Method, device and equipment for determining health state of switch cabinet and storage medium

Abstract

The embodiment of the invention provides a health state determination method and device of a switch cabinet, equipment and a storage medium. The method comprises the steps of obtaining electrical data and actual input of a plurality of mechanical structures included in the switch cabinet in a reference time period, determining health states of the mechanical structures according to the electrical data and the actual input of the mechanical structures, and then determining health states of the switch cabinet according to the health states of the mechanical structures. The health state of the switch cabinet is determined from multiple structure dimensions, and the limitation caused by only taking a circuit breaker as a research object is reduced.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of electrical safety, and in particular to a method and device for determining the health state of a switch cabinet, equipment and a storage medium. BACKGROUND

[0002] With the development of social economy, the application scale of switch cabinets is becoming larger and larger, and higher requirements are put forward for the operation and maintenance thereof.

[0003] The circuit breaker is a core component in the switch cabinet, and is of great significance to the safety and stability of the power system. At present, when evaluating the health state of the switch cabinet, the circuit breaker is mainly taken as the main research object, for example, the mechanical vibration signal of the circuit breaker is collected and analyzed, and the health state of the circuit breaker is evaluated based on the analysis result of the mechanical vibration signal.

[0004] However, there are limitations in evaluating the health state of the switch cabinet by taking the circuit breaker as the main research object. SUMMARY

[0005] Therefore, the present application provides a method and device for determining the health state of a switch cabinet, equipment and a storage medium, which can reduce the limitations of taking the circuit breaker as the main research object to evaluate the health state of the switch cabinet.

[0006] According to a first aspect of an embodiment of the present application, a method for determining the health state of a switch cabinet is provided, which comprises: acquiring electrical data and actual opening quantities of at least two mechanical structures included in the switch cabinet within a reference time period; determining a plurality of characteristic time points within the reference time period according to the change of the electrical data, and determining the electrical value and the actual opening quantity of the mechanical structure at each characteristic time point; determining the first health state of the mechanical structure according to the electrical value and the actual opening quantity of the mechanical structure at each characteristic time point; determining the second health state of the mechanical structure according to the actual opening quantity and the standard opening quantity of the mechanical structure at each characteristic time point; and determining the health state of the switch cabinet according to the first health state and the second health state of the at least two mechanical structures.

[0007] In a possible implementation manner, the at least two mechanical structures include at least two of the following: a closing and opening coil, an energy storage motor, an electric chassis car, a ground knife and a three-position knife.

[0008] In a possible implementation, the electrical data includes current waveform data; determining the plurality of feature time points within the reference time period according to the change of the electrical data includes: determining the plurality of feature time points within the reference time period from the current waveform data; wherein the feature time points corresponding to the closing and opening coil include: a starting time point of the running stage, a maximum current time point, a minimum current time point, and an ending time point of the running stage; the feature time points corresponding to the energy storage motor include: a starting time point of the starting stage, a starting time point of the running stage, a maximum current time point, and an ending time point of the running stage; the feature time points corresponding to the electric chassis vehicle include: a starting time point of the starting stage, a stopping time point of the starting stage, a stopping time point of the running stage, a maximum brake current time point, and a maximum reverse brake current time point; the feature time points corresponding to the ground cutter include: a starting time point of the running stage, a maximum brake current time point, and a maximum reverse brake current time point; and the feature time points corresponding to the three-position cutter include: a starting time point of the starting stage, a starting time point of the running stage, a maximum reverse current time point, and a motor stopping time point.

[0009] In a possible implementation, the health state of the switch cabinet is determined according to the first health state and the second health state of the at least two mechanical structures, including: determining the current health state of the mechanical structure according to the first health state and the second health state of the mechanical structure; and determining the health state of the switch cabinet according to the current health state of the at least two mechanical structures.

[0010] In a possible implementation, the current health state includes a first health state, a second health state, and a third health state from high to low; and the health state of the switch cabinet is determined according to the current health state of the at least two mechanical structures, including: determining the highest health state in the current health state of the at least two mechanical structures as the health state of the switch cabinet; wherein the first health state refers to a state in which a fault has occurred and affects the current operation, the second health state refers to a state in which there is a potential problem and does not affect the current operation, and the third health state refers to a state in which there is no fault and does not affect the current operation.

[0011] In a possible implementation, the electrical data includes current waveform data; determining the plurality of characteristic time points within the reference time period according to the change of the electrical data, and determining the electrical value and the actual opening amount of the mechanical structure at each characteristic time point, includes: calling the prediction model corresponding to the mechanical structure, determining the plurality of characteristic time points and the current value corresponding to each characteristic time point according to the current waveform data corresponding to the mechanical structure, and determining the actual opening amount corresponding to each characteristic time point based on the actual opening amount within the reference time period corresponding to the mechanical structure, wherein different mechanical structures correspond to different prediction models; determining the first health state of the mechanical structure according to the electrical value and the actual opening amount of the mechanical structure at each characteristic time point, includes: calling the prediction model corresponding to the mechanical structure, and determining the first health state of the mechanical structure according to the current value and the actual opening amount of the mechanical structure at each characteristic time point; determining the second health state of the mechanical structure according to the actual opening amount and the standard opening amount of the mechanical structure at each characteristic time point, includes: calling the prediction model corresponding to the mechanical structure, and determining the second health state of the mechanical structure according to the actual opening amount and the standard opening amount of the mechanical structure at each characteristic time point.

[0012] In a possible implementation, the current health state includes a first health state, a second health state and a third health state from high to low; determining the current health state of the mechanical structure according to the first health state and the second health state of the mechanical structure, includes: calling the prediction model corresponding to the mechanical structure, and determining the highest health state in the first health state and the second health state of the mechanical structure as the current health state of the mechanical structure; wherein the first health state refers to a state that has appeared a fault and affects the current operation, the second health state refers to a state that has potential problems and does not affect the current operation, and the third health state refers to a state that has no fault and does not affect the current operation.

