Metallized film capacitor parameter test method and system, and storage medium
By combining a capacitor graphical model and a neural network model with current change threshold judgment, the high cost of capacitor detection in large-scale power systems is solved, enabling real-time monitoring and accurate prediction of capacitor status, and reducing the consumption of manpower and material resources.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2023-10-21
- Publication Date
- 2026-03-10
AI Technical Summary
In the existing technology, regular physical inspection of metallized film capacitors in large-scale power systems requires a lot of manpower and resources, and it is difficult to effectively monitor the correlation between capacitors and the effects of aging.
By acquiring the circuit diagram data and historical state data of the capacitor, using a preset capacitor diagram model and neural network model, the state of the capacitor is predicted, the relationship between capacitors is determined by combining the current change threshold, and the state of the capacitor is monitored in real time.
This significantly shortens the capacitor parameter testing time, reduces manpower and material consumption, improves testing efficiency, enables real-time monitoring and accurate prediction of capacitor status, and allows for timely detection of potential problems.
Smart Images
Figure CN121633640A_ABST
Abstract
Description
TECHNICAL FIELD
[0001] The present application relates to the technical field of detection, and particularly relates to a metalized film capacitor parameter testing method, system and storage medium. BACKGROUND
[0002] Capacitors play an important role in power converters. In direct current alternating current conversion, a support capacitor is usually connected in parallel to the direct current bus to absorb the pulsating current on the direct current side, thereby avoiding the generation of high amplitude pulsating voltage at the impedance end of the direct current side, effectively controlling the fluctuation range of the ripple voltage, and reducing the impact of the direct current side transient overvoltage on the switching device during opening. Therefore, it is crucial to test and monitor the parameters of the capacitor. The performance and state of the capacitor will change over time and with the change of use conditions, so regular monitoring and testing are required.
[0003] The metalized film capacitor parameter testing method in the related art mainly involves physical detection, and usually requires special testing equipment and dedicated operators to directly physically measure by connecting to the electrodes of the capacitor.
[0004] However, the related art has some obvious shortcomings. For some large-scale power systems, the number of capacitors is very large. If regular physical detection is performed on each capacitor, a large amount of manpower and resources will be consumed. SUMMARY
[0005] The present application provides a metalized film capacitor parameter testing method, system and storage medium, which can greatly shorten the time of capacitor parameter testing and reduce the consumption of manpower and resources.
[0006] In a first aspect, the present application provides a metalized film capacitor parameter testing method, which comprises: acquiring circuit diagram data and historical capacitor state data of a to-be-tested capacitor; inputting the circuit diagram data into a preset capacitor graph model to calculate graph data corresponding to the circuit diagram data, the graph data comprising a first capacitor, a second capacitor connected to the first capacitor through a solid line segment, a third capacitor connected to the first capacitor through a dashed line segment, and a fourth capacitor, the first capacitor being directly connected to the second capacitor, the first capacitor being indirectly connected to the third capacitor, and the first capacitor not being connected to the third capacitor; and inputting the graph data and the historical capacitor state data into a preset neural network model to obtain first capacitor state data.
[0007] In the above embodiment, by acquiring the circuit diagram data and historical capacitor state data of the to-be-tested capacitors, and then inputting the data into the preset capacitor graph model and neural network model, the state data of each capacitor is predicted, so that special operators are not needed for special testing, the time for capacitor parameter testing is greatly shortened, the consumption of manpower and material resources is reduced, and meanwhile, since the method can consider the correlation between capacitors, when a capacitor starts to age, the influence of the aging on other capacitors can be quickly and accurately evaluated, so that more accurate prediction of capacitor monitoring is made.
[0008] With reference to the some embodiments of the first aspect, in some embodiments, after the circuit diagram data is input into the preset capacitor graph model to calculate the graph data corresponding to the circuit diagram data, the method further comprises: acquiring detection state data of part of the to-be-tested capacitors; and inputting the graph data and the detection state data into the preset neural network model to obtain second capacitor state data of all the to-be-tested capacitors.
[0009] In the above embodiment, part of the capacitors are measured in real time, and the data is input into the model to obtain the state data of all the capacitors, so that the state of all the capacitors is derived from the state of part of the capacitors, which not only improves the testing efficiency and avoids the resource consumption of comprehensive measurement, but also realizes real-time monitoring of the capacitor state, so that possible problems can be discovered and handled in time.
[0010] With reference to the some embodiments of the first aspect, in some embodiments, the preset capacitor graph model is: I nc1 +I nc2 +…+I ncj -I n1 -I n2 -…-I nk =0 In the formula, I ncj is the current of the jth capacitor connected to the node n, I nk is the current of the kth branch connected to the node n, I mk is the current of the branch mk composed of the node m and the node k, V m is the voltage of the node m, V k is the voltage of the node k, R mk is the branch resistance of the branch mk, I cj is the current of the capacitor j, C j is the capacitance value of the capacitor j, V cj is the voltage of the capacitor j, t cj is the voltage change time of the capacitor j, Let be the voltage across capacitor j and the differential of the voltage change of capacitor j, representing the rate of change of the voltage across capacitor j.
