Board card life prediction method, system, device and medium

Abstract

The invention relates to the technical field of relay protection, in particular to a method, system and device for predicting the service life of a board card and a medium, and the method comprises the following steps: constructing a board card service life prediction model based on a board card device; monitoring operation data influencing the service life of the board card, obtaining fault-free working time t2 based on the board card service life prediction model, and calculating accumulated operation time t1 of the board card; judging whether t1 and t2 meet preset conditions or not; if yes, judging that the board card is about to be in a fault state, and performing replacement prompt; and if not, the output board card is in a normal operation state. According to the invention, the service life of the board card is predicted, the prediction condition is displayed on a liquid crystal interface, and remote visualization is realized by adopting wireless data transmission; according to the method, data sharing of the smart power grid can be promoted, and the part can be used as an input characteristic quantity by analyzing the overall fault diagnosis of the power system network, so that the stability of the power system can be improved.

Description

Technical Field

[0001] The present invention relates to the technical field of relay protection, and in particular to a method, system, device and medium for predicting the life of a board. Background Art

[0002] In the field of relay protection, although the relay protection function has been widely recognized as a key factor in evaluating the stability and safety of power systems, the operational stability of the internal panels of relay protection devices has long been ignored. This situation stems from two challenges: one is the difficulty in technical implementation, that is, the lack of a systematic solution to comprehensively monitor and evaluate the status of panels; the other is the misunderstanding in cognition. It is generally believed that the replacement cycle of panels is long, thus ignoring the potential replacement needs and hidden dangers caused by sudden failures. In fact, panel damage is often sudden, and its repair and replacement are not only time-consuming and labor-intensive, but also significantly increase the operation and maintenance costs, thus bringing uncertainty to the stable operation of the power system. More importantly, the continuous operation of power system equipment is crucial to ensuring residential and industrial electricity consumption. Any unplanned downtime may cause industrial accidents and economic losses. With the continuous promotion of the construction of smart grids by the State Grid, the requirements for relay protection equipment are no longer limited to functional reliability, but also emphasize the predictability and maintainability of its operating status. Although the existing substation background monitoring system can monitor the overall operating status of the device, it lacks the ability to predict and warn of potential panel failures in advance, and it is difficult to meet the higher requirements of smart grids for safety. Summary of the invention

[0003] The purpose of the present invention is to provide a method, system, device and medium for predicting the life of a board in order to solve at least one of the above technical problems.

[0004] The present invention achieves the above-mentioned purpose through the following technical solutions:

[0005] A method for predicting the life of a board, characterized by comprising the following steps:

[0006] Construct a board life prediction model based on the influencing components that affect the board life;

[0007] Monitor the operation data that affects the life of the board, obtain the trouble-free working time t2 based on the board life prediction model, and calculate the accumulated operation time t1 of the board;

[0008] Determine whether t1 and t2 meet the preset conditions; if so, determine that the board is about to be in a fault state and issue a replacement prompt; if not, output that the board is in a normal operating state;

[0009] The preset condition is: t1>t2*0.95.

[0010] Furthermore, the board life prediction model is:

[0011]

[0012] Among them, L op Indicates the predicted life of the board capacitor; ε1 and ε2 are the first correction coefficient and the second correction coefficient respectively; L o T is the life of the capacitor at the maximum temperature; max is the maximum operating temperature of the capacitor; T h is the actual working temperature of the capacitor; I x Is the actual current value flowing through the capacitor during operation; Is is the standard allowable current; f x is the actual current frequency flowing through the capacitor, f s It is the standard industrial electrical frequency.

[0013] Furthermore, in the board life prediction model:

[0014] ε1=e η

[0015] ε2=e -2πC

[0016] Where C is the nominal capacity of the capacitor at room temperature; η is the temperature coefficient of electrostatic capacity, which is determined by the following formula:

[0017]

[0018] in, C is the electrostatic capacitance value corresponding to the upper limit temperature of the capacitor material; 25 is the electrostatic capacitance value at 25°C; T c-max is the upper limit temperature of the capacitor material.

[0019] Furthermore, the actual operating temperature of the capacitor T h The method of obtaining the temperature is as follows: a platinum resistor is designed in the circuit to be close to the lower end of the capacitor, and then an external measuring circuit is connected to obtain the voltage and current values ​​of the platinum resistor. According to the relationship between the resistance value of the platinum resistor and the temperature, the operating temperature of the capacitor is determined;

[0020] The actual current value I flowing through the capacitor during operation x The acquisition method is: real-time acquisition of the series side current value as the current flowing through the current side.

