An EMC state evaluation system and method applied to a capacitor

Abstract

The application discloses an EMC state evaluation system and method applied to capacitors, and relates to the technical field of capacitor state evaluation. The evaluation system comprises a plurality of function modules which are sequentially operated, and are respectively a layout collection module, a signal processing module, a feature extraction module, a state evaluation module and an alarm feedback module. The technical key points are as follows: the system builds a life prediction model, generates a life prediction index of the capacitor according to the change trend of the comprehensive performance evaluation, and analyzes the change trend of the life prediction index under different periods. This is helpful for discovering potential problems in time and taking corresponding maintenance measures, so as to ensure the normal operation of the capacitor and prolong the service life of the capacitor. The overall scheme not only solves the technical problems of capacitor performance and life evaluation, but also realizes real-time and accurate evaluation, thereby providing strong support for the normal operation and maintenance of the capacitor.

Description

TECHNICAL FIELD

[0001] The application relates to the technical field of capacitor state evaluation, and particularly relates to an EMC state evaluation system and method applied to a capacitor. BACKGROUND

[0002] EMC (electromagnetic compatibility) state evaluation of a capacitor is an important link for ensuring that the capacitor will not be affected in performance or cause failure due to electromagnetic interference under normal working conditions. The content of the capacitor EMC state evaluation includes: electromagnetic interference level evaluation: measuring the electromagnetic field intensity generated by the capacitor during the working process, especially the electric field or magnetic field intensity of a specific frequency band; analyzing the possible electromagnetic interference of the capacitor on surrounding equipment to ensure that the interference level is within an acceptable range; electromagnetic sensitivity evaluation: evaluating the resistance of the capacitor to external electromagnetic interference, that is, whether the capacitor can maintain normal work when subjected to electromagnetic interference; determining the performance change of the capacitor under specific electromagnetic interference through simulation or actual test; capacitor value stability evaluation: measuring the actual capacitance value of the capacitor under electromagnetic interference, and comparing it with the initial capacitance value or the rated capacitance value; analyzing the influence of electromagnetic interference on the stability of the capacitance value of the capacitor to ensure that the capacitance value change is within an acceptable range.

[0003] The scheme pointed out in the file with the publication number CN113899960A and the name of a simulation electric vehicle running state electric drive system EMI test system includes: the system is used for EMI test of the electric drive system, and includes a darkroom, an electric drive system, a copper base, an insulating base, a rotating speed and torque measuring device placed outside the darkroom, a dynamometer, a dynamometer power supply, a controller, an electric drive system control computer, a frequency spectrometer and an optical coupling isolation device. The electric drive system control computer controls the electric drive system and the load dynamometer, measures the differential mode and common mode EMI of the direct current side and the alternating current side under different working conditions of the electric drive system; the measured common mode voltage and common mode current data can be used to extract the common mode impedance of the battery pack under different working conditions; the application can test and evaluate the EMI generated by the electric drive system under various working conditions, obtain the EMC performance of the electric drive system, and the test result is accurate and reliable. However, it cannot monitor or evaluate the state of the capacitor carried by the automobile in real time.

[0004] In combination with the above file and the prior art, when the state of the automobile capacitor is evaluated in the traditional way, the data parameters considered are relatively common, such as current and voltage, but the way adopted in the subsequent evaluation is usually single, which cannot adapt to specific environments, thereby affecting the accuracy of the subsequent performance and life evaluation of the capacitor, and real-time and effective evaluation operation cannot be made. SUMMARY

[0005] (1) Technical problems solved

[0006] In view of the deficiencies of the prior art, the EMC state evaluation system and method applied to capacitors are provided, which realizes real-time and accurate evaluation of the performance and life evaluation of capacitors, provides strong support for the normal operation and maintenance of capacitors, and solves the problems in the background art.

[0007] (II) Technical solutions

[0008] To achieve the above object, the present application is realized by the following technical solutions:

[0009] An EMC state evaluation system applied to capacitors comprises:

[0010] The arrangement collection module collects physical parameters and electromagnetic field intensity data in the working process of the capacitor;

[0011] The signal processing module filters and amplifies the physical parameters and electromagnetic field intensity data, converts the physical parameters and electromagnetic field intensity data into digital signals through an A / D converter, and outputs the digital signals;

[0012] The feature extraction module receives and analyzes the digital signals, extracts feature parameters in each sub-time period within a preset period T, and establishes a feature parameter database; and synchronously acquires state defining parameters of the capacitor;

[0013] The state evaluation module has a built-in defining submodule, builds a trigger calculation model according to the state defining parameters, generates a trigger index, and sends an addition signal when the trigger index exceeds a set defining threshold; under the condition of receiving or not receiving the addition signal, the corresponding feature parameters are weighted to obtain a comprehensive performance evaluation value corresponding to each sub-time period;

[0014] The change trend of the comprehensive performance evaluation value under the same period T is analyzed, and the first analysis result obtained is outputted;

[0015] According to each comprehensive performance evaluation value under the same period T, a life prediction model is built, a life prediction index is generated, and the change trend of the life prediction index under different periods T is analyzed, and the second analysis result obtained is outputted;

[0016] The alarm feedback module receives the first analysis result and the second analysis result;

[0017] When the comprehensive performance evaluation value continuously increases in the first analysis result, a performance warning signal is sent, and a repair strategy is executed;

[0018] When the life prediction index continuously decreases in the second analysis result, a pointing warning signal is sent, and a maintenance strategy is executed.

