Low voltage draw-out switchgear monitoring system and method

Abstract

The application discloses the technical field of low-voltage draw-out type switch cabinet, and particularly relates to a low-voltage draw-out type switch cabinet monitoring system and method. First, based on the structure and key components of the switch cabinet, electrical, temperature, mechanical, insulation, environmental and image data are collected in full dimensions to provide comprehensive and accurate support for subsequent transmission and analysis. Then, through preprocessing, encrypted transmission and local caching, the data can be reliably transferred in the industrial environment through double links, and the data continuity is ensured when the network is interrupted. Based on this, through deep linkage analysis of multi-dimensional data, the application breaks through the limitation of traditional monitoring which only focuses on a single parameter, realizes complementary verification and correlation analysis of multiple types of data such as electrical, temperature, mechanical, insulation and environment, and effectively solves the pain point of isolated monitoring dimension.

Description

Technical Field

[0001] This invention relates to the field of low-voltage withdrawable switchgear technology, specifically to a low-voltage withdrawable switchgear monitoring system and method. Background Technology

[0002] Low-voltage withdrawable switchgear, as a core piece of equipment in power distribution systems, is widely used in industrial plants, commercial buildings, municipal engineering projects, and other fields. Its operational stability directly affects the safety and reliability of power supply. The MNS type low-voltage withdrawable switchgear boasts high-performance parameters such as rated current of 4000A~6300A and rated short-time withstand current of up to 100kA. It consists of several key components including the cabinet, universal circuit breaker, copper busbar, and insulating support components, and adopts a modular assembly structure to support multi-circuit power distribution needs.

[0003] However, in actual operation, current low-voltage withdrawable switchgear mainly relies on manual inspection or single-parameter monitoring equipment for maintenance, which has the following prominent problems:

[0004] 1. Lack of component condition monitoring: There is a lack of effective monitoring methods for the lifespan degradation of key components (such as universal circuit breakers and AC contactors) and the aging of insulation support components. For example, in a municipal engineering project, the AC contactors in the switch cabinet experienced contact wear due to frequent start-stop operations, which was not detected by monitoring and led to a circuit power outage fault.

[0005] 2. Poor Operation and Maintenance Collaboration: Monitoring data is disconnected from the operation and maintenance management system, making it impossible to generate targeted maintenance plans based on equipment operating status. For example, switchgear inspections at multiple sites are only carried out on a fixed schedule, without taking into account data such as actual load changes and temperature rise trends, resulting in wasted maintenance resources or the omission of critical hidden dangers. Summary of the Invention

[0006] To address the aforementioned technical problems of incomplete component status monitoring and poor operational coordination, this invention provides the following technical solution:

[0007] A low-voltage withdrawable switchgear monitoring system, comprising:

[0008] The perception layer module serves as the core of the system's data acquisition. Based on the switchgear structure and key components, it collects electrical, temperature, mechanical, insulation, environmental, and image data from all dimensions, providing comprehensive and accurate support for subsequent transmission and analysis.

[0009] The transport layer module is used to receive data from the sensing layer. After preprocessing, encrypted transmission and local caching, it ensures reliable data flow in industrial environments through dual links, while also ensuring data continuity when the network is interrupted.

[0010] The analysis layer module serves as the core of intelligent diagnosis. Based on standardized data modeling, it completes the identification of operating status, fault tracing, and component life prediction, and generates targeted decision suggestions to provide intelligent support for operation and maintenance.

[0011] The application layer module is used to support the decision-making of the analysis layer, realize the visualization of the operation status, generate accurate operation and maintenance work orders and data traceability, build a closed loop of monitoring and operation and maintenance, and improve the pertinence and efficiency of switchgear operation and maintenance.

[0012] As a preferred embodiment of the low-voltage withdrawable switchgear monitoring system of the present invention, the sensing layer module includes:

[0013] Electrical parameter acquisition unit is used to be installed on the main busbar, distribution busbar and each outgoing circuit to collect electrical parameters;

[0014] A temperature monitoring unit is used to collect temperature data by deploying distributed fiber optic sensors in areas prone to heat generation.

[0015] The mechanical condition acquisition unit is used to install on the guide rail of withdrawable components, circuit breaker operating mechanism, and connector to collect mechanical condition data and correlate it with the heating data of the temperature monitoring unit to determine the correlation between mechanical wear and temperature rise.

[0016] The insulation performance monitoring unit is used to monitor the insulation resistance and creepage distance changes of the insulation support through capacitive sensing technology, and to assess the degree of insulation aging by combining the voltage data of the electrical parameter acquisition unit.

[0017] An environmental parameter acquisition unit is deployed inside the cabinet and in the installation environment to collect environmental parameter data and correlate it with the test results of the insulation performance monitoring unit to analyze the impact of the environment on insulation performance.

[0018] The image acquisition unit is used to capture the status of the components inside the cabinet using a high-definition camera, and works in conjunction with the vibration data from the mechanical status acquisition unit to achieve complementary verification of visual and sensor data.

[0019] As a preferred embodiment of the low-voltage withdrawable switchgear monitoring system of the present invention, the transmission layer module includes:

[0020] The data preprocessing unit receives the raw data from the perception layer module and performs noise reduction, format conversion, and outlier removal to output a standardized dataset.

[0021] The data priority classification unit receives the standardized dataset output by the data preprocessing unit and classifies it into three levels: urgent, important, and routine, based on the data type and the importance of related components.

