A switch cabinet state monitoring device and switch cabinet system
By combining multi-source sensing and the adaptive DBSCAN clustering model, efficient and accurate monitoring of the insulation status of switchgear is achieved, solving the problems of insufficient real-time performance and accuracy in existing technologies, and improving the safety and stability of power systems.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- SHANDONG HAOWEI POWER EQUIP CO LTD
- Filing Date
- 2026-05-07
- Publication Date
- 2026-07-21
AI Technical Summary
Existing technologies for monitoring the insulation status of switchgear suffer from poor real-time performance, low detection accuracy, false alarms, and missed alarms, making it difficult to meet the high efficiency and accuracy requirements of modern power systems.
Multi-source sensing modules are used to collect multi-dimensional data. Combined with data preprocessing, adaptive threshold calculation and adaptive DBSCAN clustering model, the insulation status of switchgear is graded and assessed and early warning is provided. Remote monitoring and control are achieved through communication modules.
It has improved the comprehensiveness and accuracy of switchgear insulation status monitoring, reduced false alarm and missed alarm rates, enhanced the timeliness of fault early warning and operation and maintenance efficiency, and ensured the safety and stability of the power system.
Smart Images
Figure CN122430631A_ABST
Abstract
Description
Technical Field
[0001] This invention belongs to the field of switchgear technology, specifically relating to a switchgear status monitoring device and switchgear system. Background Technology
[0002] As a critical piece of equipment in a power system, the insulation condition of switchgear directly affects the safety and reliability of power transmission. During the long-term operation of a power system, the insulation condition of switchgear may deteriorate due to the complex and ever-changing external environment and the natural aging of insulation materials, leading to equipment malfunctions. To ensure the stable operation of the power system, real-time monitoring of switchgear insulation condition, accurate identification of anomalies, and timely early warning are crucial. However, traditional monitoring methods mainly rely on manual inspections and offline testing, which suffer from drawbacks such as strong subjectivity, poor real-time performance, and low detection accuracy, making it difficult to meet the demands of modern power systems for efficiency and accuracy.
[0003] While existing technologies for monitoring the insulation status of switchgear have proposed various detection methods based on single data sources, the limited information from a single data source makes it difficult to comprehensively reflect the insulation status of the switchgear. Furthermore, in complex operating environments, monitoring data is susceptible to interference, leading to unreliable results. In addition, existing methods typically rely on fixed thresholds for anomaly detection and early warning, lacking dynamic adjustment mechanisms. This makes them unable to adapt to actual needs under different operating conditions, resulting in false alarms and missed alarms, affecting the accuracy and timeliness of fault warnings. Therefore, there is an urgent need for a more intelligent and accurate method for monitoring the insulation status of switchgear to improve the safety and stability of power systems. Summary of the Invention
[0004] The purpose of this invention is to overcome the shortcomings of the prior art. In a first aspect, this invention provides a switchgear condition monitoring device, comprising: a multi-source sensing module, a data preprocessing module, an adaptive threshold calculation module, an insulation condition assessment module, an early warning module, a communication module, and a power supply module. The multi-source sensing module is used to collect multi-dimensional insulation-related monitoring data of the switchgear, including TEV data, ultrasonic data, ambient temperature data, ambient humidity data, and switchgear operating years data. The data preprocessing module, connected to the multi-source sensing module, is used to perform noise reduction and standardization processing on the collected multi-dimensional insulation-related monitoring data to eliminate the impact of environmental interference and data differences. The adaptive threshold calculation module, connected to the data preprocessing module, is used to adaptively generate dynamic threshold ranges for TEV parameter thresholds and ultrasonic parameter thresholds based on the preprocessed multi-dimensional insulation-related monitoring data, using the K-mean nearest neighbor method and mathematical expectation method. The insulation status assessment module is connected to the data preprocessing module and the adaptive threshold calculation module, respectively. It is used to input the preprocessed multi-dimensional insulation-related monitoring data into the preset adaptive DBSCAN clustering model, and combine the dynamic threshold range to classify and assess the insulation status of the switchgear and output the insulation status level. The early warning module, connected to the insulation condition assessment module, is used to issue corresponding early warning signals based on the insulation condition level. The communication module connects to the insulation condition assessment module and the early warning module respectively. It is used to transmit the insulation condition level and early warning signal to the remote monitoring terminal and to receive control commands issued by the remote monitoring terminal.
