Ring main unit online monitoring system based on multi-state quantity perception

Abstract

The invention relates to the technical field of ring main unit monitoring, and discloses a ring main unit online monitoring system based on multi-state quantity sensing. The system comprises an acquisition module used for acquiring a temperature signal and a partial discharge signal and obtaining state quantity data; the calibration module is electrically connected with the acquisition module and is used for preprocessing and calibrating the state quantity data; the analysis module is electrically connected with the calibration module and is used for performing multi-dimensional analysis on temperature and discharge characteristics; the transmission module is electrically connected with the analysis module and is used for data transmission and fusion processing; the early warning module is electrically connected with the transmission module and is used for performing multi-stage early warning analysis; and the adjusting module is electrically connected with the early warning module and is used for dynamically adjusting the monitoring parameters. According to the invention, accurate evaluation and timely early warning of the equipment operation state are realized, and the technical problems of low accuracy and poor adaptability of a single state quantity monitoring method are solved.

Description

Technical Field

[0001] The present application relates to the field of ring main unit monitoring, and in particular to an online monitoring system for a ring main unit based on multi-state quantity perception. Background Art

[0002] As a key device in the distribution network, the safe and stable operation of the ring main unit is directly related to the reliability of the power system. With the continuous deepening of the construction of intelligent distribution networks, the online monitoring technology of the ring main unit has been widely used. The existing monitoring methods are mainly based on the threshold judgment of a single state quantity, such as temperature monitoring, partial discharge monitoring or mechanical property monitoring. These monitoring systems collect equipment operation data by installing sensors and transmit the data to the monitoring center through remote communication for analysis and processing. The monitoring center evaluates the equipment status according to the preset threshold. When the monitoring parameter exceeds the threshold, the corresponding alarm signal is triggered to remind the operation and maintenance personnel to deal with it in time.

[0003] However, the existing ring main unit monitoring methods have obvious limitations. First, the monitoring of a single state quantity cannot fully reflect the operating status of the equipment, and ignores the correlation between different state quantities, resulting in low accuracy of early warning judgments. Secondly, the fixed monitoring parameters and threshold settings lack flexibility and are difficult to adapt to the dynamic changes in the operating status of the equipment. Furthermore, the timing characteristics between state quantities are not considered during data collection and processing, resulting in problems such as alarm delays or false alarms. At the same time, the allocation method of monitoring resources is relatively rigid, and it is impossible to optimize and adjust according to the real-time changes in the equipment status, which affects the operating efficiency of the monitoring system. Summary of the invention

[0004] The present application provides an online monitoring system for a ring main unit based on multi-state quantity perception, which is used to achieve accurate evaluation and timely warning of the operating status of the equipment, and solve the technical problems of low accuracy and poor adaptability of a single state quantity monitoring method.

[0005] The present application provides an online monitoring system for a ring main unit based on multi-state quantity perception, and the online monitoring system for a ring main unit based on multi-state quantity perception includes: an acquisition module, which is used to obtain state quantity data of key parts of the ring main unit through state quantity acquisition and processing according to temperature signals and partial discharge signals acquired by a SAW surface acoustic wave temperature probe and a partial discharge monitoring antenna; a calibration module, which is electrically connected to the acquisition module and is used to obtain standardized state quantity parameters by signal preprocessing and data calibration of the state quantity data; an analysis module, which is electrically connected to the calibration module and is used to analyze temperature changes based on the standardized state quantity parameters. The transmission module is electrically connected to the analysis module, and is used to utilize the state quantity characteristic correlation data to obtain the real-time operation status of the ring network cabinet through data transmission and fusion processing; the early warning module is electrically connected to the transmission module, and is used to obtain the equipment status early warning mark through multi-level early warning analysis according to the real-time operation status of the ring network cabinet; the adjustment module is electrically connected to the early warning module, and is used to dynamically adjust the monitoring parameters according to the equipment status early warning mark to obtain the monitoring and control parameters of the ring network cabinet.

[0006] In the technical solution provided by the present application, the state quantity collection and processing is carried out according to the temperature signal and partial discharge signal collected by the SAW surface acoustic wave temperature probe and the partial discharge monitoring antenna, the state quantity data of the key parts of the ring network cabinet are obtained, and the real-time monitoring of the operation status of the ring network cabinet is realized; the standardized state quantity parameters are obtained through signal preprocessing and data calibration, and the standardization and comparability of data processing are improved; the temperature change trend and the partial discharge signal characteristics are analyzed in multiple dimensions to obtain the state quantity characteristic correlation data, revealing the intrinsic connection between different state quantities; the real-time operation status of the ring network cabinet is obtained through data transmission and fusion processing, which ensures the integrity and real-time nature of the monitoring data; multi-level early warning analysis is carried out according to the real-time operation status of the ring network cabinet, the equipment status early warning mark is obtained, and a scientific early warning mechanism is established; the monitoring parameters are dynamically adjusted according to the equipment status early warning mark, and the monitoring control parameters of the ring network cabinet are obtained, realizing the adaptive optimization of the monitoring system. The whole scheme constructs a complete online monitoring link of the ring network cabinet through the collaborative analysis and dynamic adjustment of multiple state quantities, which not only solves the limitation problem of single state quantity monitoring, but also improves the accuracy and real-time nature of monitoring through the adaptive adjustment of parameters. In particular, in the collaborative analysis of the two key state quantities of temperature and partial discharge, the solution established a correlation model between the state quantities, effectively improving the accuracy of fault warning. At the same time, the dynamic adjustment mechanism of monitoring parameters ensures that the system can optimize the monitoring strategy in time according to the changes in the equipment status, improving the utilization efficiency of monitoring resources. BRIEF DESCRIPTION OF THE DRAWINGS

[0007] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0008] Figure 1 This is a schematic diagram of an embodiment of a ring main unit online monitoring system based on multi-state quantity perception in an embodiment of the present application; Figure 2 Schematic diagram of the structure of the adjustment module 106 in the embodiment of the present application. DETAILED DESCRIPTION

[0009] An embodiment of the present application provides an online monitoring system for a ring main unit based on multi-state quantity perception. The terms "first", "second", "third", "fourth", etc. (if any) in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments described herein can be implemented in an order other than that illustrated or described herein. In addition, the terms "including" or "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0010] For ease of understanding, the specific process of the embodiment of the present application is described below. Figure 1 In the embodiment of the present application, an embodiment of the ring main unit online monitoring system based on multi-state quantity perception includes: The acquisition module 101 is used to obtain the state quantity data of the key parts of the ring main unit through state quantity acquisition and processing based on the temperature signal and partial discharge signal collected by the SAW surface acoustic wave temperature probe and the partial discharge monitoring antenna; The calibration module 102 is electrically connected to the acquisition module and is used to obtain standardized state quantity parameters by performing signal preprocessing and data calibration on the state quantity data; An analysis module 103, which is electrically connected to the calibration module and is used to perform multi-dimensional feature analysis on the temperature variation trend and the partial discharge signal characteristics based on standardized state quantity parameters to obtain state quantity feature correlation data; The transmission module 104 is electrically connected to the analysis module and is used to associate data using state quantity characteristics and obtain the real-time operation status of the ring main unit through data transmission and fusion processing; The early warning module 105 is electrically connected to the transmission module and is used to obtain the equipment status early warning mark through multi-level early warning analysis according to the real-time operation status of the ring main unit; The adjustment module 106 is electrically connected to the early warning module and is used to dynamically adjust the monitoring parameters according to the equipment status early warning mark to obtain the monitoring and control parameters of the ring main unit.

[0011] It is understandable that the execution subject of the present application may be a ring main unit online monitoring system based on multi-state quantity perception, or a terminal or a server, which is not specifically limited here. The present application embodiment is described by taking a server as the execution subject as an example.