[0021] Furthermore, the process of building a board life prediction model is as follows:

[0022] Obtain the components that affect the life of the board;

[0023] Determine the impact factor of each influencing device;

[0024] An influencing component characterizing the life of the board is determined based on the influencing factor.

[0025] Furthermore, the influencing devices include chips, capacitors, resistors, inductors, and diodes;

[0026] Determine the influencing factors of each influencing device. The specific steps are:

[0027] Obtain the membership of each influencing factor; the membership is a 1x5 vector, representing the influence of each influencing device under each influencing factor;

[0028] Based on the membership degree of each influencing factor, the Euclidean distance between each influencing device and the reference centroid point is calculated;

[0029] The influence factor of each influencing component is calculated based on the Euclidean distance of each influencing component from the reference centroid point.

[0030] A system for predicting the life of a board, comprising:

[0031] A construction module is used to construct a board life prediction model;

[0032] A monitoring module, used to monitor the real-time temperature of the capacitor, obtain the trouble-free working time t2 based on the board life prediction model, and calculate the accumulated running time t1 of the board;

[0033] A judgment module, used to judge whether t1 and t2 meet the preset conditions; if so, it is judged that the board is about to be in a fault state and a replacement prompt is given; if not, it is output that the board is in a normal operating state;

[0034] The preset condition is: t1>t2*0.95.

[0035] An electronic device comprises a processor, a memory and a computer program stored in the memory and executable on the processor, wherein the computer program implements any method for predicting the life of a board when executed by the processor.

[0036] A computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the computer program implements any method for predicting the life of a board.

[0037] A computer program product comprises a computer program, which implements the steps of any method for predicting the life of a board when the computer program is executed by a processor.

[0038] The beneficial effects of the present invention are as follows: the present invention constructs a prediction model to monitor and calculate the cumulative operating time of the board and the predicted trouble-free working time in real time to predict the remaining life of the board, and provides a replacement prompt when it is close to a fault state, thereby effectively improving the timeliness and efficiency of board maintenance and extending the overall operating life of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] Figure 1 A flow chart of a method for predicting the life of a board according to an embodiment of the present invention;

[0040] Figure 2 A flow chart of a method for determining an influencing component that characterizes the life of a board according to an embodiment of the present invention;

[0041] Figure 3 A flow chart of a method for predicting the life of a board according to another embodiment of the present invention;

[0042] Figure 4 A system structure diagram for predicting the life of a board according to an embodiment of the present invention. DETAILED DESCRIPTION

[0043] The content of the present invention will now be discussed with reference to exemplary embodiments. It should be understood that the embodiments discussed are only to enable those skilled in the art to better understand and thus implement the content of the present invention, rather than implying any limitation on the scope of the present invention.

[0044] As used herein, the term “including” and variations thereof are to be interpreted as open-ended terms meaning “including, but not limited to.” The term “based on” is to be interpreted as “based, at least in part, on.” The terms “one embodiment” and “an embodiment” are to be interpreted as “at least one embodiment.”

[0045] Embodiment 1

[0046] Figure 1 FIG. 1 is a flow chart of a method for predicting the life of a board according to an embodiment of the present invention. Figure 1 As shown, according to one embodiment of the present invention, a method for predicting the life of a board includes the following steps:

[0047] Step S102: constructing a board life prediction model based on the influencing components that affect the board life;

[0048] The operating life of the entire relay protection device depends on the life of a specific hardware board. As long as one board fails to work or malfunctions, the entire device is considered to fail. Therefore, the evaluation impact of the device's operating life is converted into the board life. At the same time, if you want to study the impact of all factors on the board life, it is difficult to model the evaluation or the modeling function has too many parameters when it is actually used. Therefore, the present invention focuses on the impact of the board's own components on the board life. Compared with other coupling factors, the life of the board components has the most direct impact on the components on the board. Therefore, the characteristic quantities studied in the present invention focus on the board components, and a board life prediction model is constructed based on the board components.

[0049] Step S104: monitor the operation data that affects the life of the board, obtain the trouble-free working time t2 based on the board life prediction model, and calculate the accumulated operation time t1 of the board;

[0050] Monitor the operating data that affects the life of the board in real time, input the operating data that affects the life of the board into the board life prediction model, and calculate the trouble-free working time t2. Record and calculate the cumulative operating time t1 of the board through the timer inside the board or the external monitoring system.

[0051] Step S106: determine whether t1 and t2 meet the preset conditions; if so, determine that the board is about to fail and issue a replacement prompt; if not, output that the board is in normal operation;

[0052] The preset condition is: t1>t2*0.95.