[0013] In a possible implementation, the health state of the switch cabinet includes a first health state, a second health state and a third health state from high to low; the method further includes: if the health state of the switch cabinet is the first health state, issuing an alarm, and generating an operation and maintenance suggestion of immediate shutdown for repair for the mechanical structure in the first health state; if the health state of the switch cabinet is the second health state, issuing a pre-alarm, and generating an operation and maintenance suggestion of planned repair for the mechanical structure in the second health state; if the health state of the switch cabinet is the third health state, not issuing a prompt and not generating an operation and maintenance suggestion; wherein the first health state refers to a state that has appeared a fault and affects the current operation, the second health state refers to a state that has potential problems and does not affect the current operation, and the third health state refers to a state that has no fault and does not affect the current operation.

[0014] According to a second aspect of the embodiments of the present application, a health state determination device of a switch cabinet is provided, which comprises: a related data acquisition module, configured to acquire electrical data and actual opening quantities of at least two mechanical structures included in the switch cabinet in a reference time period; a data feature determination module, configured to determine a plurality of feature time points located in the reference time period according to changes of the electrical data, and determine electrical values and actual opening quantities of the mechanical structures at each feature time point; a first state determination module, configured to determine a first health state of the mechanical structures according to the electrical values and actual opening quantities of the mechanical structures at the feature time points, and determine a second health state of the mechanical structures according to the actual opening quantities and standard opening quantities of the mechanical structures at the feature time points; and a second state determination module, configured to determine a health state of the switch cabinet according to the first health states and the second health states of the at least two mechanical structures.

[0015] According to a third aspect of the embodiments of the present application, an electronic device is provided, which comprises a processor, a memory, a communication interface and a communication bus, the processor, the memory and the communication interface complete communication with each other through the communication bus; the memory is used to store at least one executable instruction, and the executable instruction makes the processor execute operations corresponding to the method according to any one of the first aspect.

[0016] According to a fourth aspect of the embodiments of the present application, a computer readable storage medium is provided, which stores a computer program, and the computer program is executed by a processor to implement the method according to any one of the first aspect.

[0017] According to the health state determination method of the switch cabinet provided by the embodiments of the present application, the electrical data and actual opening quantities of a plurality of mechanical structures included in the switch cabinet in a reference time period are acquired, the health states of the mechanical structures are determined according to the electrical data and actual opening quantities of the mechanical structures, and then the health state of the switch cabinet is determined according to the health states of the mechanical structures, so as to determine the health state of the switch cabinet from the multiple structure dimensions, thereby reducing the limitation brought by taking only the circuit breaker as the research object. Secondly, for the determination of the health state of each mechanical structure, a plurality of feature time points located in the reference time period are first determined according to changes of the electrical data, and the electrical values and actual opening quantities of the mechanical structures at each feature time point are determined, then the first health state of the mechanical structures is determined according to the electrical values and actual opening quantities of the mechanical structures at the feature time points, and the second health state of the mechanical structures is determined according to the actual opening quantities and standard opening quantities of the mechanical structures at the feature time points, so as to improve the accuracy of the determination of the health state of each mechanical structure, and more accurately evaluate the health state of the switch cabinet. BRIEF DESCRIPTION OF DRAWINGS

[0018] The above and other features and advantages of the present application will become more apparent by reference to the following detailed description taken in conjunction with the accompanying drawings in which:

[0019] Figure 1 Flow chart of the health state determination method of the switch cabinet provided for an exemplary embodiment of the present application.

[0020] Figure 2 Current waveform diagram of the electric chassis vehicle in a reference time period provided for an exemplary embodiment of the present application.

[0021] Figure 3 Current waveform diagram of the ground knife in a reference time period provided for an exemplary embodiment of the present application.

[0022] Figure 4 Current waveform diagram of the three-position knife in a reference time period provided for an exemplary embodiment of the present application.

[0023] Figure 5 Current waveform diagram of the closing and opening coil in a reference time period provided for an exemplary embodiment of the present application.

[0024] Figure 6 Current waveform diagram of the energy storage motor in a reference time period provided for an exemplary embodiment of the present application.

[0025] Figure 7 Schematic diagram of the health state determination device of the switch cabinet provided for an exemplary embodiment of the present application.

[0026] Figure 8 Structural schematic diagram of the electronic device provided for an exemplary embodiment of the present application.

[0027] List of reference signs:

[0028] 100: health state determination method of the switch cabinet; 101-105: method steps; T0-T20: characteristic time instants; I0-I20: current values at the characteristic time instants; 200: health state determination device of the switch cabinet; 201: related data acquisition module; 202: data characteristic determination module; 203: first state determination module; 204: second state determination module; 300: electronic device; 302: processor; 304: communication interface; 306: memory; 308: communication bus; 310: program. DETAILED DESCRIPTION

[0029] In order to make the objects, technical solutions and advantages of the present application clearer, the following embodiments are further described in detail.

[0030] Switchgear is a kind of opening and closing, controlling and protecting electrical equipment in the process of power generation, power transmission, power distribution and power conversion in power system. With the development of social economy, the application scale of switchgear is getting larger and larger, and higher requirements for its operation and maintenance are put forward. The present application provides a health state determination scheme of switchgear, which can accurately evaluate the health state of switchgear, so that the switchgear can be maintained in time in the case of operation failure or abnormal operation, thereby ensuring that the switchgear can safely, stably and reliably complete the core functions such as "turning on and off current, isolating circuit, protecting equipment" under the rated working condition.

[0031] Please refer to Figure 1 which shows a flow chart of a health state determination method 100 of switchgear provided by the embodiments of the present application, the method is applied to an electronic device, and the method comprises the following steps:

[0032] In step 101, electrical data and actual input quantities of at least two mechanical structures included in the switchgear in a reference time period are acquired.

[0033] The switchgear includes at least two mechanical structures. The mechanical structure is a structure related to the safe operation of the switchgear. For example, the mechanical structure is a core component in the switchgear that realizes functions by mechanical action. Optionally, the at least two mechanical structures include at least two of a split and close coil, an energy storage motor, an electric chassis car (also known as a handcart), a ground knife and a three-position knife. For example, each switchgear includes a split and close coil and an energy storage motor. The type of switchgear is not limited here, for example, air insulated switchgear (AIS), gas insulated switchgear (GIS), or ring network cabinet, fixed switchgear, draw-out switchgear, etc. If the switchgear adopts air insulated switchgear (AIS), the switchgear also includes an electric chassis car and a ground knife; if the switchgear adopts gas insulated switchgear (GIS), the switchgear also includes a three-position knife.