[0012] Specifically, the acquisition module 101 collects the temperature data inside the ring network cabinet through the SAW surface acoustic wave temperature probe. The SAW surface acoustic wave temperature probe is based on the surface acoustic wave sensing technology and converts the temperature change into an electrical signal. It has the characteristics of being passive and maintenance-free. At the same time, the partial discharge monitoring antenna collects the partial discharge signal generated during the operation of the electrical equipment inside the ring network cabinet. The acquisition module 101 performs preliminary processing on the collected original signal, synchronously marks the temperature signal and the partial discharge signal according to the timestamp, and forms an initial state quantity data set. The calibration module 102 receives the state quantity data transmitted by the acquisition module 101 and performs signal preprocessing, including electromagnetic interference elimination and baseline drift correction. For temperature data, high-frequency noise is eliminated by sliding average filtering; for partial discharge signals, wavelet transform is used to remove external electromagnetic interference. In the data calibration stage, the temperature signal is calibrated for the maximum and minimum values, and the temperature value is mapped to the standard interval; the partial discharge signal is logarithmically calibrated, the signal amplitude range is compressed, and standardized state quantity parameters are generated.

[0013] The analysis module 103 performs multi-dimensional feature analysis on the temperature and partial discharge signals based on standardized state quantity parameters. The temperature change rate and the partial discharge amplitude change rate are extracted according to the time series to identify key feature points. Through phase correlation analysis, the time series correspondence between temperature change and partial discharge activity is established, and the correlation coefficient between state quantities is calculated. When the temperature rises rapidly at a certain place in the ring network cabinet, the change characteristics of the partial discharge signal in the area are analyzed to determine whether there is a potential fault risk. The transmission module 104 transmits the state quantity feature-related data to the data processing center through the 4G network. During the data transmission process, the data packets are segmented and encoded, and timestamps and device identification information are added to ensure the reliability of data transmission. After receiving the data, the RTU remote terminal unit performs data packet integrity check and timing reorganization. The reorganized data is subjected to multi-source fusion processing, and the temperature change trend and the partial discharge characteristics are aligned and complementary in the time domain to obtain the real-time operation status of the ring network cabinet.

[0014] The early warning module 105 performs multi-level early warning analysis based on the real-time operating status of the ring network cabinet. The abnormal degree of temperature and partial discharge is graded and quantified, and early warning thresholds of different levels are set. The early warning level is determined by analyzing the change rate and duration of the state quantity and combining the historical abnormal records. The early warning is divided into three levels: prompt, warning, and alarm, and different levels correspond to different processing strategies. When it is detected that the temperature continues to rise or the intensity of partial discharge continues to increase, the system automatically upgrades the early warning level and generates an equipment status early warning mark. The adjustment module 106 dynamically adjusts the monitoring parameters according to the equipment status early warning mark. The early warning state sequence is analyzed in time series to extract the time correlation characteristics of temperature and partial discharge early warning. The sampling frequency is dynamically adjusted based on the early warning level, the sampling frequency is increased under high-risk conditions, and the sampling frequency is appropriately reduced under low-risk conditions. The data acquisition time window is determined by the association mapping of the state warning degree and the sampling frequency. The monitoring threshold is dynamically updated in a graded manner to achieve adaptive adjustment of the monitoring parameters and generate the monitoring and control parameters of the ring network cabinet.

[0015] During the data processing process, when it is detected that the temperature of a busbar connection point rises by more than 20°C within 15 minutes, and the amplitude of the local discharge signal in the area increases by more than 50%, the system determines the state as abnormal. By analyzing the temperature rise rate and the trend of the local discharge intensity change, the state warning level is determined. If it is determined to be a warning level, the system shortens the temperature sampling interval of the area from 15 minutes to 5 minutes, and increases the local discharge monitoring frequency from once an hour to once every 15 minutes. At the same time, the alarm thresholds of temperature and local discharge are dynamically adjusted to achieve timely detection and handling of potential faults. The entire monitoring process realizes closed-loop control of data collection, processing, analysis and early warning, and gives full play to the advantages of multi-state quantity perception in online monitoring of ring network cabinets.

[0016] In the embodiment of the present application, the state quantity collection and processing is performed according to the temperature signal and partial discharge signal collected by the SAW surface acoustic wave temperature probe and the partial discharge monitoring antenna, the state quantity data of the key parts of the ring network cabinet are obtained, and the real-time monitoring of the operation status of the ring network cabinet is realized; the standardized state quantity parameters are obtained through signal preprocessing and data calibration, and the standardization and comparability of data processing are improved; the temperature change trend and the partial discharge signal characteristics are analyzed in multiple dimensions to obtain the state quantity characteristic correlation data, revealing the intrinsic connection between different state quantities; the real-time operation status of the ring network cabinet is obtained through data transmission and fusion processing, and the integrity and real-time nature of the monitoring data are guaranteed; according to the real-time operation status of the ring network cabinet, a multi-level early warning analysis is performed to obtain the equipment status early warning mark, and a scientific early warning mechanism is established; according to the equipment status early warning mark, the monitoring parameters are dynamically adjusted to obtain the monitoring control parameters of the ring network cabinet, and the adaptive optimization of the monitoring system is realized. The whole scheme constructs a complete online monitoring link of the ring network cabinet through the collaborative analysis and dynamic adjustment of multiple state quantities, which not only solves the limitation problem of single state quantity monitoring, but also improves the accuracy and real-time nature of monitoring through the adaptive adjustment of parameters. In particular, in the collaborative analysis of the two key state quantities of temperature and partial discharge, the solution established a correlation model between the state quantities, effectively improving the accuracy of fault warning. At the same time, the dynamic adjustment mechanism of monitoring parameters ensures that the system can optimize the monitoring strategy in time according to the changes in the equipment status, improving the utilization efficiency of monitoring resources.

[0017] In a specific embodiment, the acquisition module 101 is specifically used for: (1) Use the SAW surface acoustic wave temperature probe to collect the temperature of the ring main unit switch body and busbar connection point to obtain the temperature data sequence; (2) Monitor the partial discharge signals of the electrical equipment inside the ring main unit through the partial discharge monitoring antenna to obtain the discharge signal data sequence; (3) Synchronize the temperature data sequence and the discharge signal data sequence according to the timestamp to form the initial state quantity data; (4) Eliminate electromagnetic interference and correct baseline drift of the initial state quantity data to generate preprocessed state quantity data; (5) Based on the preprocessed state quantity data, sampling points are screened and outliers are eliminated to obtain valid state quantity data; (6) The effective state quantity data is divided into time windows and calibrated to obtain the state quantity data of the key parts of the ring main unit.

[0018] Specifically, the acquisition module 101 implements multi-state quantity data acquisition and processing based on SAW surface acoustic wave technology and partial discharge monitoring. The SAW surface acoustic wave temperature probe arranges multiple temperature detection points at the switch body and busbar connection point inside the ring network cabinet, and the temperature data sequence records the temperature value of each detection point changing over time. The temperature data sequence is stored in the form of a triple 〈T, t, p〉, where T represents the temperature value, t represents the sampling timestamp, and p represents the detection point location number. The temperature sampling interval is 15 minutes, and each detection point generates 96 temperature data within 24 hours. The partial discharge monitoring antenna is strategically arranged inside the ring network cabinet to collect partial discharge signals through electromagnetic coupling. The discharge signal data sequence is recorded in the format of 〈A, φ, t, l〉, where A represents the discharge amplitude, φ represents the discharge phase angle, t represents the sampling timestamp, and l represents the antenna position number. The partial discharge monitoring is based on a cycle of 1 hour, and each monitoring records the discharge activity within 30 seconds.

