A circuit breaker operating coil current characteristic diagnostic analysis system and method

Abstract

The application provides a circuit breaker operating coil current characteristic diagnosis and analysis system and method. It belongs to the technical field of power system automation and substation state monitoring. The method comprises the following steps: synchronously recording the current of a plurality of circuit breaker operating coils on the DC feeder line screen side to generate multi-channel synchronous recording wave data; collecting SOE message information at the same time, using the time stamp and action identification information in the SOE message to automatically match and locate the specific action circuit breaker, and generating action circuit breaker positioning data; through real-time collection of the current waveform data of the circuit breaker operating coil and the use of high-precision algorithms for feature extraction and analysis, abnormal fluctuations in current changes can be accurately captured, such as excessive starting current and unstable maintenance current, thereby reducing the risk of circuit breaker misoperation or refusal caused by abnormal current characteristics, avoiding power system accidents caused by equipment failure, and improving the reliability of circuit breaker operation.

Description

TECHNICAL FIELD

[0001] The application provides a circuit breaker operating coil current characteristic diagnosis analysis system and method, and belongs to the technical field of power system automation and substation state monitoring. BACKGROUND

[0002] In power system operation, as a key device, the operating coil current characteristic of the circuit breaker directly reflects the health state of the device, and is crucial to guarantee stable operation of the power grid.

[0003] However, the existing circuit breaker mechanical characteristic test products have many defects. In terms of installation, a Hall sensor or a vibration sensor is usually installed on the side of the switch body, which requires the installation of sensors and test devices for each circuit breaker body in the high-voltage chamber, which is not only difficult to wire and requires a large amount of modification, but also leads to high cost. In terms of data, the recorded wave data is disconnected with the background SOE information, and the specific operating circuit breaker cannot be automatically and accurately positioned, forming a data island. In terms of analysis method, only the original waveform is provided, and there is no effective horizontal and vertical comparison, and it is difficult to quantify the health state of the circuit breaker. Moreover, the data needs to be uploaded to the background for analysis, and there is no intuitive display and on-site early warning, and the operation and maintenance response is seriously lagging. In addition, the manufacturers' publicity is mostly at the general level of "online monitoring" and "waveform analysis", and no one has accurately matched the DC screen remote recording wave with SOE, made horizontal and vertical comparison, and integrated three-level early warning in depth. The essence is still a recording wave instrument with storage, which cannot meet the actual operation and maintenance needs. SUMMARY

[0004] The application provides a circuit breaker operating coil current characteristic diagnosis analysis system and method to solve the problems mentioned in the background.

[0005] The application provides a circuit breaker operating coil current characteristic diagnosis analysis method, which comprises the following steps:

[0006] S1: Synchronously recording the operating coil current of a plurality of circuit breakers on the DC feeder screen side to generate multi-channel synchronous recording wave data; simultaneously collecting SOE message information, automatically matching and positioning the specific operating circuit breaker by using the time stamp and operation identification information in the SOE message, and generating operating circuit breaker positioning data;

[0007] S2: According to the operating circuit breaker positioning data, the multi-channel synchronous recording wave data is screened to extract the coil current recording wave data corresponding to the operating circuit breaker; the coil current recording wave data is preprocessed to generate preprocessed coil current waveform data;

[0008] S3: Based on the preprocessed coil current waveform data, horizontal waveform comparison is performed to generate horizontal waveform difference data; and vertical waveform comparison is performed to generate vertical waveform change data;

[0009] S4: According to the transverse waveform difference data and the longitudinal waveform change data, a key feature quantity is extracted, the key feature quantity is used, a preset health state evaluation model is combined, a health state of the action circuit breaker is quantitatively evaluated, and circuit breaker health state evaluation data is generated;

[0010] S5: According to the circuit breaker health state evaluation data, three-level early warning thresholds are set, different health state levels are corresponded respectively; the circuit breaker health state evaluation data is compared with the three-level early warning thresholds, risk early warning processing is performed, and three-level early warning data of the circuit breaker action coil current characteristics is generated.

[0011] The circuit breaker action coil current characteristic diagnosis and analysis system provided by the application comprises:

[0012] One or more processors;

[0013] A memory for storing one or more programs;

[0014] When the one or more programs are executed by the one or more processors, the one or more processors implement the method of any one of the above.

[0015] The application has the following advantages: by collecting current waveform data of the circuit breaker action coil in real time and using high-precision algorithms for feature extraction and analysis, abnormal fluctuations in current changes, such as excessive starting current and unstable maintenance current, can be accurately captured, the risk of circuit breaker misoperation or refusal caused by abnormal current characteristics is reduced, power system accidents caused by equipment failure are avoided, and the reliability of circuit breaker operation is improved; based on big data analysis technology, historical current data is deeply mined and modeled, potential failure trends of the circuit breaker action coil, such as insulation aging and coil burning, can be predicted in advance, early warning reports can be generated according to the failure risk level and maintenance suggestions can be attached, the equipment downtime caused by sudden failure is reduced, economic losses caused by unplanned maintenance are reduced, and the predictability and initiative of equipment maintenance are enhanced; by comparing current characteristic data of circuit breaker action coils of different types and specifications, circuit breaker selection and configuration schemes can be optimized, current performance of various equipment under specific working conditions can be clearly understood, and reasons for improper selection (such as load mismatch and poor environmental adaptability) can be analyzed, performance degradation problems caused by equipment selection errors are avoided, the cost of later modification and replacement is reduced, and the scientificity and economy of circuit breaker selection are improved; a standardized and automated current characteristic diagnosis process is adopted, combined with a remote monitoring and diagnosis platform, real-time and remote monitoring and analysis of the current characteristics of the circuit breaker action coil are realized, the workload and errors of on-site manual detection are reduced, inaccurate diagnosis results caused by human factors are avoided, the circuit breaker state monitoring and diagnosis work is more efficient and accurate, and the intelligent level of power system operation and maintenance is enhanced. BRIEF DESCRIPTION OF DRAWINGS

[0016] Figure 1 for the method of the present application;

[0017] Figure 2 for the waveform and characteristic quantity of the present application;

[0018] Figure 3 for the control loop current waveform comparison diagram of the present application. DETAILED DESCRIPTION

[0019] The preferred embodiments of the present application are described below in conjunction with the accompanying drawings, it should be understood that the preferred embodiments described herein are only used to explain and illustrate the present application, and are not used to limit the present application.

