A distribution network switch cabinet opening and closing characteristic monitoring method and system based on operation power supply monitoring

Abstract

This application discloses a method and system for monitoring the opening and closing characteristics of distribution network switchgear based on operating power supply monitoring, which relates to the field of power system distribution network automation technology. The disclosed method and system for monitoring the opening and closing characteristics of distribution network switchgear based on operating power supply monitoring achieves closed-loop monitoring of opening and closing characteristics by monitoring the operating power supply current and combining it with multi-dimensional analysis of waveform recording module, output relay action record and switch position change signal. This effectively simplifies the complex sensor deployment architecture in traditional solutions, avoids the need for power outage detection, and reduces construction costs.

Description

Technical Field

[0001] This application relates to the field of power system distribution network automation technology, and in particular to a method and system for monitoring the opening and closing characteristics of distribution network switchgear based on operating power supply monitoring. Background Technology

[0002] The opening and closing characteristics of distribution network switchgear directly affect the power supply reliability of the power system. Currently, the monitoring solutions commonly used in the industry suffer from several technical bottlenecks: Firstly, in terms of system architecture, traditional solutions require a separate acquisition unit to be deployed in each switch bay, with at least three sensors installed in each bay to collect the opening coil current, closing coil current, and energy storage motor current signals respectively. This architecture not only requires a dedicated aggregation unit within the distribution network terminal to integrate the data collected from each bay, but also necessitates data interaction with the distribution terminal unit according to standard protocols, resulting in an exceptionally complex system structure. Secondly, in terms of functional implementation, existing monitoring solutions are limited in function, only capable of acquiring and performing simple analysis of the waveforms of opening, closing, and energy storage currents. They cannot perform closed-loop monitoring of the entire switchgear operation process, including the complete process analysis from the relay output control signal issuance to the execution of the switch opening and closing action, and then to the feedback of the switch position signal. Furthermore, traditional offline detection methods require power outages, severely impacting the continuity of power supply. More significantly, the need to configure independent monitoring units and additional aggregation equipment for each bay leads to high engineering implementation difficulty and construction costs. Conventional distribution network terminal equipment only has basic functions such as collecting line electrical quantities and monitoring switch position status. It completely lacks the professional monitoring capability for the operating characteristics of switchgear, making it difficult to detect potential operational risks caused by abnormalities in the opening and closing mechanism in a timely manner.

[0003] The above content is only used to help understand the technical solution of this application and does not represent an admission that the above content is prior art. Summary of the Invention

[0004] The main purpose of this application is to provide a method and system for monitoring the opening and closing characteristics of distribution network switchgear based on operating power supply monitoring, which aims to simplify the system architecture, realize closed-loop monitoring, avoid power outage detection, and reduce construction costs.

[0005] To achieve the above objectives, this application proposes a method for monitoring the opening and closing characteristics of distribution network switchgear based on operating power supply monitoring. The method includes:

[0006] Monitor the operating power supply current of the distribution network switchgear to obtain the corresponding current data;

[0007] The current data is processed to determine whether the rate of change of current exceeds a preset sudden change threshold, and the waveform recording module is activated when the rate of change of current exceeds the preset sudden change threshold to obtain the waveform recording module activation signal.

[0008] In response to the start signal of the waveform recording module, the waveform of the operating power supply current is recorded by the waveform recording module to obtain current change waveform data;

[0009] Check whether there is an output relay action record within the first preset time window before the waveform recording module starts generating, in order to obtain the output action status data;

[0010] If the output action status data indicates that the output relay has been activated, then check whether the current change waveform data conforms to the basic waveform characteristics of opening and closing to obtain waveform characteristic analysis results data; the basic waveform characteristics of opening and closing include the presence of current rising edge and falling edge, and the current amplitude is greater than the preset amplitude threshold.

[0011] If the waveform feature analysis results indicate that they conform to the basic waveform features of the opening and closing circuit, then the similarity value between the current sudden change waveform data and the reference waveform in the historical opening and closing waveform database is calculated to obtain waveform similarity data.

[0012] Query whether there is only one interval of switch position change signal within the second preset time window after the start signal of the waveform recording module is generated, so as to obtain switch position change status data.

[0013] If the switch position change status data indicates only one interval change, then extract the coil closing time and current peak value corresponding to the current change waveform data to obtain the action characteristic data.

[0014] By combining the output action status data and the switch position change status data, the interval from the start time of the output relay action to the end time of the switch position change is calculated to obtain the action time data.

[0015] The waveform similarity data, motion characteristic data, and motion time data are compared to see if they exceed the normal range threshold. If so, an anomaly assessment result is generated, and an early warning signal is sent.

[0016] In one embodiment, the step of processing the current data to determine whether the current change rate exceeds a preset abrupt change threshold, and activating the waveform recording module when the current change rate exceeds the preset abrupt change threshold to obtain a waveform recording module activation signal includes:

[0017] Perform a first-order difference operation on the current data to obtain the rate of change of current;

[0018] The current change rate is compared with the preset mutation threshold. When the absolute value of the current change rate is greater than the preset mutation threshold, the waveform recording module is activated to obtain the waveform recording module activation signal.

[0019] If the rate of change of current does not exceed the threshold, then return to the step of monitoring the operating power supply current of the distribution network switchgear to obtain the corresponding current data.

[0020] In one embodiment, the step of checking whether the current surge waveform data conforms to the basic waveform characteristics of opening and closing, in order to obtain waveform characteristic analysis result data, includes:

[0021] To identify whether the current abrupt waveform data has at least one rising edge and at least one falling edge, in order to obtain edge detection result data;

[0022] If the edge detection result data indicates the presence of both a rising edge and a falling edge, the time interval between the start point of the rising edge and the end point of the falling edge is calculated to obtain the temporary coil closing time data.

[0023] The maximum current value of the current change waveform data is detected. If the maximum current value is greater than the preset amplitude threshold, waveform feature analysis result data that conforms to the basic waveform characteristics of opening and closing is generated.

[0024] If the rising or falling edge cannot be identified, or the maximum current value is insufficient, the current change waveform data is discarded.

