Method and system for diagnosing health state of switch cabinet

Abstract

The disclosure discloses a health state diagnosis method and system of a switch cabinet. The method comprises: acquiring electromechanical characteristic data through an acquisition module; generating a characteristic curve according to the electromechanical characteristic data, and extracting a characteristic value from the characteristic curve; judging whether the characteristic value satisfies a condition set in a data diagnosis model applicable to the characteristic value; and if yes, triggering display of diagnosis result information matched with the condition.

Description

Technical Field

[0001] This disclosure relates to the field of switchgear, and in particular to a method and system for diagnosing the health status of switchgear. Background Technology

[0002] Switchgear plays a crucial role in power system generation, transmission, distribution, and energy conversion, providing switching, control, and protection. Switchgear mainly consists of two parts: circuit breakers and the cabinet. With the rapid development of intelligent switchgear, networks and communications have become more flexible and sophisticated. Condition-based maintenance, centered on online monitoring, is gradually replacing traditional periodic maintenance, greatly improving power supply reliability.

[0003] However, most current online monitoring systems can only collect and organize monitoring data in a simple way, and cannot provide reliable diagnostic results or maintenance suggestions, nor can they accurately diagnose the true health status of the equipment. Summary of the Invention

[0004] To address the aforementioned technical problems, this disclosure provides a method and system for diagnosing the health status of switchgear.

[0005] This disclosure provides a method for diagnosing the health status of a switchgear, including:

[0006] Electromechanical characteristic data are acquired through the acquisition module;

[0007] A characteristic curve is generated based on the electromechanical characteristic data, and feature values ​​are extracted from the characteristic curve.

[0008] Determine whether the feature value meets the conditions applicable to the feature value setting in the data diagnostic model;

[0009] If the conditions are met, the diagnostic result information matching the conditions will be displayed.

[0010] Optionally, each condition set by the data diagnostic model is mapped to the corresponding diagnostic result information in the diagnostic strategy library, and the diagnostic result information includes fault phenomena, cause inferences, handling suggestions and / or fault codes.

[0011] Optionally, the conditions set in the data diagnostic model are determined based on the characteristic values ​​obtained from the switchgear's factory curve, historical data, or configuration parameters.

[0012] Optionally, the data diagnostic model is a set of multiple diagnostic formulas, each of which is used to diagnose the faults that have occurred and analyze the trend changes in the status of the internal components of the switchgear.

[0013] Each condition set in the data diagnostic model is determined by the corresponding diagnostic formula.

[0014] Optionally, the characteristic curve is the current-time curve of the closing trip unit. The characteristic times obtained from the current-time curve of the closing trip unit are T0, T1, T2, T3, T4, and T5. Among them, characteristic time T0 is the starting point of the operation, with T0 as the origin, and its value is 0; characteristic time T1 is the point where the trip unit current first decreases after T0, the peak of the first bulge on the curve; characteristic time T2 is the valley point of the trip unit current after T1; characteristic time T3 is the starting inflection point of the right peak of the trip unit current; characteristic time T4 is the ending inflection point of the right peak of the trip unit current; and characteristic time T5 is the end point of the operation.

[0015] The diagnostic formula used in the data diagnostic model to diagnose the status of the auxiliary switch is T5-T4>1.5(T50-T40), where T50 and T40 are calculated based on the factory curve.

[0016] The diagnostic result information mapped by the diagnostic formula in the diagnostic strategy library is as follows: Cause inference: Auxiliary switch ignition, auxiliary switch switching capability decreased; Handling suggestion: Pay attention, it is recommended to check the auxiliary switch and replace it if necessary.

[0017] Optionally, the closing time of the trip unit is t. 合 =T4+Δt 合 ;Δt 合 =t 断口 -t 辅助开关切换时间 , where Δt 合 The difference between contact closing and auxiliary switch closing; the data diagnostic model includes a closing diagnostic formula for judging the closing trend, the closing diagnostic formula being t 合 ≤μ-1.96σ or t 合 ≥μ+1.96σ, where μ and σ are the average and standard deviation of closing time analyzed from historical data;

[0018] The diagnostic information mapped by the closing diagnostic formula in the diagnostic strategy library is as follows: Cause inference: The closing time has a deteriorating trend; Handling suggestion: It is recommended to pay attention to the trend of action time change, and check the mechanism and transmission components during power outage.

[0019] Optionally, the step of acquiring electromechanical characteristic data through the acquisition module includes:

[0020] Analog signals of electromechanical characteristics are acquired through sensors;

[0021] Convert the analog signals of the electromechanical characteristics into digital signals;

[0022] The digital signal is transmitted to the aggregation node.

[0023] Optionally, the step of generating a characteristic curve based on the electromechanical characteristic data and extracting feature values ​​from the characteristic curve includes:

[0024] At the aggregation node, characteristic curves are generated based on electromechanical characteristic data using edge computing.

[0025] Extract feature values ​​from the characteristic curve;

[0026] The characteristic curves and eigenvalues ​​are saved in a predetermined format and then uploaded to a cloud platform or local computer after being converted into a predetermined communication protocol.

[0027] Optionally, the step of extracting feature values ​​from the characteristic curve includes:

[0028] Extract the curve feature values ​​of the characteristic curve at feature points, where the feature points are the start point, inflection point, vertex, valley point, or end point;

[0029] The characteristic values ​​that characterize the mechanical or electrical state of the corresponding components in the switch cabinet are calculated based on the curve characteristic values.

[0030] Optionally, the sensor is an angular displacement sensor, which collects the rotation angle of the main shaft of the switchgear. Before the step of generating a characteristic curve based on electromechanical characteristic data through edge computing at the convergence node, the method further includes:

[0031] The rotation angle is converted into stroke at the convergence node;

[0032] The formula for converting between angle and travel is:

[0033] x=(25.0*a^2+25.0*b^2-25.0*r1^2+25.0*r2^2-(25.0*b*(432964.0*b-6580.0*a*b+25.0*a^2*b-25.0*b*r1^2+25.0*b*r2^2+5.0*a*(0.04*(-25.0*a^2+6580.0*a-25.0*b^2+25.0*r1^2+50.0*r1*r2+25.0*r2^2-432964.0)*(25.0*a^2-6580.0*a+25.0*b^2-25.0*r1^2+50.0*r1*r2-25.0*r2^2+432964.0))^(1 / 2)+25.0*b^3-658.0*(0.04*(-25.0*a^2+6580.0*a-25.0*b^2+25.0*r1^2+50.0*r1*r2+25.0*r2^2-432964.0)*(25.0*a^2-6580.0*a+25.0*b^2-25.0*r1^2+50.0*r1*r2-25.0*r2^2+432964.0))^(1 / 2))) / (25.0*a^2-6580.0*a+25.0*b^2+432964.0)-432964.0) / (50.0*a-6580.0);

