Online monitoring system and method for leakage current and insulation state of power distribution network cable

Abstract

The invention discloses a power distribution network cable leakage current and insulation state on-line monitoring system and method, and belongs to the technical field of power transmission and distribution of power systems. In order to solve the problems of poor accuracy of fault prediction and low relevance of multi-source data, key features capable of representing the health state of a cable are extracted, and a more comprehensive feature set is formed by combining the multi-source data, so that information among different data sources can be matched and associated with each other; according to the method, the relevance between leakage current and temperature and humidity and the relevance between partial discharge and cable load are analyzed, multi-parameter fusion monitoring is achieved, the future leakage current data change trend is predicted, potential fault omen are rapidly found, an early warning mechanism is triggered when potential fault risks are predicted, and therefore the system can give an early warning in advance before faults occur; and the characterization characteristics of the insulation state of the cable are extracted from the fused data, so that the insulation state of the cable can be accurately evaluated in real time, and remote monitoring and early warning of the leakage current and the insulation state of the cable are realized.

Description

Technical Field

[0001] The present invention relates to the technical field of power system transmission and distribution, and particularly relates to an on-line monitoring system and method for leakage current and insulation status of distribution network cables. Background Art

[0002] Monitoring the leakage current and insulation status of distribution network cables is crucial for ensuring the safe and stable operation of the power grid. Leakage current monitoring can promptly detect cable insulation aging or damage, preventing potential fire risks, while insulation status assessment helps to identify potential fault hazards in advance and avoid sudden power outages. This kind of monitoring not only improves the reliability of the power grid, but also reduces maintenance costs, extends the service life of cables, and is of great significance for promoting the development of smart grids.

[0003] However, the following problems exist in actual operations:

[0004] Existing monitoring systems often rely only on a single data source or simple data analysis methods. Multi-source data often exists in isolation, making it difficult to conduct effective association and comprehensive analysis, and it is difficult to comprehensively and accurately predict cable faults. Summary of the Invention

[0005] The purpose of the present invention is to provide an on-line monitoring system and method for leakage current and insulation status of distribution network cables to solve the problems raised in the above background art.

[0006] To achieve the above purpose, the present invention provides the following technical solutions: An on-line monitoring system for leakage current and insulation status of distribution network cables, comprising:

[0007] A data acquisition module, used for:

[0008] Interacting with high-precision leakage current sensors, temperature sensors, humidity sensors, and partial discharge monitoring sensors deployed at key cable nodes of the distribution network, periodically or continuously collecting sensor data, and performing preliminary data formatting processing;

[0009] A data transmission module, used for:

[0010] Transmitting the data collected by the data acquisition module to the cloud server and the regional data center based on wireless communication, and encrypting the data during the data transmission process;

[0011] A data processing module, used for:

[0012] Cleaning the received data, storing the cleaned data in a distributed database, and performing standardization and normalization processing on the data;

[0013] A fault prediction module, used for:

[0014] Conduct trend analysis and fault feature extraction on the leakage current data, and predict the potential fault risks of the cable according to the analysis results;

[0015] The fusion monitoring module is used for:

[0016] Integrate multi-source data of leakage current, temperature, humidity, partial discharge, and cable operating load parameters, analyze the correlation between parameters based on data fusion, and comprehensively evaluate the insulation status of the cable in combination with the multi-parameter analysis results

[0017] The remote monitoring module is used for:

[0018] Receive control instructions remotely sent through the Web interface and mobile APP, generate a user interface to display real-time monitoring data and fault diagnosis results, and monitor the device status of each monitoring node in real time. The device status includes the working status of sensors and the power supply situation.

[0019] Furthermore, the data acquisition module includes:

[0020] The sensor interface unit is used for:

[0021] Connect high-precision leakage current sensors, temperature sensors, humidity sensors, and partial discharge monitoring sensors, trigger data acquisition tasks regularly or according to preset conditions, read the original data of each sensor. The original data includes leakage current values, temperature values, humidity values, and partial discharge information, and add time stamps and sensor ID information to each sensor's data;

[0022] The data verification unit is used for:

[0023] Check the rationality of the collected data, discard the data that fails the verification, and preprocess the data that passes the verification. The preprocessing includes data compression and data smoothing.