[0019] In the data synchronization marking stage, the temperature data sequence and the discharge signal data sequence are aligned according to the time dimension. For each detection position, a temperature-discharge correspondence table is established, and the most recent temperature sampling value is associated with the discharge signal of the corresponding period according to the timestamp. The initial state quantity data contains complete time, space and multi-state quantity information. Electromagnetic interference elimination adopts an adaptive filtering method to identify external interference components by analyzing the signal spectrum characteristics. For temperature signals, a 5-point sliding average filter is used to eliminate high-frequency noise; for discharge signals, wavelet transform decomposition is used to extract characteristic frequency bands. Baseline drift correction is achieved through long-time scale trend analysis to remove the influence of factors such as ambient temperature changes.

[0020] Sampling point screening is based on data quality assessment, and data points with too low signal-to-noise ratio or abnormal fluctuations are eliminated. For temperature data, the rate of change of adjacent sampling points is calculated, and abnormal values ​​outside the normal range are marked; for discharge data, the time distribution characteristics of the pulse group are analyzed, and sampling points that obviously deviate from statistical laws are eliminated. The effective state quantity data is divided into time windows, and the window length is dynamically adjusted according to monitoring needs. The temperature data uses a 4-hour sliding window, which slides every 15 minutes; the discharge data uses a time window corresponding to the temperature data. Data calibration converts different physical quantities to a unified dimension to facilitate subsequent analysis and processing.

[0021] Taking the temperature monitoring of a switch body as an example, three temperature detection points are set with a sampling interval of 15 minutes. During the period of 8:00-12:00, the temperature data sequence of point A is {〈38.5℃, 8:00, A〉, 〈39.2℃, 8:15, A〉, ...}, and the partial discharge data sequence of the corresponding period is {〈25pC, 30°, 8:00, L1〉, 〈28pC, 45°, 8:00, L1〉, ...}. After data synchronization, it is found that the temperature of point A rises to 42.3℃ at 8:45, and the partial discharge amplitude increases to 45pC. By eliminating electromagnetic interference and removing outliers, it is confirmed that the data at this moment reflects the real state change of the equipment. After time window division and data calibration, the state quantity data of the detection point is obtained.

[0022] In a specific embodiment, the calibration module 102 is used to: (1) Perform signal smoothing and time series baseline drift elimination on the state quantity data to obtain the reference state quantity data; (2) Using the reference state quantity data, the temperature signal and the partial discharge signal are segmented through a sliding time window to obtain segmented state quantity data; (3) Synchronize and pair the segmented state quantity data according to the sampling timestamps, and remove invalid sampling points to obtain time series correlation data; (4) Perform maximum and minimum value calibration on the temperature signal in the time series correlation data, and perform logarithmic calibration on the partial discharge signal to obtain the calibration state quantity data; (5) Detect and correct outliers based on the calibrated state quantity data to generate corrected state quantity data; (6) The corrected state quantity data is normalized to obtain the standardized state quantity parameters.

[0023] Specifically, the calibration module 102 realizes the standardization of the state quantity data of the ring main unit. Signal smoothing and time series baseline drift elimination are performed, and the WMSF (Weighted Moving Statistical Filter) composite filtering algorithm is specifically adopted, and the expression is:

[0024] in, represents the output value of the state quantity after filtering at the kth moment; X(k) represents the original state quantity input value at the kth moment; Represents the original state input value at the (ki)th moment; represents the differential value of the state quantity at the (kj)th moment; k represents the current moment; i represents the time index in the sliding window; j represents the time index of the differential calculation; n represents the length of the sliding window; m represents the order of the differential calculation; Indicates the sliding weight coefficient of the i-th sampling point, reflecting the importance of data at different times; Indicates the baseline drift compensation factor, which is used to adjust the intensity of baseline drift correction; Represents the weight coefficient of the j-th order difference, which is used to adjust the influence of the difference term.

[0025] In the ring main unit status monitoring, for temperature signals, n=60 (corresponding to a 15-minute sampling window) and m=2 (second-order difference) are usually set; for partial discharge signals, n is set to the number of sampling points in a complete voltage cycle and m=3 (third-order difference). This formula achieves the dual functions of signal smoothing and baseline drift elimination through the combination of weighted sliding average and multi-order difference.

[0026] The reference state quantity data is processed by a sliding time window, and the window length is dynamically adjusted according to the state quantity characteristics. The temperature signal uses a 15-minute fixed window with 60 data points in the window; the partial discharge signal uses a variable window that matches the discharge cycle, and the window length is a complete voltage cycle. The data segmentation adopts an overlapping sliding method, and the overlap rate of adjacent windows is 50% to ensure the temporal continuity of the state quantity. The segmented state quantity data is synchronously paired according to the sampling timestamp to establish a temporal mapping relationship between the temperature and partial discharge signals. During the pairing process, the linear interpolation method is used to deal with the problem of inconsistent sampling frequency. The determination of invalid sampling points is based on signal quality evaluation indicators, including signal-to-noise ratio, fluctuation coefficient, etc. Sampling points below the threshold are marked as invalid and eliminated.

[0027] The calibration processing of time series correlation data adopts piecewise linear mapping method. The temperature signal is mapped to the interval [0,1] through maximum and minimum value calibration, and the partial discharge signal adopts logarithmic calibration to compress the dynamic range. The calibration process takes into account the physical characteristics and numerical distribution of different state quantities. The outlier detection adopts a multi-dimensional evaluation method, which comprehensively considers the amplitude, rate of change and time series correlation of the state quantity. For the identified outliers, local average substitution or interpolation correction is used for processing. The correction process maintains the time series continuity and physical rationality of the state quantity. The corrected state quantity data is normalized, and a piecewise normalization strategy is adopted. Considering the different physical meanings of the state quantity, different normalization parameters are used for the temperature signal and the partial discharge signal to obtain the standardized state quantity parameters.

[0028] Taking the monitoring data of a ring main unit busbar connection point as an example, the original temperature data showed obvious periodic fluctuations within the 4-hour monitoring interval. After filtering with the WMSF algorithm, a smooth temperature change trend was obtained. The sliding time window segmentation processing divides the data into 16 overlapping windows, each containing 60 temperature sampling points. The partial discharge signal of the same period is segmented according to the voltage cycle, and a time series correspondence is established with the temperature data. After calibration and outlier processing, standardized parameters reflecting the operating status of the equipment are finally generated, providing a data basis for subsequent status analysis. This data processing process demonstrates the complete conversion link from the original state quantity to the standardized parameter, reflecting the systematicness and effectiveness of multi-state quantity data processing.

[0029] In a specific embodiment, the analysis module 103 is used to: (1) The standardized state quantity parameters are segmented according to the time series, and the temperature change rate and discharge amplitude change rate are extracted to obtain the state quantity change rate data; (2) Based on the state quantity change rate data, the temperature baseline fluctuation and the characteristic peak of the partial discharge signal are separated to obtain the state quantity characteristic point sequence; (3) The state quantity characteristic point sequence is calculated through phase correlation to identify the timing pairing relationship between temperature change and partial discharge signal, and a dual state quantity correlation sequence is obtained; (4) Extracting the characteristic values ​​of the dual-state quantity correlation sequence, determining the corresponding relationship between the temperature rise and the discharge intensity, and obtaining the state quantity characteristic mapping data; (5) Analyze the state quantity characteristic mapping data through time window sliding, identify the change rules of temperature and partial discharge, and obtain the state quantity change characteristic data; (6) Based on the state quantity change feature data, the state quantity feature correlation data is obtained through correlation calculation and feature fusion.