[0034] y=(0.5*(432964.0*b-6580.0*a*b+25.0*a^2*b-25.0*b*r1^2+25.0*b*r2^2+5.0*a*(0.04*(-25.0*a^2+6580.0*a-25.0*b^2+25.0*r1^2+50.0*r1*r2+25.0*r2^2-432964.0)*(25.0*a^2-6580.0*a+25.0*b^2-25.0*r1^2+50.0*r1*r2-25.0*r2^2+432964.0))^(1 / 2)+25.0*b^3-658.0*(0.04*(-25.0*a^2+6580.0*a-25.0*b^2+25.0*r1^2+50.0*r1*r2+25.0*r2^2-432964.0)*(25.0*a^2-6580.0*a+25.0*b^2-25.0*r1^2+50.0*r1*r2-25.0*r2^2+432964.0))^(1 / 2))) / (25.0*a^2-6580.0*a+25.0*b^2+432964.0);

[0035]

[0036] B = A - 32.73

[0037]

[0038] Where a and b are the coordinates of the intersection of the first and second links, r1 is the length of the first link, r2 is the length of the second link, A is the angle between the third link and the x-axis, B is A minus the starting angle, and S is the stroke of the fourth link; the first link is connected to the second link, the second link is connected to the third link, and the third link is connected to the fourth link, and both the first and fourth links are connected to the main shaft of the circuit breaker; the x-axis can be determined by the line connecting the fixed points of the first and fourth links.

[0039] Optionally, after the step of acquiring electromechanical characteristic data through the acquisition module and generating characteristic curves based on the electromechanical characteristic data, the method includes:

[0040] The characteristic curve is smoothed using Lagrange interpolation.

[0041] Optionally, the characteristic curves include the characteristic curves of the circuit breaker contact travel versus time, the characteristic curves of the current versus time of the closing trip unit, the characteristic curves of the current versus time of the three-phase main circuit, the characteristic curves of the current versus time of the circuit breaker energy storage motor, the characteristic curves of the current versus time of the circuit breaker chassis motor, and the characteristic curves of the current versus time of the grounding switch motor.

[0042] This disclosure also provides a health status diagnosis system for switchgear, comprising:

[0043] Switch cabinet;

[0044] The data acquisition module collects electromechanical characteristic data of the corresponding components inside the switch cabinet;

[0045] The data processing node is communicatively connected to the acquisition module, generates characteristic curves based on the electromechanical characteristic data, and extracts feature values ​​from the characteristic curves.

[0046] A computer device is communicatively connected to the data processing node. The computer device has a diagnostic analysis module, which is configured to determine whether the feature value meets the conditions applicable to the feature value in the data diagnostic model. If it does, it triggers the display of diagnostic result information that matches the conditions.

[0047] Optionally, the computer device further includes a diagnostic strategy library, which includes diagnostic result information mapped to the conditions set by the data diagnostic model. The diagnostic result information includes fault phenomena, cause inferences, handling suggestions, and / or fault codes.

[0048] Optionally, the computer device further includes a display device configured to display triggered diagnostic result information.

[0049] Optionally, the acquisition module includes a circuit breaker information acquisition module, a grounding switch motor current acquisition module, a cabinet partial discharge quantity acquisition module, and a cabinet temperature measurement acquisition module.

[0050] Optionally, the circuit breaker information acquisition module includes sensors such as angular displacement sensors, current sensors for tripping and opening currents, current sensors for three-phase main circuit currents, current sensors for energy storage motor currents, current sensors for chassis motor currents, and / or switch quantity monitoring sensors.

[0051] Optionally, the data processing node is a convergence node, and the convergence node uses edge computing for data processing.

[0052] Optionally, the conditions set in the data diagnostic model are determined based on the characteristic values ​​obtained from the switchgear's factory curve, historical data, or configuration parameters.

[0053] Optionally, the characteristic curve is the current-time curve of the closing trip unit. The characteristic times obtained from the current-time curve of the closing trip unit are T0, T1, T2, T3, T4, and T5. Among them, characteristic time T0 is the starting point of the operation, with T0 as the origin, and its value is 0; characteristic time T1 is the point where the trip unit current first decreases after T0, the peak of the first bulge on the curve; characteristic time T2 is the valley point of the trip unit current after T1; characteristic time T3 is the starting inflection point of the right peak of the trip unit current; characteristic time T4 is the ending inflection point of the right peak of the trip unit current; and characteristic time T5 is the end point of the operation.

[0054] The diagnostic formula used in the data diagnostic model to diagnose the status of the auxiliary switch is T5-T4>1.5(T50-T40), where T50 and T40 are calculated based on the factory curve.

[0055] The diagnostic result information mapped by the diagnostic formula in the diagnostic strategy library is as follows: Cause inference: Auxiliary switch ignition, auxiliary switch switching capability decreased; Handling suggestion: Pay attention, it is recommended to check the auxiliary switch and replace it if necessary.

[0056] Optionally, the closing time of the trip unit is t. 合 =T4+Δt 合 ;Δt 合 =t 断口 -t 辅助开关切换时间 , where Δt 合The difference between contact closing and auxiliary switch closing; the data diagnostic model includes a closing diagnostic formula for judging the closing trend, the closing diagnostic formula being t 合 ≤μ-1.96σ or t 合 ≥μ+1.96σ, where μ and σ are the average and standard deviation of closing time analyzed from historical data;

[0057] The diagnostic information mapped by the closing diagnostic formula in the diagnostic strategy library is as follows: Cause inference: The closing time has a deteriorating trend; Handling suggestion: It is recommended to pay attention to the trend of action time change, and check the mechanism and transmission components during power outage.

[0058] Optionally, the acquisition module includes:

[0059] The acquisition unit is used to acquire analog signals of electromechanical characteristics through sensors;

[0060] A conversion unit is used to convert the analog signals of the electromechanical characteristics into digital signals;

[0061] The first communication unit is used to transmit the digital signal to the aggregation node.

[0062] Optionally, the data processing node includes:

[0063] The curve generation unit is used to generate characteristic curves based on electromechanical characteristic data at the aggregation node through edge computing.

[0064] The feature extraction unit is used to extract feature values ​​from the characteristic curve;

[0065] The second communication unit is used to save the characteristic curves and feature values ​​in a predetermined format, and then upload them to the cloud platform or local computer after converting them into a predetermined communication protocol.