[0025] In one embodiment, the step of identifying whether the current abrupt change waveform data has at least one rising edge and at least one falling edge to obtain edge detection result data includes:

[0026] Calculate the first derivative of the current abrupt change waveform data to obtain gradient sequence data;

[0027] Scan the gradient sequence data to find the point where the gradient changes from negative to positive as the starting point of the rising edge and the point where it changes from positive to negative as the ending point of the falling edge, so as to obtain edge point data.

[0028] If no edge point data can be found, the output will be "No Features".

[0029] In one embodiment, the step of calculating the similarity value between the current change waveform data and a reference waveform in the historical opening and closing waveform database to obtain waveform similarity data includes:

[0030] The current sudden change waveform data is normalized to eliminate amplitude deviation and obtain standardized waveform data;

[0031] Calculate the correlation coefficient between the standardized waveform data and the reference waveforms of the same type in the historical opening and closing waveform database to obtain the initial similarity data;

[0032] If the initial similarity value is lower than the preset similarity threshold, the user will be prompted to verify whether the operation was performed locally through a manual confirmation mechanism in order to obtain manual feedback data.

[0033] The waveform similarity data is updated based on manually fed-in data or correlation coefficients that are higher than a preset similarity threshold.

[0034] In one embodiment, the step of querying whether there is only one interval of switch position change signal within the second preset time window after the waveform recording module start signal is generated, in order to obtain switch position change state data, includes:

[0035] Scan all intervals of switch position signal records within the second preset time window to obtain position count data;

[0036] If the displacement count data is greater than 1, return to the step of monitoring the operating power supply current of the distribution network switchgear to obtain the corresponding current data;

[0037] If the change count data is 0, check if the output relay was activated before the waveform recording started. If not, discard the current current change waveform data.

[0038] In one embodiment, the step of extracting the coil closing time and current peak value corresponding to the current change waveform data to obtain the operating characteristic data includes:

[0039] Analyze the current change waveform data to find the point of maximum current to obtain the current peak data;

[0040] Based on the rising edge start point and the falling edge end point, define the closing time period to obtain the temporary coil closing time;

[0041] If the temporary coil closing time exceeds the historical normal range, an anomaly indication is generated by combining the current peak data, which is then used as the action characteristic data.

[0042] In one embodiment, the step of combining the output operation status data and the switch position change status data to calculate the interval from the start time of the output relay operation to the end time of the switch position change, in order to obtain operation time data, includes:

[0043] Obtain the action start timestamp from the exit action status data and the change end timestamp from the switch position change status data to obtain timestamp pair data;

[0044] Calculate the time difference between the timestamp pair formed by the start timestamp of the action and the end timestamp of the displacement to obtain action time data;

[0045] If the time difference between the timestamp pairs exceeds a preset time threshold, it is marked as a delay anomaly and integrated into the action time data.

[0046] In one embodiment, the step of comparing whether the waveform similarity data, motion characteristic data, and motion time data exceed the normal range threshold, and if so, generating anomaly assessment result data and sending a warning signal, includes:

[0047] The waveform similarity data is compared with a preset first threshold. If it is lower than the preset first threshold, a waveform anomaly marker is generated.

[0048] The coil closing time and current peak value in the action characteristic data are compared with a preset second threshold and a preset third threshold, respectively. If they exceed the preset second threshold or the preset third threshold, an abnormal characteristic mark is generated.

[0049] The action time data is compared with a preset fourth threshold. If it exceeds the preset fourth threshold, a time anomaly marker is generated.

[0050] If at least one of the waveform anomaly marker, characteristic anomaly marker, and time anomaly marker exists, the anomaly assessment result data is generated and an early warning signal is sent.

[0051] Furthermore, to achieve the above objectives, this application also proposes a distribution network switchgear opening and closing characteristic monitoring system based on operating power supply monitoring. The system includes: a memory, a processor, and a distribution network switchgear opening and closing characteristic monitoring program based on operating power supply monitoring stored in the memory and executable on the processor. The distribution network switchgear opening and closing characteristic monitoring program based on operating power supply monitoring is configured to implement the steps of the distribution network switchgear opening and closing characteristic monitoring method based on operating power supply monitoring.

[0052] This application provides a method and system for monitoring the opening and closing characteristics of distribution network switchgear based on operating power supply monitoring. By monitoring the operating power supply current and combining it with multi-dimensional analysis of waveform recording module, output relay action record and switch position change signal, it realizes closed-loop monitoring of opening and closing characteristics, effectively simplifies the complex sensor deployment architecture in traditional solutions, avoids the need for power outage detection, and reduces construction costs. Attached Figure Description

[0053] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0054] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, for those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0055] Figure 1 This is a flowchart illustrating an embodiment of the method for monitoring the opening and closing characteristics of distribution network switchgear based on operating power supply monitoring, as provided in this application.

[0056] Figure 2 This is a schematic diagram of a structural embodiment of the distribution network switchgear opening and closing characteristic monitoring system based on operating power supply monitoring, as provided in this application.

[0057] Explanation of icon numbers:

[0058] 10. Memory; 20. Processor.

[0059] The purpose, features, and advantages of this application will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0060] The technical solutions of this application will now be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are merely some embodiments of this application, and not all embodiments. The components of this application described and shown in the accompanying drawings can generally be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of this application provided in the accompanying drawings is not intended to limit the scope of the claimed application, but merely to illustrate selected embodiments of this application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without inventive effort are within the scope of protection of this application.

[0061] It should be noted that similar reference numerals and letters in the following figures indicate similar items; therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures. Furthermore, in the description of this application, terms such as "first," "second," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0062] In existing technologies, monitoring the opening and closing characteristics of distribution network switchgear has long faced challenges such as complex structure, limited functionality, and the impact of offline detection on power supply continuity. Traditional methods require deploying multiple sensors in each bay to collect the currents of the opening coil, closing coil, and energy storage motor, and integrating the data through independent aggregation units, resulting in high hardware costs and difficult deployment. The monitoring function is limited to single waveform analysis, failing to achieve closed-loop analysis from relay control to switch action, making it difficult to comprehensively assess the operating status of the switchgear. In one substation, aging of the opening coil caused delayed action, but the traditional system failed to provide timely warnings, ultimately leading to a line fault and exposing monitoring blind spots.

