Switch cabinet state monitoring system and design method

Abstract

The invention discloses a switch cabinet state monitoring system and a design method, and relates to the technical field of intelligent monitoring and fault diagnosis of power equipment, and the method comprises the steps: collecting the operation parameters of a switch cabinet through a sensor array; performing edge calculation on the operation parameters by using an edge calculation unit to obtain a calculation result, wherein the edge calculation comprises data preprocessing, feature extraction and intelligent diagnosis; a calculation result obtained after processing is transmitted to the cloud platform through the communication module; deep analysis and state evaluation are carried out through the cloud platform, and a monitoring result and early warning information are generated; monitoring results and early warning information are displayed through a multi-terminal visual interface; real-time monitoring, fault early warning and predictive maintenance of the operation state of the switch cabinet are realized, and the safety and reliability of a power system are remarkably improved.

Description

Technical Field

[0001] This invention relates to the field of intelligent monitoring and fault diagnosis technology for power equipment, and more specifically, to a switchgear condition monitoring system and its design method. Background Technology

[0002] Switchgear, as a key piece of equipment in the power system, undertakes the important functions of closing and opening power lines and protecting system safety. With the in-depth advancement of smart grid construction, higher requirements are placed on the real-time monitoring and intelligent diagnosis of switchgear operating status.

[0003] In existing technologies, switchgear status monitoring mainly has the following limitations:

[0004] The monitoring parameters are singular. Most systems only monitor single parameters such as temperature or partial discharge, lacking the ability to conduct multi-parameter collaborative analysis, making it difficult to fully reflect the operating status of the equipment.

[0005] Data transmission relies on centralized processing, which puts a lot of pressure on network bandwidth and causes significant system response delays for large amounts of real-time monitoring data.

[0006] The lack of effective intelligent diagnostic algorithms results in insufficient early fault identification capabilities and low early warning accuracy.

[0007] The quality of monitoring data varies greatly, and noise interference is severe, affecting the reliability of condition assessment.

[0008] The low level of visualization makes it difficult for maintenance personnel to intuitively understand the status of equipment and the development trend of faults.

[0009] Therefore, there is an urgent need to develop a switchgear condition monitoring system that can achieve multi-parameter synchronous monitoring, intelligent diagnosis, and predictive maintenance to improve the safety and reliability of power systems. Summary of the Invention

[0010] The purpose of this invention is to provide a switch cabinet status monitoring system and design method to solve the problems existing in the background art.

[0011] The above-mentioned technical objective of the present invention is achieved through the following technical solution:

[0012] Firstly, this application provides a design method for a switchgear status monitoring system, comprising the following specific steps:

[0013] The operating parameters of the switchgear are collected through a sensor array;

[0014] The operating parameters are processed by edge computing units to obtain the calculation results. Edge computing includes data preprocessing, feature extraction and intelligent diagnosis.

[0015] The processed calculation results are transmitted to the cloud platform via the communication module;

[0016] In-depth analysis and status assessment are conducted through a cloud platform, generating monitoring results and early warning information;

[0017] Monitoring results and early warning information are displayed through a multi-terminal visual interface.

[0018] Based on the above technical solution, the present invention can be further improved as follows.

[0019] Furthermore, the aforementioned multi-sensor array includes:

[0020] Distributed temperature sensors, using surface acoustic wave sensors, are placed at busbar connections, circuit breaker contacts, and cable joints.

[0021] An integrated humidity sensor, using a digital humidity chip, monitors environmental parameters inside the cabinet;

[0022] The voltage and current sensor uses a 16-bit high-precision Hall sensor.

[0023] Partial discharge sensors include transient ground voltage sensors, ultrasonic sensors, and ultra-high frequency sensors.

[0024] Furthermore, the aforementioned edge computing unit includes:

[0025] The data preprocessing module uses Kalman filtering and FIR filtering for signal denoising and power frequency interference suppression;

[0026] The feature extraction module performs time-domain statistical analysis, frequency-domain FFT transformation, and partial discharge pulse identification.

[0027] The intelligent diagnostic module uses support vector machine and random forest algorithms to achieve fault classification and state prediction;

[0028] The task scheduling module implements concurrent processing of multiple tasks based on a real-time operating system.

[0029] Furthermore, the aforementioned communication module employs the following security mechanism: TLS / SSL encrypted transmission to ensure data security;

[0030] The will message mechanism enables real-time monitoring of device offline status;

[0031] QoS (Quality of Service) grading ensures reliable transmission of critical data.