[0066] The technical solutions provided by the embodiments of this disclosure may include the following beneficial effects:

[0067] This disclosure provides a method for diagnosing the health status of a switchgear. The method includes the following steps: acquiring electromechanical characteristic data through a data acquisition module; generating a characteristic curve based on the electromechanical characteristic data and extracting feature values ​​from the characteristic curve; determining whether the feature values ​​meet the conditions set for the feature values ​​in a data diagnostic model; and if so, triggering the display of diagnostic result information matching the conditions. This method acquires electromechanical characteristic data of relevant components within the switchgear through a data acquisition module, generates a characteristic curve based on the electromechanical characteristic data, extracts feature values ​​from the characteristic curve, and determines whether the feature values ​​meet the corresponding conditions set in the data diagnostic model. These conditions are used to determine whether the corresponding feature values ​​are abnormal. If abnormal, it indicates that the component within the switchgear corresponding to the feature value may have a fault. This triggers the display of diagnostic result information matching the conditions, alerting relevant personnel that a fault may have occurred or has already occurred in the relevant component or equipment (e.g., an auxiliary switch). Thus, this method can accurately diagnose faults or potential faults in components within the switchgear and display the diagnostic results to relevant personnel, enabling them to take measures to eliminate or resolve potential faults.

[0068] It should be understood that the above general description and the following detailed description are merely exemplary and do not limit this disclosure. Attached Figure Description

[0069] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with the invention and, together with the description, serve to explain the principles of the invention.

[0070] Figure 1 This is a flowchart illustrating a health status diagnosis method for a switchgear according to an exemplary embodiment.

[0071] Figure 2 It is based on Figure 1 A detailed flowchart of step 110 of one embodiment is shown in the corresponding embodiment.

[0072] Figure 3 It is based on Figure 1 A detailed flowchart of step 120 of one embodiment is shown in the corresponding embodiment.

[0073] Figure 4 It is based on Figure 1 A detailed flowchart of step 123 of one embodiment is shown in the corresponding embodiment.

[0074] Figure 5 This is a schematic diagram of the current-time curve of the energy storage motor and the characteristic values ​​derived from the analysis of this curve.

[0075] Figure 6It is a schematic diagram of the contact travel-time curve of the switchgear, the current-time curve of the closing trip unit, and the characteristic values ​​analyzed from these two curves.

[0076] Figure 7 This is a schematic diagram illustrating the architecture of a health status diagnosis system for a switchgear according to an exemplary embodiment.

[0077] Figure 8 It is based on Figure 7 A block diagram of a data acquisition module according to one embodiment is shown in the corresponding example.

[0078] Figure 9 It is based on Figure 7 A block diagram of a data processing node in one embodiment is shown in the corresponding example.

[0079] Figure 10 This is an example block diagram illustrating a computer device according to an exemplary embodiment. Detailed Implementation

[0080] To further illustrate the principles and structure of the present invention, preferred embodiments of the present invention will now be described in detail with reference to the accompanying drawings.

[0081] This disclosure provides a method for diagnosing the health status of a switchgear, which can be a high-voltage, medium-voltage, or ring network switchgear. The switchgear includes components or equipment such as circuit breakers, grounding switches, tripping devices, closing devices, energy storage motors, and chassis motors.

[0082] Specifically, in combination Figure 1 As shown, the method includes:

[0083] Step 110: Acquire electromechanical characteristic data through the acquisition module.

[0084] The acquisition module is used to collect status information of multiple components inside the switchgear. There can be multiple acquisition modules, such as circuit breaker information acquisition module, grounding switch motor current acquisition module, partial discharge quantity acquisition module and temperature measurement acquisition module inside the cabinet.

[0085] Each acquisition module includes a sensor for monitoring the electromechanical status of internal components of the switchgear (e.g., circuit breakers, grounding switches) and a data acquisition device wired to the sensor. The data acquisition device includes a digital-to-analog converter and a first communication unit. This first communication unit may be a wireless communication unit.

[0086] Each acquisition module may include multiple sensors. For example, a circuit breaker information acquisition module may include one or more of the following: an angular displacement sensor, a current sensor for the trip unit current, a current sensor for the closing trip unit current, a current sensor for the three-phase main circuit current, a current sensor for the energy storage motor current, a current sensor for the chassis motor current, and a switch quantity monitoring sensor. Each sensor is used to detect the mechanical or electrical characteristics of the corresponding internal components. For example, an angular displacement sensor is connected to the main shaft of the circuit breaker to acquire the rotation angle of the main shaft. Another example is a current sensor for the closing trip unit current, installed in the circuit circuit of the closing trip unit to acquire the current of the closing trip unit. Yet another example is a switch quantity monitoring sensor connected to the open / close circuit alarm relay of the circuit breaker to acquire the open / close circuit alarm node signal.

[0087] The partial discharge acquisition module inside the cabinet includes a TEV sensor and an ultrasonic sensor, which are used to collect partial discharge status information inside the switch cabinet.

[0088] The cabinet temperature measurement and acquisition module includes multiple wireless measurement sensors used to collect the temperature at each connection point of the main circuit inside the switch cabinet.

[0089] Optionally, the accuracy of each sensor is higher than or equal to the requirements of the NB42044 standard.

[0090] Different sensors are installed for different detection components within the switchgear to collect different electromechanical characteristic data. This electromechanical characteristic data includes mechanical characteristic data, electrical characteristic data, or both. Mechanical characteristic data can include parameters such as stroke, displacement, speed, and switching quantity. Electrical characteristic data can include parameters such as current.

[0091] Combination Figure 2 As shown, step 110 above includes:

[0092] Step 111: Acquire analog signals of electromechanical characteristics through sensors;

[0093] Step 113: Convert the analog signals of electromechanical characteristics into digital signals;

[0094] Step 115: Transmit the digital signal to the aggregation node.

[0095] In step 111, the sensor acquires an analog signal for characteristic monitoring. In step S113, the analog signal is converted into a digital signal using an analog-to-digital converter (ADC). This ADC can be an AD7606 chip, which has a 16-bit sampling accuracy and a 200 ksps sampling rate to ensure the accuracy and speed of data conversion. In step 115, the digital signal is transmitted to the aggregation node through the first communication unit of the acquisition device. The acquired data is processed at the aggregation node. The communication between the acquisition device and the aggregation node can use 2.4G wireless communication to ensure data transmission speed and real-time performance.

[0096] Furthermore, prior to step 113, the method further includes:

[0097] The acquired analog signal is filtered out using a low-pass filter to remove high-frequency environmental interference.

[0098] Before the analog signal is converted into a digital signal, the analog signal acquired by the sensor is filtered to remove environmental interference and improve sampling accuracy.

[0099] Step 120: Generate characteristic curves based on electromechanical characteristic data, and extract feature values ​​from the characteristic curves.