[0019] Further, the physical parameters include voltage, current and temperature, and the physical parameters and the electromagnetic field intensity data of the surrounding area of the capacitor are acquired by the sensor network arranged in the capacitor and the surrounding area thereof.

[0020] Further, the characteristic parameters include electromagnetic interference level, capacitance value change rate and temperature coefficient.

[0021] Further, the process of extracting the characteristic parameters in each sub-time period is as follows:

[0022] The average value of the electromagnetic field intensity of the surrounding area of the capacitor in the corresponding sub-time period is calculated to obtain the electromagnetic interference level; the actual capacitance value of the capacitor is calculated by using the collected voltage and current data, and is compared with the initial capacitance value to obtain the capacitance value change rate; the temperature and the capacitance value at the initial moment of the corresponding sub-time period are acquired, and the temperature and the capacitance value at the end moment of the corresponding sub-time period are acquired, and the slope of the capacitance value change with the temperature is calculated, that is, the temperature coefficient is obtained.

[0023] Further, the state defining parameters of the capacitor include ambient temperature and running time; wherein the ambient temperature is acquired by data grabbing from the regional meteorological station of the area where the automobile is located, and the running time is acquired by collecting the timer configured on the automobile.

[0024] Further, the process of triggering the calculation model to generate the trigger index is as follows:

[0025] Firstly, the trigger calculation model adopts a linear function to establish an influence factor calculation formula, as shown below:

[0026]

[0027] In formula (1), Ft represents the ambient temperature influence factor, a and b are both constants, and the value range is: 0

[0028] Secondly, the ambient temperature influence factor and the running time influence factor are added to obtain the trigger index:

[0029] Ci=Ft+Fh (3)

[0030] In formula (3), Ci represents the trigger index.

[0031] Further, under the condition of receiving the addition signal, the electromagnetic interference level, the capacitance value change rate and the temperature coefficient are used for weighted calculation to obtain the corresponding comprehensive performance evaluation of each sub-time period, and the formula used is:

[0032] S i=w1*E+w2*CR+w3*TC; (4)

[0033] In formula (4), S i represents the comprehensive performance evaluation corresponding to the i-th sub-time period, i is a positive integer greater than 0, E represents the electromagnetic interference level, CR = ΔC / C0, wherein ΔC is the change amount of the capacitance value, C0 is the initial capacitance value, and CR represents the capacitance value change rate; TC represents the temperature coefficient; w1, w2, and w3 are weight coefficients of the electromagnetic interference level, the capacitance value change rate, and the temperature coefficient, respectively, and the value range of each is between 0 and 1;

[0034] Under the condition that the addition signal is not received, the comprehensive performance evaluation corresponding to each sub-time period is obtained by weighted calculation according to the electromagnetic interference level and the capacitance value change rate, and the formula used is:

[0035] S i =k1*E+k2*CR;(5)

[0036] In formula (5), k1 and k2 are weight coefficients of the electromagnetic interference level and the capacitance value change rate, respectively, and the value range of each is between 0 and 1.

[0037] Further, a life prediction model is built, and the formula used to generate the life prediction index is as follows:

[0038] L=L0 / (1+G*∑(S i -S i-1 ));(6)

[0039] In formula (6), L represents the life prediction index, L0 represents the initial life estimate value of the capacitor, G represents an adjustment coefficient, and the value range is: 0 < G < 1; ∑(S i -S i-1 ) represents the cumulative change amount of the comprehensive performance evaluation S i in each time period.

[0040] Further, when analyzing the change trend of the comprehensive performance evaluation in the same period T and analyzing the change trend of the life prediction index in different periods T, a data analysis software is used, including but not limited to any one or several of Tableau, Power BI, and RapidMiner.

[0041] An EMC state evaluation method applied to a capacitor, comprising the following steps:

[0042] S1, collecting physical parameters and electromagnetic field intensity data in the working process of the capacitor;

[0043] S2, filter and amplify the physical parameters and electromagnetic field intensity data, convert the physical parameters and electromagnetic field intensity data into digital signals through an A / D converter, and output the digital signals;

[0044] S3, receive and analyze the digital signals, extract feature parameters in each sub-time period within a preset period T, and establish a feature parameter database; simultaneously obtain state defining parameters of the capacitor;

[0045] S4, build a trigger calculation model according to the state defining parameters, generate a trigger index, and send an addition signal when the trigger index exceeds a set defining threshold; under the condition of receiving or not receiving the addition signal, perform weighted calculation according to the corresponding feature parameters to obtain a comprehensive performance evaluation value corresponding to each sub-time period;

[0046] Analyze the change trend of the comprehensive performance evaluation value under the same period T, and output the obtained first analysis result;

[0047] According to each comprehensive performance evaluation value under the same period T, a life prediction model is built to generate a life prediction index, and the change trend of the life prediction index under different periods T is analyzed, and the obtained second analysis result is output;

[0048] S5, receive the first analysis result and the second analysis result;

[0049] When the first analysis result shows that the comprehensive performance evaluation value continuously increases, a performance warning signal is sent, and a maintenance strategy is executed;

[0050] When the second analysis result shows that the life prediction index continuously decreases, a pointing warning signal is sent, and a maintenance strategy is executed.