[0022] The data encryption transmission unit is used to perform AES encryption on the graded dataset using 5G+wired dual-link transmission, and optimize the transmission frequency band based on the installation location of the switch cabinet to ensure the stability of data transmission in the industrial environment.

[0023] The data caching unit is used to set up local edge cache nodes to store critical data.

[0024] As a preferred embodiment of the low-voltage withdrawable switchgear monitoring system of the present invention, the analysis layer module includes:

[0025] The status identification unit is used to receive standardized data from the transmission layer, combine it with the rated parameters of the MNS type switchgear, establish a parameter threshold model, identify normal operation, abnormal warning, and fault status, and output the status identification result.

[0026] The operating condition adaptation analysis unit is used to receive the status identification results from the status identification unit, and at the same time, backtrack the load data from the electrical parameter acquisition unit and the scene data from the environmental parameter acquisition unit to build an operating condition classification model. It also adjusts the fault judgment benchmark based on different operating conditions and outputs the status data and threshold model after adapting to the operating conditions so that the subsequent analysis process can be combined with the actual operating conditions.

[0027] The fault tracing unit is used to perform source tracing analysis by associating the output data of the working condition adaptation analysis unit with the data of multiple units in the sensing layer.

[0028] The life prediction unit receives the analysis results from the fault tracing unit, combines them with the historical operating data of key components, and uses a grey prediction model to predict the remaining life of the components, providing a basis for maintenance planning.

[0029] The linkage decision-making unit is used to integrate the results of status identification, fault tracing, and life prediction to generate targeted decision recommendations.

[0030] As a preferred embodiment of the low-voltage withdrawable switchgear monitoring system of the present invention, the application layer module includes:

[0031] The visualization monitoring unit is used to receive the results from the application layer module, display the real-time operating status of the switch cabinet through the dashboard, and also supports viewing by cabinet type. It can also be linked to the real-time screen of the perception layer module to achieve synchronous monitoring of data and vision.

[0032] The cross-cabinet linkage analysis unit is used to receive multi-cabinet operation data from the visualization monitoring unit, associate it with the status identification unit results of each cabinet, and analyze the linkage impact between cabinets; at the same time, it calls the cross-cabinet linkage data of the analysis layer module, generates linkage fault diagnosis report, and outputs it to subsequent units; enabling subsequent units to generate cross-cabinet collaborative work orders.

[0033] The operation and maintenance scheduling unit is used to automatically generate operation and maintenance work orders based on the maintenance suggestions of the analysis layer module and the remaining life data of the components in the analysis layer module, and assign them to the corresponding operation and maintenance personnel, while synchronizing the location information of the switch cabinet and the component model.

[0034] The historical data traceability unit is used to store all operational data, fault records, and maintenance logs, and supports retrieval by time, cabinet number, and fault type, providing data support for the upgrade and transformation of switchgear.

[0035] The monitoring method for low-voltage withdrawable switchgear includes the following specific steps:

[0036] S1, Sensing Layer: Used as the core of system data acquisition, based on the switch cabinet structure and key components, to collect electrical, temperature, mechanical, insulation, environmental and image data in all dimensions, providing comprehensive and accurate support for subsequent transmission and analysis;

[0037] S2, Transport Layer: Used to receive data from the Sensing Layer, preprocess, encrypt, and cache it locally, ensuring reliable data flow in industrial environments through dual links, while also ensuring data continuity during network interruptions;

[0038] S3, Analysis Layer: Used as the core of intelligent diagnosis, based on standardized data modeling, to complete operation status identification, fault tracing, component life prediction, generate targeted decision suggestions, and provide intelligent support for operation and maintenance;

[0039] S4, Application Layer: Used to support the decision-making of the analysis layer, realize the visualization of the operation status, generate accurate operation and maintenance work orders and data traceability, build a closed loop of monitoring and operation and maintenance, and improve the pertinence and efficiency of switchgear operation and maintenance.

[0040] As a preferred embodiment of the low-voltage withdrawable switchgear monitoring method of the present invention, the specific steps of step S1 are as follows:

[0041] S11, Electrical Parameter Acquisition: Installed on the main busbar, distribution busbar and each outgoing circuit to collect electrical parameters;

[0042] S12, Temperature Monitoring: Distributed fiber optic sensors are deployed in areas prone to heat generation to collect temperature data;

[0043] S13, Mechanical Condition Acquisition: Installed on the guide rail of withdrawable components, circuit breaker operating mechanism, and connector to collect mechanical condition data and correlate it with the heat generation data of the temperature monitoring step to determine the correlation between mechanical wear and temperature rise.

[0044] S14, Insulation performance monitoring: By using capacitive sensing technology, monitor the insulation resistance and creepage distance changes of the insulation support, and combine the voltage data from the electrical parameter acquisition steps to assess the degree of insulation aging.

[0045] S15, Environmental Parameter Acquisition: Deployed inside the cabinet and in the installation environment to collect environmental parameter data, and correlate with the test results of the insulation performance monitoring steps to analyze the impact of the environment on insulation performance;

[0046] S16, Image Acquisition: The status of components inside the cabinet is captured by a high-definition camera, and vibration data from the mechanical status acquisition step is used to achieve complementary verification of visual and sensor data.