[0053] When t1 approaches 95% of t2, the board is considered to be in a fault state. If t1>t2*0.95, the board is considered to be in a fault state and a replacement prompt is output. This may be achieved through an LED indicator, a buzzer on the board, or an alarm signal sent to the monitoring system. If t1≤t2*0.95, the board is output to be in a normal operating state and monitoring continues.

[0054] Preferably, in this embodiment, the board life prediction model is:

[0055]

[0056] Among them, L op Indicates the predicted life of the board capacitor; ε1 and ε2 are the first correction coefficient and the second correction coefficient respectively; L o is the life of the capacitor at the maximum temperature; T max is the maximum operating temperature of the capacitor; T h is the actual working temperature of the capacitor; I x Is the actual current value flowing through the capacitor during operation; Is is the standard allowable current; f xis the actual current frequency flowing through the capacitor, f s is the standard industrial electrical frequency. Specifically,

[0057] ε1=e η , ε2=e -2πC

[0058] Where C is the nominal capacity of the capacitor at room temperature; η is the temperature coefficient of electrostatic capacity, which is determined by the following formula:

[0059]

[0060] in, C is the electrostatic capacitance value corresponding to the upper limit temperature of the capacitor material; 25 is the electrostatic capacitance value at 25°C; T c-max is the upper limit temperature of the capacitor material.

[0061] In this embodiment, by constructing a board life prediction model and combining it with the real-time monitored capacitor temperature data, the fault-free working time t2 of the board is calculated and predicted, and the cumulative running time t1 of the board is recorded. By comparing the relationship between t1 and t2, it is determined whether the board is about to enter a fault state, so that a replacement prompt can be given in time.

[0062] The present invention monitors the operating data of board components in real time, builds a life prediction model based on component characteristics, accurately predicts and warns of board failures in advance, and effectively extends the overall operating life of the relay protection device.

[0063] According to one embodiment of the present invention, the actual operating temperature of the capacitor T h The method of obtaining the temperature is as follows: a platinum resistor is designed in the circuit to be close to the lower end of the capacitor, and then an external measuring circuit is connected to obtain the voltage and current values ​​of the platinum resistor. According to the relationship between the resistance value of the platinum resistor and the temperature, the operating temperature of the capacitor is determined;

[0064] The actual current value I flowing through the capacitor during operation x The acquisition method is: real-time acquisition of the series side current value as the current flowing through the current side.

[0065] In this implementation, in order to determine the board life prediction model, specific parameters need to be determined. The following steps are included:

[0066] Step 1: Determine the first correction coefficient ε1.

[0067] First, calculate the electrostatic capacitance temperature coefficient η of the capacitor material, and determine the maximum operating temperature T of the capacitor according to the chip manual or constant temperature oven baking. max The corresponding electrostatic capacitance value Electrostatic capacitance value C at 25℃ 25The first correction coefficient ε1 is given as a standard parameter and thus determined by calculation.

[0068] Step 2: Determine the second correction coefficient ε2.

[0069] Since the parameter compensation of the capacitor material has been performed, it is only necessary to input the basic capacitance value for calculation. The other parameters in the expression of the second correction coefficient ε2 are fixed parameters, and the value is relatively convenient to obtain.

[0070] Step 3: Actual operating temperature T of capacitor h A platinum resistor is designed in the circuit to be close to the lower end of the capacitor, and then an external measuring circuit is connected to obtain the voltage and current values ​​of the platinum resistor. According to the relationship between the resistance value of the platinum resistor and the temperature, the temperature of the capacitor during operation is determined.

[0071] Step 4: Actual current value I flowing through the capacitor during operation x Since the actual capacitance is widely distributed in the circuit, the current value of the series side is directly collected in real time as the current flowing through the current side.

[0072] Step 5: Obtain frequency f. In the relay protection algorithm, attention is paid to high-frequency harmonic interference. Therefore, the current collected in step 4 is directly subjected to discrete Fourier transform, and the highest frequency component is extracted and brought into it to obtain the actual current frequency f flowing through the capacitor. x ; Standard industrial electrical frequency f s in the country, 50 Hz is directly input for calculation.

[0073] Step 6: Capacitor life at maximum temperature L0, capacitor maximum operating temperature T max and the standard allowable current I s The maximum allowable temperature of the capacitor is T max The operating life L0 is generally given in the manual. Or you can bake it in a constant temperature oven to specify a preset high temperature. This temperature value should be controlled above the actual operating temperature as much as possible, so that the accuracy of the calibrated model will be higher. Is is the current value allowed by the capacitor design in the circuit design, which is usually a fixed value.