[0005] Furthermore, the power module includes a YAW3S05T AC / DC conversion module and a TLH4902 TADIRAN lithium battery. The AC / DC conversion module converts 220V AC power into 5V and 3.3V DC power to supply the core module.
[0006] Furthermore, the multi-source sensing module includes a TEV sensor, an ultrasonic sensor, a temperature sensor, a humidity sensor, and an operating parameter acquisition unit. The TEV sensor and ultrasonic sensor are installed at six detection points on the front, middle, and bottom of the switch cabinet, and on the front, middle, and bottom of the switch cabinet. The temperature sensor is a TMP102, and the humidity sensor is a SHT11. The operating parameter acquisition unit is connected to the control loop of the switch cabinet to collect data on the years of operation.
[0007] Furthermore, the data preprocessing module uses a PIC16F690 microcontroller as the core processor to achieve data denoising and standardization. First, it calculates the deviation between TEV data and ultrasonic data at each detection point to eliminate electromagnetic interference background noise. Then, it uses the Z-score standardization method to standardize all monitoring parameters to ensure data comparability.
[0008] Furthermore, the adaptive threshold calculation module is implemented based on the RCM6760 module. It uses the K-means nearest neighbor method to generate a candidate threshold set, calculates the MinPts candidate parameters using the mathematical expectation method, introduces a density threshold Den, and selects the candidate threshold with the smallest density threshold and stable number of clusters as the dynamic threshold range for TEV and ultrasound parameters. When K=6, the number of clusters begins to stabilize at 3, and the corresponding threshold is the optimal dynamic threshold.
[0009] Furthermore, the insulation condition assessment module uses an FPGA chip as the core processor and incorporates a pre-trained adaptive DBSCAN clustering model. The model training sample set uses 1001 sets of switchgear monitoring data, covering three insulation conditions: normal, warning, and abnormal. The model parameters are optimized using the WSVF clustering effectiveness index, with the following weight allocations for the WSVF index: silhouette coefficient 0.4, Calinski-Harabasz index 0.3, and Davies-Bouldin index 0.3. During the assessment, the pre-processed multi-dimensional data is input into the model, and combined with the dynamic threshold range, the insulation condition level is output.
[0010] Furthermore, the warning module includes LED indicator lights and a buzzer. Green LED indicator lights indicate the normal level, yellow indicates the alert level, and red indicates the abnormal level. The buzzer sounds intermittently during the alert level and continuously during the abnormal level, and can also trigger an emergency stop signal.
[0011] Furthermore, the communication module adopts an XBee PRO ZigBee module and an RJ45 Ethernet module. The ZigBee module is configured as a network coordinator to realize data interaction with the multi-source sensor module, and the Ethernet module adopts the TCP protocol to communicate with the remote monitoring terminal and transmit monitoring data and early warning signals.
[0012] Secondly, the present invention provides a switchgear condition monitoring system for controlling the switchgear condition monitoring device. When the insulation condition level is normal, a normal prompt signal is issued to indicate that the switchgear insulation condition is good and maintenance should be performed according to the normal cycle. When the insulation condition level is the attention level, a level one warning signal is issued to indicate that there is a minor insulation abnormality in the switchgear, and the detection cycle is shortened and monitoring is strengthened. When the insulation condition level is abnormal, a level 2 warning signal is issued, indicating that there is a serious insulation abnormality in the switchgear, and immediate maintenance and troubleshooting are required, and an emergency shutdown control signal is triggered. The communication module communicates with the remote monitoring terminal via Ethernet and uses the TCP protocol to remotely transmit insulation status levels, early warning signals, and raw monitoring data. It also receives control commands such as threshold adjustment and monitoring cycle adjustment issued by the remote monitoring terminal.
[0013] The beneficial effects of this invention include: 1. This invention provides a switchgear condition monitoring device, characterized in that it includes: a multi-source sensing module, a data preprocessing module, an adaptive threshold calculation module, an insulation condition assessment module, an early warning module, a communication module, and a power supply module. It adopts multi-source data fusion technology to integrate TEV data, ultrasonic data, environmental parameters, and operating years data, solving the problem of limited information from a single data source. It can comprehensively and multidimensionally reflect the insulation condition of the switchgear, covering various insulation anomaly scenarios such as partial discharge, moisture, and aging, thereby improving the comprehensiveness of monitoring.