[0024] Furthermore, the data processing module includes:

[0025] The data cleaning unit is used for:

[0026] Identify and remove outliers in the original data and handle missing values, and conduct data integrity checks and consistency verification;

[0027] The data storage unit is used for:

[0028] Store the cleaned data in a distributed database, archive the historical data, and regularly clean the historical data and back up the database;

[0029] The data standardization unit is used for:

[0030] Standardize the data collected by different sensors, convert them into standard units, normalize the data, scale the data range to the interval of (0-1), and output the standardized and normalized data to the fault prediction module.

[0031] Furthermore, the fault prediction module includes:

[0032] A feature extraction unit for:

[0033] Receive the data that has been cleaned, standardized, and normalized from the data processing module, and perform time series processing on the data based on sliding window, difference, and seasonal adjustment;

[0034] Extract feature data that can characterize the cable health status from the leakage current data. The feature data includes mean, variance, peak value, slope, waveform complexity, and combine the features of temperature, humidity, and partial discharge parameters for cross-feature extraction;

[0035] An anomaly detection unit for:

[0036] Perform time series analysis on the leakage current data, identify the long-term trend, seasonal changes, or periodic patterns of the data, predict the future change trend of the leakage current data based on the ARIMA model, perform anomaly detection on the real-time data, and identify the data points that deviate from the normal mode as potential fault omens.

[0037] Furthermore, the fault prediction module further includes:

[0038] A fault prediction unit for:

[0039] Use historical fault data and normal operation data as the training set to train the linear regression model, evaluate the trained model through the test set, and iteratively optimize the model according to the evaluation results;

[0040] Set the warning threshold according to the results of the fault prediction model. When it is predicted that the potential fault risk exceeds the threshold, automatically trigger the warning mechanism and send warning information to relevant personnel through the remote monitoring module. The warning information includes the fault type, predicted occurrence time, and impact range information.

[0041] Furthermore, the fusion monitoring module includes:

[0042] A multi-source data integration unit for:

[0043] Receive multi-source data of leakage current, temperature, humidity, partial discharge, and cable operating load that have been standardized and normalized from the data processing module, and store the integrated multi-source data in the distributed database;

[0044] A data fusion processing unit for:

[0045] Perform time alignment and interpolation operations on multi-source data, perform fusion processing on the multi-source data, synthesize the parameter information of each multi-source data through a fusion algorithm, and analyze the correlation between leakage current and temperature, humidity, and between partial discharge and cable load.

[0046] Furthermore, the fusion monitoring module further includes:

[0047] An insulation status evaluation unit for:

[0048] Extract the characterization features of the cable insulation status from the fused data, where the characterization features include the change trend of insulation resistance, the intensity and frequency of partial discharge;

[0049] Perform feature selection and dimensionality reduction on the characterization features;

[0050] Replace historical data to train the neural network model, input real-time data into the trained evaluation model for online judgment of the insulation status, and judge the cable insulation status according to the model output result.

[0051] Furthermore, the remote monitoring module includes:

[0052] A user interface generation unit for:

[0053] Real-time receive the monitoring data of the cable, the fault diagnosis result, and the cable insulation status data, generate a user interface including a Web interface and a mobile APP interface, where the user interface includes a data display area, a chart area, and a control instruction input area, display the real-time monitoring data in the form of charts, curve graphs, and dashboards on the user interface, and update the data in real time.

[0054] A control instruction unit for:

[0055] Receive the control instructions sent by the user through the Web interface or the mobile APP, parse the received control instructions, identify the instruction type and parameters, send the parsed control instructions to the corresponding module or device for execution, and receive the execution result feedback, where the execution result feedback includes execution success, execution failure, or in progress.