[0032] Encapsulates JSON data format to achieve standardized data exchange.

[0033] Furthermore, the aforementioned cloud platforms include:

[0034] A time-series database is used to store historical data from the sensor array.

[0035] Machine learning platforms that perform deep data analysis and trend prediction;

[0036] A multi-user management system that supports access control and operation auditing;

[0037] The alarm management module enables tiered alarms and closed-loop processing.

[0038] Furthermore, the above method also includes implementation via a user terminal:

[0039] Real-time data visualization, including dynamic topology maps, trend curves, and data dashboards;

[0040] Remote parameter configuration and device control;

[0041] Multi-mode alarm notifications, including app push notifications and SMS notifications;

[0042] Reports are generated automatically and historical data can be traced.

[0043] Furthermore, the aforementioned edge computing unit utilizes a 32-bit microprocessor for edge computing; the communication module transmits the computing results to the cloud platform via the MQTT protocol.

[0044] Secondly, this application provides a switchgear status monitoring system, and a design method for a switchgear status monitoring system applied to any one of the first aspects, including:

[0045] The data acquisition module is used to acquire the operating parameters of the switchgear through a sensor array;

[0046] The edge computing module is used to perform edge computing on the running parameters using the edge computing unit and obtain the calculation results. Edge computing includes data preprocessing, feature extraction and intelligent diagnosis.

[0047] The transmission module is used to transmit the processed calculation results to the cloud platform via the communication module.

[0048] The analysis and evaluation module is used to perform in-depth analysis and status assessment through the cloud platform, and generate monitoring results and early warning information;

[0049] The visualization module is used to display monitoring results and early warning information through a multi-terminal visual interface.

[0050] Thirdly, this application provides an electronic device, including: at least one processor, at least one memory, and a data bus;

[0051] In this system, the processor and memory communicate with each other via a data bus; the memory stores program instructions that can be executed by the processor, and the processor calls the program instructions to execute the method as described in any of the first aspects.

[0052] Fourthly, this application provides a non-transitory computer-readable storage medium that stores computer instructions that cause a computer to perform any of the methods in the first aspect.

[0053] Compared with the prior art, the present invention has at least the following beneficial effects:

[0054] This application implements comprehensive multi-parameter monitoring of switchgear, significantly improving information collection efficiency through multi-sensor data acquisition; adopts an architecture that combines edge computing and a cloud platform, effectively reducing network bandwidth pressure and improving system response time from the traditional second level to the millisecond level; by introducing advanced algorithms, it achieves accurate assessment of equipment status and fault prediction, significantly improving early warning accuracy; it provides rich visualization methods, enabling maintenance personnel to intuitively understand equipment status and improving maintenance efficiency; the system has good scalability, supporting the rapid integration of new sensors and algorithms to adapt to future technological development needs. Attached Figure Description

[0055] The accompanying drawings, which are included to provide a further understanding of embodiments of the invention and form part of this application, do not constitute a limitation thereof. In the drawings:

[0056] Figure 1 This is a schematic diagram showing the connection of each unit in an embodiment of the present invention;

[0057] Figure 2 This is a layered diagram of the software architecture in an embodiment of the present invention;

[0058] Figure 3 This is a flowchart illustrating the design of the MCU program in an embodiment of the present invention.

[0059] Figure 4 This is a flowchart of data communication in an embodiment of the present invention. Detailed Implementation

[0060] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. The components of the embodiments of the present invention described and shown in the accompanying drawings can generally be arranged and designed in various different configurations.

[0061] Therefore, the following detailed description of the embodiments of the invention provided in the accompanying drawings is not intended to limit the scope of the claimed invention, but merely to illustrate selected embodiments of the invention. All other embodiments obtained by those skilled in the art based on the embodiments of the invention without inventive effort are within the scope of protection of the invention.

[0062] It should be noted that similar labels and letters in the following figures indicate similar items. Therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures.

[0063] In the description of the embodiments of the present invention, "multiple" means at least two.

[0064] Example 1: This example provides a switchgear status monitoring system and design method, including the following specific steps:

[0065] S1 collects the operating parameters of the switchgear through a sensor array.

[0066] Optionally, the above-mentioned multi-sensor array includes:

[0067] Distributed temperature sensors, using surface acoustic wave sensors, are placed at busbar connections, circuit breaker contacts, and cable joints.