[0014] 2. A data preprocessing module was added to eliminate background noise caused by electromagnetic interference through deviation calculation and reduce the differences between parameters through standardization processing. This effectively improved the anti-interference ability of the monitoring data, avoided unreliable detection results caused by data distortion, and improved the accuracy of the monitoring data.
[0015] 3. An adaptive threshold calculation module is adopted, which dynamically generates threshold ranges based on the distribution characteristics of the monitoring data itself, replacing the traditional fixed threshold. This can adapt to the switchgear requirements of different substations, different voltage levels, and different operating conditions, effectively solving the problems of false alarms and missed alarms, and improving the accuracy and timeliness of fault early warning.
[0016] 4. By combining the adaptive DBSCAN clustering model and the WSVF clustering effectiveness index, the insulation status of the switchgear can be graded and evaluated. The evaluation results are more accurate and can provide clear maintenance suggestions for operation and maintenance personnel, reducing the workload of operation and maintenance. At the same time, remote monitoring and control can be realized through the communication module, improving operation and maintenance efficiency.
[0017] 5. The overall system design takes into account anti-interference, low power consumption and practicality. It can adapt to the complex operating environment of the switchgear, operate stably for a long time, effectively ensure the safety and stability of the power system, and has high engineering application value. Attached Figure Description
[0018] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.
[0019] Figure 1 This is a schematic diagram of the structure of a switchgear status monitoring device according to an embodiment of the present invention; Figure 2 This is a schematic diagram of the communication module of the present invention. Detailed Implementation
[0020] Embodiments of the present invention are described in detail below. Examples of these embodiments are illustrated in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and intended to explain the present invention, and should not be construed as limiting the present invention.
[0021] like Figure 1-2 As shown, the present invention discloses a switchgear condition monitoring device, comprising: a multi-source sensing module, a data preprocessing module, an adaptive threshold calculation module, an insulation condition assessment module, an early warning module, a communication module, and a power supply module; The multi-source sensing module is used to collect multi-dimensional insulation-related monitoring data of the switchgear, including TEV data, ultrasonic data, ambient temperature data, ambient humidity data, and switchgear service life data. Specifically, the multi-source sensing module includes 6 TEV sensors, 6 ultrasonic sensors, 1 temperature sensor, 1 humidity sensor, and 1 operating parameter acquisition unit. The TEV and ultrasonic sensors are installed at 6 detection points on the front (top, middle, bottom) and back (top, middle, bottom) of the switchgear cabinet, respectively, and their models are GH-718 (TEV sensor) and SHT11 (ultrasonic sensor). The temperature sensor is TMP102, and the humidity sensor is SHT11. The operating parameter acquisition unit is connected to the control circuit of the switchgear to collect service life data.
[0022] The data preprocessing module, connected to the multi-source sensing module, is used to denoise and standardize the acquired multi-dimensional insulation-related monitoring data, eliminating the impact of environmental interference and data differences. Specifically, the data preprocessing module uses a PIC16F690 microcontroller as the core processor to achieve data denoising and standardization. First, it calculates the deviation between TEV and ultrasonic data at each detection point to eliminate electromagnetic interference background noise. Then, it uses the Z-score standardization method to standardize all monitoring parameters, ensuring data comparability.
[0023] The adaptive threshold calculation module, connected to the data preprocessing module, is used to adaptively generate dynamic threshold ranges for TEV and ultrasonic parameters based on preprocessed multi-dimensional insulation-related monitoring data, employing the K-mean nearest neighbor method and mathematical expectation method. Specifically, the adaptive threshold calculation module is implemented based on the RCM6760 module. It uses the K-mean nearest neighbor method (K ranges from 1 to 32) to generate a candidate threshold set, calculates the MinPts candidate parameters using the mathematical expectation method, introduces a density threshold Den, and selects the candidate threshold with the smallest density threshold and stable cluster number as the dynamic threshold range for TEV and ultrasonic parameters. When K=6, the cluster number begins to stabilize at 3, and the corresponding threshold is the optimal dynamic threshold.