[0056] Furthermore, the remote monitoring module further includes:

[0057] A device status monitoring unit for:

[0058] Obtain the device status data of each monitoring node from the data acquisition module, where the device status data includes the working status of the sensor and the power supply situation, analyze the collected device status data, judge whether the device is operating normally, record the device status monitoring in the log, and generate a device status report regularly.

[0059] Further, an on-line monitoring method for leakage current and insulation status of distribution network cables is applied to the above-mentioned on-line monitoring system for leakage current and insulation status of distribution network cables, and includes the following steps:

[0060] Step 1: Data acquisition and processing. Raw data of cable nodes are collected in real time, the raw data of sensors are preliminarily formatted, data verification is carried out, the verified data is cleaned, and the cleaned data is standardized and normalized;

[0061] Step 2: Fault prediction and early warning. Characteristic data representing the health status of the cable are extracted from the cleaned and standardized data, trend analysis and anomaly detection are carried out on the leakage current data, potential fault omens are identified, an early warning threshold is set, and when the predicted potential fault risk exceeds the threshold, the early warning mechanism is automatically triggered to send early warning information to relevant personnel;

[0062] Step 3: Insulation status evaluation. Multi-source data such as leakage current, temperature, humidity, partial discharge and cable operating load are integrated and fused, the correlation between parameters is analyzed, characteristic features representing the insulation status of the cable are extracted, and the insulation status is judged and evaluated online;

[0063] Step 4: Remote monitoring and feedback. Control instructions sent by users are received, monitoring data and fault diagnosis results are displayed in real time, the device status of each monitoring node is monitored, and the device status monitoring records are logged, and reports are generated regularly.

[0064] Compared with the prior art, the beneficial effects of the present invention are:

[0065] 1. Through time series processing and cross-feature extraction, the present invention can fully mine the potential information in the data, extract key features that can represent the health status of the cable, form a more comprehensive feature set by combining multi-source data, which helps to improve the accuracy and reliability of fault prediction, track the change trend of leakage current data in real time, identify abnormal points in the data in time, predict the future change trend of leakage current data, compare it with real-time data, quickly discover potential fault omens, automatically set an early warning threshold according to the prediction result, and trigger the early warning mechanism when the predicted potential fault risk exceeds the threshold, so that the system can give an early warning before the fault occurs, and ensure that the operation and maintenance personnel can obtain fault information in time and take corresponding treatment measures.

[0066] 2. Through preprocessing steps such as time alignment and interpolation operations, the present invention synchronizes multi-source data in time, enabling the information between different data sources to match and correlate with each other. By using a fusion algorithm to comprehensively analyze the parameter information of various multi-source data, the correlation between leakage current and temperature, humidity, as well as partial discharge and cable load is analyzed, which helps to better understand the complex change process of the cable insulation state, improves the intelligence level of the monitoring system, extracts the characteristic features of the cable insulation state from the fused data, and uses a neural network model to judge the insulation state online, enabling real-time and accurate evaluation of the cable insulation state, providing a reliable decision-making basis for operation and maintenance personnel. By inputting data into the trained evaluation model in real time, the fusion monitoring module can achieve dynamic monitoring of the cable insulation state, helping to reduce the possibility of faults and improve the safety and stability of the power grid. BRIEF DESCRIPTION OF THE DRAWINGS

[0067] Figure 1 It is a schematic diagram of the modules of the online monitoring system for leakage current and insulation state of the present invention;

[0068] Figure 2 It is a schematic diagram of the flow of the online monitoring method for leakage current and insulation state of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0069] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0070] In order to solve the technical problems that existing monitoring systems often rely only on a single data source or simple data analysis methods, multi-source data often exists in isolation and is difficult to perform effective correlation and comprehensive analysis, and it is difficult to comprehensively and accurately predict cable faults, please refer to Figure 1-2 , the present invention provides the following technical solutions:

[0071] An online monitoring system for leakage current and insulation state of a distribution network cable, comprising:

[0072] A data acquisition module, configured to:

[0073] Interact with high-precision leakage current sensors, temperature sensors, humidity sensors, and partial discharge monitoring sensors deployed at key cable nodes of the distribution network, regularly or continuously collect sensor data, and perform preliminary data formatting processing;