[0100] Characteristic curves are curves generated by the acquisition module based on characteristic data over time. These curves can be the travel-time characteristic curve of circuit breaker contacts, the current-time characteristic curve of the opening and closing trip unit, the current-time characteristic curve of the three-phase main circuit, the current-time characteristic curve of the circuit breaker energy storage motor, the current-time characteristic curve of the circuit breaker chassis motor, or the current-time characteristic curve of the grounding switch motor. Each characteristic curve represents the change of the mechanical or electrical characteristics of the acquired object (e.g., circuit breaker contacts) over time.

[0101] Eigenvalues ​​are state variables extracted from characteristic curves that can be used to characterize the health status. For example, state variables such as total stroke, maximum stroke, minimum stroke, and closing speed are obtained from stroke-time curves. Eigenvalues ​​can be extracted directly from characteristic curves or calculated from eigenvalues ​​extracted directly from characteristic curves. For example, total stroke is extracted directly from stroke and time curves; while closing speed is calculated from eigenvalues ​​extracted directly from characteristic curves. Specifically, closing speed is the average speed from 6mm forward of the closing point to the closing point itself. The stroke and time from 6mm forward of the closing point to the closing point are extracted directly from the stroke-time curves, and the closing speed is calculated by dividing the extracted stroke by the time.

[0102] Step 120 is performed at the aggregation node, which is the connection point between the acquisition module and the external network. This aggregation node includes a processing unit capable of performing edge computing on the data uploaded by the acquisition module. Edge computing refers to moving the computation of applications, data, and services from the network's central node to logically edge nodes in the network. This completes the computation before the data is uploaded to the cloud platform or local computer, improving data transmission efficiency.

[0103] Edge computing includes analyzing data collected by sensors to generate characteristic curves, extracting feature values ​​from the characteristic curves, and calculating feature values ​​such as maximum, minimum, and time values.

[0104] Specifically, in combination Figure 3 As shown, step 120 includes:

[0105] Step 121: At the aggregation node, characteristic curves are generated based on electromechanical characteristic data using edge computing.

[0106] Step 123: Extract feature values ​​from the characteristic curve;

[0107] Step 125: Keep the characteristic curve and feature value data in a predetermined format, convert them into a predetermined communication protocol, and then upload them to the cloud platform or local computer.

[0108] Steps 121 to S125 described above are all completed at the aggregation node. After the aggregation node generates characteristic curves and extracts feature values ​​through edge computing, the characteristic curve data and extracted feature values ​​are saved in a predetermined format file, such as the standard COMTRAD. After being converted into a predetermined communication protocol, such as TCP / IP3, the data is uploaded to the cloud platform or local computer via 4G or a local area network (LAN). Maintaining the data in a predetermined format facilitates data parsing by third-party manufacturers. Converting to a predetermined communication protocol, such as TCP / IP3, greatly improves data transmission efficiency.

[0109] In one embodiment, the sensor of the acquisition module is an angular displacement sensor, which acquires the rotation angle of the main shaft of the switchgear. Before step 121, the above method further includes:

[0110] At the convergence node, the rotation angle is converted into stroke.

[0111] An angular displacement sensor is connected to the main shaft of the circuit breaker to collect the rotation angle of the main shaft. The first communication unit of the acquisition module uploads the collected rotation angle data to the aggregation node. At the aggregation node, the rotation angle is converted into the corresponding stroke using the stroke-angle conversion formula.

[0112] The conversion formula for the travel-angle conversion is as follows:

[0113] x=(25.0*a^2+25.0*b^2-25.0*r1^2+25.0*r2^2-(25.0*b*(432964.0*b-6580.0*a*b+25.0*a^2*b-25.0*b*r1^2+25.0*b*r2^2+5.0*a*(0.04*(-25.0*a^2+6580.0*a-25.0*b^2+25.0*r1^2+50.0*r1*r2+25.0*r2^2-432964.0)*(25.0*a^2-6580.0*a+25.0*b^2-25.0*r1^2+50.0*r1*r2-25.0*r2^2+432964.0))^(1 / 2)+25.0*b^3-658.0*(0.04*(-25.0*a^2+6580.0*a-25.0*b^2+25.0*r1^2+50.0*r1*r2+25.0*r2^2-432964.0)*(25.0*a^2-6580.0*a+25.0*b^2-25.0*r1^2+50.0*r1*r2-25.0*r2^2+432964.0))^(1 / 2))) / (25.0*a^2-6580.0*a+25.0*b^2+432964.0)-432964.0) / (50.0*a-6580.0);

[0114] y=(0.5*(432964.0*b-6580.0*a*b+25.0*a^2*b-25.0*b*r1^2+25.0*b*r2^2+5.0*a*(0.04*(-25.0*a^2+6580.0*a-25.0*b^2+25.0*r1^2+50.0*r1*r2+25.0*r2^2-432964.0)*(25.0*a^2-6580.0*a+25.0*b^2-25.0*r1^2+50.0*r1*r2-25.0*r2^2+432964.0))^(1 / 2)+25.0*b^3-658.0*(0.04*(-25.0*a^2+6580.0*a-25.0*b^2+25.0*r1^2+50.0*r1*r2+25.0*r2^2-432964.0)*(25.0*a^2-6580.0*a+25.0*b^2-25.0*r1^2+50.0*r1*r2-25.0*r2^2+432964.0))^(1 / 2))) / (25.0*a^2-6580.0*a+25.0*b^2+432964.0);

[0115]

[0116] B = A - 32.73

[0117]

[0118] Where a and b are the coordinates of the intersection point of the first and second links, r1 is the length of the first link, r2 is the length of the second link, A is the angle between the third link and the x-axis, B is A minus the initial angle, and S is the stroke of the fourth link. The first link is connected to the second link, the second link is connected to the third link, and the third link is connected to the fourth link. Both the first and fourth links are connected to the main shaft of the circuit breaker. The x-axis can be determined by the line connecting the fixed points of the first and fourth links.

[0119] Furthermore, combined with Figure 4 As shown, step 123 includes:

[0120] Step 1231: Extract the curve feature values ​​of the characteristic curve at feature points, where feature points are the start point, inflection point, vertex, valley point, or end point;

[0121] Step 1233: Calculate the characteristic values ​​that characterize the mechanical or electrical state of the corresponding components in the switch cabinet based on the curve characteristic values.

[0122] After the characteristic curve is generated, the characteristic points of the curve are identified. These characteristic points are points on the curve where there are obvious and typical fluctuations, such as the starting point, inflection point, peak, valley, or end point. Curve characteristic values ​​refer to features that can be directly read from the characteristic curve, such as total travel, maximum travel, and minimum travel. For characteristic values ​​that cannot be identified from the characteristic curve, they can be calculated from the curve characteristic values. For example, the closing speed is calculated by dividing the travel by the time; this closing speed is used to characterize the mechanical state of the circuit breaker contacts moving within the switchgear.