[0020] One embodiment of the present application, as shown in a circuit breaker operating coil current characteristic diagnostic analysis method, the method comprises: Figure 1

[0021] S1: Synchronously record the coil current of a plurality of circuit breakers on the DC feeder screen side to generate multi-channel synchronous recording data; at the same time, collect SOE (Sequence of Events) message information, use the time stamp and action identification information in the SOE message to automatically match and locate the specific action circuit breaker, and generate action circuit breaker positioning data;

[0022] S2: According to the action circuit breaker positioning data, filter the multi-channel synchronous recording data, and extract the coil current recording data corresponding to the action circuit breaker; pre-process the coil current recording data, including filtering, denoising and other operations, to generate pre-processed coil current waveform data;

[0023] S3: Based on the pre-processed coil current waveform data, perform horizontal waveform comparison, that is, compare and analyze the coil current waveform of the current action circuit breaker with the coil current waveform of other normal circuit breakers of the same type to generate horizontal waveform difference data; at the same time, perform vertical waveform comparison, that is, compare and analyze the coil current waveform of the current action circuit breaker with the coil current waveform of the action of the circuit breaker to generate vertical waveform change data;

[0024] S4: According to the horizontal waveform difference data and the vertical waveform change data, extract key characteristic quantities such as current peak value, current rise rate, action time, etc.; use these key characteristic quantities, combine with a pre-set health state evaluation model, quantitatively evaluate the health state of the action circuit breaker, and generate circuit breaker health state evaluation data;

[0025] ​S5: According to the circuit breaker health state evaluation data, set three levels of early warning threshold corresponding to different health state levels; compare the circuit breaker health state evaluation data with the three levels of early warning threshold, perform risk early warning processing, and generate three levels of early warning data of the circuit breaker operating coil current characteristics; at the same time, intuitively display the early warning data locally to realize on-site early warning and provide timely response basis for operation and maintenance personnel.

[0026] The working principle and effect of the above technical solution are:

[0027] Through centralized deployment on the DC feeder screen side and 16-way synchronous recording wave design, there is no need to install sensors and monitoring equipment on each circuit breaker at the high-voltage side, which reduces the complexity of field wiring and installation cost, avoids the maintenance burden of scattered deployment of multiple devices, and improves the monitoring deployment efficiency in the multi-circuit breaker scene; through the IRIG-B code time synchronization technology and SOE message automatic matching function, the recording wave data and the action circuit breaker information are accurately associated, the positioning error caused by time deviation is reduced, the additional wiring work of external auxiliary contact is avoided, and the accuracy and reliability of the circuit breaker action positioning are enhanced; through horizontal and vertical waveform comparison, characteristic parameter quantitative analysis and local automatic diagnosis mechanism, there is no need for manual analysis of original waveforms one by one, which reduces the workload and misjudgment risk of manual analysis, avoids the limitations of single analysis method, and improves the accuracy of circuit breaker health state diagnosis; through local three-level early warning, 10-inch touch screen intuitive display and WEB remote access function, abnormal information is pushed in real time with potential fault prompt, which reduces the probability of fault delay disposal, avoids the dependence of operation and maintenance personnel on on-site duty, enhances the timeliness of fault response and operation flexibility; through the whole-process automatic data acquisition, analysis, early warning and maintenance auxiliary closed loop, accurate decision basis is provided for operation and maintenance, resource waste caused by blind maintenance is reduced, power grid fluctuation caused by circuit breaker fault expansion is avoided, and the stability and safety redundancy of power system power supply are improved. Among them, as shown in Figure 2 According to the research of the research institute, through recording the 10kV circuit breaker opening and closing coil current waveform curve, the working state of the corresponding circuit breaker can be analyzed by the data of several key points; Figure 2 and Figure 3 is the waveform curve of the circuit breaker opening and closing coil operating current, which is usually composed of two wave peaks and one wave trough. According to the time position of the wave peak and wave trough, the waveform can be divided into five stages, representing different movement processes in the opening and closing process. Through comparison and analysis with historical data, potential defects can be found and located, which can intuitively and accurately reflect whether the operation state of each part of the circuit breaker is good or not; as can be seen from the figure, the whole process can be divided into five stages according to the movement of the iron core:

[0028] Phase 1: t=t0~t1; t0 moment is the moment of high voltage circuit breaker on-off command, coil is energized at t0 moment, current rises rapidly in t0~t1 time. t1 moment is the moment when the core starts to move, the magnetic flux in the coil rises to enough to drive the core to move. The length of phase 1 is related to the voltage of control power and coil resistance, which can reflect the state of the coil. Its characteristics are that the current rises, and the core has not moved.

[0029] Phase 2: t=t1~t2. At t1 moment, the core overcomes the resistance such as gravity, spring force and starts to move under the action of electromagnetic force. The current begins to decrease rapidly until the core stops moving, at this moment, the corresponding moment is t2, which represents that the core has touched the load of operating mechanism. Phase 2 can reflect the movement state of the core and reflect whether there is jamming, tripping and other fault conditions when the core moves.

[0030] Phase 3: t=t2~t3. When the core hits the lock door or valve of the closing lock device, the core stops moving or has a short bounce, the current begins to increase, and the opening spring begins to open.

[0031] Phase 4: t=t3~t4. This phase is a continuation of the previous phase, the current maintains a slow growth or stable trend, and the breaking process continues.

[0032] Phase 5: t=t4~t5. This is the current cut-off phase, the current value rapidly decreases to zero. In this phase, the auxiliary switch breaks, an arc is generated between the auxiliary switch contacts and is elongated, the arc voltage rapidly rises to force the coil current to rapidly decrease, the contacts are completely separated, and the equivalent resistance between the contacts rapidly increases until the arc is completely extinguished.

[0033] The current has two peak points and a valley point, the t0~t1 phase reflects the working state of the coil, which can detect whether the coil resistance is normal. The t1~t2 phase reflects the working state of the core, such as tripping, load change of energy release mechanism, and whether the structure is jammed. The t2~t4 phase is the process of driving the contacts to complete the closing or opening operation of the transmission structure. Taking t0 as the time zero point, eight characteristic parameters t1, t2, t3, t4, t5, I1, I2, I3 can be selected to analyze the operation mechanism of the circuit breaker. The quality of the supply voltage, the air gap of the core, the friction resistance of the mechanism, whether there is jamming and other working conditions. Moreover, the coil current of the circuit breaker has a fixed characteristic curve, and the curve trend represents the current change trend in the closing and opening process of the circuit breaker. Generally, the normal closing and opening curves can almost coincide, and if there is a different fault, the curve trend chart cannot coincide. Figure 3 The three curves in the figure represent the coil current waveforms of three actions of the same switch, which can almost coincide.