[0074] After steps 1-6, the model parameter determination method proposed in the present invention can determine or provide a real-time acquisition method.

[0075] The present invention integrates platinum resistance temperature measurement and current monitoring, combines material properties with discrete Fourier transform, realizes accurate perception of capacitor working state and dynamic determination of life model parameters, and improves the accuracy and real-time performance of capacitor life prediction.

[0076] According to one embodiment of the present invention, the process of constructing a board life prediction model is as follows:

[0077] Step 1: Obtain the components that affect the life of the board;

[0078] Step 2: determine the influencing factors of each influencing device;

[0079] Step 3: Determine the influencing components that characterize the life of the board based on the influencing factors.

[0080] In this embodiment, in the process of constructing a board life prediction model, first, the main influencing factors of the board life are clarified, and the focus is on identifying the key components that affect the board life. This step is the basis for building a model, because the life of a board is often affected by the combined impact of the performance degradation of multiple components inside it. Through in-depth analysis of the board structure and working environment, identify those components that play a decisive role in the overall life of the board. Preferably, in this embodiment, the influencing components include chips, capacitors, resistors, inductors, and diodes; as shown in Table 1:

[0081]

[0082] Table 1 Factors affecting device operation

[0083] Next, determine the influencing factors of each device. These influencing factors may include the device's operating temperature, operating voltage, current load, usage time, ambient humidity, vibration, etc. These factors affect the performance and life of the device through different mechanisms. For example, high temperature will accelerate material aging, and high current load will cause the device to heat up and increase the risk of damage. Through laboratory testing, on-site monitoring and data analysis, the specific impact of these influencing factors on the life of the device can be quantified. Preferably, the influencing factors of each device are determined, and the specific steps are:

[0084] Obtain the membership degree of each influencing factor; the membership degree is a 1x5 vector, which represents the influence degree of each influencing device under each influencing factor;

[0085] Based on the membership degree of each influencing factor, the Euclidean distance between each influencing device and the reference centroid point is calculated;

[0086] The influence factor of each influencing component is calculated based on the Euclidean distance of each influencing component from the reference centroid point.

[0087] Specifically, the above-described characteristics are evaluated and the method for calculating the impact factor Q of the device is determined as follows:

[0088] 1. Classification of device influencing factors

[0089] Due to the influence of multiple devices, it is necessary to abstract the above-mentioned influence levels and make a unified comparison. Therefore, it is necessary to calculate the influence factor Q of each device. The following feature classification set = {a, b, c, d, e} = {chip, capacitor, resistor, inductor, diode}.

[0090] 2. Determination of device element membership

[0091] Evaluate the corresponding membership function. Since it is impossible to use a unified model, the influence degree is evaluated here. This method is relatively simple. The membership table of the device is shown in Table 2:

[0092]

[0093] Table 2 Device membership table

[0094] Since there are multiple influencing factors of the device, the membership values ​​of the influencing factors of different devices are:

[0095] The membership degree of the influence range A is μ A (i) = {1.0, 0.8, 0, 0.1, 0.6};

[0096] The membership degree of distribution range B is μ B (i)={0.5,0.8,1.0,0.2,0.6};

[0097] The degree of membership of temperature range C is μ C (i)={0.6,0.8,1.0,0.9,0.8};

[0098] The degree of membership of D affected by temperature is μ D (i) = {0.6, 0.8, 0, 0.3, 1.0};

[0099] Among them, i = a, b, c, d, e; the membership degree referred to here can well represent multiple devices as a 1x5 vector, which is convenient for subsequent numerical processing.

[0100] The above-mentioned membership degree is to visualize the specific degree of correlation into a number for evaluation. Since different factors account for different proportions, it is not conducive to weighted average calculation of the influencing factors of each device. Classification algorithms such as decision trees, Bayesian classifiers, artificial neural networks, support vector machines, and k-nearest neighbors are not used, as these algorithms are not suitable for solving this problem. The present invention takes into account the central point deviation of the classification, is suitable for the optimal Euclidean distance, and has no sample training, so it is easy to realize simple classification evaluation.