[0051] (Three) beneficial effects

[0052] The EMC state evaluation system and method applied to the capacitor provided by the application have the following beneficial effects:

[0053] (1) The scheme collects and analyzes the physical parameters and electromagnetic field intensity data in the working process of the capacitor through the feature extraction module in real time, extracts feature parameters such as electromagnetic interference level, capacitance value change rate and temperature coefficient, and establishes a feature parameter database. These feature parameters can comprehensively reflect the working state and performance change of the capacitor;

[0054] (2) The scheme can build a trigger calculation model according to the state definition parameters, generate a real-time changing trigger index, and judge whether the temperature coefficient change needs to be considered to calculate the comprehensive performance evaluation according to the trigger index, so as to generate the comprehensive performance evaluation in combination with the actual situation and targeted; and then the extracted features are weighted and calculated to obtain the comprehensive performance evaluation corresponding to each sub-time period, which is convenient for subsequent analysis and judgment of the performance state of the capacitor;

[0055] (3) In addition, the scheme also builds a life prediction model, generates a capacitor life prediction index according to the change trend of the comprehensive performance evaluation, and analyzes the change trend of the life prediction index under different periods, which helps to find potential problems in time and take corresponding maintenance measures to ensure the normal operation of the capacitor and prolong its service life;

[0056] In summary, by adopting the above technical scheme, not only the technical problems of capacitor performance and life evaluation are solved, but also real-time and accurate evaluation is realized, which provides strong support for the normal operation and maintenance of the capacitor. BRIEF DESCRIPTION OF DRAWINGS

[0057] Figure 1 A modular schematic diagram of an EMC state evaluation system applied to a capacitor in the present application;

[0058] Figure 2 A schematic diagram of the overall steps of an EMC state evaluation method applied to a capacitor in the present application. DETAILED DESCRIPTION

[0059] The technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor fall within the scope of protection of the present application.

[0060] Embodiment 1:

[0061] Please refer to Figure 1 The present embodiment provides an EMC state evaluation system applied to a capacitor, which is aimed at: life evaluation and state detection of parts (capacitors) in a car;

[0062] The evaluation system includes a plurality of function modules running in sequence, which are arrangement collection module, signal processing module, feature extraction module, state evaluation module and alarm feedback module;

[0063] Arrangement collection module:

[0064] Real-time acquisition of physical parameters during capacitor operation and electromagnetic field intensity data in the surrounding area of the capacitor;

[0065] Among them, the physical parameters include voltage, current and temperature;

[0066] The physical parameters are obtained through the sensor network arranged in the capacitor and its surrounding area. It should be noted that the sensor network is a distributed intelligent network system composed of a large number of small sensor nodes deployed in the action area with wireless communication and computing capabilities, which can complete the specified task autonomously according to the environment; These sensor nodes can cooperatively monitor the physical or environmental conditions at different locations, such as temperature, sound, vibration, pressure or pollutants, etc.

[0067] In this embodiment, the sensor network is arranged in the capacitor and its surrounding area to real-time acquisition of various physical parameters during capacitor operation; The sensor nodes include voltage sensors, current sensors, temperature sensors and electromagnetic field intensity sensors, which are responsible for collecting voltage, current, temperature and surrounding electromagnetic field intensity data during capacitor operation, respectively;

[0068] Voltage data: collected by voltage sensor;

[0069] The voltage sensor can directly measure the voltage value across the capacitor and convert it into an electrical signal for transmission; The signal conditioning circuit pre-processes the collected voltage signal, such as filtering and amplification, to ensure the accuracy and reliability of the data;

[0070] Current data: collected by current sensor;

[0071] The current sensor usually uses the Hall effect principle or the magnetoresistance effect principle to measure the current size in the capacitor; The collected current signal also needs to be pre-processed by the signal conditioning circuit to ensure the accuracy of the data;

[0072] Temperature data: collected by temperature sensor;

[0073] The temperature sensor can sense the temperature change on the surface of the capacitor and convert it into an electrical signal; The signal conditioning circuit amplifies and filters the temperature signal to improve the stability and reliability of the data;

[0074] Electromagnetic field intensity data: collected by electromagnetic field intensity sensor;

[0075] This type of sensor can measure the electromagnetic field intensity in the space around the capacitor and convert it into an electrical signal for transmission; Since the electromagnetic field signal may be disturbed by various factors, the signal conditioning circuit pre-processes the collected electromagnetic field signal to filter out noise and improve the accuracy of the data;

[0076] Working principle of sensor network:

[0077] Each sensor node in the sensor network is connected to each other through wireless communication, forming a self-organizing network system, which can cooperatively perceive, collect and process specific information in the network coverage area, and transmit these information to the sink node or base station through multi-hop; in this scenario, the sink node or base station may be a data processing center for further processing and analysis of collected voltage, current, temperature, electromagnetic field strength and other data;

[0078] In summary, through the sensor network arranged around the capacitor and the pre-processing effect of the signal conditioning circuit, the physical parameters in the capacitor working process and the electromagnetic field strength data in the capacitor peripheral area can be collected in real time and accurately, providing strong support for the normal operation and fault diagnosis of the capacitor;

[0079] Signal processing module:

[0080] The collected physical parameters and electromagnetic field strength data are filtered and amplified to eliminate noise interference and improve signal quality; at the same time, through the A / D converter, the physical parameters and electromagnetic field strength data are converted into digital signals for subsequent processing and analysis, and output;

[0081] The purpose of filtering and amplifying here is to compensate for the lack of pre-processing of some physical parameters or electromagnetic field strength data in the collection module, further ensuring the accuracy and reliability of the data.