[0123] like Figure 5As shown, the characteristic values ​​that can be extracted from the current-time curve of the energy storage motor include energy storage time, peak starting current, stable current in the energy storage range, peak current in the energy storage range, and state quantities such as characteristic times T0, T1, T2, T3, T4, and T5. Among them, energy storage time refers to the energized time of the energy storage motor. Characteristic time T0: the starting point of the action. Characteristic time T1: the beginning of the stable current in the energy storage range after T0. Characteristic time T2: the end of the stable current in the energy storage range. Characteristic time T3: the peak time after T2. Characteristic time T4: the time when the current drops from T3 to the stable current in the energy storage range. Characteristic time T5: the end point of the action. Peak starting current: the maximum value of the energy storage motor current between T0 and T1. Stable current in the energy storage range: the stable value of the energy storage motor current after the first trough between T1 and T2. Peak current in the energy storage range: the maximum value of the energy storage motor current corresponding to time T3.

[0124] like Figure 6 As shown, the characteristic values ​​that can be extracted from the current-time curve of the closing trip unit include the minimum trip unit current (i.e., the minimum coil current), the maximum trip unit current (i.e., the maximum coil current), characteristic times T0, T1, T2, T3, T4, and T5, average operating current, and closing time. Specifically, characteristic time T0 is the starting point of the operation, with T0 as the origin and a value of 0. Characteristic time T1 is the point where the trip unit current first decreases after T0, i.e., the peak of the first spike. Characteristic time T2 is the valley point of the trip unit current after T1. Characteristic time T3 is the starting point of the right peak of the trip unit current. Characteristic time T4 is the ending point of the right peak of the trip unit current. Characteristic time T5 is the end point of the operation. Average operating current is the average value of the trip unit current from T1 to T4. Closing time is t. 合 =T4+Δt 合 , Δt 合 =t 断口 -t 辅助开关切换时间 Where Δt 合 This is the difference between the contact closing and the auxiliary switch closing.

[0125] like Figure 6 As shown, the feature values ​​that can be extracted from the current-time curve of the contact include maximum stroke, minimum stroke, total stroke, stationary position, sampling start position, sampling end position, operating stroke, and operating speed.

[0126] Furthermore, after step 120, the method of this disclosure further includes:

[0127] The characteristic curve is smoothed using Lagrange interpolation.

[0128] The diagnostic analysis module uses Lagrange interpolation on the generated characteristic curves, which makes the curves smoother and reduces errors caused by the sampling rate.

[0129] The Lagrange interpolation formula is:

[0130]

[0131] Among them, P n (x) is a Lagrange polynomial, y i This refers to the situation where the y-value of the i-th point is known, x1 to x2. n+1 It refers to the x-value of a known point, where x is the x-value of the interpolation point.

[0132] Step 140: Determine whether the feature values ​​meet the conditions applicable to feature value setting in the data diagnostic model.

[0133] Step 140 can be performed by the diagnostic analysis module of the cloud platform or the local computer. The diagnostic analysis module can be a software module, a hardware module, or a combination of both.

[0134] The data diagnostic model is a collection of multiple diagnostic formulas, each used to determine the health status of a specific component within the switchgear. For example, the formula T5-T4>1.5(T50-T40) is used to determine the switching capability of the auxiliary switch. Here, the characteristic value T5 is the calculated value at the end of the closing trip unit's operation, the characteristic value T4 is the inflection point at the right peak of the trip unit's current, and T50 and T40 are the factory-designed current versus time curves for the closing trip unit. (Closing diagnostic formula t) 合 ≤μ-1.96σ or t 合 ≥μ+1.96σ is used to determine whether the closing time of the trip unit shows a deterioration trend, where t 合 σ is the closing time of the trip unit, and μ and σ are the average and standard deviation of the closing time calculated based on historical data of the closing trip unit.

[0135] The conditions set in the data diagnostic model are determined based on characteristic values ​​obtained from the switchgear's factory curves, historical data, or configuration parameters. For example, T50 and T40 in the above formula are values ​​calculated based on the factory curves of the closing trip unit's current and time. As another example, μ and σ in the above formula are the average and standard deviation of the closing time calculated from historical data. Thus, the conditions set in the data diagnostic model can be adjusted and updated according to components manufactured by different companies to meet the different technical standards required by various manufacturers.

[0136] The diagnostic formulas in the data diagnostic model are derived from a large amount of experimental data and the experience of technical personnel. These formulas can diagnose existing faults, analyze the trend changes in the status of components within the corresponding cabinet to predict potential faults, and also predict the electrical and mechanical lifespan of the circuit breaker.

[0137] If the feature value extracted in step 120 satisfies the corresponding diagnostic formula in the data diagnostic model, then proceed to step 160. For example, if the feature value T5-T4 extracted in step 120 is 0.4ms, and the value of 1.5(T50-T40) obtained from the historical curve is 0.3, then the condition T5-T4>1.5(T50-T40) is satisfied, and step 160 is executed. Otherwise, proceed to step 180, and the process ends.

[0138] Step 160 triggers the display of diagnostic result information that matches the condition.

[0139] The diagnostic strategy library pre-stores multiple diagnostic results, each including a fault symptom, status characteristics, cause inference, remedial suggestions, and / or fault code. The diagnostic information is used to provide early warnings of potential faults, prompting relevant supervisory personnel to pay attention or take timely repair measures.

[0140] Each diagnostic result in the diagnostic strategy library is mapped to at least one condition set in the data diagnostic model; that is, each condition matches one diagnostic result. If the feature value extracted in step 120 satisfies the condition set by the corresponding diagnostic formula in the data diagnostic model, the diagnostic result corresponding to that condition will be displayed on the screen for relevant personnel to view. For example, if the condition T5-T4>1.5(T50-T40) is met, the diagnostic result mapped to that condition will be displayed, namely, "Cause inference: Auxiliary switch arcing, auxiliary switch switching capacity decreased; Handling suggestion: Pay attention, it is recommended to check the auxiliary switch, and replace it if necessary." Another example is if the closing diagnostic formula t... 合 ≤μ-1.96σ or t 合 If the condition ≥μ+1.96σ is met, the diagnostic result information corresponding to this condition will be displayed, namely, "Cause inference: There is a deterioration trend in the closing time; the handling suggestion is: pay attention to the trend of the change in the action time, and check the mechanism and transmission components during the power outage."