[0074] A data transmission module, configured to:

[0075] Based on wireless communication, the data collected by the data acquisition module is transmitted to the cloud server and the regional data center, and data encryption is performed during the data transmission process;

[0076] A data processing module, configured to:

[0077] Clean the received data, store the cleaned data in a distributed database, and perform standardization and normalization processing on the data;

[0078] A fault prediction module, configured to:

[0079] Perform trend analysis and fault feature extraction on the leakage current data, and predict the potential fault risk of the cable according to the analysis results;

[0080] A fusion monitoring module, configured to:

[0081] Integrate multi-source data of leakage current, temperature, humidity, partial discharge, and cable operating load parameters, analyze the correlation between each parameter based on data fusion, and comprehensively evaluate the cable insulation status in combination with the multi-parameter analysis results

[0082] A remote monitoring module, configured to:

[0083] Receive control instructions remotely sent through the Web interface and the mobile APP, generate a user interface to display real-time monitoring data and fault diagnosis results, and monitor the device status of each monitoring node in real time, where the device status includes the sensor working status and the power supply situation.

[0084] In the above embodiment, the system can monitor key parameters such as the leakage current, temperature, humidity, and partial discharge of the cable in real time through high-precision sensors, can timely detect the abnormal state of the cable, and predict potential fault risks, which helps to discover and solve problems in the early stage, prevent the expansion of faults, and ensure the stable operation of the power grid.

[0085] In the above embodiment, the data of multiple sensors are integrated and analyzed, and the data fusion technology is used to reveal the correlation between each parameter. The comprehensive evaluation method of multi-source data can more comprehensively and accurately reflect the insulation status and health condition of the cable, provide a more scientific decision-making basis for the operation and maintenance personnel, and provide a remote monitoring function through the Web interface and the mobile APP, so that the operation and maintenance personnel can view the real-time monitoring data and fault diagnosis results of the cable at any time.

[0086] In the above embodiments, the automation and intelligence features of the system reduce the workload of operation and maintenance personnel and the risk of human operation errors. At the same time, by detecting and predicting faults early, greater losses caused by the expansion of faults are avoided, thereby reducing operation and maintenance costs. The real-time monitoring and early warning functions help to detect and handle potential problems of cables in a timely manner, avoiding accidents such as power grid outages or equipment damage caused by cable faults, and enhancing the security and stability of the power grid.

[0087] The data acquisition module includes:

[0088] The sensor interface unit is used for:

[0089] Connecting high-precision leakage current sensors, temperature sensors, humidity sensors, and partial discharge monitoring sensors, triggering data acquisition tasks at regular intervals or according to preset conditions, reading the original data of each sensor, where the original data includes leakage current values, temperature values, humidity values, and partial discharge information, and adding a timestamp and sensor ID information to the data of each type of sensor;

[0090] The data verification unit is used for:

[0091] Checking the rationality of the collected data, discarding the data that fails the verification, and preprocessing the data that passes the verification, where the preprocessing includes data compression and data smoothing.

[0092] In the above embodiments, adding a timestamp and sensor ID information to the data of each type of sensor helps with subsequent data management and analysis, improving the traceability and reliability of the data. The data acquisition module can connect multiple types of sensors simultaneously, providing a rich data source for subsequent multi-source data fusion. The fusion analysis of multi-source data can more comprehensively reflect the insulation status and health condition of the cable, improving the accuracy and reliability of fault diagnosis.

[0093] In the above embodiments, the data acquisition module adds a timestamp and sensor ID information to the data of each type of sensor, which helps with subsequent data management and analysis work. The acquisition time of the data can be traced through the timestamp, and the data of different sensors can be distinguished through the sensor ID, providing convenience for data classification, storage, and analysis. The automation and intelligence features of the data acquisition module reduce the workload of operation and maintenance personnel and the risk of human operation errors.