[0011] In the above embodiments, the preset capacitor diagram model combines three sets of equations, taking into account both the relationship between nodes and branches and the impact of capacitors on the circuit. This model can completely describe the circuit containing resistors and capacitors, providing a scientific and accurate theoretical model for capacitor parameter testing. By solving this set of equations, the changes in voltage and current in the circuit after capacitor parameter changes can be analyzed in detail, thereby accurately predicting and understanding the impact of capacitor parameter changes on other capacitors.
[0012] In conjunction with some embodiments of the first aspect, in some embodiments, circuit diagram data is input into a preset capacitor diagram model to calculate diagram data corresponding to the circuit diagram data. Specifically, this includes: changing the parameters of the first capacitor in the circuit diagram data; determining the capacitor under test whose current change is greater than a first threshold as the second capacitor, the capacitor under test whose current change is greater than a second threshold and less than the first threshold as the third capacitor, and the capacitor under test whose current change is less than the second threshold as the fourth capacitor, wherein the first threshold is greater than the second threshold.
[0013] In the above embodiments, by measuring and calculating the current in the circuit, the linkage relationship between capacitors can be determined. This method allows for a deeper understanding of the mutual influence between capacitors, thereby making the monitoring of capacitors more accurate.
[0014] In conjunction with some embodiments of the first aspect, changing the parameters of the first capacitor in the circuit diagram data specifically includes changing the capacitance value of the first capacitor in the circuit diagram data.
[0015] In the above embodiments, by changing the capacitance value of the first capacitor and observing the change in current, the monitoring of the capacitor is made more accurate.
[0016] In conjunction with some embodiments of the first aspect, in some embodiments, changing the parameters of the first capacitor in the circuit diagram data specifically includes changing the voltage of the first capacitor in the circuit diagram data.
[0017] In the above embodiments, by changing the voltage of the first capacitor and observing the change in current, the monitoring of the capacitor is made more accurate.
[0018] In conjunction with some embodiments of the first aspect, graph data and historical capacitor state data are input into a preset neural network model to obtain first capacitor state data. Specifically, this includes inputting graph data, historical capacitor state data, historical time corresponding to the historical capacitor state data, and current time into a preset neural network model to obtain first capacitor state data.
[0019] In the above embodiments, the aging problem of capacitors over time is taken into account. By monitoring and analyzing the state of capacitors in real time, the aging trend of capacitors and their possible impact on power system performance can be predicted.
[0020] Secondly, this application provides a parameter testing system for metallized thin-film capacitors. The parameter testing system for metallized thin-film capacitors includes a server, which includes: The acquisition module is used to acquire the circuit diagram data and historical capacitor status data of the capacitor under test. The first model module is used to input circuit diagram data into a preset capacitor diagram model to calculate the diagram data corresponding to the circuit diagram data. The diagram data includes a first capacitor, a second capacitor connected to the first capacitor by solid line segments, a third capacitor and a fourth capacitor connected to the first capacitor by dashed line segments. The first capacitor and the second capacitor are directly connected, the first capacitor and the third capacitor are indirectly connected, and the first capacitor and the third capacitor are not connected. The second model module is used to input graph data and historical capacitor state data into a preset neural network model to obtain the first capacitor state data.
[0021] In conjunction with some embodiments of the second aspect, in some embodiments, the server further includes: Partial detection module, used to acquire detection status data of some capacitors under test; The third model module is used to input graph data and detection state data into a preset neural network model to obtain the second capacitor state data of all capacitors under test.
[0022] In conjunction with some embodiments of the second aspect, in some embodiments, the preset capacitor diagram model is as follows: I nc1 +I nc2 +…+I ncj -I n1 -I n2 -…-I nk =0 In the formula, I ncj Let I be the current in the j-th capacitor connected to node n. nk Let I be the current in the k-th branch connecting node n. mk Let V be the current in branch mk, which consists of nodes m and k. m Let V be the voltage at node m. k R is the voltage at node k. mk Let I be the branch resistance of branch mk. cj Let C be the current in capacitor j. jLet V be the capacitance value of capacitor j. cj Let t be the voltage across capacitor j. cj Let be the time interval of voltage change in capacitor j. Let be the voltage across capacitor j and the differential of the voltage change of capacitor j, representing the rate of change of the voltage across capacitor j.
[0023] In conjunction with some embodiments of the second aspect, in some embodiments, the first model module specifically includes: The Change submodule is used to change the parameters of the first capacitor in the circuit diagram data; The determination submodule is used to determine the capacitor under test whose current change is greater than the first threshold as the second capacitor, the capacitor under test whose current change is greater than the second threshold but less than the first threshold as the third capacitor, and the capacitor under test whose current change is less than the second threshold as the fourth capacitor, wherein the first threshold is greater than the second threshold.
[0024] In conjunction with some embodiments of the second aspect, in some embodiments, the modification of the submodule specifically includes: The first modification subunit is used to change the capacitance value of the first capacitor in the circuit diagram data. In conjunction with some embodiments of the second aspect, in some embodiments, the modification of the submodule specifically includes: The second modification subunit is used to change the voltage of the first capacitor in the circuit diagram data. In conjunction with some embodiments of the second aspect, in some embodiments, the second model module specifically includes: The second model submodule is used to input graph data, historical capacitor state data, historical time corresponding to the historical capacitor state data, and current time into a preset neural network model to obtain the first capacitor state data.
