A voltage monitoring method and system for determining the open or closed state of a switch
By collecting electrical and mechanical data of switching equipment, constructing joint transition energy curves and synchronization deviation parameters, and dynamically adjusting weights, the reliability problem of switch opening and closing status monitoring methods under complex operating conditions is solved, achieving highly accurate and robust status judgment.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- HUNAN DONGDE ZIGUANG ELECTRIC CO LTD
- Filing Date
- 2026-04-27
- Publication Date
- 2026-05-29
Smart Images
Figure CN122109807A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of electrical equipment monitoring technology, specifically to a voltage monitoring method and system for determining the open / closed state of a switch. Background Technology
[0002] The safe and stable operation of the power system is the core guarantee of power supply. As the key core equipment in the power system for realizing circuit opening and closing and fault isolation, the accurate monitoring of the switching status of the switching equipment plays a vital role in the operation control, fault early warning and accident handling of the power grid. The reliability and accuracy of the relevant monitoring methods are directly related to the operational safety of the entire power system.
[0003] For a long time, the power industry has mainly relied on two methods to determine the status of switches: one is to monitor the voltage amplitude at the switch break point and determine whether the switch is closed by setting a fixed voltage threshold; the other is to directly read the on / off signal of the auxiliary contacts of the switch body and use it as the direct basis for the open / closed status.
[0004] With the expansion of power grid scale and the increasing complexity of operating conditions, single electrical quantity monitoring is highly susceptible to interference from system harmonics, load fluctuations, and transient processes, leading to false trips or failures to trip based on fixed thresholds. Purely mechanical auxiliary contact schemes suffer from inherent defects such as increased contact resistance, mechanical jamming, and auxiliary circuit disconnections, making it difficult to guarantee the reliability of the signal itself. Currently, in practical engineering applications, switch opening and closing status monitoring methods are affected by various factors such as system noise interference, load fluctuations, and changes in equipment performance, making it difficult to consistently and stably achieve accurate judgment of switch opening and closing status. The reliability and adaptability of the monitoring results are insufficient, failing to meet the high reliability requirements of power systems for switchgear status monitoring under complex operating conditions. Summary of the Invention
[0005] To address the technical problem of insufficient reliability of existing switch open / closed state monitoring methods under complex operating conditions, the present invention aims to provide a voltage monitoring method and system for determining switch open / closed states. The specific technical solution adopted is as follows: Firstly, a voltage monitoring method for determining the open / closed state of a switch is provided. This method includes: collecting electrical and mechanical quantity data of the switchgear during operation; responding to a control command, determining a reference event time for a state transition of the switchgear based on the electrical quantity data, and determining the desired state corresponding to the control command based on the type of control command; determining a synchronization deviation parameter for each data source based on monitoring data from each data source within a time period containing the reference event time and corresponding to the data source type, the synchronization deviation parameter being used to characterize the data source response reliability; determining a dynamic weight corresponding to the current state transition event for each data source based on the synchronization deviation parameter of the current state transition event and the synchronization deviation parameter of historical state transition events; and determining a fusion confidence level characterizing the switchgear being in the desired state based on real-time monitoring data of each data source and the corresponding dynamic weight, and determining that the switchgear is in the desired state if the fusion confidence level is greater than a preset threshold.
[0006] In one possible design, in response to a control command, the reference event time for a state transition of the switching equipment is determined based on electrical quantity data. This includes: acquiring the command time when the switching equipment receives a control command for opening or closing; extracting switch break voltage data and load current data from the electrical quantity data based on the command time and a preset event monitoring window, where the preset event monitoring window is used to limit the search time range for state transition events; constructing a joint transition energy curve based on the switch break voltage data and load current data, where the joint transition energy curve is used to comprehensively characterize the coordinated change process of electric field energy and magnetic field energy; and determining the moment when the joint transition energy curve first exceeds a preset dynamic threshold and reaches a local peak within the preset event monitoring window as the reference event time.
[0007] In one possible design, the synchronization deviation parameter of each data source is determined based on the monitoring data of each data source within a time period that includes the baseline event time and corresponds to the data source type. This includes: for each data source, determining the preset diagnostic time window corresponding to the data source based on the baseline event time and the data source type; extracting the monitoring data of the data source within the preset diagnostic time window to determine the state change time corresponding to the data source; and determining the synchronization deviation parameter of the data source in the current state transition event based on the state change time corresponding to the data source, the baseline event time, and the state confirmation value of the data source after the baseline event time.
[0008] In one possible design, determining the state change time corresponding to the data source includes: when the data source is switch break voltage data, load current data, or mechanism motor current data, using a gradient peak detection algorithm within a sliding window, the moment when the gradient first exceeds a preset first multiple of the noise baseline and reaches a local peak is determined as the state change time corresponding to the data source; when the data source is a switch open / close position signal, the moment when the signal remains stable for more than a preset number of sampling periods after a steady-state transition is determined as the state change time corresponding to the data source; when the data source is an operating mechanism vibration signal, the moment when the preset frequency band energy first exceeds a preset second multiple of the background energy is determined as the state change time corresponding to the data source.
[0009] In one possible design, the synchronization deviation parameter of the data source in the current state transition event is determined based on the state change time corresponding to the data source, the reference event time, and the state confirmation value of the data source after the reference event time. This includes: determining the time deviation component based on the absolute time difference between the state change time corresponding to the data source and the reference event time, and the physical response limit duration corresponding to the data source; determining the state deviation component based on the absolute difference between the state confirmation value and the desired state value, and the preset state change amount, where the desired state value is a preset value corresponding to the desired state, and the preset state change amount is the maximum possible change amount corresponding to the data source; and determining the synchronization deviation parameter based on the preset balance weight coefficient, the time deviation component, and the state deviation component.