[0094] The data processing module includes:

[0095] The data cleaning unit is used for:

[0096] Identifying and removing outliers and handling missing values in the original data, and performing data integrity checks and consistency verification;

[0097] A data storage unit, configured to:

[0098] Store the cleaned data in a distributed database, archive the historical data, periodically clean the historical data and back up the database;

[0099] A data standardization unit, configured to:

[0100] Standardize the data collected by different sensors, convert them into standard units, normalize the data, scale the data range to the interval of (0 - 1), and output the standardized and normalized data to the fault prediction module.

[0101] In the above embodiment, standardizing and normalizing the data collected by different sensors eliminates the dimensional and order-of-magnitude differences between different data, making the data comparable, facilitating subsequent data analysis and processing, helping to more accurately extract fault features and perform trend analysis, reducing the workload of operation and maintenance personnel and improving operation and maintenance efficiency through automated and intelligent data processing processes. At the same time, high-quality data and accurate fault prediction results can help operation and maintenance personnel quickly locate problems, formulate solutions, and optimize operation and maintenance strategies.

[0102] A fault prediction module, including:

[0103] A feature extraction unit, configured to:

[0104] Receive the data that has been cleaned, standardized and normalized from the data processing module, and perform time series processing on the data based on sliding window, differencing, and seasonal adjustment;

[0105] Extract feature data that can characterize the cable health status from the leakage current data, where the feature data includes mean, variance, peak value, slope, waveform complexity, and perform cross-feature extraction in combination with the features of temperature, humidity, and partial discharge parameters;

[0106] An anomaly detection unit, configured to:

[0107] Perform time series analysis on the leakage current data, identify the long-term trend, seasonal variation or periodic pattern of the data, predict the future change trend of the leakage current data based on the ARIMA model, perform anomaly detection on the real-time data, and identify data points that deviate from the normal pattern as potential fault omens;

[0108] A fault prediction unit, configured to:

[0109] Use historical fault data and normal operation data as the training set to train a linear regression model, evaluate the trained model through a test set, and iteratively optimize the model according to the evaluation results;

[0110] Set the warning threshold according to the results of the fault prediction model. When the predicted potential fault risk exceeds the threshold, the warning mechanism is automatically triggered, and warning information is sent to relevant personnel through the remote monitoring module. The warning information includes the fault type, predicted occurrence time, and influence range information.

[0111] In the above embodiment, through time series processing and cross-feature extraction, potential information in the data can be fully mined, and key features that can characterize the cable health status are extracted. Additionally, multi-source data such as temperature, humidity, and partial discharge are combined to form a more comprehensive feature set, which helps improve the accuracy and reliability of fault prediction. The change trend of leakage current data is tracked in real time, abnormal points in the data are identified in a timely manner, the future change trend of leakage current data is predicted, and it is compared with the real-time data to quickly discover potential fault omens, enabling the system to issue early warnings before the fault occurs and giving more processing time to the operation and maintenance personnel.

[0112] In the above embodiment, the warning threshold is automatically set according to the prediction results, and the warning mechanism is triggered when the predicted potential fault risk exceeds the threshold, which can ensure that the operation and maintenance personnel can obtain fault information in a timely manner and take corresponding treatment measures. At the same time, through the Web interface and mobile APP of the remote monitoring module, the operation and maintenance personnel can view the monitoring data and warning information anytime and anywhere, realizing comprehensive monitoring and remote control of the distribution network cable status.

[0113] The fusion monitoring module includes:

[0114] The multi-source data integration unit is used for:

[0115] Receiving multi-source data of leakage current, temperature, humidity, partial discharge, and cable operating load that have completed standardization and normalization processing from the data processing module, and storing the integrated multi-source data in a distributed database;

[0116] The data fusion processing unit is used for:

[0117] Performing time alignment and interpolation operations on the multi-source data, performing fusion processing on the multi-source data, and comprehensively analyzing the parameter information of each multi-source data through a fusion algorithm to analyze the correlation between leakage current and temperature, humidity, and between partial discharge and cable load;

[0118] The insulation status evaluation unit is used for:

[0119] Extracting the characterization features of the cable insulation status from the fused data, where the characterization features include the change trend of insulation resistance, the intensity and frequency of partial discharge;

[0120] Performing feature selection and dimensionality reduction on the characterization features;

[0121] Replace the historical data to train the neural network model, input the real-time data into the trained evaluation model to perform online judgment on the insulation state, and judge the cable insulation state according to the model output result.