[0034] In one embodiment of the present application, the S1 comprises:

[0035] S11. Install a multi-channel waveform recording terminal on the DC feeder panel side of the substation, configure 16 synchronous acquisition channels, set the sampling frequency to 4-10KHZ (adapt to the circuit breaker's action time of tens of milliseconds), and enable the IRIG-B code time synchronization function to ensure the consistency of data timestamps across all channels.

[0036] S12. The waveform recording terminal monitors the total current of the DC feeder circuit in real time and adopts a sudden change triggering mechanism (based on the characteristics of load current superimposed on operating current). When the current sudden change reaches the preset threshold, it automatically starts multi-channel synchronous waveform recording, collects the current data of multiple circuit breaker operating coils, and generates a multi-channel synchronous waveform recording raw dataset.

[0037] S13. Synchronously collect SOE message information sent by the substation backend system, extract key fields in the message, including action timestamp, opening and closing action identifier, and circuit number, to form a set of key information of SOE message;

[0038] S14. Using the IRIG-B code time synchronization result of the waveform recording terminal, the time axis of the original dataset of multi-channel synchronous waveform recording and the key information set of SOE message are calibrated to eliminate time deviation and ensure that the time dimension of the two is fully aligned.

[0039] S15. Based on the calibrated timestamp and action identification information, establish a correlation mapping model between the waveform recording data and SOE messages, automatically identify the circuit and number of the circuit breaker that triggered the waveform recording, and generate accurate positioning data of the circuit breaker.

[0040] The working principle and effects of the above technical solution are as follows:

[0041] The 16-channel synchronous acquisition design eliminates the need for independent acquisition equipment on each circuit breaker, reducing on-site installation and wiring workload, avoiding the cost pressure of distributed deployment of multiple devices, and improving the acquisition deployment efficiency in multi-circuit breaker scenarios. With a 4-10kHz adaptive sampling frequency, it accurately captures the current waveform details of circuit breaker operation over tens of milliseconds, reducing waveform data distortion, avoiding feature loss due to sampling frequency mismatch, and enhancing the integrity of the raw data. IRIG-B code time synchronization and time axis calibration eliminate time deviation between the recorded waveform data and SOE messages, reducing error sources in subsequent correlation analysis, avoiding positioning errors caused by time asynchrony, and improving data time consistency. The sudden change trigger mechanism activates waveform recording only when the current is abnormal, reducing the storage of invalid data, avoiding resource waste caused by continuous waveform recording, and enhancing the targeting of data acquisition.

[0042] Through SOE message key field extraction and association mapping model, the action circuit breaker is automatically positioned, the cumbersome operation of manual matching is reduced, the additional wiring of external auxiliary contact is avoided, and the precision and convenience of the circuit breaker positioning are improved.

[0043] In one embodiment of the present application, the S12 comprises:

[0044] Based on the rated load current of the DC feeder loop, combined with the current superposition characteristics when the circuit breaker acts, the preset sudden current starting threshold (usually set as 1.2-1.5 times of I0) and the trigger delay time (5-10us, to avoid instantaneous interference false triggering) are combined with the synchronous starting logic of the 16-channel acquisition channel to generate a sudden trigger parameter configuration table;

[0045] The recording terminal continuously and high-frequency samples the total current of the DC feeder loop according to the preset 4-10KHZ sampling frequency, records the current value once every 250-100us (corresponding to the 4-10KHZ frequency) to form a real-time current sampling sequence;

[0046] The adjacent sampling points in the real-time current sampling sequence are subjected to difference calculation to obtain the current change rate; if the current change rate continuously exceeds the threshold in the sudden trigger parameter configuration table and the continuous duration reaches the trigger delay time, it is determined that the sudden trigger condition is met, and a trigger starting instruction is generated;

[0047] After receiving the trigger starting instruction, the recording terminal synchronously activates the 16-channel acquisition channel to ensure that all channels acquire the currents of the monitored multiple circuit breaker action coils in parallel at the same timestamp (based on IRIG-B code time synchronization) to form a multi-channel parallel current data stream.

[0048] The multi-channel parallel current data stream is classified according to the channel number, the accurate timestamp (accurate to the microsecond level) is added to each channel data, 2-3 unstable sampling points in the starting stage of acquisition are removed, and a multi-channel synchronous recording original data set is integrated.

[0049] The working principle and effect of the above technical scheme are:

[0050] By adapting the mutation threshold preset (1.2-1.5 times of I0) of the current superposition characteristic and the 5-10 mu s trigger delay design, the false triggering caused by transient interference is reduced, invalid recording is avoided to occupy the storage resource, and the accuracy of trigger judgment is enhanced; by 4-10KHZ high frequency continuous sampling, the current value is recorded every 250-100 mu s, the current waveform details of the breaker in dozens of ms are accurately captured, the waveform distortion and key feature loss are reduced, and the integrity of the original sampling data is improved; by the synchronous activation logic of the 16-way acquisition channel, combined with the unified time stamp ensured by the IRIG-B code time synchronization, the sampling time difference between channels is reduced, the data asynchronization affecting the subsequent correlation analysis is avoided, and the consistency of the multi-channel data is enhanced; by sorting the data stream by channel, adding the microsecond level time stamp, and matching the unstable sampling point elimination in the acquisition starting stage, the invalid data interference is reduced, the subsequent processing is avoided due to the data disorder, and the regularity and reliability of the multi-channel synchronous recording original data set are improved.

[0051] In one embodiment of the application, the S14 comprises:

[0052] The IRIG-B code time signal output in real time by the recording terminal is read, the second pulse, the minute pulse and the time coding information in the signal are analyzed, the microsecond level precision IRIG-B code standard time stamp sequence is generated, and the sequence is used as the unified reference of time calibration;

[0053] The original time stamp (generated by the terminal locally at the time of acquisition) of the multi-channel synchronous recording original data set is called, and the IRIG-B code standard time stamp sequence is compared point by point, the time deviation value (such as the deviation caused by the terminal local clock drift) of each recording data is calculated, the original time stamp is linearly compensated and corrected according to the deviation value, and the calibrated multi-channel recording data set is generated;

[0054] The action time stamp in the key information set of the SOE message is analyzed, the network transmission delay (preset delay compensation model, optimized based on historical transmission data) of the substation background and the recording terminal is combined, the SOE time stamp is aligned with the IRIG-B code standard time stamp sequence, the time deviation caused by the transmission delay is corrected, and the calibrated SOE message information set is generated;

[0055] The overlapping time interval (such as 500 ms before and after the recording trigger) in the calibrated multi-channel recording data set and the calibrated SOE message information set is selected, 20-30 time nodes are randomly extracted, the absolute deviation of the time stamps of the two is calculated, if the maximum deviation is less than or equal to 5 mu s, it is determined that the time alignment is qualified, and a time calibration accuracy verification report is generated;

[0056] Based on the time calibration accuracy verification report, the calibrated multi-channel recording data set and the calibrated SOE message information set are associated and marked according to a unified IRIG-B code time axis, so that the time dimension of each channel recording data and the corresponding SOE information is completely matched, and a time-calibrated associated data set is generated.