[0034] The sensor is connected in the loop where each mechanical structure of the switch cabinet is located, and the electrical data of the loop where the mechanical structure is located is collected through the sensor. The electrical data is the data reflecting the electrical characteristics of the loop where the mechanical structure is located, and these data are used to analyze the health status of the mechanical structure. For example, the electrical data includes voltage waveform data, and the voltage values at each time in the loop where the mechanical structure is located are collected through the sensor in a time period, and then the voltage waveform data in the time period is obtained. For another example, the electrical data also includes current waveform data, and the current values at each time in the loop where the mechanical structure is located are collected through the sensor in a time period, and then the current waveform data in the time period is obtained. After the electrical data is collected, the collected original electrical data needs to be preprocessed, including abnormal value processing, data cleaning, etc., to generate the electrical data required in this step 101.

[0035] Each mechanical structure has its own auxiliary switch, which is a state feedback accessory of the mechanical structure. It is linked with the action of the mechanical structure, and the mechanical structure acts, and the auxiliary switch also acts with it, and reflects the current state of the main structure through the "closure" or "disconnection" of its own contact. For example, when the electric chassis car is shaken to the "working position", the "working position node" of the auxiliary switch is closed, and when it is shaken to the "test position", the "test position node" of the auxiliary switch is closed. The auxiliary switches of each mechanical structure of the switch cabinet are connected to the online monitoring device, and the change of the input quantity of the mechanical structure is monitored through the online monitoring device, so as to obtain the actual input quantity in the reference time period. The input quantity indicates the state of the mechanical structure, for example, the circuit breaker is in the split position or the closed position, for another example, the electric chassis car is in the test position or the working position, and for another example, the ground knife is in the split position or the closed position.

[0036] The length of the reference time period here can be set according to actual needs, for example, the time period from the start time to the stop time of an action of the mechanical structure is taken as the reference time period, or the time period from the start to the completion of an action and the end of the recording wave for this action is taken as the reference time period. For example, the reference time period can be the time period from the start to the completion of the opening action of the opening and closing coil and the end of the recording wave for the opening action. For another example, the reference time period can also be the time period from the start time to the stop time of the switching action between the split position and the closed position of the three-position knife.

[0037] In step 102, a plurality of characteristic time points located in the reference time period are determined according to the change of the electrical data, and the electrical value and the actual input quantity of the mechanical structure at each characteristic time point are determined.

[0038] The feature time point can be understood as a time point at which the working state of the mechanical structure changes, such as a starting time point, a stopping time point, a peak current time point, and the like. The electrical value can be understood as a value in the electrical data, that is, a value corresponding to the feature time point.

[0039] If the electrical data is current waveform data, the feature time point can be understood as a time point of current change related to the change of the working state of the mechanical structure. For example, the feature time points corresponding to the closing and opening coils can include a starting time point, a maximum current time point, a minimum current time point, and an ending time point. If the electrical data is current waveform data, the electrical value of the feature time point is the current value of the feature time point in the current waveform data.

[0040] In step 103, the first health state of the mechanical structure is determined according to the electrical values of the mechanical structure at the feature time points and the actual opening amounts.

[0041] In step 104, the second health state of the mechanical structure is determined according to the actual opening amounts of the mechanical structure at the feature time points and the standard opening amounts.

[0042] The health state prediction of the mechanical structure can be realized by using a prediction model. For example, the electrical data and the actual opening amounts of a mechanical structure in a reference time period are input into the prediction model as input data, and the prediction model extracts time sequence features, statistical features, frequency domain features, correlation features, and position features from the electrical data and the actual opening amounts, determines a plurality of feature time points located in the reference time period based on these feature quantities, and determines the electrical values and the actual opening amounts of the mechanical structure at each feature time point. Further, the first health state of the mechanical structure is determined according to the electrical values and the actual opening amounts of the mechanical structure at the feature time points, and the second health state of the mechanical structure is determined according to the actual opening amounts and the standard opening amounts of the mechanical structure at the feature time points.

[0043] The standard opening amount can be determined according to the mechanical structure in the switch cabinet in the following described three-level health state, that is, the opening amount that the mechanical structure should be in under the three-level health state.

[0044] In this step 104, the second health state of the mechanical structure can be determined by comparing whether the actual opening amount and the standard opening amount are consistent, for example, if the comparison result is consistent, it is in the following described three-level health state, and if it is inconsistent, it is in the following described one-level health state. Such a comparison method is simple and convenient, and very fast. Of course, the second health state of the mechanical structure can also be determined by other methods to make the determination result of the second health state more accurate.

[0045] Step 105, determining the health state of the switch cabinet according to the first health state and the second health state of the at least two mechanical structures.

[0046] The health state includes a first health state, a second health state and a third health state from high to low. The highest health state among the first health state and the second health state of the at least two mechanical structures can be determined as the health state of the switch cabinet. For example, if there is a first health state among the first health state and the second health state of the at least two mechanical structures, it is determined that the switch cabinet is in a first health state; if there is a second health state and no first health state among the first health state and the second health state of the at least two mechanical structures, it is determined that the switch cabinet is in a second health state; if the first health state and the second health state of the at least two mechanical structures are both third health states, it is determined that the switch cabinet is in a third health state.

[0047] Optionally, the current health state of the mechanical structure is determined according to the first health state and the second health state of the mechanical structure; and the health state of the switch cabinet is determined according to the current health state of the at least two mechanical structures. For example, the electronic device can also call the prediction model to determine the current health state of the mechanical structure according to the first health state and the second health state of the mechanical structure.

[0048] Optionally, the current health state includes a first health state, a second health state and a third health state from high to low; and the highest health state among the current health states of the at least two mechanical structures is determined as the health state of the switch cabinet. For example, if there is a first health state among the first health state and the second health state of a mechanical structure, it is determined that the mechanical structure is in a first health state; if there is a second health state and no first health state among the first health state and the second health state of the mechanical structure, it is determined that the mechanical structure is in a second health state; if the first health state and the second health state of the mechanical structure are both third health states, it is determined that the mechanical structure is in a third health state.