[0082] By adopting the above technical scheme, the technical problem of real-time and accurate collection of physical parameters and electromagnetic field strength data in the capacitor working process is solved; specifically, the sensor network arranged around the capacitor can collect the voltage, current, temperature and surrounding electromagnetic field strength data in real time when the capacitor is working; these data are crucial for the normal operation and fault diagnosis of the capacitor;

[0083] At the same time, the technical scheme also achieves the following technical effects:

[0084] Improve data accuracy and reliability: each sensor node in the sensor network can cooperatively perceive, collect and process information, and through the signal conditioning circuit, the collected signal is pre-processed, such as filtering and amplification, to eliminate noise interference and improve signal quality, which ensures the accuracy and reliability of the data;

[0085] Data digitization: Through the A / D converter, the physical parameters and electromagnetic field intensity data are converted into digital signals, which are convenient for subsequent processing and analysis, and provide strong support for the intelligent management and fault diagnosis of capacitors;

[0086] Real-time monitoring and fault diagnosis: The real-time collected data can be used to monitor the running state of the capacitor, discover abnormal conditions in time, and perform fault diagnosis, which helps to improve the running efficiency and reliability of the capacitor and reduce the possibility of failure;

[0087] In summary, by adopting the above technical solutions, not only the technical problems of real-time and accurate collection of physical parameter and electromagnetic field intensity data during the operation of the capacitor are solved, but also the accuracy and reliability of the data are improved, the digitization of the data is realized, and the real-time monitoring and fault diagnosis are supported, which provides strong guarantee for the normal operation and intelligent management of the capacitor.

[0088] Feature extraction module:

[0089] Receive and analyze digital signals, extract feature parameters in each sub-time period within the preset period T, and establish a feature parameter database; simultaneously obtain the state defining parameters of the capacitor;

[0090] Among them, each sub-time period within the preset period T is equal, for example: 1 day is divided into 24-hour sub-time periods;

[0091] The feature parameters include electromagnetic interference level, capacitance value change rate, and temperature coefficient;

[0092] The process of extracting feature parameters in each sub-time period is as follows:

[0093] Calculate the average value of the electromagnetic field intensity in the surrounding area of the capacitor in the corresponding sub-time period to obtain the electromagnetic interference level;

[0094] Using the collected voltage and current data, the actual capacitance value of the capacitor is calculated and compared with the initial capacitance value to obtain the capacitance value change rate; for example, if the initial capacitance value is 100uF and the actual capacitance value is 95uF, the capacitance value change rate is -5%;

[0095] Obtain the temperature and capacitance value at the initial moment of the corresponding sub-time period, and obtain the temperature and capacitance value at the end of the corresponding sub-time period, calculate the slope of the capacitance value with temperature, i.e. the temperature coefficient; for example, if the capacitance value of the capacitor is 100uF at 25℃ and 98uF at 50℃, the temperature coefficient is -0.02 / ℃. At the same time, observe the change of temperature data with time to evaluate the thermal stability of the capacitor;

[0096] The specific description is as follows:

[0097] Electromagnetic interference level, the parameter definition is: the electromagnetic interference level refers to the strength of the electromagnetic field around the capacitor during operation, which is usually represented by the average of the electric field or magnetic field strength in a specific frequency band; extraction method: use spectrum analyzer or electromagnetic field strength meter to collect electromagnetic field strength data of a specific frequency band around the capacitor; process the collected data to calculate the average of the electromagnetic field strength in a specific frequency band; the calculation result is taken as the characteristic parameter of the electromagnetic interference level;

[0098] Capacitance value change rate, the parameter definition: the capacitance value change rate refers to the percentage deviation between the actual capacitance value and the initial or rated capacitance value of the capacitor during actual operation; extraction method: use LCR tester or similar capacitor measuring instrument to measure the actual capacitance value of the capacitor (in this embodiment, according to the ratio of voltage and current, combined with the equivalent circuit model of the capacitor, the calculation formula of the capacitance value is derived); compare the actual capacitance value with the initial or rated capacitance value to calculate the percentage deviation; the calculation result is taken as the characteristic parameter of the capacitance value change rate;

[0099] Temperature coefficient, parameter definition: the temperature coefficient refers to the degree of change of the capacitance value of the capacitor with temperature, which is usually represented by the change amount of the capacitance value per degree Celsius; extraction method: measure the capacitance value of the capacitor at different temperatures and record the data; calculate the slope of the capacitance value change with temperature, i.e. the temperature coefficient; the calculation result is taken as the characteristic parameter of the temperature coefficient.

[0100] Establish a characteristic parameter database:

[0101] Organize the extracted characteristic parameters into table form, including the characteristic parameter values of the capacitor, data collection time, etc.; import the table data into the database management system to establish the characteristic parameter database; the database should support query, insertion, update, etc. operation, convenient for subsequent data analysis and application;

[0102] The specific table form is given as follows:

[0103] Data acquisition time Electromagnetic interference level (average) Rate of change of capacitance value Temperature coefficient 2022-08-0810:00～11:00 0.5 V / m -5％ -0.02 / ℃ 2022-08-0811:00～12:00 0.6 V / m -3％ -0.01 / ℃ ... ... ... ...