[0025] Thirdly, embodiments of this application provide a parameter testing system for metallized thin-film capacitors, the system comprising: One or more processors and memory; The memory is coupled to the one or more processors and is used to store computer program code, which includes computer instructions that the one or more processors call to cause the metallized film capacitor parameter testing system to perform the methods described in the first aspect and any possible implementation thereof.
[0026] Fourthly, embodiments of this application provide a computer program product containing instructions that, when the computer program product is run on a server, cause the server to perform the method described in the first aspect and any possible implementation thereof.
[0027] Fifthly, embodiments of this application provide a computer-readable storage medium including instructions that, when executed on a server, cause the server to perform the method described in the first aspect and any possible implementation thereof.
[0028] It is understood that the metallized film capacitor parameter testing system provided in the second aspect, the metallized film capacitor parameter testing system provided in the third aspect, the computer program product provided in the fourth aspect, and the computer storage medium provided in the fifth aspect are all used to execute the metallized film capacitor parameter testing method provided in the embodiments of this application. Therefore, the beneficial effects they can achieve can be referred to the beneficial effects in the corresponding methods, and will not be repeated here.
[0029] One or more technical solutions provided in the embodiments of this application have at least the following technical effects or advantages: 1. The metallized film capacitor parameter testing method provided in this application obtains the circuit diagram data and historical capacitor state data of the capacitor under test, and then inputs these data into a preset capacitor diagram model and neural network model to predict the state data of each capacitor. Therefore, it does not require special operators to conduct the tests, which can greatly shorten the time for capacitor parameter testing and reduce the consumption of manpower and resources. At the same time, since this method can consider the correlation between capacitors, when one capacitor begins to age, it can quickly and accurately assess the impact of this aging on other capacitors, thereby making more accurate predictions for capacitor monitoring.
[0030] 2. The metallized film capacitor parameter testing method provided in this application measures a portion of the capacitors in real time and inputs these data into a model to obtain the state data of all capacitors. Therefore, the state of all capacitors can be derived from the state of a portion of the capacitors, which not only improves the efficiency of testing and avoids the resource overhead of comprehensive measurement, but also enables real-time monitoring of the capacitor state, so that potential problems can be detected and dealt with in a timely manner.
[0031] 3. The metallized film capacitor parameter testing method provided in this application uses a pre-set capacitor diagram model that combines three sets of equations. This model considers both the relationship between nodes and branches and the impact of capacitors on the circuit. This model can completely describe a circuit containing resistance and capacitance, providing a scientific and accurate theoretical model for capacitor parameter testing. By solving this set of equations, the changes in voltage and current in the circuit after changes in capacitor parameters can be analyzed in detail, thereby accurately predicting and understanding the impact of capacitor parameter changes on other capacitors. Attached Figure Description
[0032] Figure 1 This is a schematic flowchart of the parameter testing method for metallized film capacitors provided in this application.
[0033] Figure 2 Another flowchart illustrating the parameter testing method for metallized film capacitors provided in this application.
[0034] Figure 3 A schematic diagram of a modular virtual device for the metallized thin-film capacitor parameter testing system provided in this application.
[0035] Figure 4 A schematic diagram of the physical device of the metallized film capacitor parameter testing system provided in this application. Detailed Implementation
[0036] The terminology used in the following embodiments of this application is for the purpose of describing particular embodiments only and is not intended to be limiting of this application. As used in the specification and appended claims of this application, the singular expressions a, an, said, the above, and this are intended to also include the plural expressions, unless the context clearly indicates otherwise. It should also be understood that the terms used in this application refer to any or all possible combinations that include one or more of the listed items.
[0037] Hereinafter, the terms "first" and "second" are used for descriptive purposes only and should not be construed as implying or suggesting relative importance or implicitly indicating the number of indicated technical features. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature, and in the description of the embodiments of this application, unless otherwise stated, "multiple" means two or more.
[0038] The method for testing the parameters of the metallized thin-film capacitor in this embodiment is described below: like Figure 1 As shown, Figure 1 This is a schematic flowchart of the parameter testing method for metallized film capacitors provided in this application.
[0039] S101. Obtain the circuit diagram data and historical capacitor status data of the capacitor under test.
[0040] In some embodiments, circuit diagram data refers to the circuit diagram itself. In other embodiments, circuit diagram data refers to the physical structure of the circuit diagram, such as the connection method between capacitors, the number of capacitors, and their locations. Historical capacitor state data includes multiple past moments, as well as information such as the state, voltage, current, and capacitance value of the capacitors at those past moments. The method of obtaining this data can be determined according to specific circumstances. For example, circuit diagram data can be directly exported from circuit design software, while historical capacitor state data can be pre-stored in a database. No limitation is made here.
[0041] S102. Input the circuit diagram data into the preset capacitor diagram model to calculate the diagram data corresponding to the circuit diagram data. The diagram data includes a first capacitor, a second capacitor connected to the first capacitor by a solid line segment, a third capacitor and a fourth capacitor connected to the first capacitor by a dashed line segment. The first capacitor and the second capacitor are directly connected, the first capacitor and the third capacitor are indirectly connected, and the first capacitor and the third capacitor are not connected.