[0049] The highest health state among the current health states of the at least two mechanical structures is determined as the health state of the switch cabinet. For example, if there is a mechanical structure in a first health state among the at least two mechanical structures, the health state of the switch cabinet is determined as a first health state; if there is a mechanical structure in a second health state and no mechanical structure in a first health state among the at least two mechanical structures, the health state of the switch cabinet is determined as a second health state; if all the mechanical structures among the at least two mechanical structures are in a third health state, the health state of the switch cabinet is determined as a third health state.

[0050] Level 1 health status refers to a state where a fault has occurred and is affecting current operation; Level 2 health status refers to a state where there are potential problems but are not currently affecting current operation; and Level 3 health status refers to a state where there are no faults and is not affecting current operation.

[0051] In summary, the switchgear health status determination method provided in this embodiment acquires the electrical data and actual input quantities of multiple mechanical structures within the switchgear over a reference time period. Based on the electrical data and actual input quantities of each mechanical structure, the health status of each mechanical structure is determined. Then, the health status of the switchgear is determined based on the health status of multiple mechanical structures, thus determining the switchgear health status from a multi-structural perspective and reducing the limitations imposed by focusing solely on circuit breakers. Secondly, regarding the determination of the health status of each mechanical structure, firstly, multiple characteristic moments within the reference time period are determined based on changes in electrical data, and the electrical values ​​and actual input quantities of the mechanical structures at each characteristic moment are determined. Then, based on the electrical values ​​and actual input quantities of the mechanical structures at each characteristic moment, a first health status of the mechanical structure is determined. Finally, based on the actual input quantities and standard input quantities of the mechanical structures at each characteristic moment, a second health status of the mechanical structure is determined. This combination of two methods improves the accuracy of determining the health status of each mechanical structure, enabling a more accurate assessment of the switchgear health status.

[0052] In some possible implementations, the electrical data in the above embodiments includes current waveform data; the electronic device determines multiple characteristic moments within a reference time period from the current waveform data and determines the current value of the mechanical structure at each characteristic moment.

[0053] like Figure 2 The figure shows the current waveform of the electric chassis vehicle within a reference time period. The characteristic moments corresponding to the electric chassis vehicle include: the start time T0 of the start phase, the stop time T1 of the start phase, the stop time T2 of the running phase, the time of maximum braking current T3, and the time of maximum reverse braking current T4. The current values ​​at each characteristic moment include: the current value I0 at the start time T0 of the start phase, the current value I1 at the stop time T1 of the start phase, the current value I2 at the stop time T2 of the running phase, the current value I3 at the time of maximum braking current T3, and the current value I4 at the time of maximum reverse braking current T4.

[0054] like Figure 3 , is the current waveform of the grounding switch within the reference time period. The characteristic moments corresponding to the grounding switch include: the start time of the operation phase T5, the time of maximum braking current T6, and the time of maximum reverse braking current T7; the current values ​​at each characteristic moment include: the current value I5 at the start time of the operation phase T5, the current value I6 at the time of maximum braking current T6, and the current value I7 at the time of maximum reverse braking current T7.

[0055] likeFigure 4 is the current waveform of the three-position knife in the reference time period, the characteristic moments corresponding to the three-position knife include: the starting moment T8 of the starting stage, the starting moment T9 of the running stage, the maximum moment T10 of the reverse current, and the stopping moment T11 of the motor; the current values of each characteristic moment include: the current value I8 of the starting moment T8 of the starting stage, the current value I9 of the starting moment T9 of the running stage, the current value I10 of the maximum moment T10 of the reverse current, and the current value I11 of the stopping moment T11 of the motor.

[0056] As Figure 5 is the current waveform of the opening and closing coil in the reference time period, the characteristic moments corresponding to the opening and closing coil include: the starting moment T12 of the running stage, the maximum moments T13 and T15 of the current, the minimum moment T14 of the current, and the ending moment T16 of the running stage; the current values of each characteristic moment include: the current value I12 of the starting moment T12 of the running stage, the current value I13 of the maximum moment T13 of the current, the current value I14 of the minimum moment T14 of the current, the current value I15 of the maximum moment T15 of the current, and the current value I16 of the ending moment T16 of the running stage.

[0057] As Figure 6 is the current waveform of the energy storage motor in the reference time period, the characteristic moments corresponding to the energy storage motor include: the starting moment T17 of the starting stage, the starting moment T18 of the running stage, the maximum current moment T19, and the ending moment T20 of the running stage; the current values of each characteristic moment include: the current value I17 of the starting moment T17 of the starting stage, the current value I18 of the starting moment T18 of the running stage, the current value I19 of the maximum current moment T19, and the current value I20 of the ending moment T20 of the running stage.

[0058] The electronic device calls the prediction model to determine the first health state and the second health state of the mechanical structure based on the current waveform data of the mechanical structure in the reference time period and the actual opening quantity. Specifically, the prediction model extracts a plurality of characteristic moments from the current waveform data and extracts the current values of each characteristic moment, and also extracts the actual opening quantity of each characteristic moment from the actual opening quantity in the reference time period, and then determines the first health state of the mechanical structure according to the current values and the actual opening quantity of the mechanical structure at each characteristic moment; the prediction model is pre-provided with the standard opening quantity corresponding to each characteristic moment, so the prediction model also determines the second health state of the mechanical structure according to the actual opening quantity and the standard opening quantity of the mechanical structure at each characteristic moment.

[0059] In addition, the prediction model can also determine the current health state of the mechanical structure according to the first health state and the second health state of the mechanical structure.

[0060] Optionally, different mechanical structures correspond to different prediction models. That is, each mechanical structure uses a respective prediction model to determine the health state. The electronic device performs the following steps to predict the first health state and the second health state of each mechanical structure, respectively: calling the prediction model corresponding to the mechanical structure, determining a plurality of feature time points within the reference time period and the current value corresponding to each feature time point according to the current waveform data corresponding to the mechanical structure, and determining the actual opening amount corresponding to each feature time point based on the actual opening amount within the reference time period corresponding to the mechanical structure; calling the prediction model corresponding to the mechanical structure, determining the first health state of the mechanical structure according to the current value and the actual opening amount of the mechanical structure at each feature time point; calling the prediction model corresponding to the mechanical structure, determining the second health state of the mechanical structure according to the actual opening amount and the standard opening amount of the mechanical structure at each feature time point.