[0101] 3. Calculation of device impact factors

[0102] The purpose of device classification is to evaluate the impact on the life of the board, so a multi-dimensional vector can be determined, the device reference vector with the best variables. Since the influencing factors of the device, such as the impact range A, distribution range B, temperature impact D and fragility E, the larger the values, the shorter the operating life of the board, and the larger the temperature range C, the more favorable the operating life of the board, the vector is recorded as the reference centroid point:

[0103] W={w1,w2,w3,w4,w5}={1,1,0,1,1} (1)

[0104] Where W is the reference centroid vector, w1, w2, w3, w4, w5 are the elements in the reference centroid vector respectively;

[0105] Compute the Euclidean distance in multidimensional space:

[0106]

[0107] Among them, r o-i is the distance between the i-th influencing device in the membership vector and the reference centroid, i = a, b, c, d, e;

[0108] The above-mentioned Euclidean distance r based on the reference centroid o-i , represents the final calculated value Q i .

[0109]

[0110] Where Q i is the influence factor of the i-th influencing device in the membership vector.

[0111] This calculation does not require membership weighting, as the above-mentioned influencing factors have low coupling between each other and therefore will not cause spatial coupling.

[0112] The influence factor table of device types obtained by calculation is shown in Table 3:

[0113] Device Type <![CDATA[r o-i ]]> <![CDATA[Q i ]]> Chip (a) 1.3304 0.7517 Capacitance (b) 1.0050 0.9950 Resistance (c) 2.0000 0.5000 Inductance (d) 1.7320 0.5774 Diode (e) 1.0247 0.9759

[0114] Table 3 Influence factors of device types

[0115] In this implementation, according to the data in Table 3, it is determined that the impact is greatest when there is a problem with the chip. However, it should be noted that the modeling scenario is that the running board fails, and the probability of device failure needs to be considered. Chip damage will definitely have a huge impact on the logic timing of the entire board and will directly cause the entire device to fail, but the possibility of chip failure is relatively small, and the most suitable influencing factor should be selected.

[0116] It is difficult to avoid plug-in replacement in the field of relay protection, and the impact on board replacement is directly related to the corresponding life of the board. Usually, when designing highly integrated chips, measures are considered to deal with the impact of special environments such as high temperature resistance and corrosion resistance. Therefore, the life of analog components has a clearer impact on the overall impact of the board. Among capacitors, resistors and inductors, capacitors are more susceptible to external changes, so the research on the life of capacitors is more related to the life of the board.

[0117] According to the theory proposed by the present invention, the capacitor is the most suitable device for research. Although the diode is easy to break down after the voltage across the diode exceeds the upper limit of the working voltage, its distribution and comprehensive impact factor are slightly lower than that of the capacitor. Therefore, according to the above classification discussion, the capacitor is determined to be the characteristic device for evaluating the life of the board.

[0118] Based on the influencing factors, a mathematical model or algorithm is constructed to characterize the life of the board. This step is the core of model construction, which requires establishing a connection between the performance degradation of each influencing component and the overall life of the board. Advanced technologies such as machine learning and deep learning can be used to analyze and train a large amount of historical data to learn the complex relationship between the performance degradation of each influencing component and the life of the board to ensure the accuracy and reliability of the model.

[0119] Finally, the prediction effect of the model is verified through actual tests. The model is applied to the actual board life prediction, and the accuracy and reliability of the model are evaluated by comparing the differences between the model prediction results and the actual observation results. The model is adjusted and optimized as necessary according to the test results to improve its prediction accuracy and practicality.

[0120] When using the life of a capacitor to characterize the life of a board, the following situations need to be considered:

[0121] Temperature is the most basic parameter in the experimental environment. It should be considered as a factor affecting the life of the board. The heating or cooling process of the device will affect the life of the product, especially the device has a strict upper temperature limit. The impact of this temperature on the running device will not only cause insignificant cracking, expansion, deformation, etc. between materials, but also change the circuit parameters.

[0122] The Arrhenius formula in thermodynamics theory can be used to calculate the capacitor life. It is an empirical formula used to describe the relationship between the reaction rate of chemical substances and temperature, as shown below.

[0123]

[0124] Capacitor expected life L op ; Life span of capacitor at maximum temperature L o ; T max is the maximum operating temperature of the capacitor; T h is the actual operating temperature of the capacitor. However, the theoretical formula describes the capacitor life with fewer considerations and poor accuracy, and the steps for obtaining the parameters are not specified.

[0125] Therefore, based on the above problems, the present invention performs multi-parameter fusion modeling to improve the accuracy of industrial use as much as possible. The voltage across the capacitor directly affects its heat generation. c Calculation formula:

[0126]

[0127] Since the heat generation power is related to the temperature, the change in operating life due to heat generation is ΔL op The heating power is P = (I x ) 2 X c , the current changes before and after ΔI x .