[0057] The working principle and effects of the technical solution are as follows:

[0058] A microsecond standard timestamp sequence is generated by analyzing the IRIG-B code, a unified time calibration reference is established, time errors caused by local clock drift are reduced, calibration confusion caused by the lack of a unified reference is avoided, and the basic accuracy of time calibration is improved; the terminal local clock deviation is eliminated by linear compensation and correction of the original timestamp of the recording data, the time distortion of the recording data is reduced, the deviation accumulation affecting subsequent analysis is avoided, and the time consistency of the multi-channel recording data is enhanced; the time difference caused by network transmission is corrected by SOE message transmission delay compensation and standard timestamp alignment, the time misalignment of the recording and SOE message is reduced, the action positioning error caused by time mismatch is avoided, and the accuracy of the association between the two is improved; the accuracy is verified by randomly extracting 20-30 time nodes, so that the maximum deviation is less than or equal to 5 microseconds, the unqualified calibration data flowing into the subsequent link is reduced, the risk of blindly adopting the calibration result is avoided, and the reliability of time calibration is enhanced; the two types of data sets are associated and marked according to a unified time axis, so that the time dimension is completely matched, the tedious operation of manually associating data is reduced, the diagnostic deviation caused by data association failure is avoided, and a solid support is provided for subsequent precise positioning of the circuit breaker action.

[0059] In an embodiment of the present application, the S2 comprises:

[0060] S21, according to the precise positioning data of the action circuit breaker, the coil current recording data corresponding to the channel of the circuit breaker is selected from the multi-channel synchronous recording original data set, other irrelevant channel data is removed, and a single-target circuit breaker recording data set is generated;

[0061] S22, an adaptive Kalman filtering algorithm is used to filter the single-target circuit breaker recording data set, so that high-frequency interference signals such as power grid harmonics and electromagnetic radiation are filtered out, and the real change trend of the coil current is retained;

[0062] S23, a wavelet threshold denoising method is used to suppress noise of the filtered data set, eliminate random noise generated in the sampling process, repair slight distortion in the data acquisition process, and generate a denoised data set;

[0063] S24, the denoised data set is converted into a COMTRADE99 format commonly used in the power industry, a unified parameter standard is used, the parameter standard includes data range and time unit, and a standardized coil current data set is generated.

[0064] S25, integrity check is carried out on the standardized coil current data set, whether there is data missing, waveform breaking and other problems are detected, interpolation repair is carried out on the incomplete data, and preprocessed coil current waveform data is generated.

[0065] The working principle and effect of the above technical scheme are:

[0066] By screening target channel data according to positioning data and eliminating irrelevant information, the interference of redundant data on subsequent processing is reduced, the low analysis efficiency caused by data mixing is avoided, and the pertinence and convenience of data processing are improved;By adaptive Kalman filtering algorithm, the high-frequency interference such as power grid harmonic and electromagnetic radiation is filtered out, the real change trend of current is retained, the signal distortion caused by external electromagnetic environment is reduced, the interference signal is avoided to mislead the diagnosis and judgment, and the authenticity of data is enhanced;By wavelet threshold denoising method, random noise is suppressed and slight distortion is repaired, the data quality is further purified, the noise interference in the sampling process is reduced, the fine features are avoided to be covered by noise, and the purity of current waveform data is improved;By converting into COMTRADE99 universal format, unifying data range and time unit, the processing obstacles caused by format incompatibility are reduced, the influence of parameter standard confusion on cross-system analysis is avoided, and the universality and interoperability of data are enhanced;By integrity check and interpolation repair, data missing and waveform breaking are filled, the influence of incomplete data on diagnosis result is reduced, the analysis deviation caused by data defects is avoided, and the reliability and availability of preprocessed waveform data are improved.

[0067] In one embodiment of the application, the S3 comprises:

[0068] S31, collect normal circuit breaker coil current waveform data of the same type, the same operating life and no fault record, extract its typical characteristic parameters, the typical characteristics include peak value, action time and rising slope, and construct a normal waveform reference database of the same type circuit breaker;

[0069] S32, the preprocessed coil current waveform data is aligned with the waveform in the normal waveform reference database (including key nodes such as rising edge starting point, peak point and falling edge ending point), and the consistency of comparison dimension is ensured;

[0070] S33, the dynamic time warping algorithm is adopted to calculate the difference value of the current waveform and the reference waveform in the characteristic parameters and waveform form, including peak deviation rate, action time deviation rate and waveform similarity coefficient, and a horizontal waveform difference data set is generated;

[0071] S34, extract the coil current waveform data of all effective actions of the action circuit breaker in the past, arrange in time sequence, and construct a history waveform database special for the circuit breaker, and mark the key history waveforms such as the first action and the last normal action;

[0072] S35, compare the current waveform with the first action waveform and the average waveform of the last three normal actions in the history waveform database respectively, calculate the time sequence variation and the change rate of each characteristic parameter, and generate a longitudinal waveform change trend data set;

[0073] S36, fuse the transverse waveform difference data set and the longitudinal waveform change trend data set to form a complete two-dimensional waveform comparison analysis result set, and determine the difference core dimension and the change key trend.

[0074] The working principle and effect of the above technical solution are:

[0075] By constructing a normal waveform benchmark database of the same type and the same running time, the core comparison standards such as peak value and action time are determined, the comparison deviation caused by the lack of unified benchmark is reduced, the misjudgment caused by the inconsistency of the benchmark is avoided, and the reliability of the transverse comparison is improved; by aligning the key nodes (rising edge starting point, peak point, etc.) through the feature points, the comparison dimension is ensured to be uniform, the analysis error caused by waveform misplacement is reduced, the difference judgment is affected by the dimension confusion, and the accuracy of the waveform comparison is enhanced; by calculating the feature parameter difference and the waveform similarity through the dynamic time warping algorithm, the subtle morphological difference is accurately captured, the limitations of the traditional comparison method are reduced, the analysis one-sidedness caused by the feature omission is avoided, and the accuracy of the transverse difference identification is improved; by constructing a circuit breaker special history waveform database and marking the key history waveform, the self time sequence change is focused, the blindness of the longitudinal analysis is reduced, the trend misjudgment caused by the lack of history reference is avoided, and the perception of the change of the state of the equipment itself is enhanced; by fusing the transverse and longitudinal comparison results, the difference core dimension and the change key trend are determined, the one-sidedness of the single dimension analysis is reduced, the problem that the isolated data is difficult to support the diagnosis is avoided, the comprehensiveness and persuasiveness of the waveform analysis result are improved, and a solid basis is provided for subsequent health state evaluation.