[0010] In one possible design, the dynamic weight corresponding to each data source in the current state transition event is determined based on the synchronization deviation parameters of each data source in the current state transition event and the synchronization deviation parameters of historical state transition events. This includes: for each data source, determining a long-term reliability index based on the synchronization deviation parameters of the data source in the current state transition event and the synchronization deviation parameters of historical state transition events. The long-term reliability index is used to characterize the long-term synchronization and response stability of the data source during the state transition process; and determining the dynamic weight corresponding to each data source in the current state transition event based on the long-term reliability index of each data source.
[0011] In one possible design, the long-term reliability index of the data source is determined based on the synchronization deviation parameters of the data source in the current state transition event and the synchronization deviation parameters of the historical state transition events. This includes: updating the historical average level of the data source using an exponentially weighted moving average algorithm based on the synchronization deviation parameter sequence of the data source; and updating the historical long-term reliability index of the data source based on the synchronization deviation parameters of the data source in the current state transition event and the historical average level, thereby obtaining the long-term reliability index of the data source.
[0012] In one possible design, a fusion confidence level is determined based on the real-time monitoring data of each data source and the corresponding dynamic weights. This includes: converting the real-time monitoring data of each data source into a status indication value with the same value range, whereby the status indication value is used to characterize the probability that the real-time monitoring data of the data source indicates that the switchgear is in the desired state; and determining the fusion confidence level based on the status indication value of each data source and the corresponding dynamic weights.
[0013] In one possible design, electrical quantity data includes switch break voltage data and load current data, while mechanical quantity data includes switch open / close position signals, operating mechanism vibration signals, and mechanism motor current data. The acquisition of electrical and mechanical quantity data during the operation of the switchgear includes: acquiring switch break voltage data through voltage transformers installed on both sides of the switchgear break; acquiring load current data through current transformers installed on the load side; acquiring switch open / close position signals through auxiliary contacts on the switchgear body; acquiring operating mechanism vibration signals through vibration sensors installed on the operating mechanism; and acquiring mechanism motor current data through mechanism motor current sensors.
[0014] Secondly, a voltage monitoring system for determining the open / closed state of a switch is provided, comprising: a data acquisition unit for acquiring electrical and mechanical quantity data of the switchgear during operation; an event analysis unit for responding to control commands, determining the reference event time of a state transition of the switchgear based on the electrical quantity data, and determining the desired state corresponding to the control command based on the type of control command; a synchronization analysis unit for determining the synchronization deviation parameter of each data source based on monitoring data within a time period containing the reference event time and corresponding to the data source type, the synchronization deviation parameter being used to characterize the reliability of the data source response; a weight determination unit for determining the dynamic weight corresponding to each data source in the current state transition event based on the synchronization deviation parameter of each data source in the current state transition event and the synchronization deviation parameter of historical state transition events; and a state judgment unit for determining a fusion confidence level characterizing the switchgear being in the desired state based on the real-time monitoring data of each data source and the corresponding dynamic weight, and determining that the switchgear is in the desired state if the fusion confidence level is greater than a preset threshold.
[0015] The present invention has the following beneficial effects: In the voltage monitoring method for judging the opening and closing state of a switch provided by this invention, firstly, multi-dimensional data, including electrical quantities (switch contact voltage, load current) and mechanical quantities (switch opening and closing position signals, operating mechanism vibration signals, and mechanism motor current), is collected to lay a rich data foundation for comprehensively perceiving the true state of the switchgear. Then, in response to control commands, the baseline event time for state transitions is accurately determined based on the electrical quantity data, and the desired state is clarified according to the command type, providing a unified time anchor and state benchmark for all subsequent analyses. Based on this, a synchronization deviation parameter is constructed for the response performance of each data source before and after the baseline event time to quantify the reliability of its single-event response, thus transforming the abstract concept of "reliability" into a calculable and comparable numerical indicator. Subsequently, by fusing the synchronization deviation parameters of the current event and historical events, the weight of each data source is dynamically determined, enabling the system to adaptively adjust its weight based on the long-term performance of the data source, effectively avoiding misjudgments caused by fixed weights when the equipment ages or operating conditions change. Finally, the real-time monitoring data of each data source is combined with the dynamic weights to calculate the fusion confidence level and compare it with a threshold to output the final state judgment. This invention realizes a complete closed loop from multi-source data acquisition, accurate event identification, reliability quantification assessment to adaptive weight fusion, enabling the monitoring system to always tend to trust data sources with more reliable historical performance under complex conditions such as noise interference, signal conflict, and equipment performance degradation, significantly improving the overall accuracy, robustness and adaptability to complex working conditions of status judgment. Attached Figure Description
[0016] To more clearly illustrate the technical solutions and advantages in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.
[0017] Figure 1 A schematic diagram of a voltage monitoring system for determining the open / closed state of a switch, provided in an embodiment of the present invention; Figure 2 This is a schematic flowchart of a voltage monitoring method for determining the open / closed state of a switch, provided in one embodiment of the present invention. Detailed Implementation
[0018] To further illustrate the technical means and effects adopted by the present invention to achieve its intended purpose, the following, in conjunction with the accompanying drawings and preferred embodiments, details the specific implementation, structure, features, and effects of a voltage monitoring method and system for determining the on / off state of a switch according to the present invention. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. Furthermore, specific features, structures, or characteristics in one or more embodiments can be combined in any suitable form.
[0019] In embodiments of the present invention, the terms "exemplary" or "for example" are used to indicate that something is an example, illustration, or description. Any embodiment or design described as "exemplary" or "for example" in embodiments of the present invention should not be construed as being more preferred or advantageous than other embodiments or designs. Specifically, the use of the terms "exemplary" or "for example" is intended to present the relevant concepts in a specific manner.