[0030] Specifically, the analysis module 103 performs multi-dimensional feature analysis on the state quantity parameters of the ring main unit. When segmenting the standardized state quantity parameters, the state quantity change rate extraction algorithm DRCA (Dual-Rate Change Analysis) is used, and its expression is:

[0031] in: Indicates the rate of change of the comprehensive state quantity; Represents the temperature state quantity sequence; Represents the sequence of partial discharge state quantities; represents the weight coefficient of temperature change rate; Represents the discharge change rate weight coefficient; represents the first-order difference operator; represents the second-order difference operator; Indicates the sampling time interval.

[0032] For phase correlation calculation, the dual-state time series correlation algorithm TPCC (Temperature-PD CrossCorrelation) is used:

[0033] in: represents the time series correlation function; Indicates the amount of time delay; Indicates the length of the data sequence; represents the weight factor at the lth moment; represents the temperature characteristic value at the lth moment; represents the discharge characteristic value at the lth moment; represents the lth sampling moment.

[0034] The standardized state quantity parameters are segmented according to a fixed time window, with each data segment length of 4 hours and adjacent segments overlapping for 1 hour. The temperature change rate and discharge amplitude change rate are calculated by the DRCA algorithm to generate state quantity change rate data. Subsequently, the change rate data is subjected to peak-to-valley value detection, and the temperature baseline fluctuation characteristic points and local discharge characteristic peak points are extracted to form a state quantity characteristic point sequence. The TPCC algorithm performs phase correlation analysis on the state quantity characteristic point sequence, calculates the temporal correlation between the temperature change and the local discharge signal through a sliding time window, and establishes a corresponding relationship between the two state quantities. The two-state quantity correlation sequence records the correlation strength between the temperature and the discharge signal at different times.

[0035] During the feature value extraction process, the slope characteristics of temperature change and the amplitude-frequency characteristics of the discharge signal are calculated respectively, and the quantitative relationship between temperature rise and discharge intensity is established through the feature mapping table. The state quantity feature mapping data reflects the change trend and mutual influence of the two state quantities. The time window sliding analysis adopts a multi-scale sliding window method, and the window length is set to 15 minutes, 1 hour and 4 hours respectively. The state quantity feature mapping data is analyzed at multiple time scales to identify the periodic change law of temperature and partial discharge, and obtain the state quantity change feature data. The correlation degree between state quantities is calculated by the gray correlation analysis method, and feature fusion is realized by combining principal component analysis to generate state quantity feature correlation data.

[0036] Taking the monitoring of a ring main unit switch as an example, during the 8-hour continuous monitoring process, the data was divided into 6 overlapping time windows. DRCA algorithm analysis showed that the temperature change rate increased significantly during the third window, and the change rate of the partial discharge amplitude also increased. Peak detection revealed that there were 3 significant temperature fluctuation points and 2 discharge peak points in the window. TPCC algorithm calculation showed that there was a time delay of about 5 minutes between these feature points, and the temperature rise often lagged behind the discharge enhancement. Feature mapping analysis further confirmed the positive correlation between temperature and discharge intensity. Multi-scale analysis found that there were periodic fluctuation characteristics on the 1-hour scale, and finally the feature correlation index reflecting the equipment status was obtained through correlation calculation.

[0037] In a specific embodiment, the transmission module 104 is used to: (1) The state quantity feature association data is segmented and encoded through the 4G module to obtain transmission data packets; (2) Adding timestamps and device identification information to the transmission data packets, transmitting the data through the RTU remote terminal unit, and obtaining the transmission status data stream; (3) Integrate the transmission status data stream according to the time sequence and perform data packet integrity check to obtain a complete status data set; (4) Perform time domain alignment on the temperature change trend data and partial discharge characteristic data in the complete state data set to obtain multi-source state data; (5) Based on multi-source state data, cross-validation and complementary fusion of temperature and partial discharge characteristics are performed to obtain fused state data; (6) Perform time-series status judgment and comprehensive evaluation on the fused status data to obtain the real-time operating status of the ring main unit.

[0038] Specifically, the transmission module 104 realizes the remote transmission and fusion processing of the ring main unit status data. The state quantity feature association data is processed by the 4G module, and a segmented data packet processing strategy is adopted. During the data packet segmentation process, the temperature data and partial discharge data are divided into blocks according to a fixed length of 1024 bytes, and each data block contains 256 temperature sampling points or 128 discharge feature points. The data encoding adopts a differential encoding method, and only the difference information is transmitted between adjacent data points to reduce redundant data transmission. When receiving a data packet, the RTU remote terminal unit adds a 32-bit timestamp and a 16-bit device identification code to each data packet. The timestamp is accurate to the millisecond level, and the device identification code contains the ring main unit number and the monitoring point location information. The data transmission adopts the TCP / IP protocol, and the data packets are sent to the data processing center through the 4G network to form a continuous transmission status data stream.

[0039] The data packet integration stage adopts a sliding window mechanism, and the window size is set to 4096 bytes. The data packet integrity check includes two levels: the integrity of a single data packet is ensured by CRC32 check, and the continuity of the data packet is ensured by sequence number check. For lost or damaged data packets, the retransmission mechanism is started for data recovery. The complete state data set is reorganized according to the time series to restore the complete state quantity change process. The time domain alignment processing adopts different strategies for temperature and partial discharge data. The temperature data is sampled at a basic interval of 15 minutes, and the partial discharge data is sampled according to the voltage cycle. The two types of data are unified to the same time base through the linear interpolation method to generate multi-source state data with equal intervals. The physical characteristics of the data are considered in the interpolation process to ensure that the interpolation results are consistent with the actual change law. The cross-validation process is achieved through bidirectional data association analysis. The temporal correlation between temperature change and discharge characteristics is calculated to identify strongly correlated data segments. Then, the data segments are subjected to complementarity analysis, and the temperature rapid rise interval is matched with the discharge intensity increase interval to form a complementary state description. The fused state data comprehensively reflects the temperature anomaly and discharge anomaly characteristics of the equipment. The temporal state judgment adopts a hierarchical evaluation method. Perform trend analysis on temperature data to identify the characteristics of continuous temperature rise or periodic fluctuation; then analyze the intensity change and frequency distribution of partial discharge data to determine the severity of discharge activity; finally, combine the change rules of the two state quantities to comprehensively evaluate the operating status of the equipment. The real-time operating status of the ring main unit includes two parts of information: numerical status and status level.

[0040] Taking a ring main unit monitoring point as an example, during the 24-hour continuous monitoring process, a set of temperature data is generated every 15 minutes and a set of discharge data is generated every hour. These data are transmitted in packets through the 4G module, and each data packet carries an accurate timestamp and device identification information. After receiving the data, the data processing center performs packet verification and reorganization, and finds and corrects three data packet losses. The time domain alignment process interpolates the local discharge data to 15-minute intervals and establishes a corresponding relationship with the temperature data. Cross-validation found that between 2 a.m. and 4 a.m., there was a significant correlation between temperature rise and discharge enhancement. The timing state judgment shows that the equipment is in a dual abnormal state of temperature and discharge during this period.

[0041] In a specific embodiment, the early warning module 105 is used to: (1) Extract the temperature change amplitude and partial discharge intensity value from the real-time operating status of the ring main unit, compare the classification thresholds, and obtain the status abnormality degree data; (2) Based on the state abnormality degree data, the change rate of temperature and partial discharge is calculated and the threshold is judged to obtain the state change rate mark; (3) Correlate and analyze the state change rate mark with the historical abnormal records to determine the state evolution trend and obtain the state trend judgment result; (4) Perform interactive impact analysis on the temperature and partial discharge parameters in the state trend judgment results to obtain associated abnormal level data; (5) Based on the associated abnormal level data, the status is classified into three levels: prompt, warning, and alarm to obtain graded warning data; (6) Comprehensively judge and identify the graded warning data to obtain the equipment status warning identification.