[0063] To address the aforementioned issues, researchers focused on the critical signal of the operating power supply current, discovering that coil operation during the opening and closing process inevitably causes a sudden change in the operating power supply current, a feature not fully utilized in existing technologies. By analyzing the timing relationship between the current waveform and relay operation and switch position changes, a closed-loop monitoring logic was formed. Specifically, the approach involves triggering waveform recording through current sudden changes, verifying the source of the operation command by combining this with relay operation records, identifying opening and closing events using waveform characteristics, and finally correlating the switch position change signal to complete the action chain verification, thereby constructing a complete characteristic analysis system.

[0064] Based on this, this application provides a method for monitoring the opening and closing characteristics of distribution network switchgear based on operating power supply monitoring, referring to... Figure 1 The method for monitoring the opening and closing characteristics of distribution network switchgear based on operating power supply monitoring includes steps S100 to S1000, wherein:

[0065] Step S100: Monitor the operating power supply current of the distribution network switchgear to obtain the corresponding current data;

[0066] Step S200: Process the current data to determine whether the current change rate exceeds a preset sudden change threshold, and start the waveform recording module when the current change rate exceeds the preset sudden change threshold to obtain the waveform recording module start signal;

[0067] Step S300: In response to the start signal of the waveform recording module, the waveform of the operating power supply current is recorded by the waveform recording module to obtain current change waveform data;

[0068] Step S400: Check whether there is an output relay action record within the first preset time window before the waveform recording module starts generating, so as to obtain output action status data;

[0069] Step S500: If the output action status data indicates that the output relay has been activated, check whether the current change waveform data conforms to the basic waveform characteristics of opening and closing, so as to obtain waveform characteristic analysis result data; the basic waveform characteristics of opening and closing include the presence of current rising edge and falling edge, and the current amplitude is greater than the preset amplitude threshold.

[0070] Step S600: If the waveform feature analysis result data indicates that it conforms to the basic waveform features of the opening and closing, then calculate the similarity value between the current change waveform data and the reference waveform in the historical opening and closing waveform database to obtain waveform similarity data.

[0071] Step S700: Query whether there is only one interval of switch position change signal within the second preset time window after the waveform recording module start signal is generated, so as to obtain switch position change status data.

[0072] Step S800: If the switch position change status data indicates only one interval change, then extract the coil closing time and current peak value corresponding to the current change waveform data to obtain the action characteristic data.

[0073] Step S900: Combine the output action status data and the switch position change status data to calculate the interval from the start time of the output relay action to the end time of the switch position change, so as to obtain the action time data.

[0074] Step S1000: Compare whether the waveform similarity data, motion characteristic data and motion time data exceed the normal range threshold. If so, generate anomaly judgment result data and send an early warning signal.

[0075] In this embodiment, operating power supply current monitoring refers to the real-time acquisition of the current of the power supply to the switchgear control circuit. This can be achieved using a Hall sensor or a Rogowski coil, and the opening and closing events are identified by capturing sudden current changes. The current change rate threshold is set to an empirical value, for example, 10 amps per second, to distinguish between normal load fluctuations and opening and closing actions. The waveform recording module's start signal generation mechanism uses an edge-triggered method to ensure timely capture of transient waveforms. The basic waveform characteristics of opening and closing include typical current pulse shapes, such as a rising edge duration of 5 milliseconds, a falling edge duration of 8 milliseconds, and a peak current range of 3-5 amps. The waveform similarity calculation uses a normalized cross-correlation algorithm to eliminate the influence of amplitude differences. The switch position change signal verification is achieved by parsing communication messages to ensure the integrity of the action chain. The action time data is calculated to the millisecond level; for example, the standard time window from relay action to switch position change completion is set to 100 milliseconds.

[0076] In this embodiment, when the distribution network terminal detects a sudden change in the operating power supply current, it automatically triggers waveform recording to record the complete waveform. The system reviews the relay action records from the previous period to confirm whether it is a valid operation command. Feature extraction is performed on the recorded waveform to verify whether it has the typical form of opening and closing current pulses. The qualified waveform is compared with the historical database to identify abnormal waveform patterns. Changes in switch position signals are monitored synchronously to ensure that each interval corresponds to an action. Finally, by comprehensively considering parameters such as waveform similarity, action time, and current peak value, the system determines whether there are defects such as jamming or coil aging in the mechanism. For example, if a monitoring finds that the waveform rising edge is delayed by 20 milliseconds and the action time exceeds the limit, the system will generate an early warning to prompt maintenance personnel to check the operating mechanism.

[0077] Compared with existing technologies, this solution breaks through the traditional multi-sensor deployment mode, requiring only monitoring of a single current channel of the operating power supply to achieve the analysis of opening and closing characteristics, thus reducing hardware costs. By constructing a triple verification mechanism of relay action, current waveform, and switch position change, complete closed-loop monitoring from control commands to mechanical actions is achieved. Compared with offline detection methods, this technology can achieve real-time monitoring under operating conditions, reducing power outage detection time. Waveform similarity analysis combined with a manual verification mechanism effectively distinguishes between normal operation and abnormal conditions, reducing false alarm rates. Through the above technical solutions, this application successfully solves the technical bottlenecks of complex structure and limited functionality in traditional monitoring systems. Single-point monitoring of the operating power supply current replaces multi-sensor deployment, simplifying the system architecture; timing correlation analysis of relay action and switch position change achieves verification of the integrity of the action chain; and the combination of waveform feature recognition and historical data comparison improves the accuracy of anomaly detection.

[0078] In one feasible implementation, the step of processing the current data to determine whether the current change rate exceeds a preset mutation threshold, and activating the waveform recording module to obtain a waveform recording module activation signal when the current change rate exceeds the preset mutation threshold, includes: performing a first-order differential operation on the current data to obtain the current change rate; comparing the current change rate with the preset mutation threshold, and activating the waveform recording module to obtain a waveform recording module activation signal when the absolute value of the current change rate is greater than the preset mutation threshold; if the current change rate does not exceed the threshold, then returning to the step of monitoring the operating power supply current of the distribution network switchgear to obtain the corresponding current data.