[0020] In the description of this invention, unless otherwise stated, " / " means "or". For example, A / B can mean A or B. The term "and / or" in this document is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A alone, A and B simultaneously, and B alone. Furthermore, "at least one" and "more than one" refer to two or more. The terms "first," "second," etc., do not limit the quantity or order of execution, and "first," "second," etc., do not necessarily imply differences.
[0021] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains.
[0022] The following description, in conjunction with the accompanying drawings, details the specific scheme of a voltage monitoring method and system for determining the open / closed state of a switch provided by the present invention.
[0023] Please see Figure 1 The diagram illustrates a voltage monitoring system for determining the open / closed state of a switch, according to an embodiment of the present invention. Figure 1 As shown, the voltage monitoring system 10 for determining the open / closed state of a switch includes a data acquisition unit 11, an event analysis unit 12, a synchronization analysis unit 13, a weight determination unit 14, and a state determination unit 15.
[0024] The data acquisition unit 11 is used to collect electrical and mechanical data of the switchgear during operation.
[0025] The electrical data includes switch break voltage data and load current data, while the mechanical data includes switch open / close position signals, operating mechanism vibration signals, and mechanism motor current data.
[0026] In some embodiments, the data acquisition unit 11 acquires switch break voltage data in real time through voltage transformers installed on both sides of the switch equipment break, with a sampling rate of not less than 10kHz; acquires load current data in real time through current transformers installed on the load side; directly acquires switch open / close position signals through auxiliary contacts of the switch equipment body; acquires high-frequency operating mechanism vibration signals through vibration sensors installed on the operating mechanism, with a sampling rate that can be set to 50kHz; and acquires mechanism motor current data reflecting the operating mechanism's operation process through a mechanism motor current sensor.
[0027] The event analysis unit 12 is used to respond to control commands, determine the reference event time of the state transition of the switching equipment based on electrical quantity data, and determine the expected state corresponding to the control command based on the type of control command.
[0028] In some embodiments, the event analysis unit 12 first receives an externally issued control command (opening or closing command) and obtains the command time when the switchgear receives the control command. Then, based on the command time and a preset event monitoring window, it extracts the break voltage change rate and load current amplitude from the received electrical quantity data. A joint transition energy curve is then constructed based on the break voltage change rate and load current amplitude. Finally, the moment when this curve first exceeds a preset dynamic threshold and reaches a local peak within the preset event monitoring window is determined as the reference event time when the switchgear undergoes a state transition. Furthermore, the event analysis unit 12 is also used to determine and output the "desired state" corresponding to the operation based on the type of control command (opening or closing) (e.g., an opening command corresponds to a desired switch being in an open state, and a closing command corresponds to a desired switch being in a closed state).
[0029] The synchronization analysis unit 13 is used to determine the synchronization deviation parameter of each data source based on the monitoring data of each data source in the time period that includes the benchmark event time and corresponds to the data source type. The synchronization deviation parameter is used to characterize the data source response reliability.
[0030] In some embodiments, the synchronization analysis unit 13 first determines the corresponding preset diagnostic time window for each data source based on the reference event time and the type of data source, then extracts the monitoring data of the data source within the preset diagnostic time window, and uses the corresponding feature extraction method to determine the state change time of the data source in combination with the data source type; then, based on the state change time of the data source, the reference event time, and the state confirmation value of the data source after the reference event time, it performs quantitative analysis from two dimensions: time deviation and state deviation. First, it determines the time deviation component and the state deviation component respectively, and then combines the two components with the preset balance weight coefficient to finally determine the synchronization deviation parameter of each data source in the current state transition event.
[0031] The weight determination unit 14 is used to determine the dynamic weight of each data source in the current state transition event based on the synchronization deviation parameter of each data source in the current state transition event and the synchronization deviation parameter of the historical state transition events.
[0032] In some embodiments, the weight determination unit 14 first constructs a synchronization deviation parameter sequence for each data source based on the synchronization deviation parameters of its current and historical state transition events, updates the historical average level of the data source using an exponentially weighted moving average algorithm, and then updates and determines the long-term reliability index of the data source by combining the current synchronization deviation parameter and the historical average level. This index is used to characterize the long-term synchronization and response stability of the data source during the state transition process. Subsequently, based on the long-term reliability index of all data sources participating in the state judgment, normalization processing is performed to calculate and finally determine the dynamic weight of each data source corresponding to the current state transition event.
[0033] The state judgment unit 15 is used to determine the fusion confidence level, which characterizes the switchgear as being in the desired state, based on the real-time monitoring data of each data source and the corresponding dynamic weight, and to determine that the switchgear is in the desired state if the fusion confidence level is greater than a preset threshold.
[0034] In some embodiments, the state determination unit 15 first converts the real-time monitoring data of each data source into a state indication value with the same value range. This indication value is used to characterize the probability that the real-time monitoring data of the data source indicates that the switch is in the desired state. Then, a weighted fusion calculation is performed based on the state indication value of each data source and the corresponding dynamic weight to determine the fusion confidence level used to characterize that the switch is in the desired state. Finally, the fusion confidence level is compared with a preset threshold. If the fusion confidence level is greater than the preset threshold, it is determined that the switch is in the desired state, thus completing the entire monitoring and determination process of the switch's open / closed state.
[0035] Please see Figure 2The diagram illustrates a voltage monitoring method for determining the open / closed state of a switch according to an embodiment of the present invention, including the following steps S201-S205.
[0036] S201. Collect electrical and mechanical data of switchgear during operation.
[0037] The electrical data includes switch break voltage data and load current data, while the mechanical data includes switch open / close position signals, operating mechanism vibration signals, and mechanism motor current data.