[0047] As a preferred embodiment of the low-voltage withdrawable switchgear monitoring method of the present invention, the specific steps of step S2 are as follows:

[0048] S21, Data preprocessing: Receive the raw data from the perception layer step and perform noise reduction, format conversion, and outlier removal to output a standardized dataset.

[0049] S22, Data Priority Classification: Receive the standardized dataset output from the data preprocessing step and classify it into three levels: urgent / important / routine based on data type and the importance of related components;

[0050] S23, Encrypted Data Transmission: Employs 5G+wired dual-link transmission, performs AES encryption on the graded dataset, and optimizes the transmission frequency band based on the installation location of the switch cabinet to ensure the stability of data transmission in the industrial environment.

[0051] S24, Data Cache: Set up local edge cache nodes to store critical data.

[0052] As a preferred embodiment of the low-voltage withdrawable switchgear monitoring method of the present invention, the specific steps of S3 are as follows:

[0053] S31, Status Identification: Receive standardized data from the transmission layer, combine it with the rated parameters of the MNS type switchgear, establish a parameter threshold model to identify normal operation, abnormal warning, and fault status, and output the status identification result.

[0054] S32, Operating Condition Adaptation Analysis: Receives the status identification results from the status identification step, and simultaneously backtracks the load data from the electrical parameter acquisition step and the scenario data from the environmental parameter acquisition step to construct an operating condition classification model; at the same time, it adjusts the fault judgment benchmark based on different operating conditions, and outputs the status data and threshold model after adapting to the operating conditions, so that the subsequent analysis process can be combined with the actual operating conditions.

[0055] S33, Fault Source Tracing: Based on the output data of the working condition adaptation analysis steps, perform source tracing analysis by associating multi-step data from the perception layer.

[0056] S34, Lifetime Prediction: Receive the analysis results from the fault tracing steps, combine them with the historical operating data of key components, and use a grey prediction model to predict the remaining lifetime of the components, providing a basis for maintenance planning;

[0057] S35, Linked Decision Making: Integrates the results of status identification, fault tracing, and lifespan prediction to generate targeted decision recommendations.

[0058] As a preferred embodiment of the low-voltage withdrawable switchgear monitoring method of the present invention, the specific steps of S4 are as follows:

[0059] S41, Visual Monitoring: Receives the results of the application layer steps, displays the real-time operating status of the switch cabinet through the dashboard, supports viewing by cabinet type, and associates with the real-time screen of the perception layer steps to achieve synchronous monitoring of data and vision.

[0060] S42, Cross-cabinet Linkage Analysis: Receives multi-cabinet operation data from the visualization monitoring steps and associates it with the status identification results of each cabinet to analyze the linkage impact between cabinets; simultaneously, it calls the cross-cabinet association data from the analysis layer steps to generate a linkage fault diagnosis report and outputs it to subsequent steps; enabling subsequent steps to generate cross-cabinet collaborative work orders.

[0061] S43, Operation and Maintenance Scheduling: Based on the maintenance suggestions in the analysis layer steps and combined with the remaining lifespan data of the components in the analysis layer steps, automatically generate operation and maintenance work orders and assign them to the corresponding operation and maintenance personnel, and synchronize the location information of the switch cabinet and the component model.

[0062] S44, Historical Data Traceability: Stores all operational data, fault records, and maintenance logs, and supports retrieval by time, cabinet number, and fault type, providing data support for the upgrade and transformation of switchgear.

[0063] Compared with existing technologies:

[0064] This invention breaks through the limitations of traditional monitoring that focuses on only a single parameter by conducting in-depth, multi-dimensional data linkage analysis. It achieves complementary verification and correlation analysis of multiple types of data, including electrical, temperature, mechanical, insulation, and environmental data, effectively solving the pain point of isolated monitoring dimensions. Relying on real-time acquisition, intelligent preprocessing, and efficient transmission mechanisms, it significantly improves data processing and feedback efficiency, avoiding the problem of delayed fault warnings caused by relying on manual judgment. Through a specially designed monitoring unit, it comprehensively covers core dimensions such as the life decay of key components, changes in the mechanical condition and insulation performance of parts, filling the gap in traditional monitoring of component status. At the same time, it constructs a closed-loop system for the entire process from data acquisition and intelligent analysis to operation and maintenance scheduling, deeply integrating monitoring results with operation and maintenance management. It can generate precise and personalized maintenance plans, thoroughly improving the resource waste or hidden danger omissions caused by traditional fixed-cycle inspections, and achieving comprehensive control of switchgear operating status and a significant improvement in operation and maintenance efficiency. Attached Figure Description

[0065] Figure 1 This is a schematic diagram of the overall framework of the present invention;

[0066] Figure 2 This is a schematic diagram of the sensing layer module framework of the present invention;

[0067] Figure 3 This is a schematic diagram of the transport layer module framework of the present invention;

[0068] Figure 4 This is a schematic diagram of the analysis layer module framework of the present invention;

[0069] Figure 5 This is a schematic diagram of the application layer module framework of the present invention. Detailed Implementation

[0070] To make the objectives, technical solutions, and advantages of the present invention clearer, the embodiments of the present invention will be described in further detail below with reference to the accompanying drawings.

[0071] This invention provides a monitoring system for low-voltage withdrawable switchgear. Please refer to [link / reference]. Figure 1 ,include:

[0072] The perception layer module serves as the core of the system's data acquisition. Based on the switchgear structure and key components, it collects electrical, temperature, mechanical, insulation, environmental, and image data from all dimensions, providing comprehensive and accurate support for subsequent transmission and analysis.