[0061] Alternatively, after the electronic device obtains the first health state and the second health state of the mechanical structure, it also calls the prediction model corresponding to the mechanical structure to determine the current health state of the mechanical structure according to the first health state and the second health state of the mechanical structure. That is, the current waveform data and the actual opening amount of the mechanical structure within the reference time period are input into the prediction model, and the prediction model outputs the current health state of the mechanical structure.

[0062] Optionally, the electrical data includes the voltage of the mechanical structure within the reference time period. The voltage of the circuit in which the mechanical structure is located is divided into a plurality of voltage levels, and different voltage levels correspond to different current waveforms. The electronic device can call the prediction model to determine the voltage level to which the voltage of the mechanical structure within the reference time period belongs, and then determine the plurality of feature time points within the reference time period according to the voltage level and the current waveform data, and determine the current value and the actual opening amount of the mechanical structure at each feature time point.

[0063] For example, the current data of each mechanical structure is monitored in real time, and when the current waveform data is found in the current data, the health state determination method of the switch cabinet provided in the present application is executed.

[0064] It should be further noted that before executing the above-mentioned health state determination method of the switch cabinet, the prediction model corresponding to each mechanical structure needs to be trained in advance. For each mechanical structure, a to-be-trained model of the mechanical structure can be constructed based on a decision tree algorithm and a regression algorithm; the to-be-trained model of the mechanical structure is trained based on a training data set corresponding to the mechanical structure to obtain a trained model, and then the trained model is tested based on a test data set corresponding to the mechanical structure, the trained model is adjusted according to the test result, and finally the prediction model of the mechanical structure is obtained.

[0065] A plurality of sets of sample data are collected, each set of sample data including corresponding electrical data and input quantity data, and the plurality of sets of sample data are divided into a training data set and a test data set, each data set including a plurality of sets of sample data under different voltage levels. The model constructed above is trained and tested for the ability to determine the health state of the mechanical structure through machine learning or deep learning on the plurality of sets of sample data, and finally a prediction model capable of predicting the health state of the mechanical structure is obtained.

[0066] The algorithm used to construct the model includes but is not limited to decision tree algorithm and regression algorithm. When constructing the model, different characteristic quantities are selected for different mechanical structures. For example, the forward current of the electric chassis vehicle is divided into a starting stage T0-T1, a running stage T1-T2, a braking stage T2-T3, and a reverse braking stage T3-T4, and the characteristic points are obtained: the characteristic point at the start time of the starting stage (T0, I0), the characteristic point at the stop time of the starting stage (T1, I1), the characteristic point at the stop time of the running stage (T2, I2), the characteristic point at the maximum current time of the brake (T3, I3), and the characteristic point at the maximum current time of the reverse brake (T4, I4). In combination with the total running time (T4-T0) of the electric chassis vehicle, the average, maximum, minimum, and correlation coefficient of the current in the running stage T1-T2, a prediction model of the electric chassis vehicle under different voltage levels is established. The correlation coefficient refers to the correlation coefficient between the reference current waveform and the measured current waveform, which can be calculated using the Pearson algorithm, and the value is between 0 and 1.

[0067] For example, the ground cutter current is divided into a running stage T5-T6 and a braking stage T6-T7, and the characteristic points are obtained: the characteristic point at the start time of the ground cutter running stage (T5, I5), the characteristic point at the maximum current time of the brake (T6, I6), and the characteristic point at the maximum current time of the reverse brake (T7, I7). In combination with the total running time (T7-T5) of the ground cutter, the average, maximum, minimum, and correlation coefficient of the current in the running stage T5-T6, a prediction model of the ground cutter under different voltage levels is established.

[0068] For example, the three-position cutter current is divided into a starting stage T8-T9, a running stage T9-T10, and a stopping stage T10-T11, and the characteristic points are obtained: the characteristic point at the start time of the starting stage (T8, I8), the characteristic point at the start time of the running stage (T9, I9), the characteristic point at the maximum current time of the reverse current (T10, I10), and the characteristic point at the stop time of the motor (T11, I11). In combination with the total running time (T11-T8) of the cutter, the average, maximum, minimum, and correlation coefficient of the current in the total running stage T8-T11, a prediction model of the cutter under different voltage levels is established.

[0069] For example, the feature points of the closing and opening coil are obtained: the feature point at the starting time of the running stage (T12, I12), the feature point at the maximum current time (T13, I13), the feature point at the minimum current time (T14, I14), the feature point at the maximum current time (T15, I15), the feature point at the ending time of the running stage (T16, I16), and the prediction model of the closing and opening coil under different voltage levels is established in combination with the iron core impact latch time, the iron core disengagement latch time, the correlation coefficient and the like.

[0070] For example, the feature points of the energy storage motor are obtained: (T17, I17), (T18, I18), (T19, I19), (T20, I20), the total running time of the energy storage motor (T20-T17), the maximum current time (T19, I19) of the energy storage motor charging stage (T18-T20), the correlation coefficient and the like, and the prediction model of the energy storage motor under different voltage levels is established.

[0071] In summary, the health state determination method of the switch cabinet provided in the embodiment collects the current data and the input quantity of the mechanical structure, and evaluates the health state of the mechanical structure and the switch cabinet through the combination of the two. Compared with the analysis of the vibration signal of the circuit breaker, the performance requirement of the current voltage sensor is lower, the cost is lower, and more stable current voltage data can be obtained, so that the health state of the mechanical structure and the switch cabinet can be more accurately evaluated.

[0072] The prediction model constructed and trained in the embodiment also considers the influence of different voltage levels on the mechanical structure, which can more objectively evaluate the health state of the mechanical structure and the switch cabinet, and reduce the evaluation error. In the embodiment, the current data of each mechanical structure is monitored in real time, so that the occurrence of the current waveform can be found in time, and the health state of the mechanical structure and the switch cabinet can be evaluated in time, so that the existing faults or potential problems can be found in time.