[0042] It should be noted that in this step, the circuit diagram data is processed and understood by referring to the relationship diagram. The resulting diagram data can not only show the physical location and connection method of the capacitors in the circuit, but also help to understand the relationship between the capacitors, such as which capacitors are directly connected, which capacitors are indirectly connected, and which capacitors are not connected.
[0043] In some embodiments, the preset capacitor diagram is constructed based on the theory and technology of relational graphs. It can receive circuit diagram data as input and calculate the diagram data corresponding to the circuit diagram data. The diagram data includes a first capacitor, a second capacitor connected to the first capacitor by solid line segments, and a third and fourth capacitor connected to the first capacitor by dashed line segments. In this embodiment, solid line segments represent direct connections between capacitors, dashed line segments represent indirect connections between capacitors, and no line segments represent no connection between capacitors. In other embodiments, other methods may be adopted, which are not limited here.
[0044] It should be noted that the data in the figure does not only include four capacitors, namely the first capacitor, the second capacitor, the third capacitor and the fourth capacitor, but includes multiple capacitors, and each capacitor is connected to the others. The above embodiment is only an adaptive illustration and is not limited here.
[0045] For example, the parameters of the first capacitor are changed in the circuit diagram data; the capacitor under test with a current change greater than the first threshold is identified as the second capacitor, the capacitor under test with a current change greater than the second threshold and less than the first threshold is identified as the third capacitor, and the capacitor under test with a current change less than the second threshold is identified as the fourth capacitor, where the first threshold is greater than the second threshold.
[0046] It should be noted that the above solution is run on simulation software.
[0047] Specific examples are as follows: You need to load a file containing a circuit diagram, i.e., circuit diagram data. This file typically contains a series of information including capacitors and their parameters.
[0048] Locate the description of the first capacitor (C1) and change its parameters, such as the capacitance value. For example, if the original value of capacitor C1 is 10μF, you can change it to 20μF.
[0049] Use the `tran` command to perform time-domain analysis and obtain current changes. For example, perform a 1-second simulation, sampling once every 1ms.
[0050] Two thresholds are preset: a first threshold T1 and a second threshold T2, where T1 > T2. The second, third, and fourth capacitors can be determined using the following criteria: Second capacitor: The capacitor whose current change is greater than T1.
[0051] Third capacitor: A capacitor whose current change is greater than T2 but less than T1.
[0052] Fourth capacitor: A capacitor whose current change is less than T2.
[0053] This requires writing a simple script to iterate through the current data output by the simulation, calculate the current change of each capacitor, and classify it according to the above criteria.
[0054] It should be noted that this method is just a basic example. The actual operation process may vary depending on the complexity of the circuit and the function of the specific simulation software, and no limitations are made here.
[0055] The preset capacitor diagram model is as follows: I nc1 +I nc2 +…+I ncj -I n1 -I n2 -…-I nk =0 In the formula, I ncj Let I be the current in the j-th capacitor connected to node n. nk Let I be the current in the k-th branch connecting node n. mk Let V be the current in branch mk, which consists of nodes m and k. m Let V be the voltage at node m. k R is the voltage at node k. mk Let I be the branch resistance of branch mk. cj Let C be the current in capacitor j. j Let V be the capacitance value of capacitor j. cj Let t be the voltage across capacitor j. cj Let be the time interval of voltage change in capacitor j. Let be the voltage across capacitor j and the differential of the voltage change of capacitor j, representing the rate of change of the voltage across capacitor j.
[0056] These three formulas each represent different types of current: the first formula describes the current at a node, the second formula describes the current in a branch, and the third formula expresses the current in a capacitor. In practical applications, these three formulas form a set of formulas, in which any change in any parameter will affect other parameters, thereby causing a change in current. Therefore, by observing the changes in these parameters, we can understand and calculate the amount of change in current.
[0057] In some embodiments, the capacitance value of the first capacitor is changed in the circuit diagram data; in other embodiments, the voltage of the first capacitor is changed in the circuit diagram data.
[0058] It is evident that by measuring and calculating the current in the circuit, the interrelationship between capacitors can be determined. This method allows for a deeper understanding of the mutual influence between capacitors, thereby making the monitoring of capacitors more accurate.
[0059] As can be seen, the pre-defined capacitor diagram model combines three sets of equations, taking into account both the relationship between nodes and branches and the impact of capacitors on the circuit. This model can completely describe circuits containing resistors and capacitors, providing a scientific and accurate theoretical model for capacitor parameter testing. By solving this set of equations, the changes in voltage and current in the circuit after capacitor parameter changes can be analyzed in detail, thereby accurately predicting and understanding the impact of capacitor parameter changes on other capacitors.
[0060] It is evident that by changing the capacitance value of the first capacitor and observing the change in current, the monitoring of the capacitor can be made more accurate.
[0061] It is evident that by changing the voltage of the first capacitor and observing the change in current, the monitoring of the capacitor can be made more accurate.
[0062] S103. Input the graph data and historical capacitor state data into the preset neural network model to obtain the first capacitor state data.
[0063] Using graph data as weights, with earlier historical data as input and more recent historical data as output, a pre-defined neural network model is trained. This neural network model can be any known model, such as a deep neural network, a convolutional neural network, or a recurrent neural network.