[0038] As one possible approach to acquiring electrical quantity data, voltage transformers are installed on both sides of the switchgear break to collect the voltage signal at the break in real time. The collected voltage signal is then converted from analog to digital to form time-varying voltage data of the switchgear break. , The voltage amplitude at the switch break point varies with the real-time acquisition time. A continuous numerical sequence of changes is used to characterize the changes in electric field energy across the break in the switchgear. Considering the transient high-frequency components that may occur during the switching process, the sampling rate of the voltage transformer is set to no less than 10kHz to ensure a complete record of the rapid voltage change from rated value to zero (closing) or from zero to rated value (opening), providing accurate raw data for subsequent calculations of the break voltage change rate. Simultaneously, a current transformer is installed on the load side of the switchgear to acquire the load current signal in real time. The acquired current signal is then converted from analog to digital to form time-varying load current data. , The load current amplitude varies with the real-time acquisition time. A continuous sequence of changing values is used to characterize the changes in magnetic field energy in the load circuit of the switching equipment. The sampling rate of the current transformer is also set to no less than 10kHz to accurately reflect the amplitude change of the load current before and after switching operation.
[0039] For acquiring mechanical quantity data, the switch opening / closing position signal is directly obtained through the auxiliary contacts of the switchgear body. Since the auxiliary contacts may experience mechanical jitter during operation, hardware de-jitter filtering or software anti-jitter algorithms (e.g., continuous multiple sampling to confirm the status) can be used to eliminate the jitter effect and ensure that the read position signal accurately reflects the steady-state position of the switch. A vibration sensor (e.g., a piezoelectric accelerometer) is installed on the operating mechanism to acquire vibration signals in real time. Considering the high frequency of vibrations generated during the operation of the operating mechanism (typically containing components of several kHz to tens of kHz), the sampling rate of the vibration sensor is set to no less than 50 kHz to capture the complete mechanical vibration waveform. In addition, a motor current sensor (e.g., a Hall effect current sensor) is installed on the motor power supply line of the operating mechanism to acquire the motor current data in real time. This data reflects the complete process of motor start-up, operation, and stop. The sampling rate can be set to no less than 10 kHz as needed to accurately record the start-up peak, steady-state operation, and end time of the current waveform.
[0040] S202. In response to the control command, determine the reference event time of the state transition of the switching equipment based on the electrical quantity data, and determine the expected state corresponding to the control command based on the type of control command.
[0041] As one possible implementation, the switchgear receives externally issued tripping or closing control commands and simultaneously records the time when the control command is received. Set a preset event monitoring window based on the inherent mechanical operating characteristics of the switchgear. The default event monitoring window is set to the command time. to The time range is used to strictly limit the search time range for state transition events. Simultaneously, upon receiving a control command, the switch contact voltage data is immediately extracted from the electrical quantity data within the preset event monitoring window. and load current data ,in, This refers to the real-time data collection time.
[0042] It should be noted that the default event monitoring window The setting is based on the inherent mechanical operating characteristics of the switchgear. For example, for a medium-voltage vacuum circuit breaker, its typical closing time is 50–80 milliseconds, which can be... Set to 150 milliseconds to fully cover the entire operation process and eliminate noise interference during non-operation periods.
[0043] Furthermore, a joint transition energy curve is constructed based on the rate of change of the break voltage and the amplitude of the load current. This joint transition energy curve is used to comprehensively characterize the coordinated change process of electric field energy and magnetic field energy.
[0044] In some embodiments, the joint transition energy curve is defined by the following formula: In the formula, For every moment The changing joint transition energy curve; This represents the instantaneous amplitude of the voltage across the switch contacts. The rate of change of the break voltage is calculated by the central difference method from a high sampling rate voltage amplitude sequence and is used to characterize the intensity of electric field energy transfer or establishment between contacts. It represents the instantaneous amplitude of the load current, used to ensure that event detection is performed only under effective load conditions, avoiding false events caused by voltage changes but lacking magnetic field energy support during no-load or light-load commissioning.
[0045] Furthermore, the combined transition energy curve is monitored in real time within a preset event monitoring window. The change. The curve first exceeds the preset dynamic threshold. The moment when a local peak is reached is determined as the baseline event moment. Among them, the preset dynamic threshold It is not a fixed value, but rather based on the historical normal operation data of the switchgear. The statistical distribution of peak values is determined (e.g., the 30th percentile of all historical event energy peak values), thereby adapting to energy fluctuations under different load conditions and ensuring the sensitivity and robustness of event detection.
[0046] Furthermore, based on the type of control command received, the desired state for this operation is determined. If the control command is a tripping command, the desired state is that the switch is in the tripping state; if the control command is a closing command, the desired state is that the switch is in the closing state.
[0047] S203. Based on the monitoring data of each data source within the time period that includes the baseline event time and corresponds to the data source type, determine the synchronization deviation parameter of each data source.
[0048] Among them, the synchronization deviation parameter is used to characterize the reliability of the data source response.
[0049] As one possible implementation, firstly, for each data source... According to the data source Based on the physical characteristics and signal type, a preset diagnostic time window is set. This window is based on the reference event time. Centered on a central point, the diagnostic window extends forward and backward for a certain duration, with the specific range predetermined based on the response characteristics of the data source. For example, for switch break voltage data, load current data, and mechanism motor current data, whose transient processes are relatively fast, the diagnostic window can be set to... Milliseconds; for switch open / close position signals in mechanical quantities, due to mechanical transmission delay, the window can be set to... Milliseconds; for vibration signals of operating mechanisms, because they contain high-frequency oscillations and have a long duration, the window can be set to... millisecond.
[0050] Furthermore, data segments located within a preset diagnostic time window are extracted from the original monitoring data sequence of the data source, and the moment of state change in the data source itself is determined accordingly. For different types of data sources, appropriate detection algorithms are used to determine the corresponding state change times.