[0122] In the above embodiment, through preprocessing steps such as time alignment and interpolation operations, the multi-source data are synchronized in time, enabling the information between different data sources to match and correlate with each other. Subsequently, through the fusion algorithm, the parameter information of each multi-source data is integrated to analyze the correlation between the leakage current and temperature, humidity, and the correlation between partial discharge and cable load, which helps to better understand the complex change process of the cable insulation state and improves the intelligence level of the monitoring system.

[0123] In the above embodiment, the characterization features of the cable insulation state are extracted from the fused data, and the neural network model is used to perform online judgment on the insulation state, which can evaluate the cable insulation state in real time and accurately, providing a reliable decision-making basis for the operation and maintenance personnel. By inputting data into the trained evaluation model in real time, the fusion monitoring module can realize the dynamic monitoring of the cable insulation state, which helps to reduce the possibility of faults and improve the safety and stability of the power grid.

[0124] Remote monitoring module, including:

[0125] User interface generation unit, used for:

[0126] Receive the monitoring data of the cable, the fault diagnosis result, and the cable insulation state data in real time, generate a user interface including a Web interface and a mobile APP interface. The user interface includes a data display area, a chart area, and a control instruction input area, display the real-time monitoring data in the form of charts, curves, and dashboards on the user interface, and update the data in real time.

[0127] Control instruction unit, used for:

[0128] Receive the control instructions sent by the user through the Web interface or the mobile APP, parse the received control instructions, identify the instruction type and parameters, send the parsed control instructions to the corresponding module or device for execution, and receive the execution result feedback. The execution result feedback includes execution success, execution failure, or being executed.

[0129] Device status monitoring unit, used for:

[0130] Obtain the device status data of each monitoring node from the data acquisition module. The device status data includes the working status of the sensor and the power supply situation, analyze the collected device status data, judge whether the device is operating normally, record the device status monitoring in the log, and generate a device status report regularly.

[0131] In the above embodiments, the real-time and dynamic data display method greatly improves the user's interaction experience, enabling the user to easily obtain key information and timely understand the operating conditions of the cable. The remote control function not only improves the operation and maintenance efficiency but also reduces the risk of on-site operations. It obtains the device status data of each monitoring node from the data acquisition module, performs real-time analysis and judgment, helps to detect device failures or abnormal conditions in a timely manner, and takes corresponding measures for processing.

[0132] In the above embodiments, the remote monitoring module integrates multiple links such as data acquisition, data processing, fault prediction, and device status monitoring, realizing comprehensive monitoring and remote management of the leakage current and insulation status of the distribution network cable. It not only improves the overall operation and maintenance efficiency of the system but also reduces the human and material costs.

[0133] In the above embodiments, by real-time monitoring the operating status of the cable and the working conditions of the devices, the remote monitoring module can detect and handle potential problems in a timely manner, thus avoiding risks such as system paralysis or data loss caused by the expansion of faults, helping to improve the reliability and stability of the system, and ensuring the safe operation of the power grid.

[0134] To better display the on-line monitoring system for the leakage current and insulation status of the distribution network cable, this embodiment now proposes an on-line monitoring method for the leakage current and insulation status of the distribution network cable, including the following steps:

[0135] Step 1: Data acquisition and processing. Real-time collect the original data of the cable nodes, perform preliminary formatting processing on the original sensor data, conduct data verification, clean the verified data, and perform standardization and normalization processing on the cleaned data;

[0136] Step 2: Fault prediction and early warning. Extract the characteristic data representing the cable health status from the cleaned and standardized data, perform trend analysis and anomaly detection on the leakage current data, identify potential fault omens, set the early warning threshold, and automatically trigger the early warning mechanism to send early warning information to relevant personnel when the predicted potential fault risk exceeds the threshold;

[0137] Step 3: Insulation status evaluation. Integrate and fuse multi-source data such as leakage current, temperature, humidity, partial discharge, and cable operating load, analyze the correlation between parameters, extract the characteristic features of the cable insulation status, and perform on-line judgment and evaluation of the insulation status;

[0138] Step 4: Remote monitoring feedback. Receive the control instructions sent by the user, real-time display the monitoring data and fault diagnosis results, monitor the device status of each monitoring node, record the device status monitoring in the log, and generate reports regularly.