[0064] For example, historical data can be divided into input and output parts based on a timeline. For instance, assuming a year's worth of historical data, the data from the first 11 months can be used as input, and the data from the following month as output. Then, the graph data can be converted into weights, for example, based on different relationships. The input data and weights are then fed into a neural network model for training. During training, the model automatically adjusts its parameters to minimize the difference between the predicted and actual outputs. After numerous iterations and training, the model will be optimized and able to accurately predict the state of the capacitor. Using the trained model, inputting current graph data and historical data, one can obtain information such as the state and capacitance value of the capacitor at the current moment.
[0065] As can be seen, by acquiring the circuit diagram data and historical capacitor status data of the capacitor under test, and then inputting these data into the preset capacitor diagram model and neural network model, the status data of each capacitor can be predicted. Therefore, there is no need for specialized operators to conduct tests, which can greatly shorten the time for capacitor parameter testing and reduce the consumption of manpower and resources. At the same time, since this method can consider the correlation between capacitors, when one capacitor begins to age, it can quickly and accurately assess the impact of this aging on other capacitors, thereby making more accurate predictions for capacitor monitoring.
[0066] It should be noted that the state of the capacitor may gradually deteriorate over time, so the time factor is an important factor that must be considered. Therefore, in step S103, not only are the graph data and historical capacitor state data input into the preset neural network model to obtain the state data of the first capacitor, but the historical time corresponding to the historical capacitor state data and the current time are also input together. In this way, the state changes of the capacitor can be understood more comprehensively and the model can be provided with more accurate prediction capabilities.
[0067] As can be seen, considering the aging problem of capacitors over time, by monitoring and analyzing the state of capacitors in real time, the aging trend of capacitors and their potential impact on power system performance can be predicted. The above embodiment uses historical data to predict current data, but in actual use, operators will measure the state data of a portion of the capacitors. How can we infer the complete state data of all capacitors from this partial state data? To solve these problems, we will take one possible solution as an example, combined with... Figure 2 The embodiments shown below will be described in more detail below: S201. Obtain the circuit diagram data and historical capacitor status data of the capacitor under test.
[0068] In some embodiments, circuit diagram data refers to the circuit diagram itself. In other embodiments, circuit diagram data refers to the physical structure of the circuit diagram, such as the connection method between capacitors, the number of capacitors, and their locations. Historical capacitor state data includes multiple past moments, as well as information such as the state, voltage, current, and capacitance value of the capacitors at those past moments. The method of obtaining this data can be determined according to specific circumstances. For example, circuit diagram data can be directly exported from circuit design software, while historical capacitor state data can be pre-stored in a database. No limitation is made here.
[0069] S202. Input the circuit diagram data into the preset capacitor diagram model to calculate the diagram data corresponding to the circuit diagram data. The diagram data includes a first capacitor, a second capacitor connected to the first capacitor by a solid line segment, a third capacitor and a fourth capacitor connected to the first capacitor by a dashed line segment. The first capacitor and the second capacitor are directly connected, the first capacitor and the third capacitor are indirectly connected, and the first capacitor and the third capacitor are not connected.
[0070] It should be noted that in this step, the circuit diagram data is processed and understood by referring to the relationship diagram. The resulting diagram data can not only show the physical location and connection method of the capacitors in the circuit, but also help to understand the relationship between the capacitors, such as which capacitors are directly connected, which capacitors are indirectly connected, and which capacitors are not connected.
[0071] In some embodiments, the preset capacitor diagram is constructed based on the theory and technology of relational graphs. It can receive circuit diagram data as input and calculate the diagram data corresponding to the circuit diagram data. The diagram data includes a first capacitor, a second capacitor connected to the first capacitor by solid line segments, and a third and fourth capacitor connected to the first capacitor by dashed line segments. In this embodiment, solid line segments represent direct connections between capacitors, dashed line segments represent indirect connections between capacitors, and no line segments represent no connection between capacitors. In other embodiments, other methods may be adopted, which are not limited here.
[0072] It should be noted that the data in the figure does not only include four capacitors, namely the first capacitor, the second capacitor, the third capacitor and the fourth capacitor, but includes multiple capacitors, and each capacitor is connected to the others. The above embodiment is only an adaptive illustration and is not limited here.
[0073] For example, the parameters of the first capacitor are changed in the circuit diagram data; the capacitor under test with a current change greater than the first threshold is identified as the second capacitor, the capacitor under test with a current change greater than the second threshold and less than the first threshold is identified as the third capacitor, and the capacitor under test with a current change less than the second threshold is identified as the fourth capacitor, where the first threshold is greater than the second threshold.
[0074] It should be noted that the above solution is run on simulation software.
[0075] Specific examples are as follows: You need to load a file containing a circuit diagram, i.e., circuit diagram data. This file typically contains a series of information including capacitors and their parameters.
[0076] Locate the description of the first capacitor (C1) and change its parameters, such as the capacitance value. For example, if the original value of capacitor C1 is 10μF, you can change it to 20μF.
[0077] Use the `tran` command to perform time-domain analysis and obtain current changes. For example, perform a 1-second simulation, sampling once every 1ms.