[0104] The value range of T is a positive integer greater than 0;

[0105] The state defining parameters of the capacitor include: ambient temperature and running time;

[0106] The ambient temperature is obtained by data grabbing from the regional meteorological station of the area where the car is located, and the running time is collected and obtained by the timer configured on the car.

[0107] State evaluation module:

[0108] The built-in sub-module builds a trigger calculation model according to the state definition parameters, and generates a real-time changing trigger index;

[0109] When the trigger index exceeds the set definition threshold, an addition signal is sent out;

[0110] When the trigger index does not exceed the set definition threshold, no response action is taken;

[0111] Under the condition of receiving the addition signal, the electromagnetic interference level, the capacitance value change rate and the temperature coefficient are weighted to obtain the corresponding comprehensive performance evaluation of each sub-time period;

[0112] Under the condition of not receiving the addition signal, the electromagnetic interference level and the capacitance value change rate are weighted to obtain the corresponding comprehensive performance evaluation of each sub-time period;

[0113] The change trend of the comprehensive performance evaluation under the same period T is analyzed, and the first analysis result is output;

[0114] According to each comprehensive performance evaluation under the same period T, a life prediction model is built to generate the corresponding life prediction index of the capacitor, and the change trend of the life prediction index under different periods T is analyzed, and the second analysis result is output;

[0115] The process of generating the trigger index by the trigger calculation model is as follows:

[0116] Firstly, the trigger calculation model uses a linear function to establish an influence factor calculation formula, as shown below:

[0117]

[0118] In formula (1), Ft represents the environmental temperature influence factor, a and b are constants, a represents the increase of the influence factor when the environmental temperature increases by 1℃, and b is used to modify the final environmental temperature influence factor, the value range: 0

[0119] In formula (2), Fh represents the running time influence factor, d represents a constant, which represents the increase of the influence factor when the running time increases by 1 hour, the value range: 0

[0120] Secondly, the environmental temperature influence factor and the running time influence factor are added to obtain the trigger index Ci:

[0121] Ci=Ft+Fh (3)

[0122] It should be noted that the environmental temperature influencing factor Ft increases with the increase of environmental temperature, indicating the degree of influence of environmental temperature on capacitor performance, and a linear function is used to formalize this influence; the same applies to the running time influencing factor Fh, and finally the two types of influencing factors are added to obtain a comprehensive value, i.e. the trigger index Ci;

[0123] A threshold value Ch is set to determine whether the trigger index exceeds;

[0124] If Ci>Ch, it means that the influence of environmental temperature and running time on capacitor performance is large, and temperature coefficient variation needs to be considered to calculate the comprehensive value; if Ci≤Ch, it means that the influence of environmental temperature and running time on capacitor performance is small, and temperature coefficient variation does not need to be considered to calculate the comprehensive value;

[0125] When setting the threshold value Ch, the design specifications of the capacitor, working environmental conditions and historical running data should be considered comprehensively; according to the recommendations of industry standards and professional institutions, Ch should be set between the normal working environmental temperature and the limit temperature of the capacitor, and the rated running time of the capacitor should be referred to; the specific value can be determined through experimental test or simulation, which can accurately reflect the significant influence of environmental temperature and running time on capacitor performance at the moment of Ci mutation, and the value obtained at this time is the threshold value; it is recommended to consult capacitor manufacturers or industry experts for more accurate Ch setting guidance.

[0126] Overall example:

[0127] Assuming the environmental temperature wr=40℃, the running time hr=1000 hours, and the threshold value Ch=5; assuming a=0.1, b=0, and d=0.01; calculate the trigger index: Ci=0.1*40+0.01*1000=4+10=14;

[0128] Determine whether the trigger index exceeds the threshold value:

[0129] Because Ci=14>Ch=5, temperature coefficient variation needs to be considered to calculate the comprehensive value.

[0130] From this example, we can see that when the environmental temperature and running time are high, the trigger index will exceed the threshold value, triggering the logic of considering temperature coefficient variation to calculate the comprehensive value, which helps us more accurately evaluate the performance status of the capacitor under different conditions.

[0131] According to the electromagnetic interference level, the capacitance value change rate and the temperature coefficient, the comprehensive performance evaluation value corresponding to each sub-time period is calculated by weighted calculation, and the formula is:

[0132] S i= w1*E + w2*CR + w3*TC; (4)

[0133] In formula (4), S i represents the comprehensive performance evaluation corresponding to the i-th sub-time period, i is a positive integer greater than 0, E represents the electromagnetic interference level, and the unit is V / m or the corresponding electromagnetic field intensity unit; CR = ΔC / C0, wherein ΔC is the change amount of the capacitance value, C0 is the initial capacitance value, and CR represents the capacitance value change rate; TC represents the temperature coefficient, and the unit is / ℃;

[0134] w1, w2, and w3 are weight coefficients of the electromagnetic interference level, the capacitance value change rate, and the temperature coefficient, respectively, and these coefficients can be adjusted according to actual conditions, and the value range is between 0 and 1;

[0135] The weight coefficients can be determined by the coefficient of variation method, which is a method of weighting each index according to the variation degree of the current value and the target value of each index; if the numerical difference of an index is large and can clearly distinguish each evaluated object, it means that the index has rich distinguishing information, and thus the index should be given a larger weight; on the contrary, if the numerical difference of each evaluated object on an index is small, the index has weak ability to distinguish each evaluated object, and thus the index should be given a smaller weight; this method directly uses the information contained in each index, and the weight of the index is obtained by calculation, and thus the method is objective.