[0079] In this embodiment, first-order differential operation refers to calculating the difference between adjacent sampling points of the current data. Specifically, it can be implemented using the sliding window differential method to capture the instantaneous change trend of the current signal in real time. The preset abrupt change threshold refers to a pre-set trigger threshold for the rate of change of current. This threshold can be determined through historical data analysis or experimental calibration; for example, it can be set to a change of 5 amperes per second to distinguish between normal fluctuations and abrupt changes caused by opening and closing operations. The waveform recording module start signal is the control command that triggers the waveform recording function. It can be generated through digital logic circuits or software interrupt mechanisms to initiate waveform acquisition when an abnormal abrupt change is detected.

[0080] In this embodiment, after detecting the operating power supply current, the current data is input to the processing unit for first-order differential calculation, which calculates the difference between adjacent sampling points in real time and generates a current change rate sequence. This change rate sequence is continuously compared with a preset abrupt change threshold. When the absolute value of the change rate exceeds the threshold, it is determined that there is a circuit breaker operation or an abnormal event, and the waveform recording module is triggered to start. If the threshold is not exceeded, the current monitoring process continues to be executed to avoid wasting storage resources caused by invalid waveform recording. For example, when the current rises from 0 amps to 2 amps in 0.1 seconds, its change rate is 20 amps / second. If the preset threshold is 15 amps / second, the waveform recording module is triggered to record the abrupt change waveform.

[0081] In this embodiment, by dynamically monitoring the rate of change of current and setting a threshold trigger mechanism, event-driven accurate waveform recording can be achieved without deploying additional sensors. This avoids data redundancy caused by continuous waveform recording and solves the problem of power outages required for offline detection. Simultaneously, the introduction of first-order differential operations significantly improves the real-time performance of abrupt change detection, effectively addressing the issues of high deployment costs and limited functionality inherent in traditional monitoring systems due to their complex structures. The current change rate threshold trigger mechanism enables accurate identification of opening and closing actions without adding hardware, avoiding the need for multiple sensors in each bay. Furthermore, the dynamic trigger mechanism reduces invalid data storage, improves the operating efficiency of the monitoring system, and provides a high-quality data foundation for subsequent waveform analysis and fault diagnosis.

[0082] In one feasible implementation, the step of checking whether the current sudden change waveform data conforms to the basic waveform characteristics of opening and closing to obtain waveform feature analysis result data includes: identifying whether the current sudden change waveform data has at least one current rising edge and at least one current falling edge to obtain edge detection result data; if the edge detection result data indicates the presence of rising edge and falling edge, calculating the time interval between the starting point of the rising edge and the ending point of the falling edge to obtain temporary coil closing time data; detecting the maximum current value of the current sudden change waveform data; if the maximum current value is greater than the preset amplitude threshold, generating waveform feature analysis result data that conforms to the basic waveform characteristics of opening and closing; if the rising edge or falling edge cannot be identified, or the maximum current value is insufficient, discarding the current sudden change waveform data.

[0083] In this embodiment, the rising and falling edges of the current waveform refer to the abrupt transitions from low to high and from high to low, respectively. This can be achieved using first-order derivative gradient change point detection to determine whether the opening and closing actions have a complete start and stop process. The time interval refers to the duration between the start point of the rising edge and the end point of the falling edge, which can be calculated using timestamp differences. This is used to assess whether the duration of the coil closing action conforms to the normal operating range. The maximum current value refers to the peak value of the current amplitude in the waveform, which can be achieved using a peak detection algorithm to verify whether the operating power supply meets the minimum current intensity required for opening and closing.

[0084] In this embodiment, after the current surge waveform data is acquired, an edge detection algorithm is first used to identify whether a complete rising edge and falling edge exist. If both are detected, the time difference between them is further calculated as the temporary coil closing time. Simultaneously, a peak scan is performed on the waveform. If the maximum current value exceeds a preset amplitude threshold, the waveform is determined to meet the basic characteristics of opening and closing, and a valid analysis result is generated; if any condition is not met, the waveform data is discarded. For example, if the waveform only has a rising edge but no falling edge, or if the current peak value is lower than a preset value, it is determined to be an invalid interference signal, avoiding subsequent invalid analysis.

[0085] In this embodiment, by combining waveform edge features, time intervals, and amplitude thresholds for triple verification, current fluctuations unrelated to opening and closing, such as electromagnetic interference or transient signals caused by equipment malfunctions, are effectively filtered out, thereby improving monitoring accuracy. Simultaneously, through multi-dimensional waveform feature analysis, the current waveforms corresponding to actual opening and closing actions are automatically selected, reducing the amount of invalid data processing and ensuring the reliability of subsequent similarity calculations and action characteristic analysis.

[0086] In one feasible implementation, the step of identifying whether the current sudden change waveform data has at least one current rising edge and at least one current falling edge to obtain edge detection result data includes: calculating the first derivative of the current sudden change waveform data to obtain gradient sequence data; scanning the gradient sequence data to find the point where the gradient changes from negative to positive as the starting point of the rising edge and the point where it changes from positive to negative as the ending point of the falling edge to obtain edge point data; if no edge point data is found, a no-feature result is output.

[0087] In this embodiment, the first derivative refers to the rate of change of the current waveform calculated through mathematical differentiation operations. Specifically, it can be implemented using difference algorithms or numerical differentiation methods to quantify the steepness of current changes. Gradient sequence data refers to the set of continuous rate-of-change values ​​calculated from the first derivative. This can be generated using time series analysis tools to reflect the dynamic characteristics of the current waveform. Edge point data refers to the inflection points between the start of the rising edge and the end of the falling edge in the current waveform. This can be identified using threshold comparison or extreme value detection algorithms to determine the start and end times of current abrupt changes. No-feature results indicate that no rising or falling edge meeting the requirements was detected. This can be achieved through logical conditional output to exclude invalid waveform interference.

[0088] In this embodiment, after the current abrupt change waveform data is processed using the first derivative, the positive and negative change regions in the gradient sequence are extracted. For example, the position where the gradient value changes from negative to positive corresponds to the starting point of the current rising edge, while the position where it changes from positive to negative corresponds to the ending point of the falling edge. During the scanning process, if multiple consecutive sampling points satisfy the gradient sign change condition, they are determined to be valid edge points. When no region that meets the sign change condition can be found, the system automatically discards the current waveform data to avoid subsequent processing steps misjudging invalid data.