[0068] An integrated humidity sensor, using a digital humidity chip, monitors environmental parameters inside the cabinet;

[0069] The voltage and current sensor uses a 16-bit high-precision Hall sensor.

[0070] Partial discharge sensors include transient ground voltage sensors, ultrasonic sensors, and ultra-high frequency sensors.

[0071] S2 uses the edge computing unit to perform edge computing on the running parameters and obtain the calculation results. Edge computing includes data preprocessing, feature extraction and intelligent diagnosis.

[0072] The aforementioned edge computing unit includes:

[0073] The data preprocessing module uses Kalman filtering and FIR filtering for signal denoising and power frequency interference suppression;

[0074] The feature extraction module performs time-domain statistical analysis, frequency-domain FFT transformation, and partial discharge pulse identification.

[0075] The intelligent diagnostic module uses support vector machine and random forest algorithms to achieve fault classification and state prediction;

[0076] The task scheduling module implements concurrent processing of multiple tasks based on a real-time operating system.

[0077] S3 transmits the processed calculation results to the cloud platform through the communication module.

[0078] The aforementioned communication module employs the following security mechanism: TLS / SSL encrypted transmission to ensure data security;

[0079] The will message mechanism enables real-time monitoring of device offline status;

[0080] QoS (Quality of Service) grading ensures reliable transmission of critical data.

[0081] Encapsulates JSON data format to achieve standardized data exchange.

[0082] S4 performs in-depth analysis and status assessment through a cloud platform, and generates monitoring results and early warning information.

[0083] The aforementioned cloud platforms include:

[0084] A time-series database is used to store historical data from the sensor array.

[0085] Machine learning platforms that perform deep data analysis and trend prediction;

[0086] A multi-user management system that supports access control and operation auditing;

[0087] The alarm management module enables tiered alarms and closed-loop processing.

[0088] Specifically, the aforementioned edge computing unit utilizes a 32-bit microprocessor for edge computing; the communication module transmits the computing results to the cloud platform via the MQTT protocol.

[0089] S5 displays monitoring results and early warning information through a multi-terminal visual interface.

[0090] The above methods also include implementation via user terminals:

[0091] Real-time data visualization, including dynamic topology maps, trend curves, and data dashboards;

[0092] Remote parameter configuration and device control;

[0093] Multi-mode alarm notifications, including app push notifications and SMS notifications;

[0094] Reports are generated automatically and historical data can be traced.

[0095] The system structure in this embodiment is as follows: Figure 1As shown, a distributed architecture of "lower-level machine-upper-level machine" can be adopted to form a complete closed loop for status monitoring. The lower-level machine system is deployed in the switchgear field and consists of a sensor array, an A / D converter, a 32-bit microprocessor, a communication module, and a power management module. The upper-level machine system includes a cloud platform server and user terminals, which connect to the lower-level machine via the Internet. The lower-level machine system connects to various sensors through multiple bus protocols: temperature sensors are placed at key locations such as busbar connections and circuit breaker contacts; humidity sensors monitor the environment inside the cabinet; 16-bit high-precision Hall sensors collect voltage and current signals; and partial discharge detection sensors are used to detect insulation status. This multi-bus architecture ensures efficient acquisition of data from various sensors. The upper-level machine system includes a time-series database, a machine learning platform, a multi-user management system, and an alarm management module. The time-series database stores historical sensor data; the machine learning platform performs deep data analysis and trend prediction; the multi-user management system supports access control and operation auditing; and the alarm management module implements hierarchical alarms and closed-loop processing.

[0096] See Figure 2 , Figure 2 The software architecture is layered as follows: the system software adopts a five-layer architecture design. The driver layer includes sensor drivers, communication protocol drivers, and display drivers, directly operating the hardware modules. Humidity data is read via I2C, ADC sampling is controlled via SPI, temperature data is acquired via 1-Wire, and network communication is achieved via Ethernet. The logic layer runs in the MCU, implementing data preprocessing, task scheduling, and device management. The data preprocessing module filters the raw signals: moving average filtering is used for temperature data, Kalman filtering is used for electrical signals, and wavelet transform denoising is performed on partial discharge signals. The task scheduling module, based on a real-time operating system, creates multiple tasks to execute data acquisition, processing, and communication in parallel. The protocol layer establishes a unified data communication standard. Devices communicate with the cloud platform via the MQTT protocol. The MQTT client supports TLS encrypted transmission, sets QoS levels to ensure reliable transmission of important data, and configures will messages to enable offline device monitoring. The user layer provides complete business functions, including device management, data analysis, and user management. The device management module maintains sensor configuration information and device status; the data analysis module performs trend prediction and fault diagnosis; and the user management module implements access control and operation auditing.