[0073] In some possible embodiments, in the above-mentioned embodiments, after the health state of the switch cabinet is determined, if the health state of the switch cabinet is a first health state, an alarm is issued, and an immediate shutdown maintenance operation and maintenance suggestion is generated for the mechanical structure in the first health state; if the health state of the switch cabinet is a second health state, a pre-alarm is issued, and a planned maintenance operation and maintenance suggestion is generated for the mechanical structure in the second health state; if the health state of the switch cabinet is a third health state, no prompt is issued, and no operation and maintenance suggestion is generated.

[0074] If the health state of the switch cabinet is the first health state, it indicates that the switch cabinet has a fault, therefore, the electronic device issues a warning and generates an operation and maintenance suggestion of immediate shutdown for repair, which can be generated for a mechanical structure in the first health state, for example, if the electric chassis vehicle is in the first health state, an operation and maintenance suggestion of immediate shutdown for repair of the electric chassis vehicle is generated. If the health state of the switch cabinet is the second health state, it indicates that the switch cabinet has a potential problem, therefore, the electronic device issues a pre-warning and generates an operation and maintenance suggestion of planned repair, which can be generated for a mechanical structure in the second health state, for example, if the on-off coil is in the second health state, an operation and maintenance suggestion of planned repair of the on-off coil is generated. The warning mode and the pre-warning mode can adopt at least one of vibration, sound and text prompt, and there is a difference between the warning mode and the pre-warning mode, for example, different vibration frequencies or different prompt sounds.

[0075] If the health state of the switch cabinet is the third health state, it indicates that the switch cabinet is running normally and has no fault or potential problem, at this time, no prompt or operation and maintenance suggestion can be issued, or a prompt of normal operation can also be issued.

[0076] In the method provided in this embodiment, the problem source can be accurately located based on the health state of the mechanical structure, and corresponding operation and maintenance suggestions are given, so that the fault or potential problem can be quickly solved, and the loss caused by equipment failure is greatly reduced.

[0077] Please refer to Figure 7 which shows a schematic diagram of a health state determination device 200 of a switch cabinet provided in the embodiments of the present application, and the device comprises:

[0078] A related data acquisition module 201 is configured to acquire electrical data and actual input quantities of at least two mechanical structures included in the switch cabinet within a reference time period;

[0079] A data feature determination module 202 is configured to determine a plurality of characteristic time points within the reference time period according to changes in the electrical data, and determine electrical values and actual input quantities of the mechanical structures at each characteristic time point;

[0080] A first state determination module 203 is configured to determine a first health state of the mechanical structure according to the electrical values and actual input quantities of the mechanical structure at each characteristic time point, and determine a second health state of the mechanical structure according to the actual input quantities and standard input quantities of the mechanical structure at each characteristic time point;

[0081] A second state determination module 204 is configured to determine a health state of the switch cabinet according to the first health states and the second health states of the at least two mechanical structures.

[0082] In a possible implementation, the at least two mechanical structures include at least two of the closing and opening coil, the energy storage motor, the electric chassis vehicle, the ground knife, and the three-position knife.

[0083] In a possible implementation, the electrical data includes current waveform data; the first state determining module 203 is configured to determine a plurality of feature time points in a reference time period from the current waveform data; wherein the feature time points corresponding to the closing and opening coil include a starting time point of the running phase, a maximum current time point, a minimum current time point, and an ending time point of the running phase; the feature time points corresponding to the energy storage motor include a starting time point of the starting phase, a starting time point of the running phase, a maximum current time point, and an ending time point of the running phase; the feature time points corresponding to the electric chassis vehicle include a starting time point of the starting phase, a stopping time point of the starting phase, a stopping time point of the running phase, a maximum brake current time point, and a maximum reverse brake current time point; the feature time points corresponding to the ground knife include a starting time point of the running phase, a maximum brake current time point, and a maximum reverse brake current time point; and the feature time points corresponding to the three-position knife include a starting time point of the starting phase, a starting time point of the running phase, a maximum reverse current time point, and a motor stopping time point.

[0084] In a possible implementation, the first state determining module 203 is configured to determine a current health state of the mechanical structure according to the first health state and the second health state of the mechanical structure; and the second state determining module 204 is configured to determine the health state of the switch cabinet according to the current health states of the at least two mechanical structures.

[0085] In a possible implementation, the current health state includes a first health state, a second health state, and a third health state from high to low; the second state determining module 204 is configured to determine the highest health state among the current health states of the at least two mechanical structures as the health state of the switch cabinet; wherein the first health state refers to a state in which a fault has occurred and affects the current operation, the second health state refers to a state in which there is a potential problem and does not affect the current operation, and the third health state refers to a state in which there is no fault and does not affect the current operation.

[0086] In one possible implementation, the electrical data includes current waveform data; the data feature determination module 202 is used to call the prediction model corresponding to the mechanical structure, determine multiple characteristic moments and the current value corresponding to each characteristic moment based on the current waveform data corresponding to the mechanical structure, and determine the actual input amount corresponding to each characteristic moment based on the actual input amount within the reference time period corresponding to the mechanical structure, wherein different mechanical structures correspond to different prediction models; the first state determination module 203 is used to call the prediction model corresponding to the mechanical structure, determine the first health state of the mechanical structure based on the current value and actual input amount of the mechanical structure at each characteristic moment; and call the prediction model corresponding to the mechanical structure to determine the second health state of the mechanical structure based on the actual input amount and standard input amount of the mechanical structure at each characteristic moment.

[0087] In one possible implementation, the current health status includes a first-level health status, a second-level health status, and a third-level health status, ranked from high to low. The first-state determination module 203 is used to call the prediction model corresponding to the mechanical structure and determine the highest-level health status between the first-level and second-level health statuses of the mechanical structure as the current health status of the mechanical structure. Among them, the first-level health status refers to the state in which a fault has occurred and affects the current operation, the second-level health status refers to the state in which there is a potential problem but does not affect the current operation, and the third-level health status refers to the state in which there is no fault and does not affect the current operation.