[0141] Another aspect of this disclosure provides a health status diagnosis system for switchgear, combined with Figure 7 As shown, the system includes a switch cabinet 11, a data acquisition module 12, a data processing node 13, and a computer device 14.

[0142] Switchgear 11 can be a medium-voltage switchgear, a high-voltage switchgear, or a ring network switchgear.

[0143] The acquisition module 12 is configured to collect electromechanical characteristic data of corresponding components within the switchgear. The acquisition module 12 includes sensors installed within the switchgear and a data acquisition device wired to these sensors. This data acquisition device is wirelessly connected to the data processing node 13. The acquisition module 12 includes a circuit breaker information acquisition module, a grounding switch motor current acquisition module, a cabinet partial discharge quantity acquisition module, and a cabinet temperature measurement acquisition module. The sensors in the acquisition module are independently installed and operate to collect multi-dimensional data, which is then integrated into the same diagnostic system, facilitating unified data analysis. Specifically, the circuit breaker information acquisition module includes sensors such as angular displacement sensors, current sensors for tripping and closing tripping, current sensors for three-phase main circuit current, current sensors for energy storage motor current, current sensors for chassis motor current, and switch quantity monitoring sensors. Each sensor is used to detect mechanical or electrical performance, such as displacement and current information.

[0144] Specifically, in combination Figure 8 As shown, the acquisition module 12 also includes:

[0145] The acquisition unit 122 is used to acquire analog signals of electromechanical characteristics through sensors;

[0146] The conversion unit 124 is used to convert analog signals of electromechanical characteristics into digital signals;

[0147] The first communication unit 126 is used to transmit digital signals to the aggregation node.

[0148] Data processing node 13 is configured to generate characteristic curves based on the electromechanical characteristic data collected by acquisition module 12, and extract feature values ​​from the characteristic curves. Data processing node 13 can be a aggregation node, and the aggregation node 13 uses edge computing for data processing. Data processing node 13 is wirelessly connected to computer device 14.

[0149] Furthermore, combined with Figure 9 As shown, data processing node 13 includes:

[0150] Curve generation unit 132 is used to generate characteristic curves based on electromechanical characteristic data at the aggregation node through edge computing.

[0151] Feature extraction unit 134 is used to extract feature values ​​from the characteristic curve;

[0152] The second communication unit 136 is used to save the characteristic curve and characteristic value data in a predetermined format, and then upload them to the cloud platform or local computer after converting them into a predetermined communication protocol.

[0153] The computer device 14 can be any device with computing and processing capabilities. It can be a portable mobile device, such as a smartphone, tablet, laptop, or PDA (Personal Digital Assistant), or a fixed device, such as a computer, field terminal, desktop computer, server, or workstation. It can also be a collection of multiple devices, such as the physical infrastructure of cloud computing or a server cluster, or a physical infrastructure for cloud computing. The computer device 14 has a diagnostic analysis module, which is configured to determine whether the feature values ​​extracted by the data processing node meet the conditions set for the feature values ​​in the data diagnostic model. If they meet the conditions, it triggers the display of diagnostic result information that matches those conditions.

[0154] The diagnostic analysis module can be various software systems, such as a data analysis and management system. This diagnostic analysis module can run on the computer device 14 to achieve health status analysis and diagnosis.

[0155] The diagnostic analysis module can be implemented entirely in hardware, entirely in software (including firmware, microcode, etc.), or a combination of hardware and software.

[0156] The computer device 14 also includes a diagnostic strategy library, which includes diagnostic result information that maps to the conditions set by the data diagnostic model. The diagnostic result information includes fault symptoms, cause inferences, handling suggestions and / or fault codes.

[0157] As mentioned earlier, the data diagnostic model is a collection of multiple diagnostic formulas. Each formula defines a condition, and each condition is calculated based on characteristic values ​​obtained from the switchgear's factory curves, historical data, or configuration parameters. For example, for the diagnostic formula T5-T4>1.5(T50-T40), the values ​​of characteristic time T40 (the inflection point at the right peak of the trip current at the factory setting) and characteristic time T50 (the end point of the operation at the factory setting) can be calculated based on the factory-set current-time curve of the closing trip unit. Another example is the closing diagnostic formula t... 合 ≤μ-1.96σ or t 合 ≥μ+1.96σ, where μ and σ are the average and standard deviation of closing time analyzed from historical data.

[0158] The computer device 14 also includes a display device configured to display the triggered diagnostic result information.

[0159] The diagnostic system described above can provide early warnings of potential faults in switchgear, diagnose existing faults in switchgear, and predict the electrical and mechanical lifespan of circuit breakers.

[0160] The following example illustrates how this diagnostic system diagnoses health status.

[0161] For example, a current sensor for the closing trip unit current is installed in the closing trip unit current circuit to detect the current of the closing trip unit. The acquired current is converted into a digital signal by the acquisition device and transmitted to the data processing node for processing. The data processing node generates a closing trip unit current-time characteristic curve based on the acquired current signal and time, and extracts state quantities such as the minimum value of the trip unit current, the maximum value of the trip unit current, characteristic times T0, T1, T2, T3, T4, T5, average operating current, and closing time from the closing trip unit current-time characteristic curve. Among them, characteristic time T0: the starting point of the operation, with T0 as the origin, and its value is 0; characteristic time T1: the point where the trip current first drops after T0, i.e., the peak of the first spike; characteristic time T2: the valley point of the trip current after T1; characteristic time T3: the starting point of the right peak of the trip current; characteristic time T4: the ending point of the right peak of the trip current; characteristic time T5: the end point of the operation; the average operating current is the average value of the trip current from T1 to T4; closing time: t 合 =T4+Δt 合 ;Δt 合 =t 断口 -t 辅助开关切换时间 , where Δt 合 This represents the difference between the contact closure and the auxiliary switch. The data processing node uploads the extracted feature values ​​and curve data to the computer equipment. The diagnostic analysis module in the computer equipment determines whether the feature values ​​meet the conditions set for the corresponding feature values ​​in the data diagnostic model. For example, the diagnostic analysis module determines whether the feature value T5-T4 meets the condition determined by the diagnostic formula T5-T4>1.5(T50-T40) in the diagnostic model. If it does, it triggers the display of the diagnostic result information: "Cause inference: Auxiliary switch sparking, auxiliary switch switching capability decreased; Handling suggestion: Pay attention, it is recommended to check the auxiliary switch, and replace it if necessary." That is, this information prompts relevant personnel that the auxiliary switch may have a problem with decreased switching capability, and relevant personnel should check the auxiliary switch in time to eliminate possible faults. For example, the diagnostic analysis module determines the feature value t 合 Does it satisfy the closing diagnostic formula t in the diagnostic model? 合 ≤μ-1.96σ or t 合 If the condition ≥μ+1.96σ is met, the diagnostic result information "Cause inference: Closing time shows a deterioration trend; Recommendation: Pay attention to the trend of action time changes, and check the mechanism and transmission components during power outages" will be triggered. Thus, the diagnostic system and method of this disclosure can automatically analyze the trend changes in the state of equipment components, provide early warnings of potential faults, and conduct troubleshooting in the early stages of fault occurrence, thereby improving equipment reliability.