[0089] In this embodiment, dynamic gradient scanning combined with sign change rules can more accurately capture the true abrupt changes in the current waveform. For example, in existing technologies using amplitude threshold detection, transient interference may be misidentified as valid edges. Gradient sign change rules, however, can effectively distinguish between real operating current and noise fluctuations, solving the problem of traditional edge detection methods being susceptible to noise interference in current waveform analysis and improving the accuracy of rising and falling edge identification. Through the dynamic scanning mechanism of gradient sequences, waveform distortion caused by non-operating currents can be effectively eliminated, reducing the probability of invalid data entering subsequent analysis processes, thereby improving the overall reliability of the opening and closing characteristic monitoring system.

[0090] In one feasible implementation, the step of calculating the similarity value between the current sudden change waveform data and the reference waveform in the historical opening and closing waveform database to obtain waveform similarity data includes: normalizing the current sudden change waveform data to eliminate amplitude deviation and obtain standardized waveform data; calculating the correlation coefficient between the standardized waveform data and the reference waveforms of the same type in the historical opening and closing waveform database to obtain initial similarity data; if the initial similarity data is lower than a preset similarity threshold, prompting the user to verify whether it is an on-site operation through a manual confirmation mechanism to obtain manual feedback data; and updating the waveform similarity data based on the manual feedback data or the correlation coefficient being higher than the preset similarity threshold.

[0091] In this embodiment, normalization refers to adjusting current waveforms with different amplitudes to a unified dimension range. This can be achieved using linear scaling or maximum value standardization methods to eliminate amplitude deviations caused by differences in sensor sensitivity or environmental interference, ensuring the comparability of waveform shape characteristics. The correlation coefficient measures the similarity between two waveforms in the time domain. This can be achieved using the Pearson correlation coefficient or dynamic time warping algorithm, numerically evaluating the matching degree between the current waveform and historical reference waveforms. The manual verification mechanism triggers manual intervention when the automatic similarity is insufficient. This can be achieved by pushing alarm information via a mobile terminal or generating a pending verification work order, avoiding misjudgments caused by special operating scenarios or equipment malfunctions.

[0092] In this embodiment, after acquiring the current surge waveform data, amplitude differences are first eliminated through normalization processing. For example, the waveform data is scaled to the 0-1 range based on the maximum value, making waveforms acquired at different times comparable. Then, the standardized waveform is compared point-by-point with reference waveforms of the same type of opening and closing operation in the historical database. For example, the Pearson correlation coefficient is calculated; if the coefficient is higher than a preset threshold, they are considered similar. When the correlation coefficient is lower than the threshold, the system automatically triggers a manual confirmation process, such as sending a notification containing a waveform comparison diagram to maintenance personnel for manual judgment of whether it is a normal local operation or an equipment malfunction. Finally, the similarity data is updated based on the manual feedback or automatic judgment results, for example, manually confirmed abnormal waveforms are marked as new samples in the reference library.

[0093] In this embodiment, normalization processing is used to improve the accuracy of waveform feature comparison. Combined with the dual mechanism of automatic calculation and manual verification, it effectively distinguishes between normal operation and equipment abnormality, reduces the probability of misjudgment caused by the limitations of a single algorithm, solves the problem of waveform misjudgment caused by amplitude differences in traditional monitoring methods, improves the reliability of opening and closing characteristic analysis, and reduces false alarms in special operation scenarios through the manual confirmation mechanism, thereby reducing operation and maintenance costs.

[0094] In one feasible implementation, the step of querying whether there is only one interval of switch position change signal within the second preset time window after the waveform recording module start signal is generated, in order to obtain switch position change status data, includes: scanning all intervals of switch position signal records within the second preset time window to obtain change count data; if the change count data is greater than 1, then returning to the step of monitoring the operating power supply current of the distribution network switch cabinet to obtain the corresponding current data; if the change count data is 0, then checking whether there is an output relay action before the waveform recording starts, if not, then discarding the current current change waveform data.

[0095] In this embodiment, the second preset time window refers to a pre-set time range for detecting switch position change signals. Specifically, it can be implemented using a fixed duration or a dynamically adjusted window, for example, set to 500 milliseconds to 2 seconds, to ensure timely capture of the change signal after the opening and closing action. The change count data refers to the statistical value of the number of changes obtained by scanning the switch position signals of each interval. Specifically, it can be implemented using a counter module or a logical judgment algorithm, used to determine whether there is a reliable change in a single interval. Discarding current current mutation waveform data means removing waveform data that does not meet the conditions from the processing flow. Specifically, it can be implemented by releasing memory or marking invalid data to avoid invalid data interfering with subsequent analysis.

[0096] In this embodiment, after the waveform recording module is started, the system automatically sets a second preset time window, for example, starting from the start of waveform recording and lasting for 1 second, and scans the switch position signals of all intervals within this time period. If multiple interval change signals are detected, it is determined that there may be external interference or equipment abnormality. At this time, the current analysis process is interrupted and the operating power supply current is re-monitored to rule out false triggering. If no change signal is detected, it is further checked whether there is an output relay action record before the waveform recording starts. If there is no relevant record, the current waveform data is directly discarded to avoid misjudgment caused by current sudden changes due to non-opening and closing operations.

[0097] In this embodiment, by limiting the time window and combining the correlation verification between position change count and relay action record, it is possible to effectively distinguish between actual opening and closing actions and noise interference, reducing the amount of invalid data processing. It also solves the misjudgment problem caused by the lack of a position change signal verification mechanism in traditional monitoring methods, thus improving the accuracy of opening and closing characteristic analysis. Furthermore, by dynamically discarding invalid data, system resource consumption is reduced, and the impact of redundant calculations on real-time monitoring performance is avoided.

[0098] In one feasible implementation, the step of extracting the coil closing time and current peak value corresponding to the current mutation waveform data to obtain the action characteristic data includes: analyzing the current mutation waveform data, finding the current maximum value point to obtain the current peak value data; defining a closing time period based on the rising edge start point and falling edge end point to obtain the temporary coil closing time; if the temporary coil closing time exceeds the historical normal range, then generating an abnormal indication in combination with the current peak value data as the action characteristic data.