[0097] like Figure 3As shown in the MCU program flowchart, after the lower-level system powers on, it first executes the initialization sequence. This includes initializing hardware peripherals, configuring the ADC sampling rate, setting the watchdog timer, and establishing a network connection. System parameters are loaded, including the sensor address mapping table, alarm threshold configuration, and communication parameters. The system performs multi-tasking operations through a real-time operating system. A temperature acquisition task is created, cyclically reading temperature data with a 1-second sampling interval. An electrical parameter acquisition task is created, controlling the sensors to synchronously sample three-phase voltage and current via the SPI interface, calculating the effective values ​​and harmonic content. A partial discharge monitoring task is created, simultaneously acquiring TEV, ultrasonic, and UHF signals at sampling frequencies of 100MHz, 2MHz, and 500MHz, respectively. The data processing task processes the acquired raw data through a multi-stage process: first, data cleaning to remove obvious outliers and supplement missing data; then, signal processing is performed: temperature data uses a sliding window filter, voltage and current signals are noise-removed using an FIR filter, and partial discharge signals undergo wavelet packet transform to extract features; finally, feature extraction is performed, calculating statistical features, time-domain features, and frequency-domain features from the preprocessed data. The edge computing unit executes an intelligent diagnostic algorithm. A support vector machine-based classifier identifies partial discharge types, accurately distinguishing between internal discharge, surface discharge, and corona discharge. A random forest regression model predicts device health trends, with inputs including 20 features such as temperature change rate, partial discharge intensity, and current harmonic content. DS evidence theory integrates multi-source information, combining diagnostic results from temperature, electrical parameters, and partial discharge data to output a unified state assessment.

[0098] like Figure 4As shown in the data communication flowchart, the system establishes a complete data transmission mechanism. During the communication initialization phase, the lower-level device loads a digital certificate, establishes a TLS secure channel, and initiates a connection request to the MQTT Broker. Identity authentication is performed, and communication parameters are negotiated, including heartbeat interval, message size, and QoS level. The data upload process adopts an asynchronous transmission mechanism. The lower-level device encapsulates the processed data into JSON format, including device ID, timestamp, data payload, and quality identifier. It publishes the data to a specified topic via the MQTT protocol. The QoS level is set to 1 to ensure that critical data is transmitted at least once. After receiving the data, the cloud platform performs data verification and storage. Data integrity is verified, timestamp continuity is checked, and data quality is evaluated. Qualified data is stored in a time-series database, and abnormal data is marked and logged. Simultaneously, a real-time analysis process is triggered, including trend detection, correlation analysis, and anomaly warning. The command issuance process supports remote control. Users send control commands through a web interface, and the cloud platform encapsulates the commands into MQTT messages and publishes them to the topics subscribed to by the device. After receiving the commands, the lower-level device parses and executes the corresponding operations, such as modifying sampling parameters, triggering diagnostic tests, and updating algorithm models. A communication guarantee mechanism ensures transmission reliability. Implement heartbeat monitoring, sending a heartbeat packet every 30 seconds to maintain the connection. Configure will messages, automatically reporting the status when the device goes offline abnormally. Employ a disconnection reconnection mechanism, caching data when the network is interrupted and automatically resuming transmission upon recovery. A message confirmation mechanism ensures reliable delivery of important instructions.

[0099] Example 2: This application provides a switchgear status monitoring system, applied to the design method of a switchgear status monitoring system in Example 1, including:

[0100] The data acquisition module is used to acquire the operating parameters of the switchgear through a sensor array;

[0101] The edge computing module is used to perform edge computing on the running parameters using the edge computing unit and obtain the calculation results. Edge computing includes data preprocessing, feature extraction and intelligent diagnosis.

[0102] The transmission module is used to transmit the processed calculation results to the cloud platform via the communication module.

[0103] The analysis and evaluation module is used to perform in-depth analysis and status assessment through the cloud platform, and generate monitoring results and early warning information;

[0104] The visualization module is used to display monitoring results and early warning information through a multi-terminal visual interface.

[0105] Example 3: This application provides an electronic device, including: at least one processor, at least one memory, and a data bus;

[0106] In this embodiment, the processor and the memory communicate with each other through a data bus; the memory stores program instructions that can be executed by the processor, and the processor calls the program instructions to execute the method as described in Embodiment 1.