[0076] In one embodiment of the application, the S36 comprises:

[0077] S361, extract the characteristic parameters of the transverse waveform difference data set (including peak value deviation rate, action time deviation rate, and waveform similarity coefficient) and the longitudinal waveform change trend data set (including feature parameter time sequence variation and change rate), unify the parameter unit (such as keeping 2 decimal places for the deviation rate and taking % / time as the unit for the change rate) and the data format, and generate a normalized two-dimensional feature data set;

[0078] S362, based on the circuit breaker historical fault case library, analyze the contribution of horizontal difference (comparison of the same type) and longitudinal change (self time sequence comparison) to fault diagnosis, determine the fusion weight of each feature parameter (such as peak deviation rate weight 0.3, time sequence change rate weight 0.35) by using analytic hierarchy process (AHP), and generate a two-dimensional feature fusion weight matrix;

[0079] S363, taking the normalized two-dimensional feature data set as input, combining the fusion weight matrix, weighting and summing the same feature dimensions (such as horizontal peak deviation rate and longitudinal peak change amount), and splicing the different feature dimensions (such as waveform similarity coefficient and action time length change rate) to generate a preliminary two-dimensional fusion data set;

[0080] S364, establish a verification rule (such as horizontal peak deviation rate ≤5% and longitudinal peak change rate ≤8% / time is judged as logical consistency), mark the contradictory data (such as horizontal normal but longitudinal abnormal) in the preliminary two-dimensional fusion data set, correct the abnormal value combined with the recent operation condition of the circuit breaker (such as environment temperature and humidity, operation times), and generate a verified fusion data set;

[0081] S365, from the verified fusion data set, select the top 3 feature parameters (such as peak deviation rate, action time length change rate, and waveform similarity coefficient) as the difference core dimension, extract the trend (such as action time length is prolonged successively) in the time sequence change for more than 3 times as the change key trend, and integrate to form a complete two-dimensional waveform comparison analysis result set.

[0082] The working principle and effect of the above technical scheme are:

[0083] By unifying the unit of characteristic parameters and the data format, a normalized two-dimensional characteristic data set is generated, which reduces the fusion confusion caused by mixed data of different formats, avoids the calculation error caused by the non-uniform unit, and improves the compatibility and accuracy of data fusion; By analytic hierarchy process combined with historical fault cases to determine the fusion weight, the contribution of horizontal and vertical analysis is quantified, the deviation of weight allocation dominated by subjective experience is reduced, the unreasonable weight setting affecting the fusion effect is avoided, and the scientificity and pertinence of data fusion are enhanced; By the same dimension weighted summation and the different dimension characteristic splicing fusion mode, the horizontal and vertical core information is comprehensively integrated, the limitation of single fusion logic is reduced, the key features are avoided to be missed, and the comprehensiveness and integrity of the fusion data are improved; By checking rule to mark contradictory data, combining with running condition to correct abnormal value, eliminating the influence of data conflict and distortion, reducing the analysis deviation caused by contradictory data, avoiding the risk of blindly adopting the fusion result, and enhancing the reliability of the fusion data set; By screening the top 3 core features and continuous time sequence trend, focusing on the key diagnostic information, reducing the interference of redundant data on subsequent analysis, avoiding the key information blurred caused by information overload, improving the efficiency and accuracy of fault diagnosis, and providing high-quality data support for circuit breaker health assessment.

[0084] In one embodiment of the application, the S363 comprises:

[0085] Traverse the normalized two-dimensional characteristic data set, identify and extract the corresponding same characteristic dimension parameter pairs (such as the horizontal peak deviation rate and the vertical peak change amount, the horizontal action time deviation rate and the vertical action time change amount) of the horizontal and vertical, group them according to the characteristic categories (peak category, time length category), and generate a same characteristic dimension parameter pair set;

[0086] For each group of parameters in the same characteristic dimension parameter pair set, the weight value of the corresponding parameter in the fusion weight matrix is called (such as the horizontal peak deviation rate weight 0.3, the vertical peak change amount weight 0.35), the weighted sum of each group of parameters is calculated according to the formula (horizontal parameter value x horizontal weight) + (vertical parameter value x vertical weight), and a same characteristic dimension weighted fusion result set is generated;

[0087] Again traverse the normalized two-dimensional characteristic data set, and screen out independent characteristic parameters without horizontal and vertical corresponding relationship (such as the waveform similarity coefficient unique to the horizontal and the action time change rate unique to the vertical), and classify and arrange them according to the horizontal independent parameters and the vertical independent parameters, to generate a different characteristic dimension independent parameter set;

[0088] The same characteristic dimension weighted fusion result set and the different characteristic dimension independent parameter set are spliced according to the fixed characteristic order of the peak category, the time length category, the similarity category and the rate category, the data field format (including parameter name, weighted value / original value, weight identifier) is unified, and a preliminary two-dimensional fusion data set is generated.

[0089] The working principle and effect of the above technical solution are:

[0090] By identifying and extracting the same feature dimension parameter pairs and grouping them by category, the fusion interference caused by the mixing of different types of features is reduced, the calculation error caused by feature classification confusion is avoided, and the pertinence and accuracy of the same dimension data fusion are improved; by calling the weight matrix to calculate the weighted sum according to the formula, the contribution of the same features in the horizontal and vertical directions is quantitatively integrated, the fusion deviation dominated by subjective experience is reduced, the one-sidedness of single dimension data is avoided, and the rationality of the same feature dimension fusion result is enhanced; by screening independent feature parameters without corresponding relationship and classifying and arranging, the key information unique to the horizontal and vertical directions is completely retained, the information loss caused by feature omission is reduced, the incompleteness of the fusion data is avoided, and the comprehensiveness of the fusion data set is improved; by fixing the feature order to splice the two types of data sets and unify the field format, the data organization form is standardized, the subsequent processing obstacles caused by data disorder are reduced, the analysis efficiency caused by format inconsistency is avoided, the regularity and usability of the preliminary fusion data set are enhanced, and a good foundation is laid for subsequent data verification and core feature extraction.