[0099] In this embodiment, the coil closing time refers to the time span from the rising edge to the falling edge of the current during the opening and closing operation. Specifically, it can be calculated by determining the time difference between the rising and falling edges in the current waveform, reflecting the response speed of the mechanical components. The current peak value refers to the maximum amplitude in the current surge waveform, which can be obtained by scanning the extreme points in the waveform data, used to determine whether the coil drive energy meets the standard. The temporary coil closing time refers to the action time parameter initially calculated based on a single waveform recording. Specifically, it can be generated by calibrating the timestamp difference between the rising edge and the falling edge, serving as the basis for comparison with historical data. The historical normal range refers to a pre-established statistical interval of action time for similar equipment, specifically determined by collecting historical opening and closing operation data and calculating the average value and standard deviation, used to identify abnormal action events.

[0100] In this embodiment, after acquiring the current surge waveform data, the maximum current point is first located using an extreme value detection algorithm, for example, by using a sliding window method to traverse the waveform data and record the maximum sampled value. Then, the rising edge start point and falling edge end point are determined based on the edge detection results, for example, by using the zero-crossing point of the first derivative to determine the waveform inflection point, and the time difference between the two points is used as the closing time. When this closing time exceeds the historical data statistical range, for example, exceeding three times the standard deviation of the average value, an anomaly indication generation mechanism is triggered. Simultaneously, the current peak value is compared with a preset threshold, for example, set to the range of 80% to 120% of the rated current value; if it exceeds this range, an anomaly marker is superimposed. Finally, the closing time anomaly state and the current peak value anomaly state are logically combined to form the action characteristic data output.

[0101] In this embodiment, by extracting two key parameters—closing time and peak current—a multi-dimensional operational characteristic evaluation mechanism is established, which can simultaneously detect potential faults in mechanical components and electrical circuits. Through the above technical solution, this application can effectively identify opening and closing operation defects caused by coil aging, mechanical wear, or abnormal power supply. For example, when the closing time is abnormally prolonged, it can be inferred that there is jamming in the mechanical transmission mechanism; when the peak current is below the threshold, it can be determined that the operating power supply voltage is insufficient or there is a short circuit between coil turns. This dual-parameter verification mechanism reduces the probability of misjudgment based on a single indicator, improves the reliability of monitoring results, and achieves refined analysis of operational characteristics without the need for additional sensors.

[0102] In one feasible implementation, the step of combining the exit action status data and the switch position change status data to calculate the interval from the start time of the exit relay action to the end time of the switch position change to obtain action time data includes: obtaining the action start timestamp from the exit action status data and the change end timestamp from the switch position change status data to obtain timestamp pair data; calculating the time difference between the timestamp pair formed by the action start timestamp and the change end timestamp to obtain action time data; if the time difference of the timestamp pair exceeds a preset time threshold, it is marked as a delay anomaly and integrated into the action time data.

[0103] In this embodiment, the output action status data refers to the status information recording the trigger time of the output relay action, which can be implemented through the control signal acquisition module of the distribution network terminal to determine the start time point of the opening and closing operation. The switch position change status data refers to the termination time information of the switch position signal state change, which can be implemented through the switch auxiliary contact signal acquisition circuit to determine the completion time point of the mechanical action. The timestamp pair data refers to the combination of timing markers consisting of the action start time and the change end time, which can be implemented using a high-precision clock synchronization module to establish the time correlation between the control signal and the mechanical action. The preset time threshold refers to the maximum allowable delay range set based on historical normal operation data, which can be determined through statistical analysis methods to determine whether abnormal delays occur in the opening and closing actions.

[0104] In this embodiment, after detecting the operation of the outlet relay, the control signal acquisition module records the precise trigger time of the action, while the switch position signal monitoring unit continuously tracks the changes in the contact state. When the switch position completes the change, the system captures the end time of the change and calculates the difference with the start time of the action. This time difference is compared with a preset threshold in real time; if it exceeds the allowable range, a delay anomaly flag is generated. For example, if the switch position does not change within 200 milliseconds after the relay control signal is issued, it is determined to be a mechanical transmission anomaly. This process effectively identifies jamming or transmission failure problems in the opening and closing operations by monitoring the timing relationship between the control signal and the mechanical action in a closed loop.

[0105] In this embodiment, closed-loop monitoring of the entire opening and closing operation process is achieved by accurately calculating the overall time interval from relay triggering to switch change completion. Common misjudgments in existing technologies, such as false alarms caused by asynchronous signal acquisition, are eliminated in this solution through a timestamp synchronization mechanism. Furthermore, this application achieves time-series monitoring of the entire opening and closing operation chain, accurately identifying complex faults such as control signal transmission delays and mechanical transmission jamming. Compared to traditional single-signal monitoring methods, this solution can effectively distinguish between electrical control anomalies and mechanical execution faults, reducing maintenance errors caused by misjudgments of local signals and improving the reliability of distribution network switchgear status assessment.

[0106] In one feasible implementation, the step of comparing whether the waveform similarity data, motion characteristic data, and motion time data exceed the normal range threshold, and generating anomaly judgment result data and sending an early warning signal if so, includes: comparing the waveform similarity data with a preset first threshold; if it is lower than the preset first threshold, generating a waveform anomaly marker; comparing the coil closing time and current peak value in the motion characteristic data with a preset second threshold and a preset third threshold, respectively; if they exceed the preset second threshold or the preset third threshold, generating a characteristic anomaly marker; comparing the motion time data with a preset fourth threshold; if it exceeds the preset fourth threshold, generating a time anomaly marker; and if at least one of the waveform anomaly marker, characteristic anomaly marker, and time anomaly marker exists, generating the anomaly judgment result data and sending an early warning signal.

[0107] In this embodiment, waveform similarity data refers to the degree of matching between the current waveform and historical reference waveforms, calculated using a correlation coefficient. Specifically, this can be achieved by calculating the Pearson correlation coefficient after normalization. This data is used to determine whether the operating power supply current pattern deviates from the normal opening and closing mode. Action characteristic data includes two dimensions: coil closing time and current peak value. Specifically, it can be extracted by analyzing the time difference between the rising and falling edges of the current waveform and the maximum amplitude of the waveform. This data is used to detect whether the mechanical component's action speed and driving capability are abnormal. Action time data refers to the time interval from relay action to switch position change. Specifically, it can be calculated using timestamp differences. This data is used to evaluate the response delay of the control loop and mechanical transmission system. The normal range thresholds include four independent thresholds, which can be determined using the statistical distribution of historical operating data. For example, the first preset threshold can be set to a correlation coefficient of 0.85, the second preset threshold to the range of 50ms to 200ms, the third preset threshold to the range of 2A to 5A, and the fourth preset threshold to the range of 100ms to 500ms.