[0162] The following reference Figure 10To describe the computer device 14 according to an embodiment of the present invention. Figure 10 The computer device 14 shown is merely an example and should not impose any limitation on the functionality and scope of use of the embodiments of the present invention.

[0163] like Figure 10 As shown, the computer device 14 is presented in the form of a general-purpose computing device. The components of the computer device 14 may include, but are not limited to: at least one processing unit 610, at least one storage unit 620, and a bus 630 connecting different system components (including storage unit 620 and processing unit 610).

[0164] The storage unit stores program code, which can be executed by the processing unit 610, causing the processing unit 610 to perform the steps described in the "Embodiment Methods" section of this specification according to various exemplary embodiments of the present invention.

[0165] Storage unit 620 may include readable media in the form of volatile storage units, such as random access memory (RAM) 621 and / or cache memory 622, and may further include read-only memory (ROM) 623.

[0166] Storage unit 620 may also include a program / utility 624 having a set (at least one) of program modules 625, including but not limited to: an operating system, one or more application programs, other program modules, and program data, each or some combination of these examples may include an implementation of a network environment.

[0167] Bus 630 can represent one or more of several types of bus structures, including a memory cell bus or memory cell controller, a peripheral bus, a graphics acceleration port, a processing unit, or a local bus using any of the various bus structures.

[0168] Computer device 14 can also communicate with one or more external devices 800 (e.g., keyboard, pointing device, Bluetooth device, etc.), and with one or more devices that enable a user to interact with computer device 14, and / or with any device that enables computer device 14 to communicate with one or more other computing devices (e.g., router, modem, etc.). This communication can be performed via input / output (I / O) interface 650. Furthermore, computer device 14 can also communicate with one or more networks (e.g., local area network (LAN), wide area network (WAN), and / or public networks, such as the Internet) via network adapter 660. As shown, network adapter 660 communicates with other modules of computer device 14 via bus 630. It should be understood that, although not shown in the figures, other hardware and / or software modules can be used in conjunction with computer device 14, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems.

[0169] Furthermore, the above figures are merely illustrative of the processes included in the method according to exemplary embodiments of the present invention, and are not intended to be limiting. It is readily understood that the processes shown in the above figures do not indicate or limit the temporal order of these processes. Additionally, it is readily understood that these processes may be executed synchronously or asynchronously, for example, in multiple modules.

[0170] It should be understood that the present invention is not limited to the precise structure described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope. The scope of the invention is limited only by the appended claims.

Claims

1. A method for diagnosing the health status of a switchgear, characterized in that, include: Electromechanical characteristic data are acquired through the acquisition module; A characteristic curve is generated based on the electromechanical characteristic data, and multiple feature values ​​are extracted from the characteristic curve. The multiple feature values ​​are directly read from the characteristic curve or calculated by directly reading the feature values ​​from the characteristic curve. The multiple feature values ​​include the starting point, inflection point, peak, valley point or end point of the characteristic curve where there are obvious fluctuations. Each feature value characterizes the mechanical or electrical state of the corresponding component in the switch cabinet. The system determines whether at least two of the multiple feature values ​​satisfy a condition applicable to the feature value setting in the data diagnostic model. The data diagnostic model is a set of multiple diagnostic formulas, and each condition set in the data diagnostic model corresponds to one of the diagnostic formulas in the set of multiple diagnostic formulas. The fixed parameters set in the diagnostic formula are determined based on feature values ​​obtained from the switchgear's factory curve, historical data, or configuration parameters. Each condition set in the data diagnostic model maps to corresponding diagnostic result information in the diagnostic strategy library, and the diagnostic result information includes cause inference and handling suggestions. If the conditions are met, the diagnostic result information matching the conditions will be displayed.

2. The health status diagnosis method for switchgear according to claim 1, characterized in that, The characteristic curve is the current-time curve of the closing trip unit. The characteristic times obtained from the current-time curve of the closing trip unit are T0, T1, T2, T3, T4, and T5. Among them, characteristic time T0 is the starting point of the operation, with T0 as the origin, and its value is 0; characteristic time T1 is the point where the trip unit current first decreases after T0, the peak of the first bulge on the curve; characteristic time T2 is the valley point of the trip unit current after T1; characteristic time T3 is the starting inflection point of the right peak of the trip unit current; characteristic time T4 is the ending inflection point of the right peak of the trip unit current; and characteristic time T5 is the end point of the operation. The diagnostic formula used in the data diagnostic model to diagnose the status of the auxiliary switch is T5-T4>1.5(T50-T40), where T50 and T40 are calculated based on the factory curve. The diagnostic result information mapped by the diagnostic formula in the diagnostic strategy library is as follows: Cause inference: Auxiliary switch ignition, auxiliary switch switching capability decreased; Handling suggestion: Pay attention, it is recommended to check the auxiliary switch and replace it if necessary.

3. The health status diagnosis method for switchgear according to claim 2, characterized in that, The closing time of the trip unit is t. 合 = T4+Δt 合 ;△t 合 = t 断口 - t 辅助开关切换时间 , where △t 合 The difference between contact closing and auxiliary switch closing; the data diagnostic model includes a closing diagnostic formula for judging the closing trend, the closing diagnostic formula being t 合 ≤μ-1.96σ or t 合 ≥μ+1.96σ, where μ and σ are the average and standard deviation of closing time analyzed from historical data; The diagnostic information mapped by the closing diagnostic formula in the diagnostic strategy library is as follows: Cause inference: The closing time has a deteriorating trend; Handling suggestion: It is recommended to pay attention to the trend of action time change, and check the mechanism and transmission components during power outage.

4. The health status diagnosis method for switchgear according to claim 1, characterized in that, The step of acquiring electromechanical characteristic data through the acquisition module includes: Analog signals of electromechanical characteristics are acquired through sensors; Convert the analog signals of the electromechanical characteristics into digital signals; The digital signal is transmitted to the aggregation node.

5. The health status diagnosis method for switchgear according to claim 4, characterized in that, The step of generating a characteristic curve based on the electromechanical characteristic data and extracting feature values ​​from the characteristic curve includes: At the aggregation node, characteristic curves are generated based on electromechanical characteristic data using edge computing. Extract feature values ​​from the characteristic curve; The characteristic curves and eigenvalues ​​are saved in a predetermined format and then uploaded to a cloud platform or local computer after being converted into a predetermined communication protocol.