[0042] Specifically, the early warning module 105 implements the early warning analysis of the ring main unit state. The state quantity numerical features are extracted from the real-time operation state of the ring main unit, and three-level thresholds [45°C, 60°C, 75°C] are set for the temperature change amplitude, and corresponding thresholds [100pC, 500pC, 1000pC] are set for the local discharge intensity. The threshold comparison adopts a hierarchical progressive method. When the temperature or discharge exceeds any level threshold, the abnormal mark of the corresponding level is triggered to generate state abnormality degree data. The state change rate calculation is based on the time window difference method. The temperature change rate adopts a 15-minute time window to calculate the temperature rise rate within the window; the local discharge change rate calculates the growth trend of the discharge amplitude and frequency per unit time. The corresponding early warning threshold is set for the change rate. The temperature change rate threshold is [2°C / min, 5°C / min, 8°C / min], and the discharge change rate threshold is [20pC / min, 50pC / min, 100pC / min]. The calculation result is compared with the threshold, and the time period with drastic changes is marked to form a state change rate mark.

[0043] The state evolution trend analysis combines the current state change rate mark and historical anomaly records. The historical anomaly records contain abnormal events in the past 24 hours, recording the time, duration and degree of anomaly. By comparing the current state with the historical records, the trend of intensification or weakening of state changes can be identified. Trend discrimination uses a three-dimensional feature space, including anomaly duration, anomaly degree and change rate, and a comprehensive evaluation is performed to obtain the state trend discrimination result. The interactive impact analysis focuses on the interaction between temperature anomalies and discharge anomalies. Through time series correlation analysis, the time delay and intensity correlation between temperature changes and discharge activities are calculated. When temperature anomalies and discharge anomalies are significantly correlated in time and intensity, they are marked as interactive anomaly states. The associated anomaly level data reflects the severity of the dual anomalies.

[0044] The hierarchical early warning processing maps the associated abnormality level data to a three-level early warning system. The prompt level corresponds to a minor abnormality of a single state quantity; the warning level corresponds to a serious abnormality of a single state quantity or a minor abnormality of a dual state quantity; the alarm level corresponds to a serious abnormality of a dual state quantity. Each early warning level sets a corresponding processing strategy to form hierarchical early warning data. The early warning mark generation process comprehensively considers multiple early warning factors, including the degree of abnormality, the rate of change, the evolution trend, and the interactive impact. The final early warning level is calculated by the weighted fusion method to generate an equipment status early warning mark containing the early warning level, the cause of the early warning, and the processing suggestion.

[0045] Taking the monitoring of the busbar connection point of a ring main unit as an example, the temperature sensor recorded a temperature increase from 40°C to 65°C between 10:00 and 11:00, with an average temperature increase rate of 5.2°C / min, exceeding the warning level threshold. Partial discharge monitoring during the same period showed that the discharge amplitude increased from 80pC to 600pC, and the discharge frequency increased significantly. Comparing historical records, it was found that the monitoring point had similar temperature-discharge coupling anomalies in the previous 12 hours, but the degree was relatively mild. Based on the state evolution trend analysis, it was determined that the degree of anomaly showed an aggravating trend. The interactive impact analysis showed that the temperature rise and the enhanced discharge showed an obvious causal relationship, with a time delay of about 3 minutes. After comprehensive evaluation, an alarm-level warning mark was generated, indicating "compound abnormality of temperature-discharge at the busbar connection point, and immediate inspection is recommended."

[0046] In a specific embodiment, if Figure 2 FIG. 1 is a schematic diagram of the structure of the adjustment module 106. The adjustment module 106 includes: The analysis unit 1061 is used to perform warning level judgment and time series correlation analysis on the equipment status warning mark to obtain warning status sequence data; The adjustment unit 1062 is used to adjust the temperature sampling frequency and the partial discharge monitoring frequency according to the warning state sequence data to obtain sampling frequency adjustment data; A mapping unit 1063 is used to associate and map the sampling frequency adjustment data with the state warning degree, dynamically determine the data collection time window, and obtain the collection window parameter; An updating unit 1064 is used to dynamically update the temperature threshold and the partial discharge alarm threshold in a hierarchical manner based on the acquisition window parameter to obtain a monitoring threshold parameter; The comparison unit 1065 is used to compare and analyze the monitoring threshold parameters with the historical warning records, establish parameter update rules, and obtain parameter adjustment strategies; The configuration unit 1066 is used to comprehensively optimize the parameter adjustment strategy and configure the parameters to obtain the monitoring and control parameters of the ring main unit.

[0047] Specifically, the adjustment module 106 realizes the adaptive adjustment of the monitoring parameters of the ring network cabinet. The analysis unit 1061 conducts an in-depth analysis of the equipment status warning mark, classifies the warning level into three levels: prompt, warning, and alarm, and establishes a time series database of warning events. The time series association analysis adopts a sliding time window method, with a window length of 4 hours, sliding once every 15 minutes, recording the occurrence frequency, level distribution and time interval of the warning event in the window, and generating warning state sequence data. The adjustment unit 1062 dynamically adjusts the monitoring frequency according to the warning state sequence data. Under normal conditions, the temperature sampling interval is 15 minutes, and the partial discharge monitoring interval is 1 hour; when a prompt-level warning occurs, the temperature sampling interval is shortened to 10 minutes, and the partial discharge monitoring interval is shortened to 30 minutes; when a warning-level warning is issued, it is further increased to 5 minutes for temperature and 15 minutes for discharge sampling; the alarm-level warning is adjusted to 1 minute for temperature and 5 minutes for discharge sampling. The adjustment of the sampling frequency takes into account the state change rate. The higher the abnormality, the faster the sampling frequency, forming the sampling frequency adjustment data.

[0048] The mapping unit 1063 establishes an associated mapping relationship between the sampling frequency and the warning degree. A hierarchical mapping strategy is adopted to set corresponding data collection time windows for different warning levels. The prompt level warning adopts a 2-hour collection window, the warning level adopts a 1-hour window, and the alarm level adopts a 30-minute window. The window length is dynamically adjusted with the change of the warning level. When the warning level increases, the window is shortened to improve the real-time monitoring. When the warning level decreases, the window is appropriately extended to reduce the amount of data. Finally, the collection window parameters including the window start and end time and the sliding step are generated. The updating unit 1064 dynamically adjusts the monitoring threshold based on the collection window parameters. The temperature threshold and the partial discharge threshold adopt a three-level dynamic threshold mechanism, and the threshold value is updated as the state changes in the monitoring window. For example, when a rapid temperature rise trend is detected, the temperature warning threshold is correspondingly lowered; when the discharge activity is enhanced, the discharge warning threshold is lowered. The threshold update process takes into account the influence of equipment operating conditions and environmental factors, and generates monitoring threshold parameters that reflect real-time monitoring needs.

[0049] The comparison unit 1065 compares and analyzes the current monitoring threshold parameters with the historical warning records. The historical warning records contain the warning event data of the past 7 days, recording the threshold settings and warning results when the warning is triggered. By statistically analyzing the warning accuracy in the historical data, the rationality of the existing thresholds is evaluated, and parameter update rules including the threshold adjustment direction and step size are established to form a parameter adjustment strategy. The configuration unit 1066 optimizes the parameter adjustment strategy, comprehensively considering the balance between monitoring accuracy and system load. The parameter configuration process includes three aspects: sampling frequency configuration, time window configuration, and threshold configuration, and the logical consistency between the various parameters is maintained. Finally, the complete ring network cabinet monitoring and control parameters are generated to guide the subsequent status monitoring process.