[0139] As described above, it is only the preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art within the technical scope disclosed by the present invention, according to the technical solution and inventive concept of the present invention, making equivalent replacements or changes, shall be covered by the protection scope of the present invention.

Claims

1. An online monitoring system for cable leakage current and insulation status in a distribution network, characterized in that: include: Data acquisition module for: Interact with high-precision leakage current sensors, temperature sensors, humidity sensors and partial discharge monitoring sensors deployed at key cable nodes in the distribution network, collect sensor data regularly or continuously, and perform preliminary data formatting; Data transmission module for: The data collected by the data acquisition module is transmitted to the cloud server and the regional data center based on wireless communication, and the data is encrypted during the data transmission process; Data processing module for: Clean the received data, store the cleaned data in a distributed database, and standardize and normalize the data; Fault prediction module for: Perform trend analysis and fault feature extraction on leakage current data, and predict potential cable fault risks based on the analysis results; Fusion monitoring module for: Integrate multi-source data of leakage current, temperature, humidity, partial discharge, and cable operating load parameters, analyze the correlation between parameters based on data fusion, and conduct a comprehensive assessment of the cable insulation status based on multi-parameter analysis results. Remote monitoring module for: Receive control commands sent remotely through the Web interface and mobile APP, generate a user interface to display real-time monitoring data and fault diagnosis results, and monitor the device status of each monitoring node in real time, including the sensor working status and power supply status.

2. The online monitoring system for leakage current and insulation status of cables in a distribution network as claimed in claim 1, characterized in that: The data acquisition module comprises: Sensor interface unit for: Connect high-precision leakage current sensors, temperature sensors, humidity sensors and partial discharge monitoring sensors, trigger data collection tasks regularly or according to preset conditions, read the original data of each sensor, the original data includes leakage current value, temperature value, humidity value and partial discharge information, and add timestamp and sensor ID information to each sensor data; Data verification unit, used for: The collected data is checked for rationality, data that fails the check is discarded, and data that passes the check is preprocessed, the preprocessing includes data compression and data smoothing.

3. The online monitoring system for leakage current and insulation status of cables in a distribution network as claimed in claim 2, characterized in that: The data processing module comprises: Data cleaning unit, used for: Identify and remove outliers in raw data and handle missing values, perform data integrity checks and consistency verification; Data storage unit for: Store the cleaned data in a distributed database, archive historical data, clean up historical data regularly and back up the database; Data normalization unit for: The data collected by different sensors are standardized and converted into standard units. The data are normalized and the data range is scaled to the interval of (0-1). The standardized and normalized data are output to the fault prediction module.

4. The online monitoring system for leakage current and insulation status of cables in a distribution network as claimed in claim 3, characterized in that: The fault prediction module comprises: Feature extraction unit, used to: Receive cleaned, standardized and normalized data from the data processing module, and perform time series processing on the data based on sliding windows, differences, and seasonal adjustments; Extract characteristic data that can characterize the health status of the cable from the leakage current data, wherein the characteristic data includes mean, variance, peak value, slope, waveform complexity, and combines the characteristics of temperature, humidity, and partial discharge parameters to perform cross-feature extraction; Anomaly detection unit, used to: Perform time series analysis on leakage current data to identify long-term trends, seasonal changes or periodic patterns in the data, predict future leakage current data change trends based on the ARIMA model, perform anomaly detection on real-time data, and identify data points that deviate from normal patterns as potential fault precursors.