[0073] The transport layer module is used to receive data from the sensing layer. After preprocessing, encrypted transmission and local caching, it ensures reliable data flow in industrial environments through dual links, while also ensuring data continuity when the network is interrupted.

[0074] The analysis layer module serves as the core of intelligent diagnosis. Based on standardized data modeling, it completes the identification of operating status, fault tracing, and component life prediction, and generates targeted decision suggestions to provide intelligent support for operation and maintenance.

[0075] The application layer module is used to support the decision-making of the analysis layer, realize the visualization of the operation status, generate accurate operation and maintenance work orders and data traceability, build a closed loop of monitoring and operation and maintenance, and improve the pertinence and efficiency of switchgear operation and maintenance.

[0076] Please see Figure 2 The perception layer module includes:

[0077] Electrical parameter acquisition unit is used to install on the main bus, distribution bus and each outgoing circuit to collect electrical parameters such as rated current (6300A~400A), voltage (400V / 380V / 690V), frequency (50Hz), and short-circuit withstand current;

[0078] The temperature monitoring unit uses distributed fiber optic sensors deployed at locations prone to heat generation, such as main busbar joints, circuit breaker contacts, and insulating support components, to collect temperature data (accuracy ±0.5℃). This data, combined with current data from the electrical parameter acquisition unit, provides a basis for judging thermal faults.

[0079] The mechanical condition acquisition unit is used to install on the guide rail of the withdrawable component, the circuit breaker operating mechanism, and the connector to collect mechanical condition data such as the number of times the drawer is inserted and removed, the opening and closing status of the switch, and the mechanical vibration frequency. It is also correlated with the heat generation data of the temperature monitoring unit to determine the correlation between mechanical wear and temperature rise.

[0080] The insulation performance monitoring unit is used to monitor the insulation resistance and creepage distance changes (reference value ≥12.5mm) of the insulation support components (DMC material, epoxy resin material) through capacitive sensing technology, and to assess the degree of insulation aging by combining the voltage data of the electrical parameter acquisition unit.

[0081] The environmental parameter acquisition unit is deployed inside the cabinet and in the installation environment to collect environmental parameter data such as temperature, humidity, and dust concentration, and correlates them with the test results of the insulation performance monitoring unit to analyze the impact of the environment on insulation performance.

[0082] The image acquisition unit is used to capture the status of internal components (such as busbar corrosion and loose connectors) using a high-definition camera, and works in conjunction with the vibration data from the mechanical status acquisition unit to achieve complementary verification of visual and sensor data.

[0083] Please see Figure 3 The transport layer module includes:

[0084] The data preprocessing unit receives the raw data from the perception layer module and performs noise reduction, format conversion, and outlier removal (such as removing spike interference in current data) to output a standardized dataset.

[0085] The data priority classification unit receives the standardized dataset output by the data preprocessing unit and classifies it into three levels—urgent, important, and routine—based on data type (fault data, early warning data, and normal operation data) and the importance of related components (e.g., main bus data has higher priority than environmental data). The classification results are then synchronized to the data encryption transmission unit, instructing it to prioritize the transmission of urgent data (e.g., short-circuit fault current, insulation breakdown early warning), followed by important and routine data. Simultaneously, the data caching unit prioritizes caching of urgent data. This approach maintains the existing "preprocessing-transmission-caching" link while improving the transmission efficiency of fault data through priority allocation, thus resolving the problem of critical information delays caused by concurrent data transmission in industrial environments.

[0086] The data encryption transmission unit is used to perform AES encryption on the graded dataset using 5G+wired dual-link transmission, and optimize the transmission frequency band based on the installation location of the switch cabinet (indoor type) to ensure the stability of data transmission in the industrial environment.

[0087] The data caching unit is used to set up local edge caching nodes to store critical data for nearly 72 hours (such as short-circuit current peaks and abnormal temperature rise records). When the network is interrupted, the cached data can be retrieved by the data preprocessing unit to ensure data continuity.

[0088] Please see Figure 4 The analysis layer module includes:

[0089] The status identification unit is used to receive standardized data from the transmission layer, combine it with the rated parameters of the MNS type switchgear (such as the rated current of the main busbar 6300A and the short-time withstand current 100kA), establish a parameter threshold model, identify normal operation, abnormal warning, and fault status, and output the status identification results.

[0090] The operating condition adaptation analysis unit receives the status identification results from the status identification unit, and simultaneously backtracks the load data (such as loop current load rate) from the electrical parameter acquisition unit and the scenario data (such as production / idle periods in industrial plants) from the environmental parameter acquisition unit to construct an operating condition classification model (such as heavy load, light load, and standby conditions). It also adjusts the fault judgment criteria based on different operating conditions (e.g., the temperature rise threshold can be dynamically relaxed by 5% under heavy load to avoid misjudgment), and outputs the adapted status data and threshold model so that the subsequent analysis process (by the fault tracing unit) can be combined with the actual operating conditions, avoiding misjudgment / missed judgment due to fixed thresholds.

[0091] The fault tracing unit is used to perform source tracing analysis by associating the output data of the operating condition adaptation analysis unit with data from multiple units in the sensing layer. For example, when the status identification unit determines that the temperature rise is abnormal, it calls the current data from the electrical parameter acquisition unit, the contact resistance data from the mechanical status acquisition unit, and the temperature and humidity data from the environmental parameter acquisition unit to locate the cause of the fault (such as overload, poor contact, or overheating).