[0024] The insulation status assessment module connects to both the data preprocessing module and the adaptive threshold calculation module. It inputs preprocessed multi-dimensional insulation-related monitoring data into a pre-defined adaptive DBSCAN clustering model, and, combined with dynamic threshold intervals, performs a graded assessment of the switchgear's insulation status, outputting the insulation status level. Specifically, the insulation status assessment module uses an FPGA chip as its core processor and incorporates a pre-trained adaptive DBSCAN clustering model. The model training sample set uses 1001 sets of switchgear monitoring data, covering three insulation states: normal, warning, and abnormal. The model parameters are optimized using the WSVF clustering effectiveness index, with the following weight allocation: silhouette coefficient 0.4, Calinski-Harabasz index 0.3, and Davies-Bouldin index 0.3. During assessment, the preprocessed multi-dimensional data is input into the model, combined with dynamic threshold intervals, to output the insulation status level.
[0025] The early warning module, connected to the insulation condition assessment module, is used to issue corresponding early warning signals based on the insulation condition level. Specifically, the early warning module includes LED indicator lights and a buzzer. Green LEDs indicate a normal level, yellow indicates a warning level, and red indicates an abnormal level. The buzzer sounds intermittently during the warning level and continuously during the abnormal level, and can also trigger an emergency stop signal.
[0026] The communication module connects to both the insulation condition assessment module and the early warning module. It transmits insulation condition levels and early warning signals to the remote monitoring terminal and receives control commands from the terminal. Specifically, the communication module uses an XBee PRO ZigBee module and an RJ45 Ethernet module. The ZigBee module is configured as the network coordinator, enabling data interaction with the multi-source sensor modules. The Ethernet module uses the TCP protocol to communicate with the remote monitoring terminal, transmitting monitoring data and early warning signals.
[0027] The power module includes a YAW3S05T AC / DC conversion module and a TLH4902 TADIRAN lithium battery. The AC / DC conversion module converts 220V AC power into 5V and 3.3V DC power to supply the core module. The lithium battery powers the wireless sensing unit and adopts a sleep-wake mechanism to collect data every 15 seconds to reduce power consumption.
[0028] The multi-source sensing module includes a TEV sensor, an ultrasonic sensor, a temperature sensor, a humidity sensor, and an operating parameter acquisition unit; TEV sensors are used to collect TEV data from different detection points of the switchgear. The detection points include at least three positions on the front of the switchgear cabinet (top, middle, and bottom) and three positions on the back of the switchgear cabinet (top, middle, and bottom). The ultrasonic sensor corresponds one-to-one with the detection point of the TEV sensor and is used to collect ultrasonic data at each detection point. Temperature and humidity sensors are used to collect temperature and humidity data of the environment in which the switch cabinet is located; The operating parameter acquisition unit is used to collect data on the service life of the switchgear.
[0029] The noise reduction process in the data preprocessing module includes: The deviation between TEV data and ultrasonic data is calculated separately to eliminate background noise caused by electromagnetic interference. The calculation formula is as follows:
[0030]
[0031] in, Let TEV be the TEV deviation at the i-th detection point of the k-th switchgear. This is the raw TEV data for the i-th detection point of the k-th switchgear. Let TEV be the background value of the kth switchgear; Let be the ultrasonic deviation at the i-th detection point of the k-th switchgear. This represents the original ultrasonic data from the i-th detection point of the k-th switchgear. Let be the ultrasonic background value of the kth switchgear; The data preprocessing module uses Z-score normalization, calculated using the following formula:
[0032] in, The values are standardized, and Z is the mean of the sample data. The standard deviation of the sample data is used; the objects of standardization processing include the denoised TEV deviation, ultrasonic deviation, and environmental temperature data, environmental humidity data, and years of operation data.
[0033] The specific working process of the adaptive threshold calculation module includes: 1) Using the K-mean nearest neighbor method, calculate the K-nearest neighbor distance for each data point in the preprocessed multi-dimensional insulation-related monitoring data, and take the average of all K-nearest neighbor distances to generate a candidate threshold set; 2) Using the mathematical expectation method, calculate the expected value of the number of samples in the r-neighborhood corresponding to each candidate threshold, and use it as the candidate parameter of MinPts; 3) Introduce a density threshold Den, defined as the number of MinPts data points within a circle with the candidate threshold as its radius. The calculation formula is as follows:
[0034] Where r is the candidate threshold and MinPts is the corresponding expected value; 4) Select the candidate threshold with the smallest density threshold Den and stable cluster number as the dynamic threshold range for TEV parameters and ultrasonic parameters.