[0107] Example 4: This application provides a non-transitory computer-readable storage medium that stores computer instructions that cause a computer to execute the method of Example 1.

[0108] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0109] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0110] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0111] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0112] Those skilled in the art will understand that all or part of the steps in the above facts and methods can be implemented by a program instructing related hardware. The program or the program described therein can be stored in a computer-readable storage medium. When the program is executed, it includes the following steps: at this time, the corresponding method steps are introduced. The storage medium can be ROM / ROM, magnetic disk, optical disk, etc.

[0113] The specific embodiments described above further illustrate the purpose, technical solution, and beneficial effects of the present invention. It should be understood that the above description is only a specific embodiment of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A design method for a switchgear status monitoring system, characterized in that, The specific steps include the following: The operating parameters of the switchgear are collected through a sensor array; The operating parameters are processed by an edge computing unit to obtain the calculation results. The edge computing includes data preprocessing, feature extraction and intelligent diagnosis. The processed calculation results are transmitted to the cloud platform via the communication module. The cloud platform is used for in-depth analysis and status assessment, and monitoring results and early warning information are generated. The monitoring results and early warning information are displayed through a multi-terminal visual interface.

2. The design method of a switchgear status monitoring system according to claim 1, characterized in that, The multi-sensor array includes: Distributed temperature sensors, using surface acoustic wave sensors, are placed at busbar connections, circuit breaker contacts, and cable joints. An integrated humidity sensor, using a digital humidity chip, monitors environmental parameters inside the cabinet; The voltage and current sensor uses a 16-bit high-precision Hall sensor. Partial discharge sensors include transient ground voltage sensors, ultrasonic sensors, and ultra-high frequency sensors.

3. The design method of a switchgear status monitoring system according to claim 1, characterized in that, The edge computing unit includes: The data preprocessing module uses Kalman filtering and FIR filtering for signal denoising and power frequency interference suppression; The feature extraction module performs time-domain statistical analysis, frequency-domain FFT transformation, and partial discharge pulse identification. The intelligent diagnostic module uses support vector machine and random forest algorithms to achieve fault classification and state prediction; The task scheduling module implements concurrent processing of multiple tasks based on a real-time operating system.

4. The design method of a switchgear status monitoring system according to claim 1, characterized in that, The communication module employs the following security mechanism: TLS / SSL encrypted transmission to ensure data security; The will message mechanism enables real-time monitoring of device offline status; QoS (Quality of Service) grading ensures reliable transmission of critical data. Encapsulates JSON data format to achieve standardized data exchange.

5. The design method of a switchgear status monitoring system according to claim 1, characterized in that, The cloud platform includes: A time-series database is used to store historical data from the sensor array; Machine learning platforms that perform deep data analysis and trend prediction; A multi-user management system that supports access control and operation auditing; The alarm management module enables tiered alarms and closed-loop processing.

6. The design method of a switchgear status monitoring system according to claim 1, characterized in that, The method also includes implementation via a user terminal: Real-time data visualization, including dynamic topology maps, trend curves, and data dashboards; Remote parameter configuration and device control; Multi-mode alarm notifications, including app push notifications and SMS notifications; Reports are generated automatically and historical data can be traced.

7. The design method of a switchgear status monitoring system according to claim 1, characterized in that, The edge computing unit uses a 32-bit microprocessor to perform edge computing; the communication module transmits the computing results to the cloud platform via the MQTT protocol.

8. A switchgear status monitoring system, characterized in that, include: The data acquisition module is used to acquire the operating parameters of the switchgear through a sensor array; An edge computing module is used to perform edge computing on the operating parameters using an edge computing unit and obtain the calculation results. The edge computing includes data preprocessing, feature extraction, and intelligent diagnosis. The transmission module is used to transmit the processed calculation results to the cloud platform via the communication module; The analysis and evaluation module is used to perform in-depth analysis and status assessment through the cloud platform, and generate monitoring results and early warning information; The visualization module is used to display the monitoring results and the early warning information through a multi-terminal visualization interface.

9. An electronic device, characterized in that, include: At least one processor, at least one memory, and a data bus; The processor and the memory communicate with each other via the data bus. The memory stores program instructions that can be executed by the processor, which invokes the program instructions to perform the method as described in any one of claims 1-7.

10. A non-transitory computer-readable storage medium, characterized in that, The non-transitory computer-readable storage medium stores computer instructions that cause the computer to perform the method of any one of claims 1-7.

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