[0108] In this embodiment, after completing waveform feature matching, action parameter extraction, and time series analysis, the system jointly judges the multi-dimensional monitoring data with preset thresholds. When the waveform similarity is lower than the first threshold, it indicates that the current pattern is distorted, possibly caused by poor contact or coil aging; when the coil closing time exceeds the second threshold, it indicates that there is a risk of jamming in the mechanical transmission mechanism; when the current peak value is lower than the third threshold, it indicates an abnormal drive power supply or a short circuit between coil turns; when the action time exceeds the fourth threshold, it reflects a response delay in the control circuit or mechanical components. The system achieves layered detection by setting independent thresholds. When an abnormal marker appears in any dimension, an early warning is triggered, effectively covering various problems that may occur during the opening and closing process, such as abnormal electrical characteristics, mechanical performance degradation, and control circuit failures.

[0109] In some specific implementations, after the warning signal is generated, it can be linked with the equipment management system to automatically generate a maintenance work order. For example, when the coil closing time is detected to exceed the second threshold three times consecutively, a red warning is triggered and an emergency maintenance command is pushed. For yellow warnings with waveform similarity in the range of 0.8 to 0.85, the system can start a high-frequency sampling mode for continuous monitoring.

[0110] In this embodiment, by establishing a multi-dimensional detection model, the collaborative analysis of electrical characteristics, mechanical parameters, and time sequence relationships is achieved, enabling accurate differentiation between transient interference and actual faults. The existing manual threshold setting method is replaced by a dynamic threshold adjustment mechanism based on historical data, effectively avoiding misjudgments caused by fixed thresholds and solving the technical defects of high false alarm rates and inaccurate fault location in traditional monitoring systems. This achieves a comprehensive health status assessment of the opening and closing process. By establishing joint criteria based on waveform similarity, action parameters, and time sequence data, complex faults such as coil aging, mechanical jamming, and control circuit abnormalities can be accurately identified, significantly improving the accuracy and reliability of distribution network switchgear status monitoring.

[0111] In this embodiment, the method for monitoring the opening and closing characteristics of distribution network switchgear based on operating power supply monitoring achieves closed-loop monitoring of opening and closing characteristics by monitoring the operating power supply current and combining it with multi-dimensional analysis of waveform recording module, output relay action record and switch position change signal. This effectively simplifies the complex sensor deployment architecture in traditional solutions, avoids the need for power outage detection, and reduces construction costs.

[0112] This application also provides a system for monitoring the opening and closing characteristics of distribution network switchgear based on operating power supply monitoring. Please refer to [reference needed]. Figure 2 The system includes: a memory 10, a processor 20, and a power supply monitoring program for monitoring the opening and closing characteristics of a distribution network switchgear based on operating power supply monitoring, which is stored in the memory 10 and can run on the processor 20. The power supply monitoring program for monitoring the opening and closing characteristics of a distribution network switchgear based on operating power supply monitoring is configured to implement the steps of the power supply monitoring method for monitoring the opening and closing characteristics of a distribution network switchgear based on operating power supply monitoring.

[0113] The distribution network switchgear opening and closing characteristic monitoring system based on operating power supply monitoring provided in this application adopts the opening and closing characteristic monitoring method based on operating power supply monitoring in the above embodiments, which can simplify the system architecture, realize closed-loop monitoring, avoid power outage detection, and reduce construction costs. Compared with the prior art, the beneficial effects of the distribution network switchgear opening and closing characteristic monitoring system based on operating power supply monitoring provided in this application are the same as the beneficial effects of the distribution network switchgear opening and closing characteristic monitoring method based on operating power supply monitoring provided in the above embodiments, and other technical features of the distribution network switchgear opening and closing characteristic monitoring system based on operating power supply monitoring are the same as the features disclosed in the methods of the above embodiments, and will not be repeated here.

[0114] The above description is only a part of the embodiments of this application and does not limit the patent scope of this application. All equivalent structural transformations made under the technical concept of this application and using the contents of the specification and drawings of this application, or direct / indirect applications in other related technical fields, are included in the patent protection scope of this application.

Claims

1. A method for monitoring the switching characteristics of a power distribution switchgear based on the monitoring of the operating power supply, characterized in that, The method includes: Monitor the operating power supply current of the distribution network switchgear to obtain the corresponding current data; The current data is processed to determine whether the rate of change of current exceeds a preset sudden change threshold, and the waveform recording module is activated when the rate of change of current exceeds the preset sudden change threshold to obtain the waveform recording module activation signal; In response to the start signal of the waveform recording module, the waveform of the operating power supply current is recorded by the waveform recording module to obtain current change waveform data; Check whether there is an output relay action record within the first preset time window before the waveform recording module starts generating, in order to obtain the output action status data; If the output action status data indicates that the output relay has been activated, then check whether the current change waveform data conforms to the basic waveform characteristics of opening and closing to obtain waveform characteristic analysis results data; the basic waveform characteristics of opening and closing include the presence of current rising edge and falling edge, and the current amplitude is greater than the preset amplitude threshold. If the waveform feature analysis results indicate that they conform to the basic waveform features of the opening and closing circuit, then the similarity value between the current sudden change waveform data and the reference waveform in the historical opening and closing waveform database is calculated to obtain waveform similarity data. Query whether there is only one interval of switch position change signal within the second preset time window after the start signal of the waveform recording module is generated, so as to obtain switch position change status data. If the switch position change status data indicates only one interval change, then extract the coil closing time and current peak value corresponding to the current change waveform data to obtain the action characteristic data. By combining the output action status data and the switch position change status data, the interval from the start time of the output relay action to the end time of the switch position change is calculated to obtain the action time data. The waveform similarity data, motion characteristic data, and motion time data are compared to see if they exceed the normal range threshold. If so, an anomaly assessment result is generated, and an early warning signal is sent.