[0035] Furthermore, the insulation condition assessment module The training process of the adaptive DBSCAN clustering model includes: 1) Collect multi-dimensional monitoring data of switchgear under different insulation conditions (normal, warning, abnormal), and use the data as a training sample set after data preprocessing; 2) The WSVF clustering effectiveness index with weighted ensemble is used to perform clustering training on the training sample set, optimize the model parameters, and determine the optimal r parameter and MinPts parameter; 3) The WSVF index is obtained by weighted integration of the silhouette coefficient, Davies-Bouldin index, and Calinski-Harabasz index, and the calculation formula is as follows:
[0036] in, Let be the weight of the i-th effectiveness indicator, and , For the profile coefficient, The Calinski-Harabasz index, The Davies-Bouldin index after monotonicity processing; The graded assessment process of the insulation condition assessment module includes: 1) Input the preprocessed multi-dimensional insulation-related monitoring data into the trained adaptive DBSCAN clustering model to obtain the clustering results; 2) Combining the dynamic threshold range generated by the adaptive threshold calculation module, the clustering results are mapped to three insulation status levels: normal level, attention level, and abnormal level; 3) Output insulation condition level and corresponding assessment report. The assessment report includes the specific values of each monitoring parameter, the degree of deviation from the dynamic threshold, and insulation condition analysis.
[0037] The power module includes an AC-to-DC converter and a low-power supply unit; The AC-to-DC conversion unit is used to convert 220V AC power to 5V and 3.3V DC power to supply core modules such as the data preprocessing module, adaptive threshold calculation module, and insulation status assessment module. The low-power power supply unit uses a high-energy lithium battery to power the wireless sensing unit in the multi-source sensing module. It adopts a sleep-wake mechanism to reduce power consumption and extend service life.
[0038] The switchgear body includes core components such as circuit breakers, disconnect switches, load switches, and bushings. The multi-source sensing module of the condition monitoring device is installed at the corresponding position on the switch cabinet body: TEV sensor and ultrasonic sensor are installed at the detection points on the front and back of the switch cabinet body, temperature sensor and humidity sensor are installed inside the switch cabinet, and the operating parameter acquisition unit is connected to the control loop of the switch cabinet. The early warning module of the status monitoring device is installed on the surface of the switchgear body for easy on-site inspection by maintenance personnel; the communication module is installed on the top of the switchgear body to ensure stable communication signals. The status monitoring device is connected to the circuit breaker of the switch cabinet body. When a secondary warning signal is issued, the circuit breaker can be controlled to disconnect according to the preset command to prevent the insulation fault from spreading.
[0039] Example 2 This embodiment provides a switchgear condition monitoring system for controlling the switchgear condition monitoring device. When the insulation condition level is normal, a normal prompt signal is issued to indicate that the switchgear insulation condition is good and maintenance should be performed according to the normal cycle. When the insulation condition level is the attention level, a level one warning signal is issued to indicate that there is a minor insulation abnormality in the switchgear, and the detection cycle is shortened and monitoring is strengthened. When the insulation condition level is abnormal, a level 2 warning signal is issued, indicating that there is a serious insulation abnormality in the switchgear, requiring immediate inspection and troubleshooting, and triggering an emergency shutdown control signal; The communication module communicates with the remote monitoring terminal via Ethernet and uses the TCP protocol to remotely transmit insulation status levels, early warning signals, and raw monitoring data. It also receives control commands such as threshold adjustment and monitoring cycle adjustment issued by the remote monitoring terminal.