[0051] In some embodiments, if the data source is switch break voltage data, load current data, or mechanism motor current data (all of which are analog signals), a gradient peak detection algorithm within a sliding window is used. First, the gradient (i.e., the first-order difference) of the data sequence within the sliding window is calculated. When the gradient value first exceeds a preset first multiple (e.g., 3 times) of the noise baseline and reaches a local peak, this moment is determined as the state change moment of the data source. This is to effectively capture the starting point of rapid changes in analog signals and avoid noise interference.
[0052] If the data source is a switch open / close position signal (switching signal), an edge detection algorithm with debouncing is used. Since the auxiliary contact may experience mechanical jitter during operation, the system continuously samples the signal. When a signal transition is detected (e.g., from 0 to 1 or from 1 to 0) and the state after the transition is maintained for more than a preset number of sampling periods (e.g., three consecutive sampling points maintain the new state), the moment of the transition is determined as the state change moment. .
[0053] If the data source is the vibration signal of the operating mechanism (high-frequency oscillation signal), then the wavelet packet energy analysis algorithm is used. First, the vibration signal is decomposed using wavelet packets to extract the energy sequence of a preset frequency band (e.g., the frequency band corresponding to the characteristic frequency of the mechanism's movement). When the energy of this frequency band first exceeds a preset second multiple (e.g., 5 times) of the background energy, that moment is determined as the state change moment. This allows us to highlight the sudden increase in energy in a specific frequency band caused by mechanical action and effectively identify the starting point of vibration events.
[0054] At the moment of obtaining the state change of each data source Then, based on the state change time corresponding to the data source, the reference event time, and the state confirmation value of the data source after the reference event time, the synchronization deviation parameter of the data source in the current state transition event is determined.
[0055] In some embodiments, the formula for calculating the synchronization deviation parameter of the data source in the current state transition event is as follows: In the formula, As the baseline event time, For the first The state change times corresponding to each data source For the first The absolute time difference between the state change time corresponding to each data source and the base event time. For the first The physical response limit time corresponding to each data source It is a preset non-zero constant. For example, the typical mechanical transmission time of the auxiliary contact is 15 milliseconds, and the transmission delay of the voltage transformer is less than 1 millisecond. It is used to normalize the absolute time difference, so that data sources with different response speeds are comparable in the time dimension, and obtain the normalized first... The time deviation component of each data source in the current state transition event . The preset balance weight coefficient is used to adjust the relative importance of the time deviation component and the state deviation component. Its value can be optimized based on historical data, for example, 0.7, to further emphasize time synchronization. For the first The state confirmation value of each data source within a preset stable time window after the reference event time. The preset stable time window is located after the reference event time and is used to read the steady-state value after the transient process ends (for analog data, the state confirmation value is the average value of all sampling points within the preset stable time window; for digital signals, the state confirmation value is the steady-state logic value within the preset stable time window; for vibration signals, the state confirmation value is the energy integral or root mean square value within the preset stable time window). For the first The expected state value corresponding to each data source is the preset value corresponding to the expected state (open or closed). For example, if the expected state is closed, the expected state value is 0 (or close to 0) for switch break voltage data; the expected state value is 1 (indicating closed) for switch open / close position signal; and the expected state value is the rated current value (indicating on-load) for load current, etc. The absolute difference between the confirmed state value and the expected state value directly reflects the degree of deviation between the actual state and the theoretical expected state of the data source after the state transition. A larger difference indicates a less accurate state response from the data source (e.g., voltage not dropping to zero, auxiliary contacts not closing), and lower reliability. For the first The preset state change amount corresponding to the data source (the first) The maximum possible change corresponding to the nth data source (not zero) is used to normalize the absolute difference to a uniform dimension, resulting in the normalized nth... The state deviation components of each data source in the current state transition event . For the first The synchronization deviation parameter of a data source in the current state transition event is negatively correlated with the response reliability of the data source. That is, the smaller the synchronization deviation parameter, the better the response synchronization of the data source in the current state transition event and the higher the data reliability; conversely, the larger the value, the lower the reliability of the data source.
[0056] Based on this, the synchronization deviation parameters of all data sources in the current state transition event can be obtained.
[0057] Understandably, in this embodiment of the invention, preset diagnostic time windows matching the physical response characteristics of different types of electrical and mechanical data sources are set to ensure that the extraction of the state change moment of each data source is carried out within its most suitable time range. By extracting the original monitoring data from the diagnostic window and accurately determining the state change moment of each data source, the response start point of different physical quantities such as voltage, current, vibration, and position signals during the state transition process can be effectively captured. Furthermore, by combining the benchmark event moment and the state confirmation value to construct a synchronization deviation parameter, not only is the synchronization degree between the data source and the benchmark event in the time dimension considered, but the state confirmation value is also used to quantify whether it accurately reaches the expected state after the event, thereby comprehensively reflecting the response accuracy and stability of the data source.
[0058] S204. Based on the synchronization deviation parameters of each data source in the current state transition event and the synchronization deviation parameters of historical state transition events, determine the dynamic weight corresponding to each data source in the current state transition event.
[0059] As one possible implementation, firstly, for each data source... Maintaining data sources Sequence of synchronization deviation parameters in historical state transition events ,in This serves as an index for the current state transition events. Since the synchronicity deviation parameter of a single event may fluctuate due to random noise or transient disturbances, it cannot be directly used for weight allocation. Therefore, it is necessary to construct an indicator that can smooth historical fluctuations and reflect long-term trends. To this end, an exponentially weighted moving average method is used to calculate and update the historical average level for each data source. Its formula is expressed as ,in, For the first The historical average level of each data source after the previous state transition event. For the first Synchronization deviation parameters of each data source in the current state transition event. This is the forgetting factor, typically set between 0.85 and 0.95, for example, a value of 0.9, used to control the length of historical memory. The larger the value, the more persistent the impact of historical data on the current average level, and the stronger the smoothing effect. The smaller the value, the more sensitive it is to changes in recent events. Through this recursive update, It can dynamically track the slow changing trends of data source performance, such as response characteristic drift caused by equipment aging or environmental changes.