[0136] According to the electromagnetic interference level and the capacitance value change rate, the comprehensive performance evaluation corresponding to each sub-time period is obtained by weighted calculation, and the formula is as follows:

[0137] S i = k1*E + k2*CR; (5)

[0138] In formula (5), k1 and k2 are weight coefficients of the electromagnetic interference level and the capacitance value change rate, respectively, and these coefficients can be adjusted according to actual conditions, and the value range is between 0 and 1;

[0139] The formula for building a life prediction model to generate a life prediction index is as follows:

[0140] L = L0 / (1 + G*∑(S i -S i-1 ))(6)

[0141] In formula (6), L represents the life prediction index, L0 represents the initial life estimation value of the capacitor (which can be an empirical value or obtained according to historical data), G represents an adjustment coefficient, and the value range is 0 < G < 1; ∑(S i -S i-1 ) represents the comprehensive performance evaluation S iThe cumulative change amount under each time period; for different periods T, at least 10 consecutive periods T need to be considered when analyzing, for example: analyzing different periods T within 10 days or a month;

[0142] It should be noted that the life of the capacitor is inversely proportional to the speed of its performance decline, that is, the faster the performance declines, the shorter the life; by conducting comprehensive evaluation and building a life prediction model, and combining with the characteristic parameter data of each period, we can effectively evaluate the performance of the capacitor and predict its life; this helps us to discover potential problems in time and take appropriate maintenance measures to ensure the normal operation of the capacitor and prolong its service life.

[0143] When analyzing the trend of the comprehensive performance evaluation under the same period T, and when analyzing the trend of the life prediction index under different periods T, data analysis software is used, including but not limited to: Tableau, Power BI, and any one or several of RapidMiner;

[0144] The first analysis result includes continuous increase in comprehensive performance evaluation or non-continuous increase in comprehensive performance evaluation (including, for example: the comprehensive value remains stable or decreases slightly, indicating that the performance of the capacitor remains good); the second analysis result includes continuous decrease in life prediction index or non-continuous decrease in life prediction index (including, for example: the life prediction index remains stable or increases slightly, indicating that the life of the capacitor still has a long remaining time);

[0145] It should be noted that:

[0146] Analyze the change of comprehensive performance evaluation:

[0147] If the comprehensive performance evaluation gradually increases from the 1st sub-time period to the 24th sub-time period, it means that the performance of the capacitor is gradually declining; this may be due to an increase in electromagnetic interference level, an increase in capacitance value change rate, or a more unfavorable temperature coefficient; if the comprehensive performance evaluation suddenly increases a lot in a certain period, it may mean that the capacitor has experienced some abnormal events such as overvoltage, overcurrent, or a sharp change in environmental temperature during that sub-time period; if the comprehensive performance evaluation remains relatively stable or decreases slightly, it means that the performance of the capacitor remains good during that period of time without obvious signs of decline;

[0148] Analyze the change of life prediction index:

[0149] If the life prediction index gradually decreases from the 1st cycle to the 10th cycle, it indicates that the expected life of the capacitor is shortening. This may be due to the cumulative effect of performance degradation; if the life prediction index suddenly decreases a lot at a certain cycle, it may mean that the performance of the capacitor has decreased significantly at that cycle, and close attention should be paid to its subsequent development; if the life prediction index remains relatively stable or slightly increases, it means that the expected life of the capacitor still has a long time left, and it can continue to be used normally.

[0150] By adopting the above technical solution, the technical problems of capacitor performance and life evaluation are mainly solved, and the technical effects of real-time and accurate evaluation of capacitor performance and prediction of its life are achieved.

[0151] Specifically, the technical solution collects and analyzes the physical parameters and electromagnetic field intensity data in the working process of the capacitor in real time through the feature extraction module, extracts feature parameters such as electromagnetic interference level, capacitance value change rate and temperature coefficient, and establishes a feature parameter database. These feature parameters can comprehensively reflect the working state and performance change of the capacitor.

[0152] At the same time, the state evaluation module builds a trigger calculation model according to the state definition parameters, generates a real-time changing trigger index, and judges whether the temperature coefficient change needs to be considered to calculate the comprehensive performance evaluation according to the trigger index, so as to generate the comprehensive performance evaluation in combination with the actual situation and targetedly; then, the corresponding comprehensive performance evaluation of each sub-time period is obtained by weighting calculation according to the extracted features, which is convenient for subsequent analysis and judgment of the performance state of the corresponding capacitor.

[0153] In addition, the technical solution also builds a life prediction model, generates a life prediction index of the capacitor according to the change trend of the comprehensive performance evaluation, and analyzes the change trend of the life prediction index under different cycles, which helps to find potential problems in time and take corresponding maintenance measures to ensure the normal operation of the capacitor and prolong its service life.

[0154] In summary, by adopting the above technical solution, not only the technical problems of capacitor performance and life evaluation are solved, but also real-time and accurate evaluation is achieved, which provides strong support for the normal operation and maintenance of the capacitor.