[0078] Two thresholds are preset: a first threshold T1 and a second threshold T2, where T1 > T2. The second, third, and fourth capacitors can be determined using the following criteria: Second capacitor: The capacitor whose current change is greater than T1.
[0079] Third capacitor: A capacitor whose current change is greater than T2 but less than T1.
[0080] Fourth capacitor: A capacitor whose current change is less than T2.
[0081] This requires writing a simple script to iterate through the current data output by the simulation, calculate the current change of each capacitor, and classify it according to the above criteria.
[0082] It should be noted that this method is just a basic example. The actual operation process may vary depending on the complexity of the circuit and the function of the specific simulation software, and no limitations are made here.
[0083] The preset capacitor diagram model is as follows: I nc1 +I nc2 +…+I ncj -I n1 -I n2 -…-I nk =0 In the formula, I ncj Let I be the current in the j-th capacitor connected to node n. nk Let I be the current in the k-th branch connecting node n. mk Let V be the current in branch mk, which consists of nodes m and k. m Let V be the voltage at node m. k R is the voltage at node k.mk Let I be the branch resistance of branch mk. cj Let C be the current in capacitor j. j Let V be the capacitance value of capacitor j. cj Let t be the voltage across capacitor j. cj Let be the time interval of voltage change in capacitor j. Let be the voltage across capacitor j and the differential of the voltage change of capacitor j, representing the rate of change of the voltage across capacitor j.
[0084] These three formulas each represent different types of current: the first formula describes the current at a node, the second formula describes the current in a branch, and the third formula expresses the current in a capacitor. In practical applications, these three formulas form a set of formulas, in which any change in any parameter will affect other parameters, thereby causing a change in current. Therefore, by observing the changes in these parameters, we can understand and calculate the amount of change in current.
[0085] In some embodiments, the capacitance value of the first capacitor is changed in the circuit diagram data; in other embodiments, the voltage of the first capacitor is changed in the circuit diagram data.
[0086] It is evident that by measuring and calculating the current in the circuit, the interrelationship between capacitors can be determined. This method allows for a deeper understanding of the mutual influence between capacitors, thereby making the monitoring of capacitors more accurate.
[0087] As can be seen, the pre-defined capacitor diagram model combines three sets of equations, taking into account both the relationship between nodes and branches and the impact of capacitors on the circuit. This model can completely describe circuits containing resistors and capacitors, providing a scientific and accurate theoretical model for capacitor parameter testing. By solving this set of equations, the changes in voltage and current in the circuit after capacitor parameter changes can be analyzed in detail, thereby accurately predicting and understanding the impact of capacitor parameter changes on other capacitors.
[0088] It is evident that by changing the capacitance value of the first capacitor and observing the change in current, the monitoring of the capacitor can be made more accurate.
[0089] It is evident that by changing the voltage of the first capacitor and observing the change in current, the monitoring of the capacitor can be made more accurate.
[0090] S203. Obtain the detection status data of some capacitors under test.
[0091] It is easy to imagine that it is necessary to collect some detection status data of the capacitor under test. This data may come from real-time circuit monitoring system or from regular circuit maintenance and inspection, which is not limited here.
[0092] These test status data reflect the current operating state of the capacitors, which can help understand potential problems with the capacitors and whether the capacitors are operating stably. For capacitors that are not directly measured, their status can be indirectly inferred from their position in the circuit diagram and the status of other capacitors connected to them.
[0093] S204. Input the graph data and detection status data into the preset neural network model to obtain the second capacitor status data of all capacitors under test.
[0094] Using graph data as weights, a pre-defined neural network model is trained with partial data as input data and complete data as output data. This neural network model can be any known model, such as deep neural networks, convolutional neural networks, or recurrent neural networks.
[0095] For example, the input and output parts of the training data can be divided based on completeness. For instance, assuming a complete dataset, a portion of the data can be used as input, and the complete dataset as output. The graph data can then be transformed into weights, for example, based on different relationships. The input data and weights are then fed into a neural network model for training. During training, the model automatically adjusts its parameters to minimize the difference between the predicted and actual outputs. After numerous iterations and training, the model will be optimized and able to accurately predict the state of the capacitor. Using the trained model, inputting the detected state data and the graph data, a second capacitor state data can be obtained.
[0096] As can be seen, by measuring a portion of the capacitors in real time and inputting this data into the model to obtain the state data of all capacitors, the state of all capacitors can be derived from the state of a portion of the capacitors. This not only improves the efficiency of the test and avoids the resource overhead of comprehensive measurement, but also enables real-time monitoring of the capacitor state, allowing potential problems to be detected and dealt with in a timely manner.
[0097] The following are device embodiments of this application, which can be used to execute the method embodiments of this application. For details not disclosed in the device embodiments of this application, please refer to the method embodiments of this application.
[0098] refer to Figure 3 This application provides a parameter testing system for metallized film capacitors. The parameter testing system for metallized film capacitors includes a server, which includes: The acquisition module 301 is used to acquire the circuit diagram data and historical capacitor status data of the capacitor under test; The first model module 302 is used to input circuit diagram data into a preset capacitor diagram model to calculate diagram data corresponding to the circuit diagram data. The diagram data includes a first capacitor, a second capacitor connected to the first capacitor by solid line segments, a third capacitor and a fourth capacitor connected to the first capacitor by dashed line segments. The first capacitor and the second capacitor are directly connected, the first capacitor and the third capacitor are indirectly connected, and the first capacitor and the third capacitor are not connected. The second model module 303 is used to input graph data and historical capacitor state data into a preset neural network model to obtain the first capacitor state data.