[0088] In one possible implementation, the health status of the switchgear includes three levels, from high to low: Level 1, Level 2, and Level 3. The device is further configured to: issue an alarm if the switchgear is in Level 1 health, and generate an immediate shutdown and maintenance recommendation for the mechanical structure in Level 1 health; issue a pre-warning alarm if the switchgear is in Level 2 health, and generate a planned maintenance recommendation for the mechanical structure in Level 2 health; and not issue an alert or generate a maintenance recommendation if the switchgear is in Level 3 health. Here, Level 1 health refers to a state where a fault has occurred and affects current operation; Level 2 health refers to a state where there are potential problems but do not currently affect current operation; and Level 3 health refers to a state where there are no faults and does not affect current operation.

[0089] Figure 8 This is a schematic block diagram of an electronic device 300 provided in an embodiment of the present invention. The specific implementation of the electronic device 300 is not limited by the specific embodiments of the present invention. Figure 8 As shown, the electronic device 300 may include: a processor 302, a communications interface 304, a memory 306, and a communication bus 308. Wherein:

[0090] The processor 302, the communication interface 304, and the memory 306 communicate with each other through a communication bus 308.

[0091] The communication interface 304 is configured to communicate with other electronic devices or servers.

[0092] The processor 302 is configured to execute the program 310, and specifically can execute the related steps in any of the preceding embodiments.

[0093] Specifically, the program 310 can include program code including computer operation instructions.

[0094] The processor 302 can be a CPU, or an application specific integrated circuit (ASIC), or one or more integrated circuits configured to implement the embodiments of the present application. The one or more processors included in the smart device can be the same type of processor, such as one or more CPUs; or can be different types of processors, such as one or more CPUs and one or more ASICs.

[0095] RISC-V is an open-source instruction set architecture based on the principle of reduced instruction set (RISC), which can be applied to various aspects such as single-chip microcomputers and FPGA chips. Specifically, it can be applied in the fields of Internet of Things security, industrial control, mobile phones, personal computers, etc. And because it takes into account the realities of small, fast, and low power consumption when designing, it is particularly suitable for modern computing devices such as warehouse-scale computers, high-end mobile phones, and tiny embedded systems. With the rise of artificial intelligence Internet of Things (AIoT), the RISC-V instruction set architecture has also received more and more attention and support, and is expected to become the next generation of widely used CPU architecture.

[0096] The computer operation instructions in the embodiments of the present application can be computer operation instructions based on the RISC-V instruction set architecture, and correspondingly, the processor 302 can be designed based on the RISC-V instruction set. Specifically, the chip of the processor in the electronic device provided by the embodiments of the present application can be a chip designed based on the RISC-V instruction set, which can execute executable code based on the configured instructions, and further implement the health state determination method of the switch cabinet in the above embodiments.

[0097] The memory 306 is configured to store the program 310. The memory 306 can include a high-speed RAM memory, and can also include a non-volatile memory such as at least one disk memory.

[0098] The program 310 can specifically be used to cause the processor 302 to perform the method in any of the preceding embodiments.

[0099] The specific implementation of each step in the program 310 can refer to the corresponding description in the corresponding step and unit in any of the preceding method embodiments, which will not be repeated here. It can be clearly understood by those skilled in the art that, for the convenience and brevity of description, the specific working process of the above-described device and module can refer to the corresponding process description in the preceding method embodiments, which will not be repeated here.

[0100] The present application also provides a computer-readable storage medium storing instructions for causing a machine to perform the health state determination method of the switch cabinet as described herein. Specifically, a system or device equipped with a storage medium can be provided, on which a software program code realizing the functions of any of the above-described embodiments is stored, and the computer (or CPU or MPU) of the system or device reads out and executes the program code stored in the storage medium.

[0101] In this case, the program code read from the storage medium itself can realize the functions of any of the above-described embodiments, and therefore the program code and the storage medium storing the program code constitute a part of the present application.

[0102] The storage medium for providing the program code includes a floppy disk, a hard disk, a magneto-optical disk, an optical disk (such as CD-ROM, CD-R, CD-RW, DVD-ROM, DVD-RAM, DVD-RW, DVD+RW), a magnetic tape, a non-volatile memory card, and a ROM. Alternatively, the program code can be downloaded from a server computer via a communication network.

[0103] The present application also provides a computer program product including computer instructions instructing a computing device to perform any corresponding operation in the above-described method embodiments.

[0104] It should be noted that, according to the needs of implementation, each component / step described in the embodiments of the present application can be split into more components / steps, or two or more components / steps or part of the operations of the components / steps can be combined into a new component / step, to achieve the purpose of the embodiments of the present application.

[0105] The methods according to the embodiments of the present application described above can be implemented in hardware, firmware, or software, or a combination of them, and can be stored in a recording medium such as a CD ROM, RAM, floppy disk, hard disk, or magneto-optical disk, or be downloaded by a network from a remote recording medium or a non-transitory machine-readable medium originally stored in a local recording medium and then stored in a local recording medium, so that the methods described herein can be processed by such software on a recording medium using a general-purpose computer, a special-purpose processor, or programmable or special-purpose hardware such as an ASIC or an FPGA. It can be understood that the computer, processor, microprocessor controller, or programmable hardware includes a storage component (for example, RAM, ROM, flash memory, etc.) that can store or receive software or computer code, when the software or computer code is accessed and executed by the computer, processor, or hardware, the methods described herein are implemented. Furthermore, when a general-purpose computer accesses code for implementing the methods shown herein, the execution of the code will convert the general-purpose computer into a special-purpose computer for executing the methods shown herein.

[0106] Those skilled in the art can realize that the units and method steps of each example described in combination with the embodiments disclosed herein can be realized in electronic hardware, or a combination of computer software and electronic hardware. Whether the functions are realized in hardware or software depends on the specific application and design constraints of the technical solution. A person skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of the embodiments of the present application.

[0107] The nouns and pronouns referring to persons in this patent application are not limited to a specific gender.

[0108] The above only describes the preferred embodiments of the present application and is not intended to limit the present application. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included in the protection scope of the present application.

Claims

1. A method of determining a health state of a switchgear cabinet, characterized in that, The method comprises: obtaining electrical data and actual opening quantities of at least two mechanical structures included in a switch cabinet within a reference time period; determining a plurality of characteristic time points within the reference time period according to changes in the electrical data, and determining electrical values and actual opening quantities of the mechanical structures at each of the characteristic time points; determining a first health state of the mechanical structures according to the electrical values and the actual opening quantities of the mechanical structures at the characteristic time points; determining a second health state of the mechanical structures according to the actual opening quantities and standard opening quantities of the mechanical structures at the characteristic time points; determining a health state of the switch cabinet according to the first health state and the second health state of the at least two mechanical structures.