[0091] An embodiment of the present application, the S4, comprises:

[0092] S41, from the double-dimension waveform comparison analysis result set, automatically extract key feature quantities, including current peak value, current rise rate, current drop rate, action total time length, peak value appearance time, and each stage (attraction stage, holding stage) time length, form a multi-dimension feature parameter set;

[0093] S42, based on a machine learning algorithm (such as a random forest algorithm), input historical fault case data and normal operation data for model training, construct a circuit breaker health state quantitative evaluation model, and set feature parameter weight coefficients in combination with power industry circuit breaker fault diagnosis standards;

[0094] S43, normalize the multi-dimension feature parameter set to eliminate the range difference between different parameters, ensure that the weight proportion of each feature in the evaluation model is reasonable, and generate a standardized feature parameter set;

[0095] S44, input the standardized feature parameter set into the health state quantitative evaluation model, calculate the circuit breaker health state score (score range 0-100) through the model, and the higher the score, the better the health state;

[0096] S45, according to the health state score result, generate circuit breaker health state evaluation data in combination with a preset grade division rule (such as 85 points and above for high-quality state, 70-84 points for good state, 50-69 points for general state, and 50 points or below for risk state).

[0097] The working principle and effect of the above technical solution are:

[0098] By automatically extracting current peak value, rising rate, and multi-dimensional key features such as each stage duration, the full-stage information of the coil action is covered, the one-sidedness of single feature evaluation is reduced, the key state information is avoided to be missed, and the comprehensiveness of health evaluation is improved; by combining the machine learning algorithm with the power industry diagnosis standard, the model is trained with historical faults and normal data, the feature parameter weight is quantified, the deviation of subjective experience judgment is reduced, the problem of non-uniform evaluation standard is avoided, and the scientificity of health evaluation is enhanced; by normalizing the multi-dimensional feature parameters, the range difference of different parameters is eliminated, the reasonable proportion of each feature weight is ensured, the model misjudgment caused by parameter range interference is reduced, the feature importance imbalance is avoided, and the effectiveness of model input data is improved; by quantifying the score (0-100 points) to intuitively present the health status, the fuzzy qualitative judgment is replaced, the ambiguity of state definition is reduced, the subjectivity of artificial judgment is avoided, and the recognizability of health status is enhanced; by generating evaluation data according to the preset grade division rule, the excellent, good, general, and risk levels are determined, the blindness of operation and maintenance decision is reduced, the misjudgment and misdisposal of the device state are avoided, and the pertinence of subsequent early warning and maintenance work is improved.

[0099] In an embodiment of the present application, the S42 comprises:

[0100] Collecting circuit breaker historical fault case data (including waveform features and diagnosis results of fault types such as coil short circuit and iron core jamming), normal operation data (feature parameters without fault record), eliminating abnormal samples with a data missing rate greater than 5%, labeling sample tags according to four health levels of excellent / good / general / risk, and generating a labeled model training data set;

[0101] According to the size of the training data set (such as sample amount>1000), the core parameters of the random forest are configured, the number of decision trees is set to 100-200 (balance model accuracy and calculation efficiency), the maximum tree depth is set to 8-12 layers (avoid overfitting), and the Gini coefficient is used as the feature selection criterion to generate a random forest algorithm parameter configuration table;

[0102] The labeled model training data set is divided into a training set and a validation set according to a 7:3 ratio, the training set is used as input, the model training is started in combination with the random forest algorithm parameter configuration table, the feature parameter mapping relationship corresponding to different health levels is learned, and an initial evaluation model of the circuit breaker health state is generated;

[0103] The power industry standards such as the DL / T1573-2016 High Voltage Circuit Breaker State Evaluation Guide are called to determine the priority of the characteristics such as current peak deviation rate and action time length change (for example, the peak deviation weight is higher than the time deviation in the coil short circuit fault) for fault diagnosis, the weight coefficient is adjusted based on the importance of the characteristics output by the initial model to meet the requirements of the industry standards, and a feature parameter weight coefficient matrix is generated;

[0104] The initial evaluation model is input into the verification set, the model health level judgment accuracy is calculated (the requirement is greater than or equal to 92%), if the accuracy does not meet the requirement, the number of random forest decision trees and the maximum depth parameters are adjusted, the model is retrained and the weight is calibrated until the accuracy requirement is met, and a circuit breaker health state quantitative evaluation model is generated.

[0105] The working principle and effect of the above technical scheme are as follows:

[0106] By removing the abnormal samples with a data missing rate greater than 5% and labeling the labels according to four health levels, the interference of poor data on model training is reduced, the learning deviation caused by sample label confusion is avoided, and the reliability and effectiveness of the training data set are improved. By configuring the random forest parameters (100-200 decision trees, 8-12 layers of maximum depth) according to the sample size, the model precision and calculation efficiency are balanced, the overfitting or underfitting problems caused by unreasonable parameter configuration are reduced, the performance loss caused by blind parameter setting is avoided, and the pertinence of model training is enhanced. By dividing the training set and the verification set in a ratio of 7:3, the model fully learns the feature mapping relationship while retaining an independent verification space, the generalization ability caused by excessive dependence on training data is reduced, the one-sidedness of single data set training is avoided, and the adaptability of the initial model is improved. By adjusting the feature weight according to the power industry standards, the fault diagnosis priority is clear, the deviation between subjective weight allocation and industry specifications is reduced, the problem that the model output does not meet the actual operation and maintenance requirements is avoided, and the compliance and practicality of the evaluation model are enhanced. By verifying the accuracy of the verification set (the requirement is greater than or equal to 92%), iteratively optimizing the parameters and weights, the model is ensured to meet the diagnosis requirements, the risk of using unqualified models is reduced, the health state misjudgment caused by insufficient model accuracy is avoided, and the precision and credibility of the circuit breaker health state quantitative evaluation are improved.

[0107] One embodiment of the present application, the S5, comprises:

[0108] S51, in combination with the requirements of the circuit breaker equipment manual, the industry operation and maintenance standards and the statistical results of historical fault data, three-level early warning thresholds (normal threshold, attention threshold and abnormal threshold) are set, and dynamic adjustment according to the equipment operation period and environmental conditions is supported;

[0109] S52, compare the circuit breaker health state assessment data with the three-level early warning threshold, and determine the normal early warning level when the health state score is within the normal threshold range, the attention early warning level when it is lower than the normal threshold but higher than the attention threshold, the abnormal early warning level when it is lower than the attention threshold but higher than the abnormal threshold, and the emergency early warning level when it is lower than the abnormal threshold, and generate circuit breaker action coil current characteristic three-level early warning data;

[0110] S53, for different early warning levels, automatically generate corresponding early warning information, including early warning level, trigger reason (such as peak deviation exceeding standard, action time length extension, etc.), possible fault type (reference mechanical characteristic data analysis criterion), suggested treatment measures and priority, form a complete early warning information package;

[0111] S54, on the 10-inch touch screen of the wave recording terminal, intuitively display the corresponding early warning information, including early warning level (identified by different colors: green-normal, yellow-attention, orange-abnormal, red-emergency), current waveform diagram, horizontal and vertical comparison results, characteristic parameter abnormal items, etc. Corresponding early warning information, and support field operation and maintenance personnel to view and operate;

[0112] S55, through the WEB remote access function, synchronize the three-level early warning data, early warning information package, and original waveform data to the remote operation and maintenance platform, support operation and maintenance personnel to view at any time and anywhere, and record early warning historical data.