[0128] Combining formula (4) we can get the following formula:

[0129]

[0130] Where ε'=e -2πC ;I x Represents the actual current value flowing through the capacitor during operation; I s is the standard allowable current, obtained by calibration; for the convenience of calculation, the two current changes ΔI x Use I x and I s ratio.

[0131] If the frequency f in the running circuit is distorted and high-frequency pulses are generated, the heating of the capacitor will be more obvious. In addition, the capacitor itself has the characteristics of AC conduction and DC resistance, so the interference of high-frequency harmonic components must be considered.

[0132]

[0133] Among them, ΔL op ′ is the expected life span change of the capacitor considering the high-frequency harmonic components, f x is the actual current frequency flowing through the capacitor, f s It is the standard industrial electrical frequency.

[0134] Industrial capacitors are made of metal film, polypropylene, thin porcelain, etc. Different capacitors have different losses, leakage, high temperature resistance, etc. Therefore, different capacitor materials will definitely affect the life of the capacitor. Affecting the temperature of the device will also directly lead to changes in the operating life.

[0135] Capacitors made of different materials have different effects on their electrostatic capacitance at different temperatures, so it is best to consider the effect of the capacitor material. The electrostatic capacitance temperature coefficient corresponding to the capacitor material is η.

[0136]

[0137] The upper limit temperature T of capacitor material c-max ; Electrostatic capacitance value C at 25℃ 25, which can be obtained from the chip manual; the electrostatic capacitance value corresponding to the upper limit temperature of the capacitor material

[0138] According to the temperature coefficient of electrostatic capacitance, we can know the offset of the stable operating capacitor charge at different temperatures, and thus get the temperature T x The corresponding capacitance C x , and further obtain the corrected value of the capacitance.

[0139] C x =η(T x -T 25 )+C 25 (9)

[0140] For the convenience of calculation, the above formula can be abstracted into:

[0141] C x =ηC (10)

[0142] Where C is the theoretical value at a relatively stable temperature. For a 1uF capacitor, the corresponding capacitance value at 25 degrees Celsius is 1uF; C is the nominal capacity at room temperature. The correction parameters caused by the material are:

[0143] ε=e -2πηC =e η ×e -2πC =ε1ε2 (11)

[0144] Where ε1 = e η , ε2=e -2πC These two coefficients are the empirical parameters of the life operation prediction model and must be obtained through experimental calibration in actual use.

[0145] Combining formulas (4)(6)(7)(11), we can get the estimated life model of capacitor operation.

[0146]

[0147] According to the model formula, it can be concluded that the service life of the capacitor is related to the current value flowing through the capacitor, the frequency of the current, and the temperature value at which the capacitor actually operates. Obviously, the model conclusion is consistent with the theoretical analysis.

[0148] The present invention proposes a board life prediction model based on influencing factor analysis, which improves the accuracy and practicality of the prediction by identifying key influencing components, quantifying their influencing factors, and applying mathematical models to predict the board life, thereby providing a scientific basis for the maintenance and replacement of the board.

[0149] Embodiment 2

[0150] The operating life of the entire relay protection device depends on the life of a specific hardware board. As long as one board fails to work or malfunctions, the entire device is considered to be ineffective. Therefore, the evaluation of the operating life of the device is transformed into the life of the board. At the same time, if you want to study the impact of all factors on the life of the board, it is difficult to model the evaluation or the modeling function has too many parameters when it is actually used. Therefore, the present invention focuses on the impact of the board's own components on the life of the board. Compared with other coupling factors, the life of the board components has the most direct impact on them. Therefore, the characteristic quantities studied in the present invention focus on the board components.

[0151] Figure 2 A flow chart of a method for determining an influencing component that characterizes the life of a board according to an embodiment of the present invention; Figure 3 FIG. 4 is a flow chart of a method for predicting the life of a board according to another embodiment of the present invention. Figure 2-3 As shown, according to an embodiment of the present invention, for board components, through experience accumulation, it can be determined that the influencing factors include chips, capacitors, resistors, inductors, diodes, etc. Other unconventional components are not reflected in the evaluation. According to the impact of each component, a table of factors affecting the operation of the device is listed as shown in Table 1:

[0152]

[0153]

[0154] Table 1 Factors affecting device operation

[0155] According to the above data, it is determined that the impact is greatest when there is a problem with the chip. However, it should be noted that the modeling scenario is a failure of the running board, and the probability of device failure needs to be considered. Chip damage will definitely have a huge impact on the logic timing of the entire board and will directly cause the entire device to fail, but the possibility of chip failure is relatively small, so the most suitable influencing factor should be selected.