[0050] Taking the monitoring of a ring network switch as an example, when the device changes from a normal state to a prompt-level warning, the analysis unit 1061 records the warning event and updates the warning state sequence. The adjustment unit 1062 then adjusts the temperature sampling interval from 15 minutes to 10 minutes, and the partial discharge monitoring interval from 1 hour to 30 minutes. The mapping unit 1063 sets the acquisition window to 2 hours, sliding it every 10 minutes. Since the cause of the warning is a slight temperature abnormality, the update unit 1064 temporarily reduces the temperature warning threshold by 5°C to strengthen the monitoring of temperature changes. The comparison unit 1065 analyzes the historical records and finds that the warning accuracy under similar working conditions is high, so the current parameter adjustment plan is retained. The configuration unit 1066 finally generates a monitoring control parameter set containing all updated parameters. The entire parameter adjustment process demonstrates the monitoring system's ability to respond quickly to changes in equipment status, and reflects the flexibility and effectiveness of parameter adaptive adjustment.

[0051] In a specific embodiment, the adjustment unit 1062 is used to: (1) Decompose the warning state sequence data in the time dimension, separate the temperature warning sequence and the partial discharge warning sequence, and obtain dual-state warning data; (2) The dual-state warning data is segmented and counted according to the warning level, and the frequency of temperature and partial discharge warning is calculated to obtain the warning frequency distribution data; (3) Perform time correlation analysis on the warning frequency distribution data, identify the time correlation characteristics between temperature and partial discharge warning, and obtain status warning correlation data; (4) Based on the status warning correlation data, calculate the minimum sampling interval and maximum sampling interval of temperature and partial discharge monitoring, and obtain the sampling interval limit; (5) Perform frequency mapping between the sampling interval limit and the current warning level, determine the sampling time intervals for temperature and partial discharge, and obtain the sampling interval parameters; (6) The sampling interval parameters are divided into frequency segments to obtain the sampling frequency adjustment data.

[0052] Specifically, the adjustment unit 1062 realizes the processing conversion from the warning state sequence data to the sampling frequency adjustment data. In the time dimension decomposition stage, the warning state sequence data is classified according to the state quantity type, and the temperature warning event and the partial discharge warning event are extracted. Each warning event record contains a timestamp, a warning level, a duration, and a triggering reason. The decomposed temperature warning sequence and the partial discharge warning sequence constitute dual-state warning data, which maintains the temporal integrity of the warning event. The warning frequency statistics adopt a multi-time scale analysis method, and are counted in three time windows of 15 minutes, 1 hour, and 4 hours. For each time window, the temperature and partial discharge warning events are counted according to the three warning levels of prompt, warning, and alarm, and the number of occurrences and time distribution of warnings of different levels are recorded. The statistical results form multi-dimensional warning frequency distribution data, reflecting the time aggregation characteristics of warning events.

[0053] The time correlation analysis focuses on the correlation between temperature warning and partial discharge warning. By calculating the time interval distribution of the two types of warning events, the warning sequence with significant correlation is identified. The correlation analysis uses a sliding time window method to calculate the sequence and time delay of warning events in each time window, generate state warning correlation data, and reveal the occurrence mode of dual warnings. The determination of the sampling interval limit is based on the warning correlation characteristics. The minimum sampling interval of temperature monitoring is determined by the warning upgrade speed, and the maximum sampling interval considers the stability of the equipment status; the sampling interval of partial discharge monitoring is also set based on the warning characteristics. The upper and lower limits. The sampling interval limit provides a constraint range for the subsequent sampling frequency adjustment.

[0054] The frequency mapping process establishes the correspondence between the warning level and the sampling interval. For different warning levels, the corresponding sampling time interval coefficient is set. For example, the minimum sampling interval is used in the alarm level warning state, the medium sampling interval is used in the warning level warning, and the larger sampling interval is used in the prompt level warning. The mapping result forms the sampling interval parameter to guide the time control of data collection. The segment frequency configuration is refined and adjusted according to the monitoring area and equipment characteristics. The ring network cabinet is divided into different monitoring areas, and the baseline sampling frequency is set for each area according to the importance and fault risk. On this basis, the influence of the warning state is superimposed to form a complete sampling frequency adjustment data.

[0055] Taking the monitoring of the switch body of a ring main unit as an example, 12 temperature warnings and 8 partial discharge warnings were recorded within an 8-hour monitoring cycle. After decomposition of the time dimension, it was found that the temperature warnings were mainly concentrated in the first 4 hours, mainly at the prompt level; the partial discharge warnings increased in the last 4 hours, and the level gradually increased. The warning frequency statistics show that within the 1-hour time window, the temperature prompt level warning reached a maximum of 3 times / hour, and the partial discharge warning level warning reached a maximum of 2 times / hour. Time correlation analysis found that the partial discharge warning usually leads the temperature warning by 3-5 minutes. Based on these characteristics, the sampling interval interval of temperature monitoring is set to [1 minute, 15 minutes], and the sampling interval interval of partial discharge monitoring is set to [5 minutes, 60 minutes]. When a warning level warning occurs, the temperature sampling interval is automatically adjusted to 5 minutes, and the partial discharge sampling interval is adjusted to 15 minutes. Considering that the switch body belongs to the key monitoring area, the sampling frequency is always maintained at a high level.

[0056] In a specific embodiment, the mapping unit 1063 is used to: (1) Perform statistical analysis on the sampling frequency adjustment data according to the time series distribution, extract the frequency change law, and obtain the frequency change characteristic data; (2) Match the frequency change characteristic data with the status warning degree in time series, identify the key warning time points, and obtain the warning time node data; (3) Calculate the time interval of the warning time node data, determine the correlation delay between temperature and partial discharge signal, and obtain the state correlation delay data; (4) Preliminarily divide the sampling time window through the state-related delay data, divide the basic collection interval, and obtain the collection interval data; (5) Dynamically expand and shrink the collected interval data according to the status warning level to obtain the window adjustment parameters; (6) Perform boundary constraints and timing optimization on the window adjustment parameters to obtain the acquisition window parameters.

[0057] Specifically, the mapping unit 1063 implements adaptive mapping from sampling frequency to acquisition window. The sampling frequency adjustment data is subjected to time series statistical analysis, and the sliding window method is used to count and trend the sampling frequency changes at different time scales. The statistical window is divided into three levels: 15 minutes, 1 hour and 4 hours. The number of changes, change amplitude and change direction of temperature and partial discharge sampling frequency are recorded in each window, the dynamic change law of sampling frequency is summarized, and frequency change characteristic data is generated. The time series matching process of frequency characteristics and warning degree adopts a two-way scanning method. Scan forward along the time axis to mark the time point when the warning level changes significantly; then scan backward to confirm the sampling frequency adjustment before and after each warning change point. By comparing the time relationship between the warning level change and the frequency adjustment, the key moments of warning upgrade and warning release are identified, and these time points constitute the warning time node data.

[0058] The time interval calculation focuses on analyzing the timing relationship between temperature anomalies and partial discharge anomalies. For each pair of adjacent warning time nodes, the time difference between the temperature warning and the partial discharge warning is calculated, and the time delay distribution under different warning levels is statistically analyzed. The statistical results show the timing characteristics between temperature changes and partial discharge activities, forming state-related delay data. The division of the basic acquisition interval is based on the state-related delay data. Using a segmented division strategy, the monitoring time axis is divided into multiple acquisition intervals according to the warning characteristics. The length of each interval is determined by the warning level and the state-related delay within the interval. For high-frequency warning areas, the length of the acquisition interval is shorter; for areas with less warning activities, the acquisition interval is extended accordingly. The division results form preliminary acquisition interval data.