[0113] The working principle and effect of the above technical scheme are:

[0114] The three-level early warning threshold is set in combination with the equipment manual, industry standards and historical data, supports dynamic adjustment according to the operation life and working conditions, reduces the adaptation limitation of the fixed threshold, avoids the false early warning or missed early warning caused by unreasonable threshold, improves the scientificity and adaptability of the early warning threshold; the four-level early warning level (normal / attention / abnormal / emergency) is accurately determined, the equipment health state is clearly divided, the decision-making difficulty caused by the fuzzy state definition is reduced, the risk degree misjudgment of the operation and maintenance personnel is avoided, and the recognition of the early warning level is enhanced; the early warning information package containing the trigger reason, fault type, processing measure and priority is automatically generated, without additional manual analysis, the time cost of blind maintenance is reduced, the fault expansion caused by improper measures is avoided, and the pertinence and efficiency of the maintenance response are improved; the 10-inch touch screen locally and intuitively displays the early warning information, uses color identification level, synchronously presents the waveform and abnormal parameter, supports on-site quick viewing operation, reduces the information acquisition difficulty of on-site operation and maintenance, avoids the response delay caused by complex operation, and enhances the convenience of on-site disposal; the WEB remotely pushes the early warning data and historical record, supports viewing at any time and anywhere, reduces the dependence on on-site duty, avoids the problem that the remote fault cannot be known in time, improves the flexibility and coverage of operation and maintenance, and provides complete data support for subsequent traceability analysis.

[0115] In one embodiment of the present application, the circuit breaker operating coil current characteristic diagnosis and analysis system comprises:

[0116] One or more processors;

[0117] Memory for storing one or more programs;

[0118] When the one or more programs are executed by the one or more processors, the one or more processors implement the method of any one of the above.

[0119] Obviously, those skilled in the art can make various modifications and variations to the present application without departing from the spirit and scope of the present application. Thus, if these modifications and variations of the present application belong to the scope of the claims of the present application and their equivalent technologies, the present application also intends to include these modifications and variations.

Claims

1. A method for diagnosing and analyzing the current characteristics of a circuit breaker's operating coil, characterized in that, The method includes: S1: Synchronously record the current of multiple circuit breaker operating coils on the DC feeder panel side to generate multi-channel synchronous recording data; at the same time, collect SOE message information, and use the timestamp and operation identification information in the SOE message to automatically match and locate the specific operating circuit breaker and generate operating circuit breaker location data. S2: Based on the circuit breaker positioning data, filter the multi-channel synchronous waveform data and extract the coil current waveform data of the corresponding circuit breaker; preprocess the coil current waveform data to generate preprocessed coil current waveform data; S3: Based on the preprocessed coil current waveform data, perform a horizontal waveform comparison to generate horizontal waveform difference data; at the same time, perform a vertical waveform comparison to generate vertical waveform change data. S4: Based on the transverse waveform difference data and the longitudinal waveform change data, extract key feature quantities. Using the key feature quantities and combined with the preset health status assessment model, quantitatively assess the health status of the operating circuit breaker and generate circuit breaker health status assessment data. S5: Based on the circuit breaker health status assessment data, set three-level early warning thresholds, each corresponding to a different health status level; compare the circuit breaker health status assessment data with the three-level early warning thresholds, perform risk early warning processing, and generate three-level early warning data for the circuit breaker operating coil current characteristics. S1 includes: S11. Install a multi-channel waveform recording terminal on the DC feeder panel side of the substation, configure 16 synchronous acquisition channels, set the sampling frequency to 4-10KHZ, and enable the IRIG-B code time synchronization function. S12. The waveform recording terminal monitors the total current of the DC feeder circuit in real time. It adopts a sudden change triggering mechanism. When the current sudden change reaches the preset threshold, it automatically starts multi-channel synchronous waveform recording, collects the current data of multiple circuit breaker operating coils, and generates a multi-channel synchronous waveform recording raw dataset. S13. Synchronously collect SOE message information sent by the substation backend system, extract key fields in the message, and form a set of key information of SOE message; S14. Using the IRIG-B code time synchronization result of the waveform recording terminal, perform time axis calibration on the original dataset of multi-channel synchronous waveform recording and the key information set of SOE messages. S15. Based on the calibrated timestamp and action identification information, establish a correlation mapping model between the waveform recording data and SOE messages, automatically identify the circuit and number of the circuit breaker that triggered the waveform recording, and generate accurate positioning data of the circuit breaker.

2. The method for diagnosing and analyzing the current characteristics of a circuit breaker operating coil according to claim 1, characterized in that, S14 includes: Read the IRIG-B code time synchronization signal output in real time from the waveform recording terminal, analyze the second pulse, minute pulse and time code information in the signal, and generate a standard timestamp sequence of IRIG-B code with microsecond-level precision as a unified reference for time calibration. Retrieve the original timestamps of the multi-channel synchronous waveform recording dataset, compare them point by point with the IRIG-B code standard timestamp sequence, calculate the time deviation value of each waveform recording data, and perform linear compensation correction on the original timestamps based on the deviation value to generate a calibrated multi-channel waveform recording dataset. Analyze the action timestamps in the key information set of SOE messages, combine the network transmission delay between the substation backend and the waveform recording terminal, align the SOE timestamps with the IRIG-B code standard timestamp sequence, correct the time deviation caused by the transmission delay, and generate a calibrated SOE message information set. Select the overlapping time intervals between the calibrated multi-channel waveform dataset and the calibrated SOE message information set, randomly select 20-30 time nodes, calculate the absolute deviation of the timestamps of the two, and if the maximum deviation is ≤5μs, the time alignment is deemed qualified and a time calibration accuracy verification report is generated. Based on the time calibration accuracy verification report, the calibrated multi-channel waveform recording dataset and the calibrated SOE message information set are associated and marked according to a unified IRIG-B code time axis to generate a time-calibrated associated dataset.