[0040] The communication module adopts a hybrid communication method combining ZigBee wireless communication and Ethernet. The communication module interacts with the multi-source sensing module and the data preprocessing module via ZigBee wireless communication, and adopts a 2.4GHz Mesh network architecture to ensure communication stability in complex environments. By adopting multi-source data fusion technology, integrating TEV data, ultrasonic data, environmental parameters and operating years data, it solves the problem of limited information from a single data source. It can comprehensively and multi-dimensionally reflect the insulation status of the switchgear, covering various insulation anomaly scenarios such as partial discharge, moisture, and aging, thus improving the comprehensiveness of monitoring. An additional data preprocessing module was added to eliminate background noise caused by electromagnetic interference through deviation calculation and reduce the differences between various parameters through standardization processing. This effectively improved the anti-interference ability of the monitoring data, avoided unreliable detection results caused by data distortion, and improved the accuracy of the monitoring data. An adaptive threshold calculation module is adopted, which dynamically generates threshold ranges based on the distribution characteristics of the monitoring data itself, replacing the traditional fixed thresholds. This can adapt to the switchgear requirements of different substations, different voltage levels, and different operating conditions, effectively solving the problems of false alarms and missed alarms, and improving the accuracy and timeliness of fault early warning. By combining the adaptive DBSCAN clustering model and the WSVF clustering effectiveness index, a graded assessment of the insulation status of switchgear can be achieved. The assessment results are more accurate, providing clear maintenance suggestions for operation and maintenance personnel, reducing the workload of operation and maintenance, and at the same time, remote monitoring and control can be achieved through the communication module, improving operation and maintenance efficiency. The overall system design takes into account anti-interference, low power consumption and practicality. It can adapt to the complex operating environment of the switchgear, operate stably for a long time, effectively ensure the safety and stability of the power system, and has high engineering application value.
[0041] The switchgear body is a 10kV switchgear, including components such as circuit breakers, disconnecting switches, and bushings. The multi-source sensor module of the status monitoring device is installed in the corresponding position of the switchgear, the early warning module is installed on the surface of the switchgear, and the communication module is installed on the top of the switchgear. The status monitoring device is connected to the circuit breaker of the switchgear. When a secondary early warning signal is issued, the circuit breaker is automatically controlled to open to prevent the fault from escalating.
[0042] The working process of this switchgear system is as follows: 1) The multi-source sensing module collects TEV data, ultrasonic data, ambient temperature, humidity data, and service life data from 6 detection points of the switchgear in real time; 2) The data preprocessing module performs noise reduction and standardization on the collected data to eliminate interference and data differences; 3) The adaptive threshold calculation module dynamically generates threshold ranges for TEV and ultrasound parameters based on the preprocessed data; 4) The insulation condition assessment module inputs the preprocessed data into the adaptive DBSCAN clustering model and, combined with the dynamic threshold range, assesses the insulation condition level of the switchgear. 5) The early warning module issues a corresponding early warning signal based on the insulation status level, and at the same time, the communication module transmits the insulation status level and the early warning signal to the remote monitoring terminal; 6) Maintenance personnel shall take corresponding maintenance measures based on the early warning signals and assessment reports; if the level is abnormal, the status monitoring device shall automatically control the circuit breaker to disconnect to prevent the fault from escalating.
[0043] The above are merely preferred embodiments of the present invention and are not intended to limit the present invention in any other way. Any person skilled in the art may make changes or modifications to the above-disclosed technical content to create equivalent embodiments that can be applied to other fields. However, any simple modifications, equivalent changes, and modifications made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the protection scope of the present invention.
Claims
1. A status monitoring device for a switchgear, characterized in that, include: Multi-source sensing module, data preprocessing module, adaptive threshold calculation module, insulation status assessment module, early warning module, communication module and power supply module; The multi-source sensing module is used to collect multi-dimensional insulation-related monitoring data of the switchgear, including TEV data, ultrasonic data, ambient temperature data, ambient humidity data, and switchgear operating years data. The data preprocessing module, connected to the multi-source sensing module, is used to perform noise reduction and standardization processing on the collected multi-dimensional insulation-related monitoring data to eliminate the impact of environmental interference and data differences. The adaptive threshold calculation module, connected to the data preprocessing module, is used to adaptively generate dynamic threshold ranges for TEV parameter thresholds and ultrasonic parameter thresholds based on the preprocessed multi-dimensional insulation-related monitoring data, using the K-mean nearest neighbor method and mathematical expectation method. The insulation status assessment module is connected to the data preprocessing module and the adaptive threshold calculation module, respectively. It is used to input the preprocessed multi-dimensional insulation-related monitoring data into the preset adaptive DBSCAN clustering model, and combine the dynamic threshold range to classify and assess the insulation status of the switchgear and output the insulation status level. The early warning module, connected to the insulation condition assessment module, is used to issue corresponding early warning signals based on the insulation condition level. The communication module connects to the insulation condition assessment module and the early warning module respectively. It is used to transmit the insulation condition level and early warning signal to the remote monitoring terminal and to receive control commands issued by the remote monitoring terminal.