[0060] It should be noted that for newly commissioned equipment, the historical average level... It can be uniformly preset to 0.1.
[0061] In obtaining the first Historical average level of data sources Then, further analysis is performed based on the synchronization deviation parameters of the data source during the current state transition event. and historical average level Update the historical long-term reliability metrics of the data source to obtain the long-term reliability metrics of the data source. Long-term reliability indicators Used to characterize the long-term synchronization and response stability of a data source during state transitions.
[0062] In some embodiments, the formula for calculating the long-term reliability metric of the data source is as follows: In the formula, For the first The historical long-term reliability metric of the data source, i.e., the first The long-term reliability metrics determined by each data source after the previous state transition event; This is a smoothing factor, typically set between 0.8 and 0.9, for example, a value of 0.85, used to control the speed of indicator updates. The larger the value, the greater the impact of historical reliability on current indicators, and the smoother the indicator changes. The smaller the value, the more significant the impact of the current event's performance on the indicator. For the first Synchronization deviation parameters of each data source in the current state transition event. For the first The historical average level of each data source It is a very small positive number, and an empirical value of 0.0001 can be taken to prevent the denominator from being zero. For the natural constant An exponential function with base 0. For the first Long-term reliability metrics for each data source.
[0063] Among them, in the first Synchronization deviation parameters of each data source in the current state transition event Compared to the historical average level of the data source itself When it is small, the exponential term A value close to 1 indicates excellent performance and helps improve long-term reliability metrics; when When it is significantly greater than its historical average, the index term A rapid decay to near zero indicates an anomaly in performance, and long-term reliability metrics subsequently decline. Through use... As a normalized benchmark, it enables personalized evaluation of data sources with different characteristics. For data sources with inherently larger response latency and higher historical average levels, their tolerance for single fluctuations is correspondingly higher; while for high-precision, fast-response data sources, the same absolute deviation will lead to a more significant decrease in indicators. This recursive calculation method makes... It not only contains information about the performance of current events, but also inherits the memory of historical reliability, forming a long-term reliability profile that can evolve dynamically.
[0064] It should be noted that for newly commissioned equipment, long-term reliability indicators... It can be uniformly preset to 1.0.
[0065] Furthermore, long-term reliability metrics for each data source are obtained. Then, based on the long-term reliability metrics of each data source, the dynamic weight corresponding to the current state transition event of each data source is determined.
[0066] In some embodiments, the formula for calculating the dynamic weight corresponding to each data source in the current state transition event is as follows: In the formula, The total number of data sources participating in this status assessment. For the first Long-term reliability metrics for each data source, denominator The sum of long-term reliability metrics for all data sources (if If the value is zero, it will not participate in the calculation, and a fault message indicating a switchgear malfunction will be output. This normalization calculation makes the sum of the dynamic weights of all data sources equal to 1, forming a competitive weight allocation mechanism. Data sources with higher long-term reliability indicators receive greater weights and contribute more to subsequent fusion judgments; conversely, data sources with lower long-term reliability indicators have their weights automatically suppressed, thereby reducing the interference of their unreliable signals on the final judgment.
[0067] Understandably, in this embodiment of the invention, a correlation analysis is first established between the synchronization deviation parameter of the current state transition event and the historical event sequence for each data source. By introducing the core concept of long-term reliability index, the instantaneous performance of a single event is organically integrated with historical performance. This allows the reliability assessment of the data source to respond promptly to recent performance changes while retaining the memory of its long-term stability, effectively avoiding drastic fluctuations in weights caused by single abnormal disturbances. Furthermore, the long-term reliability index of each data source is normalized to form dynamic weights. This gives data sources with more stable historical performance and more accurate responses higher weight in the fusion judgment, while the weight of data sources with lower long-term reliability is automatically suppressed. Through this mechanism, the performance degradation of each data source caused by equipment aging, environmental changes, or changes in operating conditions can be adaptively tracked. In the event of signal conflicts or abnormal interference, the system automatically tends to trust more reliable data sources, significantly enhancing the robustness and accuracy of multi-source information fusion. It also avoids the limitations of manual intervention and fixed threshold settings, possessing autonomous decision-making and adaptive capabilities.
[0068] S205. Based on the real-time monitoring data of each data source and the corresponding dynamic weight, determine the fusion confidence level used to characterize the switchgear being in the desired state, and determine that the switchgear is in the desired state if the fusion confidence level is greater than a preset threshold.
[0069] As one possible approach, the current real-time monitoring data of each data source is first acquired, including switch break voltage data, load current data, switch open / close position signals, operating mechanism vibration signals, and mechanism motor current data.
[0070] It should be noted that real-time monitoring data are raw sampled values with different physical dimensions and numerical ranges, making direct weighted fusion impossible. Therefore, before performing fusion calculations, these heterogeneous data need to be converted into comparable state indication values with uniform dimensions.
[0071] In some embodiments, for each data source According to the data source The signal type and physical meaning, the data source Real-time monitoring data Mapped to a state indicator value in the range [0,1]. The magnitude of this value represents the probability that the data source indicates the switching device is in the desired state.
[0072] For example, if the desired state is closed, then for switch contact voltage data, the lower the voltage value (closer to 0), the closer the status indication value is to 1. For switch open / close position signals, if the signal is 1 (indicating closed), the status indication value is 1; if it is 0, the status indication value is 0. For load current data, the corresponding probability value can be determined based on whether the current reaches the rated load range. For vibration signals and motor current data, the status indication value can be converted through a preset matching algorithm based on whether their waveform characteristics match the characteristics of historical closing operations.