3. The method for diagnosing and analyzing the current characteristics of a circuit breaker operating coil according to claim 1, characterized in that, S2 includes: S21. Based on the precise positioning data of the circuit breaker, select the coil current recording data of the corresponding channel of the circuit breaker from the original multi-channel synchronous recording dataset, remove other irrelevant channel data, and generate a single-target circuit breaker recording dataset. S22. Adaptive Kalman filtering algorithm is used to filter the waveform data of a single-target circuit breaker. S23. Apply wavelet thresholding to suppress noise in the filtered dataset and generate a denoised dataset. S24. Convert the noise-reduced dataset into the COMTRADE99 format commonly used in the power industry, unify parameter standards, and generate a standardized coil current dataset. S25. Perform integrity verification on the standardized coil current dataset, interpolate and repair incomplete data, and generate preprocessed coil current waveform data.

4. The method for diagnosing and analyzing the current characteristics of a circuit breaker operating coil according to claim 1, characterized in that, The S3 includes: S31. Collect coil current waveform data of normal circuit breakers of the same type, with the same service life and no fault records, extract their typical characteristic parameters, and construct a normal waveform benchmark database of circuit breakers of the same type. S32. Align the feature points of the preprocessed coil current waveform data with those of the waveform in the normal waveform reference database. S33. Using the dynamic time warping algorithm, calculate the difference values ​​between the current waveform and the reference waveform in terms of characteristic parameters and waveform morphology, and generate a horizontal waveform difference dataset. S34. Extract the coil current waveform data of all past effective operations of the circuit breaker, organize them in the order of operation time, and build a historical waveform database dedicated to the circuit breaker. S35. Compare the current waveform with the first action waveform and the average waveform of the three most recent normal actions in the historical waveform database, calculate the time-series change amount and change rate of each characteristic parameter, and generate a longitudinal waveform change trend dataset. S36. Merge the horizontal waveform difference dataset with the vertical waveform change trend dataset to form a complete two-dimensional waveform comparison analysis result set.

5. The method for diagnosing and analyzing the current characteristics of a circuit breaker operating coil according to claim 4, characterized in that, S36 includes: S361. Extract the feature parameters of the horizontal waveform difference dataset and the vertical waveform change trend dataset, unify the parameter units and data format, and generate a normalized two-dimensional feature dataset. S362. Based on the historical fault case library of circuit breakers, analyze the contribution of horizontal differences and vertical changes to fault diagnosis, use the analytic hierarchy process to determine the fusion weight of each feature parameter, and generate a two-dimensional feature fusion weight matrix. S363. Using the normalized two-dimensional feature dataset as input, and combining it with the fusion weight matrix, perform weighted summation on the same feature dimensions and feature splicing on different feature dimensions to generate a preliminary two-dimensional fusion dataset. S364. Establish verification rules, mark contradictory data in the preliminary two-dimensional fusion dataset, correct outliers based on the recent operating conditions of the circuit breaker, and generate a verified fusion dataset. S365. From the fused dataset after verification, the top 3 feature parameters with the highest weights are selected as the core dimensions of difference. The trends of more than 3 consecutive changes in the time series are extracted as the key trends of change. The results are then integrated to form a complete set of two-dimensional waveform comparison analysis results.

6. The method for diagnosing and analyzing the current characteristics of a circuit breaker operating coil according to claim 5, characterized in that, S363 includes: Traverse the normalized two-dimensional feature dataset, identify and extract the same feature dimension parameter pairs corresponding in the horizontal and vertical directions, group them by feature category, and generate a set of the same feature dimension parameter pairs. For each set of parameters in the same feature dimension parameter pair set, retrieve the weight value of the corresponding parameter in the fusion weight matrix, calculate the weighted sum of each set of parameters according to the formula (horizontal parameter value × horizontal weight) + (vertical parameter value × vertical weight), and generate a weighted fusion result set with the same feature dimension. The normalized two-dimensional feature dataset is traversed again to filter out independent feature parameters that have no horizontal or vertical correspondence. These parameters are then classified and organized according to their horizontal and vertical independence to generate independent parameter sets for different feature dimensions. The weighted fusion result set with the same feature dimensions and the independent parameter sets with different feature dimensions are concatenated according to the fixed feature order of peak, duration, similarity and rate, and the data field format is unified to generate a preliminary two-dimensional fusion dataset.

7. The method for diagnosing and analyzing the current characteristics of a circuit breaker operating coil according to claim 1, characterized in that, The S4 includes: S41. From the set of dual-dimensional waveform comparison analysis results, key feature quantities are automatically extracted to form a multi-dimensional feature parameter set; S42. Based on machine learning algorithms and combined with the power industry circuit breaker fault diagnosis standards, input historical fault case data and normal operation data to train the model, construct a quantitative assessment model of circuit breaker health status, and set the feature parameter weight coefficients. S43. Normalize the multi-dimensional feature parameter set to eliminate the range differences between different parameters and generate a standardized feature parameter set. S44. Input the standardized feature parameter set into the health status quantitative assessment model, and obtain the circuit breaker health status score through model calculation. The higher the score, the better the health status. S45. Based on the health status score results and combined with the preset level classification rules, generate circuit breaker health status assessment data.

8. The method for diagnosing and analyzing the current characteristics of a circuit breaker operating coil according to claim 1, characterized in that, The S5 includes: S51. Set three-level early warning thresholds and support dynamic adjustment based on equipment operating years and environmental conditions; S52. Compare the circuit breaker health status assessment data with the three-level early warning threshold. If the health status score is within the normal threshold range, it is determined to be a normal early warning level; if it is below the normal threshold but above the attention threshold, it is determined to be a attention early warning level; if it is below the attention threshold but above the abnormal threshold, it is determined to be an abnormal early warning level; if it is below the abnormal threshold, it is determined to be an emergency early warning level. Generate three-level early warning data for the circuit breaker operating coil current characteristics. S53. For different warning levels, automatically generate corresponding warning information, including the warning level and triggering reason, to form a complete warning information package; S54. On the 10-inch touch screen of the waveform recording terminal, the corresponding warning information is displayed intuitively, including the warning level; S55. Through the WEB remote access function, the three-level early warning data, early warning information package, and raw waveform data are synchronously pushed to the remote operation and maintenance platform.

9. A circuit breaker operating coil current characteristic diagnostic analysis system, comprising: One or more processors; Memory, used to store one or more programs; Wherein, when the one or more programs are executed by the one or more processors, the one or more processors implement the method of any one of claims 1 to 8.

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