6. The health status diagnosis method for switchgear according to claim 5, characterized in that, The sensor is an angular displacement sensor, which collects the rotation angle of the main shaft of the switchgear. Before the step of generating a characteristic curve based on electromechanical characteristic data through edge computing at the convergence node, the following steps are also included: The rotation angle is converted into stroke at the convergence node; The formula for converting between angle and travel is: Where a and b are the coordinates of the intersection of the first and second links, r1 is the length of the first link, r2 is the length of the second link, A is the angle between the third link and the x-axis, B is A minus the starting angle, and S is the stroke of the fourth link; the first link is connected to the second link, the second link is connected to the third link, and the third link is connected to the fourth link, and both the first and fourth links are connected to the main shaft of the circuit breaker; the x-axis can be determined by the line connecting the fixed points of the first and fourth links.

7. The health status diagnosis method for switchgear according to claim 1, characterized in that, After the steps of acquiring electromechanical characteristic data through the acquisition module and generating characteristic curves based on the electromechanical characteristic data, the following steps are included: The characteristic curve is smoothed using Lagrange interpolation.

8. The health status diagnosis method for switchgear according to claim 1, characterized in that, The characteristic curves include the characteristic curves of the circuit breaker contact travel versus time, the characteristic curves of the current versus time of the closing trip unit, the characteristic curves of the current versus time of the three-phase main circuit, the characteristic curves of the current versus time of the circuit breaker energy storage motor, the characteristic curves of the current versus time of the circuit breaker chassis motor, and the characteristic curves of the current versus time of the grounding switch motor.

9. A health status diagnosis system for switchgear, characterized in that, include: Switch cabinet; The data acquisition module collects electromechanical characteristic data of the corresponding components inside the switch cabinet; The data processing node is communicatively connected to the acquisition module. It generates a characteristic curve based on the electromechanical characteristic data and extracts multiple feature values ​​from the characteristic curve. The multiple feature values ​​are directly read from the characteristic curve or calculated from the feature values ​​directly read from the characteristic curve. The multiple feature values ​​include the starting point, inflection point, peak, valley point or end point of the characteristic curve where there are obvious fluctuations. Each feature value characterizes the mechanical or electrical state of the corresponding component in the switch cabinet. A computer device is communicatively connected to the data processing node. The computer device has a diagnostic analysis module configured to determine whether at least two of the plurality of feature values ​​satisfy a condition in a data diagnostic model applicable to the feature value setting. The data diagnostic model is a set of multiple diagnostic formulas. Each condition set in the data diagnostic model corresponds to one of the diagnostic formulas in the set. The fixed parameters set in the diagnostic formula are determined based on feature values ​​obtained from the switchgear's factory curve, historical data, or configuration parameters. Each condition set in the data diagnostic model maps to corresponding diagnostic result information in a diagnostic strategy library. The diagnostic result information includes cause inference and treatment suggestions. If satisfied, the diagnostic result information matching the condition is triggered for display.

10. The health status diagnosis system for switchgear according to claim 9, characterized in that, The computer device also includes a display device configured to display triggered diagnostic result information.

11. The health status diagnosis system for switchgear according to claim 9, characterized in that, The acquisition module includes a circuit breaker information acquisition module, a grounding switch motor current acquisition module, a cabinet partial discharge quantity acquisition module, and a cabinet temperature measurement acquisition module.

12. The health status diagnosis system for switchgear according to claim 11, characterized in that, The circuit breaker information acquisition module includes sensors such as angular displacement sensors, current sensors for tripping and closing current, current sensors for three-phase main circuit current, current sensors for energy storage motor current, current sensors for chassis motor current, and / or switch quantity monitoring sensors.

13. The health status diagnosis system for switchgear according to claim 9, characterized in that, The data processing node is a convergence node, and the convergence node uses edge computing for data processing.

14. The health status diagnosis system for the switchgear according to claim 9, characterized in that, The characteristic curve is the current-time curve of the closing trip unit. The characteristic times obtained from the current-time curve of the closing trip unit are T0, T1, T2, T3, T4, and T5. Among them, characteristic time T0 is the starting point of the operation, with T0 as the origin, and its value is 0; characteristic time T1 is the point where the trip unit current first decreases after T0, the peak of the first bulge on the curve; characteristic time T2 is the valley point of the trip unit current after T1; characteristic time T3 is the starting inflection point of the right peak of the trip unit current; characteristic time T4 is the ending inflection point of the right peak of the trip unit current; and characteristic time T5 is the end point of the operation. The diagnostic formula used in the data diagnostic model to diagnose the status of the auxiliary switch is T5-T4>1.5(T50-T40), where T50 and T40 are calculated based on the factory curve. The diagnostic result information mapped by the diagnostic formula in the diagnostic strategy library is as follows: Cause inference: Auxiliary switch ignition, auxiliary switch switching capability decreased; Handling suggestion: Pay attention, it is recommended to check the auxiliary switch and replace it if necessary.

15. The health status diagnosis system for switchgear according to claim 14, characterized in that, The closing time of the trip unit is t. 合 = T4+Δt 合 ;△t 合 = t 断口 - t 辅助开关切换时间 , where △t 合 The difference between contact closing and auxiliary switch closing; the data diagnostic model includes a closing diagnostic formula for judging the closing trend, the closing diagnostic formula being t 合 ≤μ-1.96σ or t 合 ≥μ+1.96σ, where μ and σ are the average and standard deviation of closing time analyzed from historical data; The diagnostic information mapped by the closing diagnostic formula in the diagnostic strategy library is as follows: Cause inference: The closing time has a deteriorating trend; Handling suggestion: It is recommended to pay attention to the trend of action time change, and check the mechanism and transmission components during power outage.

16. The health status diagnosis system for switchgear according to claim 9, characterized in that, The acquisition module includes: The acquisition unit is used to acquire analog signals of electromechanical characteristics through sensors; A conversion unit is used to convert the analog signals of the electromechanical characteristics into digital signals; The first communication unit is used to transmit the digital signal to the aggregation node.

17. The health status diagnosis system for switchgear according to claim 16, characterized in that, The data processing node includes: The curve generation unit is used to generate characteristic curves based on electromechanical characteristic data at the aggregation node through edge computing. The feature extraction unit is used to extract feature values ​​from the characteristic curve; The second communication unit is used to save the characteristic curves and feature values ​​in a predetermined format, and then upload them to the cloud platform or local computer after converting them into a predetermined communication protocol.

Images