[0099] In some embodiments, the server further includes: Partial detection module, used to acquire detection status data of some capacitors under test; The third model module is used to input graph data and detection state data into a preset neural network model to obtain the second capacitor state data of all capacitors under test.
[0100] In some embodiments, the preset capacitor diagram model is: I nc1 +I nc2 +…+I ncj -I n1 -I n2 -…-I nk =0 In the formula, I ncj Let I be the current in the j-th capacitor connected to node n. nk Let I be the current in the k-th branch connecting node n. mk Let V be the current in branch mk, which consists of nodes m and k. m Let V be the voltage at node m. k R is the voltage at node k. mk Let I be the branch resistance of branch mk. cj Let C be the current in capacitor j. j Let V be the capacitance value of capacitor j. cj Let t be the voltage across capacitor j. cj Let be the time interval of voltage change in capacitor j. Let be the voltage across capacitor j and the differential of the voltage change of capacitor j, representing the rate of change of the voltage across capacitor j.
[0101] In some embodiments, the first model module specifically includes: The Change submodule is used to change the parameters of the first capacitor in the circuit diagram data; The determination submodule is used to determine the capacitor under test whose current change is greater than the first threshold as the second capacitor, the capacitor under test whose current change is greater than the second threshold but less than the first threshold as the third capacitor, and the capacitor under test whose current change is less than the second threshold as the fourth capacitor, wherein the first threshold is greater than the second threshold.
[0102] In some embodiments, changing a submodule specifically includes: The first modification subunit is used to change the capacitance value of the first capacitor in the circuit diagram data. In some embodiments, changing a submodule specifically includes: The second modification subunit is used to change the voltage of the first capacitor in the circuit diagram data. In some embodiments, the second model module specifically includes: The second model submodule is used to input graph data, historical capacitor state data, historical time corresponding to the historical capacitor state data, and current time into a preset neural network model to obtain the first capacitor state data.
[0103] This application also discloses a parameter testing system for metallized thin-film capacitors. (Refer to...) Figure 4 This is a schematic diagram of the physical device of the metallized film capacitor parameter testing system provided in this application. The information interaction device 400 may include: at least one processor 401, at least one network interface 404, a user interface 403, a memory 405, and at least one communication bus 402.
[0104] The communication bus 402 is used to enable communication between these components.
[0105] The user interface 403 may include a display screen and a camera. Optionally, the user interface 403 may also include a standard wired interface and a wireless interface.
[0106] The network interface 404 may optionally include a standard wired interface or a wireless interface (such as a Wi-Fi interface).
[0107] The processor 401 may include one or more processing cores. The processor 401 connects to various parts of the server using various interfaces and lines, and performs various server functions and processes data by running or executing instructions, programs, code sets, or instruction sets stored in memory 405, and by calling data stored in memory 405. Optionally, the processor 401 may be implemented using at least one hardware form of Digital Signal Processing (DSP), Field-Programmable Gate Array (FPGA), or Programmable Logic Array (PLA). The processor 401 may integrate one or a combination of several of the following: Central Processing Unit (CPU), Graphics Processing Unit (GPU), and modem. The CPU primarily handles the operating system, user interface, and applications; the GPU is responsible for rendering and drawing the content required for display; and the modem handles wireless communication. It is understood that the modem may also be implemented as a separate chip without being integrated into the processor 401.
[0108] The memory 405 may include random access memory (RAM) or read-only memory. Optionally, the memory 405 may include a non-transitory computer-readable storage medium. The memory 405 may be used to store instructions, programs, code, code sets, or instruction sets. The memory 405 may include a program storage area and a data storage area, wherein the program storage area may store instructions for implementing an operating system, instructions for at least one function (such as touch function, sound playback function, image playback function, etc.), instructions for implementing the above-described method embodiments, etc.; the data storage area may store data involved in the above-described method embodiments, etc. Optionally, the memory 405 may also be at least one storage device located remotely from the aforementioned processor 401. (Refer to...) Figure 4 The memory 405, which serves as a computer storage medium, may include an operating system, a network communication module, a user interface module, and an application program for testing the parameters of metallized film capacitors.
[0109] exist Figure 4In the information interaction device 400 shown, the user interface 403 is mainly used to provide an input interface for the user and to acquire user input data; while the processor 401 can be used to call the application program for testing the parameters of the metallized film capacitor stored in the memory 405. When executed by one or more processors 401, the information interaction device 400 performs one or more of the methods described in the above embodiments. It should be noted that, for the foregoing method embodiments, for the sake of simplicity, they are all described as a series of actions. However, those skilled in the art should understand that this application is not limited to the described order of actions, because according to this application, some steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also understand that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily necessary for this application.
[0110] In the above embodiments, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions in other embodiments.
[0111] In the various embodiments provided in this application, it should be understood that the disclosed apparatus can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some service interface; the indirect coupling or communication connection between apparatuses or units may be electrical or other forms.