2. The method of claim 1, wherein, The at least two mechanical structures include at least two of a closing and opening coil, an energy storage motor, an electric chassis vehicle, a ground knife, and a three-position knife.

3. The method of claim 2, wherein, The electrical data includes current waveform data. The determining of the plurality of characteristic time points within the reference time period according to changes in the electrical data includes determining the plurality of characteristic time points within the reference time period from the current waveform data. The characteristic time points corresponding to the closing and opening coil include a running phase start time point, a current maximum value time point, a current minimum value time point, and a running phase end time point. The characteristic time points corresponding to the energy storage motor include a starting phase start time point, a running phase start time point, a maximum current time point, and a running phase end time point. The characteristic time points corresponding to the electric chassis vehicle include a starting phase start time point, a starting phase stop time point, a running phase stop time point, a maximum brake current time point, and a maximum reverse brake current time point. The characteristic time points corresponding to the ground knife include a running phase start time point, a maximum brake current time point, and a maximum reverse brake current time point. The characteristic time points corresponding to the three-position knife include a starting phase start time point, a running phase start time point, a maximum reverse current time point, and a motor stop time point.

4. The method of claim 1, wherein, The determining of the health state of the switch cabinet according to the first health state and the second health state of the at least two mechanical structures includes: determining a current health state of the mechanical structures according to the first health state and the second health state of the mechanical structures; determining the health state of the switch cabinet according to the current health states of the at least two mechanical structures.

5. The method of claim 4, wherein, The current health state includes a first health state, a second health state, and a third health state from high to low. The determining of the health state of the switch cabinet according to the current health states of the at least two mechanical structures includes: determining the highest level of health state among the current health states of the at least two mechanical structures as the health state of the switch cabinet. The first health state refers to a state in which a fault has occurred and affects the current operation, the second health state refers to a state in which there is a potential problem and does not affect the current operation, and the third health state refers to a state in which there is no fault and does not affect the current operation.

6. The method of claim 4, wherein, The electrical data includes current waveform data. The determining the plurality of characteristic time points located in the reference time period according to the change of the electrical data, and the electrical value and the actual opening amount of the mechanical structure at each characteristic time point comprises: calling the prediction model corresponding to the mechanical structure, determining the plurality of characteristic time points and the current value corresponding to each characteristic time point according to the current waveform data corresponding to the mechanical structure, and determining the actual opening amount corresponding to each characteristic time point based on the actual opening amount in the reference time period corresponding to the mechanical structure, wherein different prediction models correspond to different mechanical structures; The determining the first health state of the mechanical structure according to the electrical value and the actual opening amount of the mechanical structure at each characteristic time point comprises: calling the prediction model corresponding to the mechanical structure, and determining the first health state of the mechanical structure according to the current value and the actual opening amount of the mechanical structure at each characteristic time point; The determining the second health state of the mechanical structure according to the actual opening amount and the standard opening amount of the mechanical structure at each characteristic time point comprises: calling the prediction model corresponding to the mechanical structure, and determining the second health state of the mechanical structure according to the actual opening amount and the standard opening amount of the mechanical structure at each characteristic time point.

7. The method of claim 6, wherein, The current health state comprises a first health state, a second health state and a third health state from high to low; The determining the current health state of the mechanical structure according to the first health state and the second health state of the mechanical structure comprises: calling the prediction model corresponding to the mechanical structure, and determining the highest health state in the first health state and the second health state of the mechanical structure as the current health state of the mechanical structure; The first health state refers to a state that has appeared a fault and affects current operation, the second health state refers to a state that has potential problems and does not affect current operation, and the third health state refers to a state that has no fault and does not affect current operation.

8. The method of claim 4, wherein, The health state of the switch cabinet comprises a first health state, a second health state and a third health state from high to low; The method further comprises: if the health state of the switch cabinet is the first health state, issuing an alarm, and generating an operation and maintenance suggestion of immediate shutdown for repair for the mechanical structure in the first health state; if the health state of the switch cabinet is the second health state, issuing a pre-alarm, and generating an operation and maintenance suggestion of planned repair for the mechanical structure in the second health state; if the health state of the switch cabinet is the third health state, not issuing a prompt and not generating an operation and maintenance suggestion; The first health state refers to a state that has appeared a fault and affects current operation, the second health state refers to a state that has potential problems and does not affect current operation, and the third health state refers to a state that has no fault and does not affect current operation.

9. A health state determination apparatus (200) of a switchgear, characterized in that, The method comprises: a related data acquisition module (201) configured to acquire electrical data and actual opening amounts of at least two mechanical structures included in a switch cabinet in a reference time period; The data feature determination module (202) is configured to determine a plurality of feature time points within the reference time period according to the change of the electrical data, and determine the electrical value and the actual opening amount of the mechanical structure at each feature time point; The first state determination module (203) is configured to determine a first health state of the mechanical structure according to the electrical value and the actual opening amount of the mechanical structure at each feature time point, and determine a second health state of the mechanical structure according to the actual opening amount and the standard opening amount of the mechanical structure at each feature time point; The second state determination module (204) is configured to determine the health state of the switch cabinet according to the first health state and the second health state of the at least two mechanical structures.

10. An electronic device (300), characterized by The electronic device (300) comprises a processor (302), a communication interface (304), a memory (306) and a communication bus (308), the processor (302), the communication interface (304) and the memory (306) complete communication with each other through the communication bus (308); the memory (306) is used for storing at least one executable instruction, and the executable instruction makes the processor (302) execute the operation corresponding to the health state determination method of the switch cabinet according to any one of claims 1-8.

11. A computer readable storage medium, characterized in that, A computer program is stored thereon, and the computer program is executed by a processor to implement the health state determination method of the switch cabinet according to any one of claims 1-8. A computer program is stored thereon, and the computer program is executed by a processor to implement the health state determination method of the switch cabinet according to any one of claims 1-8.

Images