according to claim 3, characterized in that: The independent fault visual value of a faulty component is obtained as follows: determine all fault types of a faulty component, and then obtain the fault fitting value of the component in each fault type, sum up the fault fitting values ​​of all fault types and take the average to obtain the fault fitting mean Pksz, set the fault fitting limit, and when the fault fitting value of the fault type is greater than the fault fitting limit, mark the fault type as a possible fault type, mark the total number of possible fault types as Bnce, and use the formula The independent fault visible value Vtgs of the faulty component is obtained, where k1 is the fault fitting coefficient and k2 is the possible fault coefficient.

7. A high-voltage metal-enclosed switch cabinet fault visualization monitoring system according to claim 6, characterized in that: The fault fitting value of a component in a fault type is obtained by the following steps: obtaining fault parameters involved in a fault type, inputting the fault parameters involved in the fault type into a switch cabinet simulation model, the switch cabinet simulation model simulates and runs for one cycle, and then collecting various types of operating data of the component in the switch cabinet simulation model, performing feature extraction on each type of operating data and combining them into a simulation operating feature set in the form of a feature set, combining the component operating feature set of the component and the simulation operating feature set into an operating fault comparison group, obtaining a fault fitting analysis model for the component, using the operating fault comparison group as input data of the fault fitting analysis model, and the fault fitting analysis model outputs the fault fitting value.

8. A high-voltage metal-enclosed switch cabinet fault visualization monitoring system according to claim 1, characterized in that: Determine the color depth level of each faulty component according to the comprehensive fault visual value, specifically: obtain the comprehensive fault visual value Cwed of the faulty component, d=1, 2, ..., d-1, d; Cwe1<Cwe2<…<Cwed-1<Cwed, there are d color depth levels in total, namely color depth level 1, color depth level 2, ..., color depth level d-1, color depth level d, set the comprehensive fault visual value in each range to correspond to a color depth level, the range of the comprehensive fault visual value includes (0, Cwe1], (Cwe1, Cwe2], ..., (Cwed-1, Cwed], if the comprehensive fault visual value ∈ (Cwe1, Cwe2], it is determined to be the thermal runaway secondary warning level 2, the color depth of color depth level 1 < the color depth of color depth level 2 < ... < the color depth of color depth level d-1 < the color depth of color depth level d.

9. A visual monitoring device for high-voltage metal-enclosed switch cabinet faults, characterized in that: The monitoring device is used to store a high-voltage metal-enclosed switchgear fault visualization monitoring system as described in claim 1.

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