[0092] The life prediction unit receives the analysis results from the fault tracing unit and combines them with the historical operating data (such as the number of opening and closing times and overload duration) of key components (such as universal circuit breakers and AC contactors). Using a grey prediction model, it predicts the remaining life of the components and provides a basis for maintenance planning.

[0093] The linkage decision unit is used to integrate the results of status identification, fault tracing, and life prediction to generate targeted decision suggestions (such as emergency shutdown, planned maintenance, and component replacement). The decision logic is optimized based on the structural characteristics of the switchgear (such as overall lifting method and modular assembly) to ensure the feasibility of the suggestions.

[0094] Please see Figure 5 The application layer module includes:

[0095] The visualization monitoring unit is used to receive the results from the application layer module and display the operating status of the switchgear (electrical parameters, temperature distribution, fault warning) in real time through the dashboard. It also supports viewing by cabinet type (incoming cabinet, feeder cabinet, control cabinet) and is associated with the real-time screen of the perception layer module to achieve synchronous monitoring of data and vision.

[0096] The cross-cabinet linkage analysis unit receives multi-cabinet operation data from the visualization monitoring unit (such as incoming line cabinets, feeder cabinets, and control cabinets in the same power distribution system), and associates it with the status identification unit results of each cabinet (such as abnormal current in the incoming line cabinet and temperature rise warning in the feeder cabinet). It analyzes the linkage effects between cabinets (such as an incoming line cabinet busbar fault causing overload in the downstream feeder cabinet). At the same time, it calls the cross-cabinet linkage data from the analysis layer module to generate a linkage fault diagnosis report (such as identifying the fault source cabinet and the scope of affected cabinets), and outputs it to the subsequent unit (operation and maintenance scheduling unit). This enables the subsequent unit (operation and maintenance scheduling unit) to generate cross-cabinet collaborative work orders (such as simultaneously inspecting the incoming line cabinet and the associated feeder cabinet), filling the limitations of the original single-cabinet independent analysis, while reusing the multi-cabinet data from the visualization monitoring and the traceability results from the analysis layer to maintain logical consistency.

[0097] The operation and maintenance scheduling unit is used to automatically generate operation and maintenance work orders (such as circuit breaker contact replacement and busbar cleaning) based on the maintenance suggestions of the analysis layer module and the remaining life data of the components in the analysis layer module, and assign them to the corresponding operation and maintenance personnel, while synchronizing the location information of the switch cabinet and the component model (such as CW3-6300H / 3P circuit breaker).

[0098] The historical data traceability unit is used to store all operational data, fault records, and maintenance logs. It supports retrieval by time, cabinet number, and fault type, providing data support for the upgrade and transformation of switchgear (such as protection level optimization and busbar cross-sectional area adjustment). Its retrieval results can be called and displayed by the visualization monitoring unit.

[0099] The monitoring method for low-voltage withdrawable switchgear includes the following specific steps:

[0100] S1, Sensing Layer: Based on the switch cabinet structure and key components, collect electrical, temperature, mechanical, insulation, environmental and image data in all dimensions;

[0101] The specific steps of S1 are as follows:

[0102] S11, Electrical Parameter Acquisition: Installed on the main busbar, distribution busbar and each outgoing circuit to collect electrical parameters such as rated current (6300A~400A), voltage (400V / 380V / 690V), frequency (50Hz), and short-circuit withstand current;

[0103] S12, Temperature Monitoring: Distributed fiber optic sensors are deployed at locations prone to heat generation, such as main busbar joints, circuit breaker contacts, and insulating support components, to collect temperature data (accuracy ±0.5℃). This data is then combined with current data from the electrical parameter collection steps to provide a basis for thermal fault diagnosis.

[0104] S13, Mechanical Status Acquisition: Installed on the guide rail of withdrawable components, circuit breaker operating mechanism, and connector to collect mechanical status data such as the number of drawer insertions and removals, switch opening and closing status, and mechanical vibration frequency, and correlated with the heat generation data of the temperature monitoring step to determine the correlation between mechanical wear and temperature rise.

[0105] S14, Insulation performance monitoring: Using capacitive sensing technology, monitor the insulation resistance and creepage distance changes of the insulation support components (DMC material, epoxy resin material) (reference value ≥12.5mm), and combine the voltage data from the electrical parameter acquisition steps to assess the degree of insulation aging.

[0106] S15, Environmental Parameter Acquisition: Deployed inside the cabinet and in the installation environment to collect environmental parameter data such as temperature, humidity, and dust concentration, and correlate them with the test results of the insulation performance monitoring steps to analyze the impact of the environment on insulation performance;

[0107] S16, Image Acquisition: The status of components inside the cabinet (such as busbar corrosion and loose connectors) is captured by a high-definition camera, and vibration data from the mechanical status acquisition step is used in conjunction with the acquisition to achieve complementary verification of visual and sensor data.

[0108] S2, Transport Layer: The data collected by S1 is processed sequentially through preprocessing, encrypted transmission, and local caching;

[0109] The specific steps of S2 are as follows:

[0110] S21, Data preprocessing: Receive the raw data from the perception layer step and perform noise reduction, format conversion, and outlier removal (such as removing spike interference in current data) to output a standardized dataset.