[0073] If the desired state is open, then for the switch contact voltage data, after successful opening, the contact voltage should recover to near the system's rated voltage. Therefore, the higher the voltage value (the closer to the rated voltage), the closer the status indication value is to 1; the lower the voltage value (the closer to 0), the closer the status indication value is to 0. For the switch open / close position signal, after opening, it is expected that the auxiliary contact will be open, i.e., the signal is 0. Therefore, if the real-time position signal is 0, the status indication value is 1; if the signal is 1, the status indication value is 0. For the load current data, after opening, the load current should drop to 0 (or no-load current). Therefore, the status indication value can be determined in reverse based on the ratio of the real-time current value to the rated current. The lower the current value, the closer the status indication value is to 1; when the current value is close to the rated value, the status indication value is closer to 0. For vibration signals and motor current data, the status indication value can be converted into a preset matching algorithm based on whether its waveform characteristics match the characteristics of historical opening operations.
[0074] Furthermore, after obtaining the status indication value for each data source... Then, the fusion confidence level is determined based on the status indication value of each data source and the corresponding dynamic weight.
[0075] In some embodiments, the formula for determining the fusion confidence is as follows: In the formula, The total number of data sources participating in this fusion judgment; For the first Dynamic weights of each data source; For the first Status indicator values corresponding to each data source. Fusion confidence level. It is a dimensionless numerical value, ranging from [0,1], and its physical meaning is the confidence level of the integrated support from multi-source data for the switching equipment to be in the desired state. Since the dynamic weights are adaptively allocated based on the historical reliability of the data sources, the fused confidence level can more objectively reflect the true state, with reliable data sources contributing more and the influence of unreliable data sources being suppressed.
[0076] Finally, the confidence scores will be merged. With a preset threshold (For example This is compared with the previous one. The preset threshold can be optimized based on historical operating data and desired sensitivity and specificity requirements. If the switchgear successfully reaches the desired state (e.g., successful closing and in the closed state, or successful opening and in the open state), then it is determined that the switchgear has successfully reached the desired state (e.g., successful closing and in the closed state, or successful opening and in the open state); otherwise, if If the switchgear does not reach the expected state, it is determined that there may be a fault or abnormality.
[0077] In some embodiments, when When an abnormality occurs during the state switching of the output switching equipment, a fault alarm is triggered to prompt maintenance personnel to perform maintenance.
[0078] Understandably, in the voltage monitoring method for judging the switch opening and closing state provided in this embodiment of the invention, firstly, multi-dimensional data, including electrical quantities (switch open / close position signal, operating mechanism vibration signal, and mechanism motor current), is collected to lay a rich data foundation for comprehensively perceiving the real state of the switchgear. Then, in response to control commands, the baseline event time for state transition is accurately determined based on the electrical quantity data, and the desired state is clarified according to the command type, providing a unified time anchor and state benchmark for all subsequent analyses. Based on this, a synchronization deviation parameter is constructed for the response performance of each data source before and after the baseline event time to quantify the reliability of its single-event response, thereby transforming the abstract "reliability" into a calculable and comparable numerical indicator. Subsequently, by fusing the synchronization deviation parameters of the current event and historical events, the weight of each data source is dynamically determined, enabling the system to adaptively adjust its weight based on the long-term performance of the data source, effectively avoiding misjudgments caused by fixed weights when the equipment ages or operating conditions change. Finally, the real-time monitoring data of each data source is combined with the dynamic weights to calculate the fusion confidence level and compare it with a threshold to output the final state judgment. This invention realizes a complete closed loop from multi-source data acquisition, accurate event identification, reliability quantification assessment to adaptive weight fusion, enabling the monitoring system to always tend to trust data sources with more reliable historical performance under complex conditions such as noise interference, signal conflict, and equipment performance degradation, significantly improving the overall accuracy, robustness and adaptability to complex working conditions of status judgment.
[0079] It should be noted that the order of the above embodiments of the present invention is merely for descriptive purposes and does not represent the superiority or inferiority of the embodiments. The processes depicted in the accompanying drawings do not necessarily require a specific or sequential order to achieve the desired result. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.
[0080] The various embodiments in this specification are described in a progressive manner. The same or similar parts between the various embodiments can be referred to each other. Each embodiment focuses on describing the differences from other embodiments.
Claims
1. A voltage monitoring method for determining the open / closed state of a switch, characterized in that, The method includes: Collect electrical and mechanical data of switchgear during operation; In response to a control command, the reference event time for a state transition of the switching device is determined based on the electrical quantity data, and the desired state corresponding to the control command is determined based on the type of the control command. Based on the monitoring data of each data source within the time period that includes the benchmark event time and corresponds to the data source type, a synchronization deviation parameter is determined for each data source. The synchronization deviation parameter is used to characterize the data source response reliability. Based on the synchronization deviation parameters of each data source in the current state transition event and the synchronization deviation parameters of historical state transition events, determine the dynamic weight corresponding to each data source in the current state transition event. Based on the real-time monitoring data of each data source and the corresponding dynamic weight, a fusion confidence level is determined to characterize the switching device as being in the desired state. If the fusion confidence level is greater than a preset threshold, the switching device is determined to be in the desired state.
2. The voltage monitoring method for determining the switch open / closed state according to claim 1, characterized in that, In response to a control command, the reference event time for a state transition of the switching equipment is determined based on the electrical quantity data, including: The time when the switching device receives the control command for opening or closing is obtained; Based on the command time and the preset event monitoring window, switch break voltage data and load current data are extracted from the electrical quantity data. The preset event monitoring window is used to limit the search time range of state transition events. Based on the switch-off voltage data and the load current data, a joint transition energy curve is constructed. The joint transition energy curve is used to comprehensively characterize the coordinated change process of electric field energy and magnetic field energy. The moment when the joint transition energy curve first exceeds the preset dynamic threshold and reaches a local peak within the preset event monitoring window is determined as the reference event moment.