5. The online monitoring system for leakage current and insulation status of cables in a distribution network as claimed in claim 4, characterized in that: The fault prediction module further includes: Fault prediction unit for: The linear regression model is trained using historical fault data and normal operation data as training sets, and the trained model is evaluated using the test set. The model is iteratively optimized based on the evaluation results. The warning threshold is set according to the results of the fault prediction model. When the potential fault risk is predicted to exceed the threshold, the warning mechanism is automatically triggered, and the warning information is sent to relevant personnel through the remote monitoring module. The warning information includes the fault type, predicted occurrence time, and impact range information.

6. The online monitoring system for leakage current and insulation status of cables in a distribution network as claimed in claim 5, characterized in that: The fusion monitoring module includes: Multi-source data integration unit for: Receive the leakage current, temperature, humidity, partial discharge and cable operating load multi-source data that have been standardized and normalized from the data processing module, and store the integrated multi-source data in a distributed database; Data fusion processing unit, used for: The multi-source data are time aligned and interpolated, and fused. The parameter information of each multi-source data is integrated through the fusion algorithm to analyze the correlation between leakage current and temperature, humidity, and between partial discharge and cable load.

7. The online monitoring system for leakage current and insulation status of cables in a distribution network as claimed in claim 6, characterized in that: The fusion monitoring module further includes: Insulation condition assessment unit for: Extracting the characteristic features of the cable insulation status from the fused data, wherein the characteristic features include the variation trend of the insulation resistance, the intensity and frequency of the partial discharge; Perform feature selection and dimensionality reduction on the representation features; Replace historical data to train the neural network model, input real-time data into the trained evaluation model, make online judgments on the insulation status, and judge the cable insulation status based on the model output results.

8. The online monitoring system for leakage current and insulation status of cables in a distribution network as claimed in claim 7, characterized in that: The remote monitoring module comprises: A user interface generation unit, for: Receive cable monitoring data, fault diagnosis results and cable insulation status data in real time, generate a user interface including a Web interface and a mobile APP interface, the user interface includes a data display area, a chart area, and a control instruction input area, and display the real-time monitoring data on the user interface in the form of charts, curves, and dashboards, and update the data in real time. Control command unit, used for: Receive control instructions sent by users through a web interface or mobile APP, parse the received control instructions, identify instruction types and parameters, send the parsed control instructions to the corresponding module or device for execution, and receive execution result feedback, which includes execution success, execution failure, or execution in progress.

9. The online monitoring system for leakage current and insulation status of cables in a distribution network as claimed in claim 8, characterized in that: The remote monitoring module further includes: Equipment status monitoring unit for: The device status data of each monitoring node is obtained from the data acquisition module. The device status data includes the working status of the sensor and the power supply status. The collected device status data is analyzed to determine whether the device is operating normally. The device status monitoring is recorded in the log and a device status report is generated regularly.

10. A method for online monitoring of leakage current and insulation status of cables in a distribution network, applied to an online monitoring system for leakage current and insulation status of cables in a distribution network as claimed in any one of claims 9, characterized in that: The steps include: Step 1: Data collection and processing: real-time collection of raw data from cable nodes, preliminary formatting of raw sensor data, data verification, cleaning of verified data, and standardization and normalization of cleaned data; Step 2: Fault prediction and early warning: extract characteristic data representing the health status of the cable from the cleaned and standardized data, perform trend analysis and anomaly detection on the leakage current data, identify potential fault signs, set early warning thresholds, and automatically trigger the early warning mechanism when the potential fault risk is predicted to exceed the threshold, sending early warning information to relevant personnel; Step 3: Insulation status assessment: Integrate and fuse multi-source data such as leakage current, temperature, humidity, partial discharge and cable operating load, analyze the correlation between various parameters, extract the characterization features of the cable insulation status, and make online judgment and assessment of the insulation status; Step 4: Remote monitoring feedback, receiving control instructions sent by users, displaying monitoring data and fault diagnosis results in real time, monitoring the equipment status of each monitoring node, recording equipment status monitoring in logs, and generating reports regularly.

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