[0059] The dynamic adjustment process of the window is carried out according to the degree of state warning. Set the window adjustment coefficient for different warning levels: the window maintains the basic length in the prompt-level warning state, the window is shortened to half of the basic length in the warning-level warning state, and the window is further shortened to a quarter of the basic length in the alarm-level warning state. At the same time, the duration of the warning is considered. The long-term warning state corresponds to a shorter acquisition window. The adjusted results are recorded in the window adjustment parameters. Optimize the window adjustment parameters. Set the upper and lower limits of the window length to ensure that the acquisition window is not too long or too short. Timing optimization focuses on the window switching process to avoid drastic fluctuations in the window length and maintain the continuity of data collection. The optimized results are used as the final acquisition window parameters.

[0060] Taking the monitoring of the busbar connection point of a ring main unit as an example, within the 12-hour monitoring cycle, statistical analysis shows that the temperature sampling frequency is 15 minutes, 10 minutes and 5 minutes in the three states of normal, prompt and warning, respectively, and the partial discharge sampling frequency is 60 minutes, 30 minutes and 15 minutes accordingly. Through timing matching, it is found that the device has a temperature prompt-level warning in the 4th hour, which is upgraded to a warning-level warning in the 6th hour, accompanied by partial discharge abnormalities. Time interval analysis shows that partial discharge abnormalities usually lead temperature abnormalities by 3-5 minutes. Based on this, the monitoring time is divided into three basic acquisition intervals: 0-4 hours is the normal monitoring interval, 4-6 hours is the prompt-level monitoring interval, and 6-12 hours is the warning-level monitoring interval. In the warning-level monitoring interval, considering the double abnormality, the acquisition window is dynamically shortened from the basic 60 minutes to 15 minutes. The final generated acquisition window parameters not only ensure timely response to abnormal conditions, but also avoid excessive consumption of data acquisition resources.

[0061] In a specific embodiment, the updating unit 1064 is configured to: (1) Decompose the acquisition window parameters according to the two dimensions of time series and frequency to obtain time-frequency feature data; (2) Screen the temperature anomaly points and partial discharge anomaly points within the sample interval based on the time-frequency feature data to obtain the key anomaly point set; (3) The correlation between temperature and partial discharge threshold is analyzed through the key abnormal point set to obtain the threshold correlation parameter; (4) Calculate the graded thresholds for the threshold-related parameters, distinguish the normal, caution, and danger thresholds for temperature and partial discharge, and obtain the graded monitoring thresholds; (5) Perform timing calibration and parameter matching on the hierarchical monitoring threshold and the current monitoring window to obtain threshold matching data; (6) Perform boundary constraints and dynamic smoothing on the threshold matching data to obtain the monitoring threshold parameters.

[0062] Specifically, the updating unit 1064 realizes the dynamic update of the monitoring threshold. The acquisition window parameters are subjected to two-dimensional decomposition analysis. The time series dimension records the start and end time of the acquisition window and the sliding step length, and the frequency dimension contains the change of the sampling frequency. The characteristic parameters such as window length, sliding rate and sampling density are extracted through time-frequency analysis. These parameters together constitute the time-frequency characteristic data, which reflects the dynamic characteristics of the data acquisition process. The abnormal points are screened according to the time-frequency characteristic data, and the temperature and partial discharge data in each acquisition window are analyzed. The determination of temperature abnormal points is based on two indicators: temperature value and temperature change rate, and the determination of partial discharge abnormal points is based on discharge amplitude and discharge frequency. The screening process adopts the sliding window method to compare and analyze the data of multiple consecutive windows, and mark the data points that deviate significantly from the normal range. These abnormal points are sorted according to the time sequence and abnormal degree to form a set of key abnormal points.

[0063] The threshold correlation analysis focuses on the correlation characteristics of temperature anomalies and partial discharge anomalies. By pairing the temperature anomaly points and partial discharge anomaly points where the key anomaly points are concentrated, the temporal relationship and intensity correspondence between the two abnormal states are studied. The analysis found that when the partial discharge anomaly occurs, the temperature anomaly often appears after a certain time delay, and the degree of anomaly is positively correlated. This correlation feature is quantified as a threshold correlation parameter to guide the subsequent threshold setting. The hierarchical threshold calculation is based on the threshold correlation parameter. The temperature threshold is divided into a normal operating threshold (35-45℃), a caution state threshold (45-60℃) and a dangerous state threshold (above 60℃); the partial discharge threshold is correspondingly divided into a normal level (below 100pC), a caution level (100-500pC) and a dangerous level (above 500pC). The threshold settings of different levels take into account the equipment operation characteristics and state evolution laws to form a complete hierarchical monitoring threshold system.

[0064] The threshold timing calibration process matches the hierarchical monitoring threshold with the current monitoring window. The threshold is dynamically adjusted according to the sampling frequency and data characteristics of the monitoring window. In the high-frequency sampling interval, the threshold is appropriately lowered to improve sensitivity; in the low-frequency sampling interval, the threshold is correspondingly increased to reduce false alarms. The calibrated results are recorded as threshold matching data. Dynamic smoothing ensures the smooth change of threshold parameters. The maximum step size limit of the threshold change is set to prevent sudden changes in the threshold; the exponential smoothing method is used to process the threshold sequence to eliminate the impact of short-term fluctuations. The processed results are used as the final monitoring threshold parameters.

[0065] Taking the monitoring of the switch body of a ring main unit as an example, the acquisition window length is 60 minutes and the sliding step length is 15 minutes within the 4-hour monitoring period. Time-frequency analysis shows that partial discharge anomalies began to appear in the second hour, and the discharge amplitude gradually increased from 50pC to 300pC. At the same time, the temperature began to rise after 20 minutes, from 38°C to 52°C. The abnormal point screening marked 4 partial discharge abnormal points and 3 temperature abnormal points during this period. Correlation analysis confirmed that there was a time delay of about 20 minutes between the enhancement of partial discharge and the temperature rise. Based on this correlation feature, the temperature attention threshold was dynamically adjusted to 48°C, and the partial discharge attention threshold was adjusted to 200pC. Considering the abnormal development trend, the monitoring window was also shortened to 30 minutes, and the sampling frequency was increased accordingly.

[0066] As described above, the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. An online monitoring system for ring main unit based on multi-state quantity perception, characterized in that: The ring main unit online monitoring system based on multi-state quantity perception includes: The acquisition module is used to obtain the state quantity data of the key parts of the ring main unit through state quantity acquisition and processing based on the temperature signal and partial discharge signal collected by the SAW surface acoustic wave temperature probe and the partial discharge monitoring antenna; A calibration module, the calibration module is electrically connected to the acquisition module, and is used to obtain standardized state quantity parameters by performing signal preprocessing and data calibration on the state quantity data; An analysis module, the analysis module is electrically connected to the calibration module, and is used to perform multi-dimensional feature analysis on the temperature variation trend and the partial discharge signal characteristics based on the standardized state quantity parameters to obtain state quantity feature correlation data; A transmission module, the transmission module is electrically connected to the analysis module, and is used to use the state quantity feature to associate data, and obtain the real-time operation status of the ring main unit after data transmission and fusion processing; An early warning module, the analysis and early warning module is electrically connected to the transmission module, and is used to obtain an equipment status early warning mark through multi-level early warning analysis according to the real-time operating status of the ring main unit; An adjustment module, the adjustment module is electrically connected to the early warning module, and is used to dynamically adjust the monitoring parameters according to the equipment status early warning mark to obtain the ring network cabinet monitoring control parameters.