[0111] S22, Data Priority Classification: Receives the standardized dataset output from the data preprocessing step and classifies it into three levels—urgent, important, and routine—based on data type (fault data, early warning data, normal operation data) and the importance of related components (e.g., main bus data has higher priority than environmental data). The classification results are synchronized to the data encryption and transmission step, instructing it to prioritize the transmission of urgent data (e.g., short-circuit fault current, insulation breakdown early warning), followed by important and routine data. Simultaneously, the data caching step prioritizes caching urgent data. This approach does not disrupt the original "preprocessing-transmission-caching" link and improves the transmission efficiency of fault data through priority allocation, solving the problem of critical information delays caused by concurrent data transmission in industrial environments.

[0112] S23, Encrypted Data Transmission: Employs 5G+wired dual-link transmission, performs AES encryption on the graded dataset, and optimizes the transmission frequency band based on the installation location of the switch cabinet (indoor type) to ensure the stability of data transmission in the industrial environment.

[0113] S24, Data caching: Set up local edge cache nodes to store critical data for the past 72 hours (such as short-circuit current peaks and abnormal temperature rise records). When the network is interrupted, the cached data can be recalled by the data preprocessing steps to ensure data continuity.

[0114] S3, Analysis Layer: Based on the standardized data transmitted by S2, it performs modeling, completes operation status identification, fault tracing, component life prediction, and generates targeted decision-making suggestions;

[0115] The specific steps of S3 are as follows:

[0116] S31, Status Identification: Receive standardized data from the transmission layer, combine it with the rated parameters of the MNS type switchgear (such as main bus rated current 6300A, short-time withstand current 100kA), establish a parameter threshold model to identify normal operation, abnormal warning, and fault status, and output the status identification result.

[0117] S32, Operating Condition Adaptation Analysis: Receives the status identification results from the status identification step, and simultaneously backtracks the load data (e.g., loop current load rate) from the electrical parameter acquisition step and the scenario data (e.g., production / idle periods in industrial plants) from the environmental parameter acquisition step, constructing an operating condition classification model (e.g., heavy load, light load, standby conditions); it also adjusts the fault judgment criteria based on different operating conditions (e.g., the temperature rise threshold can be dynamically relaxed by 5% under heavy load to avoid misjudgment), and outputs the status data and threshold model after adapting to the operating conditions, so that the subsequent analysis process (fault tracing step) is combined with the actual operating conditions, avoiding misjudgment / missed judgment due to fixed thresholds.

[0118] S33, Fault Source Tracing: Based on the output data of the operating condition adaptation analysis step, perform source tracing analysis by associating multi-step data from the sensing layer. For example, when the status identification step determines that the temperature rise is abnormal, call the current data from the electrical parameter acquisition step, the contact resistance data from the mechanical status acquisition step, and the temperature and humidity data from the environmental parameter acquisition step to locate the cause of the fault (such as overload, poor contact, or overheating of the environment).

[0119] S34, Life Prediction: Receive the analysis results of the fault tracing steps, combine them with the historical operating data (such as the number of opening and closing times and overload duration) of key components (universal circuit breakers, AC contactors, etc.), and use a grey prediction model to predict the remaining life of the components, providing a basis for maintenance plans.

[0120] S35, Linked Decision Making: Integrates the results of status identification, fault tracing, and life prediction to generate targeted decision suggestions (such as emergency shutdown, planned maintenance, and component replacement). The decision logic is optimized based on the structural characteristics of the switchgear (such as overall lifting method and step-by-step assembly) to ensure the feasibility of the suggestions.

[0121] S4, Application Layer: Based on the targeted decision-making suggestions generated by S3, it enables visualization of operational status, generation of accurate maintenance work orders, and data traceability;

[0122] The specific steps of S4 are as follows:

[0123] S41, Visual Monitoring: Receives the results of the application layer steps and displays the real-time operating status of the switchgear (electrical parameters, temperature distribution, fault warning) through the dashboard. It also supports viewing by cabinet type (incoming cabinet, feeder cabinet, control cabinet) and associates it with the real-time screen of the perception layer steps to achieve synchronous monitoring of data and vision.

[0124] S42, Cross-cabinet Linkage Analysis: Receives multi-cabinet operation data from the visualization monitoring steps (such as incoming line cabinets, feeder cabinets, and control cabinets in the same power distribution system), and associates it with the status identification results of each cabinet (such as abnormal current in the incoming line cabinet and temperature rise warning in the feeder cabinet). Analyzes the linkage effects between cabinets (such as an incoming line cabinet busbar fault causing overload in the downstream feeder cabinet). Simultaneously, it calls the cross-cabinet linkage data from the analysis layer steps to generate a linkage fault diagnosis report (such as identifying the fault source cabinet and the scope of affected cabinets), and outputs it to subsequent steps (maintenance scheduling steps). This enables subsequent steps (maintenance scheduling steps) to generate cross-cabinet collaborative work orders (such as simultaneously inspecting the incoming line cabinet and associated feeder cabinets), filling the limitations of the original single-cabinet independent analysis, while reusing the multi-cabinet data from visualization monitoring and the tracing results from the analysis layer to maintain logical consistency.