[0156] The above described characteristics are evaluated to determine the method for calculating the impact factor Q of the device.

[0157] 1. Classification of device influencing factors

[0158] Due to the influence of multiple devices, it is necessary to abstract the above-mentioned influence levels and make a unified comparison. Therefore, it is necessary to calculate the influence factor Q of each device. The following feature classification set = {a, b, c, d, e} = {chip, capacitor, resistor, inductor, diode}.

[0159] 2. Determination of device element membership

[0160] Evaluate the corresponding membership function. Since it is impossible to use a unified model, the influence degree is evaluated here. This method is relatively simple. The membership table of the device is shown in Table 2:

[0161]

[0162] Table 2 Device membership table

[0163] Since there are multiple influencing factors of the device, the membership values ​​of the influencing factors of different devices are:

[0164] The membership degree of the influence range A is μ A (i) = {1.0, 0.8, 0, 0.1, 0.6};

[0165] The membership degree of distribution range B is μ B (i)={0.5,0.8,1.0,0.2,0.6};

[0166] The degree of membership of temperature range C is μ C (i)={0.6,0.8,1.0,0.9,0.8};

[0167] The degree of membership of D affected by temperature is μ D (i) = {0.6, 0.8, 0, 0.3, 1.0};

[0168] Among them, i = a, b, c, d, e; the membership degree referred to here can well represent multiple devices as a 1x5 vector, which is convenient for subsequent numerical processing.

[0169] The above-mentioned membership degree is to visualize the specific degree of correlation into a number for evaluation. Since different factors account for different proportions, it is not conducive to weighted average calculation of the influencing factors of each device. Classification algorithms such as decision trees, Bayesian classifiers, artificial neural networks, support vector machines, and k-nearest neighbors are not used, as these algorithms are not suitable for solving this problem. The present invention takes into account the central point deviation of the classification, is suitable for the optimal Euclidean distance, and has no sample training, so it is easy to realize simple classification evaluation.

[0170] 3. Calculation of device impact factors

[0171] The purpose of device classification is to evaluate the impact on the life of the board, so a multi-dimensional vector can be determined, the device reference vector with the best variables. Since the influencing factors of the device, such as the impact range A, distribution range B, temperature impact D and fragility E, the larger the values, the shorter the operating life of the board, and the larger the temperature range C, the more favorable the operating life of the board, the vector is recorded as the reference centroid point:

[0172] W={w1,w2,w3,w4,w5}={1,1,0,1,1} (1)

[0173] Where W is the reference centroid vector, w1, w2, w3, w4, w5 are the elements in the reference centroid vector respectively;

[0174] Compute the Euclidean distance in multidimensional space:

[0175]

[0176] Among them, r o-i is the distance between the i-th influencing device in the membership vector and the reference centroid, i = a, b, c, d, e;

[0177] The above-mentioned Euclidean distance r based on the reference centroid o-i , represents the final calculated value Q i .

[0178]

[0179] Where Q i is the influence factor of the i-th influencing device in the membership vector.

[0180] This calculation does not require membership weighting, as the above-mentioned influencing factors have low coupling between each other and therefore will not cause spatial coupling.

[0181] The influence factor table of device types obtained by calculation is shown in Table 3:

[0182]

[0183]

[0184] Table 3 Influence factors of device types

[0185] It is difficult to avoid plug-in replacement in the field of relay protection, and the impact on board replacement is directly related to the corresponding life of the board. Usually, when designing highly integrated chips, measures are considered to deal with the impact of special environments such as high temperature resistance and corrosion resistance. Therefore, the life of analog components has a clearer impact on the overall impact of the board. Among capacitors, resistors and inductors, capacitors are more susceptible to external changes, so the research on the life of capacitors is more related to the life of the board.

[0186] According to the theory proposed by the present invention, the capacitor is the most suitable for research. Although the diode is easy to break down after the voltage across the two ends exceeds the upper limit of the working voltage, its distribution and comprehensive impact factor are slightly lower than that of the capacitor. Therefore, according to the above classification discussion, it is determined that the capacitor is a characteristic device for evaluating the life of the board. The life of the capacitor will be used to characterize the life of the board in the following.

[0187] According to an embodiment of the present invention, a method for predicting the life of a board based on capacitor reliability includes:

[0188] Temperature is the most basic parameter in the experimental environment. It should be considered as a factor affecting the life of the board. The heating or cooling process of the device will affect the life of the product, especially the device has a strict upper temperature limit. The impact of this temperature on the running device will not only cause insignificant cracking, expansion, deformation, etc. between materials, but also change the circuit parameters.