2. The ring main unit online monitoring system based on multi-state quantity perception according to claim 1 is characterized in that: The acquisition module is used for: The temperature of the ring main unit switch body and busbar connection point is collected through the SAW surface acoustic wave temperature probe to obtain the temperature data sequence; The partial discharge signal of the electrical equipment inside the ring main unit is monitored by the partial discharge monitoring antenna to obtain the discharge signal data sequence; Synchronously marking the temperature data sequence and the discharge signal data sequence according to the timestamp to form initial state quantity data; Eliminating electromagnetic interference and correcting baseline drift on the initial state quantity data to generate preprocessed state quantity data; Based on the pre-processed state quantity data, sampling points are screened and outliers are eliminated to obtain valid state quantity data; The effective state quantity data is divided into time windows and calibrated to obtain the state quantity data of the key parts of the ring main unit.

3. The ring main unit online monitoring system based on multi-state quantity perception according to claim 1 is characterized in that: The calibration module is used for: Performing signal smoothing processing and time series baseline drift elimination on the state quantity data to obtain reference state quantity data; Using the reference state quantity data, segmenting the temperature signal and the partial discharge signal through a sliding time window to obtain segmented state quantity data; Synchronously pairing the segmented state quantity data according to sampling timestamps and removing invalid sampling points to obtain time series correlation data; Performing maximum and minimum value calibration on the temperature signal in the time series correlation data, and performing logarithmic calibration on the partial discharge signal, to obtain calibration state quantity data; Perform abnormal value detection and correction based on the calibrated state quantity data to generate corrected state quantity data; The corrected state quantity data is normalized to obtain standardized state quantity parameters.

4. The ring main unit online monitoring system based on multi-state quantity perception according to claim 1 is characterized in that: The analysis module is used to: The standardized state quantity parameters are segmented according to the time series, and the temperature change rate and the discharge amplitude change rate are extracted to obtain the state quantity change rate data; Based on the state quantity change rate data, the temperature baseline fluctuation and the local discharge signal characteristic peak value are separated to obtain a state quantity characteristic point sequence; The state quantity characteristic point sequence is calculated through phase correlation to identify the time sequence pairing relationship between the temperature change and the partial discharge signal, thereby obtaining a dual state quantity correlation sequence; Extracting characteristic values ​​of the dual-state quantity association sequence, determining the corresponding relationship between the temperature rise and the discharge intensity, and obtaining state quantity characteristic mapping data; The state quantity characteristic mapping data is analyzed by sliding a time window to identify the change rules of temperature and partial discharge, and obtain state quantity change characteristic data; Based on the state quantity change characteristic data, state quantity characteristic association data is obtained through correlation degree calculation and feature fusion.

5. The ring main unit online monitoring system based on multi-state quantity perception according to claim 1 is characterized in that: The transmission module is used for: The state quantity characteristic associated data is segmented and encoded by a 4G module to obtain a transmission data packet; Adding a timestamp and device identification information to the transmission data packet, performing data transmission through an RTU remote terminal unit, and obtaining a transmission status data stream; Integrate the transmission status data stream according to the time sequence, and perform data packet integrity check to obtain a complete status data set; Performing time domain alignment on the temperature change trend data and the partial discharge characteristic data in the complete state data set to obtain multi-source state data; Based on the multi-source state data, cross-validation and complementary fusion of temperature and partial discharge characteristics are performed to obtain fused state data; The fusion status data is subjected to time sequence status determination and comprehensive evaluation to obtain the real-time operation status of the ring main unit.

6. The ring main unit online monitoring system based on multi-state quantity perception according to claim 1 is characterized in that: The early warning module is used to: Extract the temperature change amplitude and partial discharge intensity value from the real-time operating status of the ring main unit, perform classification threshold comparison, and obtain status abnormality degree data; Based on the state abnormality degree data, the change rates of temperature and partial discharge are calculated and threshold values ​​are determined to obtain a state change rate mark; Correlation analysis is performed on the state change rate mark and the historical abnormal records to determine the state evolution trend and obtain the state trend discrimination result; Performing interactive impact analysis on the temperature and partial discharge parameters in the state trend determination result to obtain associated abnormality level data; Based on the associated abnormal level data, the status is classified into three levels: prompt, warning, and alarm to obtain graded early warning data; The graded warning data is comprehensively judged and marked to obtain a device status warning mark.

7. The ring main unit online monitoring system based on multi-state quantity perception according to claim 1 is characterized in that: The adjustment module comprises: An analysis unit, used to perform warning level judgment and time series correlation analysis on the equipment status warning mark to obtain warning status sequence data; An adjustment unit, used for performing interval adjustment on the temperature sampling frequency and the partial discharge monitoring frequency according to the warning state sequence data to obtain sampling frequency adjustment data; A mapping unit, used to associate and map the sampling frequency adjustment data with the state warning degree, dynamically determine the data collection time window, and obtain a collection window parameter; An updating unit, configured to dynamically update the temperature threshold and the partial discharge alarm threshold in a hierarchical manner based on the acquisition window parameter to obtain a monitoring threshold parameter; A comparison unit, used to compare and analyze the monitoring threshold parameters with historical warning records, establish parameter update rules, and obtain parameter adjustment strategies; The configuration unit is used to comprehensively optimize and configure the parameter adjustment strategy to obtain the monitoring and control parameters of the ring network cabinet.

8. The ring main unit online monitoring system based on multi-state quantity perception according to claim 7 is characterized in that: The regulating unit is used for: Decomposing the warning state sequence data in a time dimension to separate a temperature warning sequence and a partial discharge warning sequence to obtain dual-state warning data; The dual-state warning data is segmented and counted according to the warning level, and the temperature and partial discharge warning occurrence frequency are calculated to obtain warning frequency distribution data; Performing time correlation analysis on the warning frequency distribution data, identifying the time correlation characteristics between temperature and partial discharge warning, and obtaining status warning correlation data; Based on the state warning associated data, the minimum sampling interval and the maximum sampling interval of the temperature and partial discharge monitoring are calculated to obtain the sampling interval limit; Perform frequency mapping of the sampling interval limit value and the current warning level, determine the sampling time interval of temperature and partial discharge, and obtain the sampling interval parameter; The sampling interval parameter is subjected to segment frequency configuration to obtain sampling frequency adjustment data.

9. The ring main unit online monitoring system based on multi-state quantity perception according to claim 7 is characterized in that: The mapping unit is used for: Performing statistical analysis on the sampling frequency adjustment data according to time series distribution, extracting frequency change rules, and obtaining frequency change characteristic data; Perform time series matching of the frequency change characteristic data and the state warning degree, identify key warning time points, and obtain warning time node data; Calculating the time interval of the warning time node data, determining the correlation delay between the temperature and the partial discharge signal, and obtaining the state correlation delay data; The sampling time window is preliminarily divided by the state-associated delay data to divide the basic collection interval and obtain the collection interval data; Dynamically expand and shrink the acquisition interval data according to the state warning degree to obtain the window adjustment parameter; Boundary constraints and timing optimization are performed on the window adjustment parameters to obtain acquisition window parameters.

10. The ring main unit online monitoring system based on multi-state quantity perception according to claim 7 is characterized in that: The updating unit is used for: Decomposing the acquisition window parameters according to the two dimensions of time sequence and frequency to obtain time-frequency feature data; Screening temperature anomaly points and partial discharge anomaly points within a sample interval according to the time-frequency characteristic data to obtain a key anomaly point set; Performing correlation analysis between temperature and partial discharge threshold through the key abnormal point set to obtain threshold correlation parameters; Performing graded threshold calculation on the threshold-related parameters, distinguishing normal, caution and danger thresholds of temperature and partial discharge, and obtaining graded monitoring thresholds; Performing timing calibration and parameter matching on the hierarchical monitoring threshold and the current monitoring window to obtain threshold matching data; The threshold matching data is subjected to boundary constraint and dynamic smoothing processing to obtain monitoring threshold parameters.

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