[0125] S43, Operation and Maintenance Scheduling: Based on the maintenance suggestions in the analysis layer steps and combined with the remaining life data of components in the analysis layer steps, automatically generate operation and maintenance work orders (such as circuit breaker contact replacement, busbar cleaning), and assign them to the corresponding operation and maintenance personnel, while synchronizing the location information of the switch cabinet and the component model (such as CW3-6300H / 3P circuit breaker).

[0126] S44, Historical Data Traceability: Stores all operational data, fault records, and maintenance logs, and supports retrieval by time, cabinet number, and fault type. It provides data support for the upgrade and transformation of switchgear (such as protection level optimization and busbar cross-sectional area adjustment), and its retrieval results can be called and displayed in the visualization monitoring steps.

[0127] Although the present invention has been described above with reference to embodiments, various modifications can be made and components can be replaced with equivalents without departing from the scope of the invention. In particular, as long as there is no structural conflict, the features in the disclosed embodiments can be combined with each other in any manner. The lack of an exhaustive description of these combinations in this specification is merely for the sake of brevity and resource conservation. Therefore, the present invention is not limited to the specific embodiments disclosed herein, but includes all technical solutions falling within the scope of the claims.

Claims

1. A monitoring system for low-voltage withdrawable switchgear, characterized in that, include: The sensing layer module is used to collect electrical, temperature, mechanical, insulation, environmental and image data from all dimensions based on the switch cabinet structure and key components; The transport layer module is used to ensure that the data collected by the perception layer module undergoes preprocessing, encrypted transmission, and local caching in sequence. The analysis layer module is used to model based on the standardized data transmitted by the transmission layer module, complete the identification of operating status, fault tracing, component life prediction, and generate targeted decision suggestions. The application layer module is used to realize the visualization of operation status, the generation of accurate operation and maintenance work orders, and data traceability based on the targeted decision suggestions generated by the analysis layer module; The analysis layer module includes: The status identification unit is used to receive standardized data from the transmission layer, combine it with the rated parameters of the MNS type switchgear, establish a parameter threshold model, identify normal operation, abnormal warning, and fault status, and output the status identification result. The operating condition adaptation analysis unit is used to receive the status identification results from the status identification unit, and at the same time, backtrack the load data from the electrical parameter acquisition unit and the scene data from the environmental parameter acquisition unit to build an operating condition classification model. It also adjusts the fault judgment benchmark based on different operating conditions and outputs the status data and threshold model after adapting to the operating conditions so that the subsequent analysis process can be combined with the actual operating conditions. The fault tracing unit is used to perform source tracing analysis by associating the output data of the working condition adaptation analysis unit with the data of multiple units in the sensing layer. The life prediction unit receives the analysis results from the fault tracing unit, combines them with the historical operating data of key components, and uses a grey prediction model to predict the remaining life of the components, providing a basis for maintenance planning. The linkage decision-making unit is used to integrate the results of status identification, fault tracing, and life prediction to generate targeted decision recommendations; The application layer module includes: The visualization monitoring unit is used to receive the results from the application layer module, display the real-time operating status of the switch cabinet through the dashboard, and also supports viewing by cabinet type. It can also be linked to the real-time screen of the perception layer module to achieve synchronous monitoring of data and vision. The cross-cabinet linkage analysis unit is used to receive multi-cabinet operation data from the visualization monitoring unit, associate it with the status identification unit results of each cabinet, and analyze the linkage impact between cabinets; at the same time, it calls the cross-cabinet linkage data of the analysis layer module, generates linkage fault diagnosis report, and outputs it to subsequent units; enabling subsequent units to generate cross-cabinet collaborative work orders. The operation and maintenance scheduling unit is used to automatically generate operation and maintenance work orders based on the maintenance suggestions of the analysis layer module and the remaining life data of the components in the analysis layer module, and assign them to the corresponding operation and maintenance personnel, while synchronizing the location information of the switch cabinet and the component model. The historical data traceability unit is used to store all operational data, fault records, and maintenance logs, and supports retrieval by time, cabinet number, and fault type, providing data support for the upgrade and transformation of switchgear.

2. The low-voltage withdrawable switchgear monitoring system according to claim 1, characterized in that, The perception layer module includes: Electrical parameter acquisition unit is used to be installed on the main busbar, distribution busbar and each outgoing circuit to collect electrical parameters; A temperature monitoring unit is used to collect temperature data by deploying distributed fiber optic sensors in areas prone to heat generation. The mechanical condition acquisition unit is used to install on the guide rail of withdrawable components, circuit breaker operating mechanism, and connector to collect mechanical condition data and correlate it with the heating data of the temperature monitoring unit to determine the correlation between mechanical wear and temperature rise. The insulation performance monitoring unit is used to monitor the insulation resistance and creepage distance changes of the insulation support through capacitive sensing technology, and to assess the degree of insulation aging by combining the voltage data of the electrical parameter acquisition unit. An environmental parameter acquisition unit is deployed inside the cabinet and in the installation environment to collect environmental parameter data and correlate it with the test results of the insulation performance monitoring unit to analyze the impact of the environment on insulation performance. The image acquisition unit is used to capture the status of the components inside the cabinet using a high-definition camera, and works in conjunction with the vibration data from the mechanical status acquisition unit to achieve complementary verification of visual and sensor data.