[0155] The alarm feedback module receives the first analysis result and the second analysis result;

[0156] When the comprehensive performance evaluation in the first analysis result continuously increases, a performance warning signal is issued, indicating that the performance of the capacitor is gradually decreasing, a maintenance strategy is executed, and the driver is prompted to go to the factory to replace or maintain the capacitor;

[0157] When the second analysis result shows that the life prediction index continuously decreases, a performance warning signal is sent, indicating that the life of the capacitor is gradually shortened, and a maintenance strategy is executed, prompting the driver to go to the factory for maintenance or repair of the capacitor;

[0158] It should be noted that the performance warning signal and the pointing warning signal have different forms of expression, for example: the performance warning signal sends a warning through a yellow sound and light form, and the pointing warning signal sends a warning through a red sound and light form.

[0159] Embodiment 2:

[0160] Please refer to Figure 2 Based on Embodiment 1, this embodiment also provides an EMC state evaluation method applied to a capacitor, comprising the following steps:

[0161] S1, collecting physical parameters and electromagnetic field intensity data during the operation of the capacitor;

[0162] S2, filtering and amplifying the physical parameters and electromagnetic field intensity data, synchronously converting the physical parameters and electromagnetic field intensity data into digital signals through an A / D converter, and outputting the digital signals;

[0163] S3, receiving and analyzing the digital signals, extracting feature parameters in each sub-time period within a preset period T, and establishing a feature parameter database; synchronously obtaining state defining parameters of the capacitor;

[0164] S4, building a trigger calculation model according to the state defining parameters, generating a trigger index, and sending an addition signal when the trigger index exceeds a set defining threshold; under the condition of receiving or not receiving the addition signal, performing weighted calculation according to the corresponding feature parameters to obtain a comprehensive performance evaluation value corresponding to each sub-time period;

[0165] Analyzing the change trend of the comprehensive performance evaluation value under the same period T, and outputting the obtained first analysis result;

[0166] According to each comprehensive performance evaluation value under the same period T, a life prediction model is built to generate a life prediction index, and the change trend of the life prediction index under different periods T is analyzed, and the obtained second analysis result is outputted;

[0167] S5, receiving the first analysis result and the second analysis result;

[0168] When the first analysis result shows that the comprehensive performance evaluation value continuously increases, a performance warning signal is sent, and a repair strategy is executed;

[0169] When the second analysis result shows that the life prediction index continuously decreases, a pointing warning signal is sent, and a maintenance strategy is executed.

[0170] In the application, the several formulas involved are all calculated by taking the values of the de-dimensioned ones, and the establishment of the formulas is obtained by collecting a large amount of data to simulate a formula of the nearest real situation, and part of the coefficients or weights in the formulas are set by the person skilled in the art according to the actual situation, so here is not more elaborated.

[0171] The above-described embodiments can be implemented in whole or in part by software, hardware, firmware, or any combination thereof. When implemented by software, the above-described embodiments can be implemented in whole or in part in the form of a computer program product. Those of ordinary skill in the art can realize that the units and algorithm steps of the examples described in connection with the embodiments disclosed herein can be realized by electronic hardware, or a combination of computer software and electronic hardware. Whether the functions are executed in hardware or software depends on the specific application and design constraints of the technical solutions.

[0172] The units described as separate components can or can not be physically separate, and the components shown as units can or can not be physical units, and can be located in one place or distributed on multiple network units. Part or all of the units can be selected to achieve the purpose of the embodiments according to actual needs.

[0173] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto, and any person skilled in the art can easily think of changes or replacements within the technical scope disclosed in the present application, which should be covered within the protection scope of the present application.

Claims

1. An EMC condition assessment system for capacitors, the system comprising: Set up a collection module to collect physical parameters and electromagnetic field strength data during the operation of the capacitor; The physical parameters include voltage, current and temperature. The physical parameters and the electromagnetic field strength data of the corresponding capacitor's surrounding area are obtained through a sensor network, which is arranged in the capacitor and its surrounding area. The signal processing module filters and amplifies the physical parameters and electromagnetic field strength data, and simultaneously converts the physical parameters and electromagnetic field strength data into digital signals via an A / D converter, and outputs them; its characteristic is that it also includes: The feature extraction module receives and analyzes digital signals, extracts feature parameters in each sub-time period within a preset period T, and establishes a feature parameter database; it also synchronously acquires the state definition parameters of the capacitor; the feature parameters include electromagnetic interference level, capacitance change rate, and temperature coefficient; the state definition parameters of the capacitor include ambient temperature and operating time; the ambient temperature is obtained by capturing data from the local meteorological station in the area where the car is located, and the operating time is collected by the timer configured on the car; The state assessment module has a built-in definition submodule. Based on the state definition parameters, it builds a trigger calculation model and generates a trigger index. When the trigger index exceeds the set definition threshold, it sends an addition signal. Under the condition of receiving or not receiving the addition signal, it performs a weighted calculation based on the corresponding feature parameters to obtain the comprehensive performance estimate corresponding to each sub-time period. Analyze the changing trend of the comprehensive performance estimate under the same period T, and output the first analysis result; Based on the comprehensive performance estimate under the same period T, a life prediction model is built, life prediction indicators are generated, and the changing trend of life prediction indicators under different periods T is analyzed. The second analysis results are then output. The alarm feedback module receives the first analysis result and the second analysis result; If the first analysis results show that the overall performance estimate continues to increase, a performance warning signal will be issued and a maintenance strategy will be implemented. If the second analysis shows that the life prediction index continues to decrease, an early warning signal will be issued and a maintenance strategy will be implemented.