2. The switchgear status monitoring device as described in claim 1, characterized in that, The power module includes a YAW3S05T AC / DC converter module and a TLH4902 TADIRAN lithium battery. The AC / DC converter module converts 220V AC power into 5V and 3.3V DC power to supply the core module.
3. The switchgear status monitoring device as described in claim 1, characterized in that, The multi-source sensing module includes a TEV sensor, an ultrasonic sensor, a temperature sensor, a humidity sensor, and an operating parameter acquisition unit. The TEV sensor and ultrasonic sensor are installed at six detection points on the front, middle, and bottom of the switch cabinet, and on the front, middle, and bottom of the switch cabinet. The temperature sensor is a TMP102, and the humidity sensor is a SHT11. The operating parameter acquisition unit is connected to the control circuit of the switch cabinet to collect data on the number of years of operation.
4. The switchgear status monitoring device as described in claim 1, characterized in that, The data preprocessing module uses a PIC16F690 microcontroller as the core processor to achieve data denoising and standardization. First, it calculates the deviation between TEV data and ultrasonic data at each detection point to eliminate electromagnetic interference background noise. Then, it uses the Z-score standardization method to standardize all monitoring parameters to ensure data comparability.
5. The switchgear status monitoring device as described in claim 1, characterized in that, The adaptive threshold calculation module is implemented based on the RCM6760 module. It uses the K-means nearest neighbor method to generate a candidate threshold set, calculates the MinPts candidate parameters using the mathematical expectation method, introduces the density threshold Den, and selects the candidate threshold with the smallest density threshold and stable number of clusters as the dynamic threshold range for TEV and ultrasound parameters. When K=6, the number of clusters begins to stabilize at 3, and the corresponding threshold is the optimal dynamic threshold.
6. The switchgear status monitoring device as described in claim 1, characterized in that, The insulation condition assessment module uses an FPGA chip as the core processor and has a built-in trained adaptive DBSCAN clustering model. The model training sample set uses 1001 sets of switchgear monitoring data, covering three insulation conditions: normal, warning, and abnormal. The model parameters are optimized using the WSVF clustering effectiveness index, with the following weight allocation for the WSVF index: silhouette coefficient 0.4, Calinski-Harabasz index 0.3, and Davies-Bouldin index 0.
3. During the assessment, the preprocessed multi-dimensional data is input into the model, and combined with the dynamic threshold range, the insulation condition level is output.
7. The switchgear status monitoring device as described in claim 1, characterized in that, The warning module includes LED indicator lights and a buzzer. Green LED indicator lights indicate the normal level, yellow indicates the alert level, and red indicates the abnormal level. The buzzer sounds intermittently during the alert level and continuously during the abnormal level, and can also trigger an emergency stop signal.
8. The switchgear status monitoring device as described in claim 1, characterized in that, The communication module uses an XBeePRO ZigBee module and an RJ45 Ethernet module. The ZigBee module is configured as a network coordinator to realize data interaction with the multi-source sensor module, and the Ethernet module uses the TCP protocol to communicate with the remote monitoring terminal to transmit monitoring data and early warning signals.
9. A status monitoring system for a switchgear, characterized in that, The device is used to control the status monitoring of the switchgear as described in claim 1. When the insulation status level is normal, it issues a normal prompt signal to indicate that the insulation status of the switchgear is good and maintenance should be performed according to the normal cycle. When the insulation condition level is the attention level, a level one warning signal is issued to indicate that there is a minor insulation abnormality in the switchgear, and the detection cycle is shortened and monitoring is strengthened. When the insulation condition level is abnormal, a level 2 warning signal is issued, indicating that there is a serious insulation abnormality in the switchgear, requiring immediate inspection and troubleshooting, and triggering an emergency shutdown control signal; The communication module communicates with the remote monitoring terminal via Ethernet and uses the TCP protocol to remotely transmit insulation status levels, early warning signals, and raw monitoring data. It also receives control commands such as threshold adjustment and monitoring cycle adjustment issued by the remote monitoring terminal.