3. The low-voltage withdrawable switchgear monitoring system according to claim 1, characterized in that, The transport layer module includes: The data preprocessing unit receives the raw data from the perception layer module and performs noise reduction, format conversion, and outlier removal to output a standardized dataset. The data priority classification unit receives the standardized dataset output by the data preprocessing unit and classifies it into three levels: urgent, important, and routine, based on the data type and the importance of related components. The data encryption transmission unit is used to perform AES encryption on the graded dataset using 5G+wired dual-link transmission, and optimize the transmission frequency band based on the installation location of the switch cabinet to ensure the stability of data transmission in the industrial environment. The data caching unit is used to set up local edge cache nodes to store critical data.

4. A monitoring method for low-voltage withdrawable switchgear, characterized in that, The specific steps are as follows: S1 collects electrical, temperature, mechanical, insulation, environmental and image data from all dimensions based on the switch cabinet structure and key components; S2 causes the data collected by S1 to undergo preprocessing, encrypted transmission, and local caching in sequence; S3 models the data transmitted by S2 to identify operating status, trace faults, predict component lifespan, and generate targeted decision-making suggestions. S4, based on the targeted decision-making suggestions generated by S3, enables visualization of operational status, generation of accurate maintenance work orders, and data traceability; The specific steps of S3 are as follows: S31, Status Identification: Receive standardized data from the transmission layer, combine it with the rated parameters of the MNS type switchgear, establish a parameter threshold model to identify normal operation, abnormal warning, and fault status, and output the status identification result. S32, Operating Condition Adaptation Analysis: Receives the status identification results from the status identification step, and simultaneously backtracks the load data from the electrical parameter acquisition step and the scenario data from the environmental parameter acquisition step to construct an operating condition classification model; at the same time, it adjusts the fault judgment benchmark based on different operating conditions, and outputs the status data and threshold model after adapting to the operating conditions, so that the subsequent analysis process can be combined with the actual operating conditions. S33, Fault Source Tracing: Based on the output data of the working condition adaptation analysis steps, perform source tracing analysis by associating multi-step data from the perception layer. S34, Lifetime Prediction: Receive the analysis results from the fault tracing steps, combine them with the historical operating data of key components, and use a grey prediction model to predict the remaining lifetime of the components, providing a basis for maintenance planning; S35, Linked Decision Making: Integrates the results of status identification, fault tracing, and lifespan prediction to generate targeted decision recommendations.

5. The monitoring method for low-voltage withdrawable switchgear according to claim 4, characterized in that, The specific steps of S1 are as follows: S11, Electrical Parameter Acquisition: Installed on the main busbar, distribution busbar and each outgoing circuit to collect electrical parameters; S12, Temperature Monitoring: Distributed fiber optic sensors are deployed in areas prone to heat generation to collect temperature data; S13, Mechanical Condition Acquisition: Installed on the guide rail of withdrawable components, circuit breaker operating mechanism, and connector to collect mechanical condition data and correlate it with the heat generation data of the temperature monitoring step to determine the correlation between mechanical wear and temperature rise. S14, Insulation performance monitoring: By using capacitive sensing technology, monitor the insulation resistance and creepage distance changes of the insulation support, and combine the voltage data from the electrical parameter acquisition steps to assess the degree of insulation aging. S15, Environmental Parameter Acquisition: Deployed inside the cabinet and in the installation environment to collect environmental parameter data, and correlate with the test results of the insulation performance monitoring steps to analyze the impact of the environment on insulation performance; S16, Image Acquisition: The status of components inside the cabinet is captured by a high-definition camera, and vibration data from the mechanical status acquisition step is used to achieve complementary verification of visual and sensor data.

6. The monitoring method for low-voltage withdrawable switchgear according to claim 4, characterized in that, The specific steps of S2 are as follows: S21, Data preprocessing: Receive the raw data from the perception layer step and perform noise reduction, format conversion, and outlier removal to output a standardized dataset. S22, Data Priority Classification: Receive the standardized dataset output from the data preprocessing step and classify it into three levels: urgent / important / routine based on data type and the importance of related components; S23, Encrypted Data Transmission: Employs 5G+wired dual-link transmission, performs AES encryption on the graded dataset, and optimizes the transmission frequency band based on the installation location of the switch cabinet to ensure the stability of data transmission in the industrial environment. S24, Data Cache: Set up local edge cache nodes to store critical data.

7. The monitoring method for low-voltage withdrawable switchgear according to claim 4, characterized in that, The specific steps of S4 are as follows: S41, Visual Monitoring: Receives the results of the application layer steps, displays the real-time operating status of the switch cabinet through the dashboard, supports viewing by cabinet type, and associates with the real-time screen of the perception layer steps to achieve synchronous monitoring of data and vision. S42, Cross-cabinet Linkage Analysis: Receives multi-cabinet operation data from the visualization monitoring steps and associates it with the status identification results of each cabinet to analyze the linkage impact between cabinets; simultaneously, it calls the cross-cabinet association data from the analysis layer steps to generate a linkage fault diagnosis report and outputs it to subsequent steps; enabling subsequent steps to generate cross-cabinet collaborative work orders. S43, Operation and Maintenance Scheduling: Based on the maintenance suggestions in the analysis layer steps and combined with the remaining lifespan data of the components in the analysis layer steps, automatically generate operation and maintenance work orders and assign them to the corresponding operation and maintenance personnel, and synchronize the location information of the switch cabinet and the component model. S44, Historical Data Traceability: Stores all operational data, fault records, and maintenance logs, and supports retrieval by time, cabinet number, and fault type, providing data support for the upgrade and transformation of switchgear.

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