[0112] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.
[0113] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.
[0114] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage device (CMD). Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a memory and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this application. The aforementioned memory includes various media capable of storing program code, such as USB flash drives, portable hard drives, magnetic disks, or optical disks.
[0115] The above description is merely an exemplary embodiment of this disclosure and should not be construed as limiting the scope of this disclosure. Any equivalent changes and modifications made in accordance with the teachings of this disclosure shall still fall within the scope of this disclosure. Other embodiments of this disclosure will be readily apparent to those skilled in the art upon consideration of the specification and the disclosure of practical truths.
[0116] This application is intended to cover any variations, uses, or adaptations of this disclosure that follow the general principles of this disclosure and include common knowledge or customary techniques in the art not described in this disclosure. The specification and embodiments are to be considered exemplary only, and the scope and spirit of this disclosure are defined by the claims.
Claims
1. A method of testing parameters of a metalized film capacitor, characterized by, The method comprises: obtaining circuit diagram data and historical capacitor state data of a to-be-tested capacitor; inputting the circuit diagram data into a preset capacitor graph model to calculate graph data corresponding to the circuit diagram data, wherein the graph data comprises a first capacitor, a second capacitor connected to the first capacitor through a solid line segment, a third capacitor connected to the first capacitor through a dashed line segment, and a fourth capacitor, the first capacitor is directly connected to the second capacitor, the first capacitor is indirectly connected to the third capacitor, and the first capacitor is not connected to the third capacitor; inputting the graph data and the historical capacitor state data into a preset neural network model to obtain first capacitor state data.
2. The method of claim 1, wherein After the circuit diagram data is inputted into the preset capacitor graph model to calculate the graph data corresponding to the circuit diagram data, the method further comprises: obtaining detection state data of part of the to-be-tested capacitor; 3. The method of claim 1, wherein the metalized film capacitor parameters are tested by: inputting the graph data and the detection state data into a preset neural network model to obtain second capacitor state data of all the to-be-tested capacitor. I nc1 +I nc2 +…+I ncj -I n1 -I n2 -…-I nk =0 where I ncj is the current of the jth capacitor connected to node n, I nk is the current of the kth branch connected to node n, I mk is the current of the branch mk consisting of node m and node k, V m is the voltage of node m, V k is the voltage of node k, R mk is the branch resistance of the branch mk, I cj is the current of the capacitor j, C j is the capacitance value of the capacitor j, V cj is the voltage of the capacitor j, t cj is the voltage change time of the capacitor j, is the voltage of the capacitor j and the voltage change differential of the capacitor j, indicating the voltage change speed of the capacitor j.
4. The method of claim 1, wherein the metalized film capacitor parameters are tested by: The preset capacitor graph model comprises: The circuit diagram data is inputted into the preset capacitor graph model to calculate the graph data corresponding to the circuit diagram data, specifically comprising: changing a parameter of the first capacitor in the circuit diagram data; 5. The method of claim 4, wherein the metalized film capacitor parameter test method is characterized by, determining a to-be-tested capacitor with a current change greater than a first threshold value as the second capacitor, a to-be-tested capacitor with a current change greater than a second threshold value and less than the first threshold value as the third capacitor, and a to-be-tested capacitor with a current change less than the second threshold value as the fourth capacitor, the first threshold value being greater than the second threshold value. The parameter of the first capacitor in the circuit diagram data is changed, specifically comprising:
6. The method of claim 4, wherein the metalized film capacitor parameter test method is characterized by, changing a capacitance value of the first capacitor in the circuit diagram data. The parameter of the first capacitor in the circuit diagram data is changed, specifically comprising:
7. The method of claim 1, wherein changing a voltage of the first capacitor in the circuit diagram data.
8. A metallized film capacitor parameter testing system comprising a server, characterized in that, The graph data and the historical capacitor state data are inputted into the preset neural network model to obtain the first capacitor state data, specifically comprising: inputting the graph data, the historical capacitor state data, historical time corresponding to the historical capacitor state data, and current time into the preset neural network model to obtain the first capacitor state data. The server comprises: an obtaining module configured to obtain circuit diagram data and historical capacitor state data of a to-be-tested capacitor; 9. A metalized film capacitor parameter testing system, characterized by, a first model module configured to input the circuit diagram data into a preset capacitor graph model to calculate graph data corresponding to the circuit diagram data, wherein the graph data comprises a first capacitor, a second capacitor connected to the first capacitor through a solid line segment, a third capacitor connected to the first capacitor through a dashed line segment, and a fourth capacitor, the first capacitor is directly connected to the second capacitor, the first capacitor is indirectly connected to the third capacitor, and the first capacitor is not connected to the third capacitor; a second model module configured to input the graph data and the historical capacitor state data into a preset neural network model to obtain first capacitor state data. comprises: one or more processors and a memory; the memory coupled with the one or more processors, the memory to store computer program code comprising computer instructions to cause the metalized film capacitor parameter testing system to perform the method of any of claims 1-7.
10. A computer-readable storage medium comprising instructions, characterized in that, When the instructions are executed on the metalized film capacitor parameter testing system, the metalized film capacitor parameter testing system is caused to perform the method of any of claims 1-7.