2. The EMC condition assessment system for capacitors according to claim 1, characterized in that: The process of extracting feature parameters in each sub-time period is as follows: The mean electromagnetic field strength in the area surrounding the capacitor is calculated within the corresponding sub-time period to obtain the electromagnetic interference level. The actual capacitance value of the capacitor is calculated using the collected voltage and current data and compared with the initial capacitance value to obtain the capacitance change rate. Temperature and capacitance values ​​are obtained at the beginning and end of the corresponding sub-time period, and the slope of capacitance change with temperature is calculated to obtain the temperature coefficient.

3. The EMC condition assessment system for capacitors according to claim 1, characterized in that: The process of generating the trigger index using the trigger calculation model is as follows: First, the trigger calculation model uses a linear function to establish the formula for calculating the impact factor, as shown below: ， In equation (1), Ft represents the ambient temperature influence factor, a and b are constants, and the range of values ​​is: 0 < a < 1, 0 ≤ b < 1; In equation (2), Fh represents the running time influence factor, d represents a constant, and the range of values ​​is: 0 < d < 0.1; wr represents the ambient temperature, and hr represents the running time. Secondly, the trigger index is obtained by adding the environmental temperature influence factor and the runtime influence factor: ， In equation (3), Ci represents the trigger index.

4. The EMC condition assessment system for capacitors according to claim 1, characterized in that: Under the condition of receiving the added signal, a weighted calculation is performed based on the electromagnetic interference level, capacitance change rate, and temperature coefficient to obtain the comprehensive performance estimate for each sub-time period. The formula used is as follows: ， In equation (4), This represents the comprehensive performance estimate corresponding to the i-th sub-time period, where i is a positive integer greater than 0, E represents the electromagnetic interference level, CR=ΔC / C0, where ΔC is the change in capacitance value, C0 is the initial capacitance value, and CR represents the rate of change of capacitance value; TC represents the temperature coefficient. These are the weighting coefficients for electromagnetic interference level, capacitance change rate, and temperature coefficient, respectively, all ranging from 0 to 1; If no additional signal is received, a weighted calculation is performed based on the electromagnetic interference level and the rate of change of capacitance value to obtain the comprehensive performance estimate for each sub-time period. The formula used is as follows: ， In equation (5), These are the weighting coefficients for the electromagnetic interference level and the rate of change of capacitance value, respectively, with values ​​ranging from 0 to 1.

5. The EMC condition assessment system for capacitors according to claim 4, characterized in that: The formulas used to build the lifespan prediction model and generate lifespan prediction indicators are as follows: ， In equation (6), L represents the life prediction index, L0 represents the initial life estimate of the capacitor, and G represents the adjustment coefficient, with a value range of 0 < G < 1. Indicates overall performance valuation The cumulative change over each time period.

6. The EMC condition assessment system for capacitors according to claim 1, characterized in that: When analyzing the changing trend of comprehensive performance estimates under the same period T, and when analyzing the changing trend of life prediction indicators under different periods T, data analysis software is used, including but not limited to: Tableau, Power BI, and RapidMiner, one or more of them.

7. A method for EMC condition assessment of capacitors, using the system described in any one of claims 1 to 6, characterized in that: Includes the following steps: S1. Collect physical parameters and electromagnetic field strength data during the operation of the capacitor. The physical parameters include voltage, current and temperature. The physical parameters and the corresponding electromagnetic field strength data of the capacitor's surrounding area are obtained through a sensor network, which is arranged in the capacitor and its surrounding area. S2. Filter and amplify the physical parameters and electromagnetic field strength data, and simultaneously convert the physical parameters and electromagnetic field strength data into digital signals through an A / D converter and output them; S3. Receive and analyze digital signals, extract feature parameters in each sub-time period within a preset period T, and establish a feature parameter database. Synchronously acquire the state definition parameters of the capacitor; the characteristic parameters include electromagnetic interference level, capacitance change rate and temperature coefficient; the state definition parameters of the capacitor include ambient temperature and running time; among them, the ambient temperature is obtained by capturing data from the local meteorological station in the area where the car is located, and the running time is collected by the timer configured on the car; S4. Based on the state definition parameters, build a trigger calculation model and generate a trigger index. When the trigger index exceeds the set threshold, an add signal is issued. Under the condition of receiving or not receiving the add signal, a weighted calculation is performed based on the corresponding feature parameters to obtain the comprehensive performance estimate corresponding to each sub-time period. Analyze the changing trend of the comprehensive performance estimate under the same period T, and output the first analysis result; Based on the comprehensive performance estimate under the same period T, a life prediction model is built, life prediction indicators are generated, and the changing trend of life prediction indicators under different periods T is analyzed. The second analysis results are then output. S5. Receive the first analysis result and the second analysis result; If the first analysis results show that the overall performance estimate continues to increase, a performance warning signal will be issued and a maintenance strategy will be implemented. If the second analysis shows that the life prediction index continues to decrease, an early warning signal will be issued and a maintenance strategy will be implemented.

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