3. The voltage monitoring method for determining the switch open / closed state according to claim 1, characterized in that, Based on the monitoring data of each data source within the time period corresponding to the data source type and including the baseline event time, determine the synchronization deviation parameter for each data source, including: For each data source, a preset diagnostic time window is determined based on the baseline event time and the type of the data source. Extract monitoring data from the data source within the preset diagnostic time window, and determine the time of state change corresponding to the data source; Based on the state change time corresponding to the data source, the reference event time, and the state confirmation value of the data source after the reference event time, the synchronization deviation parameter of the data source in the current state transition event is determined.
4. The voltage monitoring method for determining the switch open / closed state according to claim 3, characterized in that, Determining the state change time corresponding to the data source includes: When the data source is switch break voltage data, load current data, or mechanism motor current data, a gradient peak detection algorithm within a sliding window is used to determine the moment when the gradient first exceeds the noise baseline by a preset first multiple and reaches a local peak as the state change moment corresponding to the data source. When the data source is a switch on / off position signal, the moment when the signal remains stable for more than a preset number of sampling periods after a steady-state transition is determined as the state change moment corresponding to the data source. When the data source is the vibration signal of the operating mechanism, the moment when the preset frequency band energy first exceeds the background energy by a preset second multiple is determined as the state change moment corresponding to the data source.
5. The voltage monitoring method for determining the switch open / closed state according to claim 3, characterized in that, Based on the state change time corresponding to the data source, the reference event time, and the state confirmation value of the data source after the reference event time, the synchronization deviation parameter of the data source in the current state transition event is determined, including: The time deviation component is determined based on the absolute time difference between the state change time corresponding to the data source and the baseline event time, as well as the physical response limit duration corresponding to the data source. Based on the absolute difference between the confirmed state value and the expected state value, and the preset state change amount, the state deviation component is determined. The expected state value is a preset value corresponding to the expected state, and the preset state change amount is the maximum possible change amount corresponding to the data source. The synchronization deviation parameter is determined based on the preset balance weight coefficient, the time deviation component, and the state deviation component.
6. The voltage monitoring method for determining the switch open / closed state according to claim 1, characterized in that, Based on the synchronization deviation parameters of each data source in the current state transition event and the synchronization deviation parameters of historical state transition events, determine the dynamic weight corresponding to each data source in the current state transition event, including: For each data source, a long-term reliability index is determined based on the synchronization deviation parameter of the data source in the current state transition event and the synchronization deviation parameter of the historical state transition events. The long-term reliability index is used to characterize the long-term synchronization and response stability of the data source during the state transition process. Based on the long-term reliability metrics of each data source, determine the dynamic weight corresponding to the current state transition event for each data source.
7. The voltage monitoring method for determining the switch open / closed state according to claim 6, characterized in that, Based on the synchronization deviation parameters of the data source in the current state transition event and the synchronization deviation parameters of historical state transition events, the long-term reliability indicators of the data source are determined, including: The historical average level of the data source is updated using an exponentially weighted moving average algorithm based on the synchronization deviation parameter sequence of the data source. Based on the synchronization deviation parameter of the data source in the current state transition event and the historical average level, the historical long-term reliability index of the data source is updated to obtain the long-term reliability index of the data source.
8. The voltage monitoring method for determining the switch open / closed state according to claim 1, characterized in that, Based on real-time monitoring data from each data source and its corresponding dynamic weights, a fusion confidence level is determined to characterize the switching device as being in the desired state, including: The real-time monitoring data from each data source is converted into a status indication value with the same range of values. The status indication value is used to characterize the probability that the real-time monitoring data from the data source indicates that the switching device is in the desired state. The fusion confidence level is determined based on the status indication value of each data source and its corresponding dynamic weight.
9. The voltage monitoring method for determining the switch open / closed state according to claim 1, characterized in that, The electrical quantity data includes switch contact voltage data and load current data; the mechanical quantity data includes switch open / close position signals, operating mechanism vibration signals, and mechanism motor current data. The electrical and mechanical quantity data collected during the operation of the switchgear includes: Voltage data of the switch break is collected by voltage transformers installed on both sides of the switch equipment break, and load current data is collected by current transformers installed on the load side. The switch opening / closing position signal is acquired through the auxiliary contacts of the switch body, the vibration signal of the operating mechanism is acquired through the vibration sensor installed on the operating mechanism, and the current data of the mechanism motor is acquired through the mechanism motor current sensor.
10. A voltage monitoring system for determining the open / closed state of a switch, characterized in that, include: The data acquisition unit is used to collect electrical and mechanical data of the switchgear during operation. The event analysis unit is used to respond to control commands, determine the reference event time of the state transition of the switching equipment based on the electrical quantity data, and determine the desired state corresponding to the control command based on the type of the control command. The synchronization analysis unit is used to determine the synchronization deviation parameter of each data source based on the monitoring data of each data source in the time period that includes the benchmark event time and corresponds to the data source type. The synchronization deviation parameter is used to characterize the data source response reliability. The weight determination unit is used to determine the dynamic weight of each data source in the current state transition event based on the synchronization deviation parameter of each data source in the current state transition event and the synchronization deviation parameter of the historical state transition events. The state determination unit is used to determine the fusion confidence level, which characterizes the switching device as being in the desired state, based on the real-time monitoring data of each data source and the corresponding dynamic weight, and to determine that the switching device is in the desired state if the fusion confidence level is greater than a preset threshold.