2. The method for monitoring the switching characteristics of the power distribution switchgear based on the operation power supply monitoring according to claim 1, characterized in that, The steps of processing the current data to determine whether the current change rate exceeds a preset abrupt change threshold, and activating the waveform recording module when the current change rate exceeds the preset abrupt change threshold to obtain the waveform recording module activation signal include: Perform a first-order difference operation on the current data to obtain the rate of change of current; The current change rate is compared with the preset mutation threshold. When the absolute value of the current change rate is greater than the preset mutation threshold, the waveform recording module is activated to obtain the waveform recording module activation signal. If the rate of change of current does not exceed the threshold, then return to the step of monitoring the operating power supply current of the distribution network switchgear to obtain the corresponding current data.

3. The method for monitoring the switching characteristics of the power distribution switchgear based on the operation power supply monitoring according to claim 1, characterized in that, The step of checking whether the current surge waveform data conforms to the basic waveform characteristics of opening and closing, and obtaining waveform characteristic analysis results data, includes: To identify whether the current abrupt waveform data has at least one rising edge and at least one falling edge, in order to obtain edge detection result data; If the edge detection result data indicates the presence of both a rising edge and a falling edge, the time interval between the start point of the rising edge and the end point of the falling edge is calculated to obtain the temporary coil closing time data. The maximum current value of the current change waveform data is detected. If the maximum current value is greater than the preset amplitude threshold, waveform feature analysis result data that conforms to the basic waveform characteristics of opening and closing is generated. If the rising or falling edge cannot be identified, or the maximum current value is insufficient, the current change waveform data is discarded.

4. The method for monitoring the switching characteristics of the power distribution switchgear based on the operation power supply monitoring according to claim 3, characterized in that, The step of identifying whether the current abrupt waveform data has at least one rising edge and at least one falling edge to obtain edge detection result data includes: Calculate the first derivative of the current abrupt change waveform data to obtain gradient sequence data; Scan the gradient sequence data to find the point where the gradient changes from negative to positive as the starting point of the rising edge and the point where it changes from positive to negative as the ending point of the falling edge, so as to obtain edge point data. If no edge point data can be found, the output will be "No Features".

5. The method for monitoring the opening and closing characteristics of distribution network switchgear based on operating power supply monitoring as described in claim 1, characterized in that, The step of calculating the similarity value between the current surge waveform data and the reference waveform in the historical opening and closing waveform database to obtain waveform similarity data includes: The current sudden change waveform data is normalized to eliminate amplitude deviation and obtain standardized waveform data; Calculate the correlation coefficient between the standardized waveform data and the reference waveforms of the same type in the historical opening and closing waveform database to obtain the initial similarity data; If the initial similarity value is lower than the preset similarity threshold, the user will be prompted to verify whether the operation was performed locally through a manual confirmation mechanism in order to obtain manual feedback data. The waveform similarity data is updated based on manually fed-in data or correlation coefficients that are higher than a preset similarity threshold.

6. The method for monitoring the opening and closing characteristics of distribution network switchgear based on operating power supply monitoring as described in claim 1, characterized in that, The step of querying whether there is only one interval of switch position change signal within the second preset time window after the waveform recording module start signal is generated, in order to obtain switch position change status data, includes: Scan all intervals of switch position signal records within the second preset time window to obtain position count data; If the displacement count data is greater than 1, return to the step of monitoring the operating power supply current of the distribution network switchgear to obtain the corresponding current data; If the change count data is 0, check if the output relay was activated before the waveform recording started. If not, discard the current current change waveform data.

7. The method for monitoring the opening and closing characteristics of distribution network switchgear based on operating power supply monitoring as described in claim 1, characterized in that, The step of extracting the coil closing time and current peak value corresponding to the current change waveform data to obtain the operating characteristic data includes: Analyze the current change waveform data to find the point of maximum current to obtain the current peak data; Based on the rising edge start point and the falling edge end point, define the closing time period to obtain the temporary coil closing time; If the temporary coil closing time exceeds the historical normal range, an anomaly indication is generated by combining the current peak data, which is then used as the action characteristic data.

8. The method for monitoring the opening and closing characteristics of distribution network switchgear based on operating power supply monitoring as described in claim 1, characterized in that, The step of combining the output action status data and the switch position change status data to calculate the interval from the start time of the output relay action to the end time of the switch position change, in order to obtain the action time data, includes: Obtain the action start timestamp from the exit action status data and the change end timestamp from the switch position change status data to obtain timestamp pair data; Calculate the time difference between the timestamp pair formed by the start timestamp of the action and the end timestamp of the displacement to obtain action time data; If the time difference between the timestamp pairs exceeds a preset time threshold, it is marked as a delay anomaly and integrated into the action time data.

9. The method for monitoring the opening and closing characteristics of distribution network switchgear based on operating power supply monitoring as described in claim 1, characterized in that, The step of comparing whether the waveform similarity data, motion characteristic data, and motion time data exceed the normal range threshold, and if so, generating anomaly assessment result data and sending an early warning signal includes: The waveform similarity data is compared with a preset first threshold. If it is lower than the preset first threshold, a waveform anomaly marker is generated. The coil closing time and current peak value in the action characteristic data are compared with a preset second threshold and a preset third threshold, respectively. If they exceed the preset second threshold or the preset third threshold, an abnormal characteristic mark is generated. The action time data is compared with a preset fourth threshold. If it exceeds the preset fourth threshold, a time anomaly marker is generated. If at least one of the waveform anomaly marker, characteristic anomaly marker, and time anomaly marker exists, the anomaly assessment result data is generated and an early warning signal is sent.

10. A monitoring system for the opening and closing characteristics of a distribution network switchgear based on operating power supply monitoring, characterized in that, The system includes: a memory, a processor, and a power supply monitoring program for monitoring the opening and closing characteristics of a distribution network switchgear based on operating power supply monitoring, which is stored in the memory and can run on the processor. The power supply monitoring program for monitoring the opening and closing characteristics of a distribution network switchgear based on operating power supply monitoring is configured to implement the steps of the power supply monitoring method for monitoring the opening and closing characteristics of a distribution network switchgear based on operating power supply monitoring as described in any one of claims 1 to 9.

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