[0189] The Arrhenius formula in thermodynamics theory can be used to calculate the capacitor life. It is an empirical formula used to describe the relationship between the reaction rate of chemical substances and temperature, as shown below.

[0190]

[0191] Among them, L op is the expected life of the capacitor; L o is the life of the capacitor at the maximum temperature; T max is the maximum operating temperature of the capacitor; T h is the actual working temperature of the capacitor. However, the theoretical formula describes the capacitor life with fewer considerations and poor accuracy, and the steps for obtaining the parameters are not specified.

[0192] Therefore, based on the above problems, the present invention performs multi-parameter fusion modeling to improve the accuracy of industrial use as much as possible. The voltage across the capacitor directly affects its heat generation. c Calculation formula:

[0193]

[0194] Since the heat generation power is related to the temperature, the change in operating life due to heat generation is ΔL op The heating power is P = (I x ) 2 X c , the current changes before and after ΔI x .

[0195] Combining formula (4) we can get the following formula:

[0196]

[0197] Among them, ΔL op I is the expected life span change of the capacitor; x Represents the actual current value flowing through the capacitor during operation; I s is the standard allowable current, obtained by calibration; ΔI x is the change of the two currents before and after, through I xand Is is determined by the ratio; ε′ is the first coefficient, ε′=e -2πC .

[0198] If the frequency f in the running circuit is distorted and high-frequency pulses are generated, the heating of the capacitor will be more obvious. In addition, the capacitor itself has the characteristics of AC conduction and DC resistance, so the interference of high-frequency harmonic components must be considered.

[0199]

[0200] Where, ΔL op ′ is the expected life span change of the capacitor considering the high-frequency harmonic components, f x is the actual current frequency flowing through the capacitor, f s It is the standard industrial electrical frequency.

[0201] Industrial capacitors are made of metal film, polypropylene, thin porcelain, etc. Different capacitors have different losses, leakage, high temperature resistance, etc. Therefore, different capacitor materials will definitely affect the life of the capacitor. Affecting the temperature of the device will also directly lead to changes in the operating life.

[0202] Capacitors made of different materials have different effects on their electrostatic capacitance at different temperatures, so it is best to consider the effect of the capacitor material. The electrostatic capacitance temperature coefficient corresponding to the capacitor material is η.

[0203]

[0204] Where η is the temperature coefficient of electrostatic capacitance, and the upper limit temperature T of the capacitor material is c-max ; Electrostatic capacitance value C at 25℃ 25 , which can be obtained from the chip manual; the electrostatic capacitance value corresponding to the upper limit temperature of the capacitor material

[0205] According to the temperature coefficient of electrostatic capacitance, we can know the offset of the stable operating capacitor charge at different temperatures, and thus get the temperature T x The corresponding capacitance C x , and further obtain the corrected value of the capacitance.

[0206] C x =η(T x -T 25 )+C 25 (9)

[0207] For the convenience of calculation, the above formula can be abstracted into:

[0208] C x =ηC (10)

[0209] Where C represents the nominal capacity at room temperature, which is the theoretical value at a relatively stable temperature; the correction parameters caused by the material are:

[0210] ε=e -2πηC =e η ×e -2πC =ε1ε2 (11)

[0211] Where ε is the correction parameter caused by the material; ε1 and ε2 are the first correction coefficient and the second correction coefficient, ε1 = e η , ε2=e -2πC These two coefficients are the empirical parameters of the life operation prediction model and must be obtained through experimental calibration when used in practice.

[0212] Combining formulas (4)(6)(7)(11), we can get the estimated life model of capacitor operation.

[0213]

[0214] Among them, L op Indicates the predicted life of the board capacitor; ε1 and ε2 are the first correction coefficient and the second correction coefficient respectively; L o is the life of the capacitor at the maximum temperature; T max is the maximum operating temperature of the capacitor; T h is the actual working temperature of the capacitor; I x Is the actual current value flowing through the capacitor during operation; Is is the standard allowable current; f x is the actual current frequency flowing through the capacitor, f s It is the standard industrial electrical frequency.

[0215] According to the model formula, it can be concluded that the service life of the capacitor is related to the current value flowing through the capacitor, the frequency of the current, and the temperature value at which the capacitor actually operates. Obviously, the model conclusion is consistent with the theoretical analysis.

[0216] In order to determine the capacitor life model, specific parameters need to be determined.

[0217] Step 1: Determine the first correction coefficient ε1.

[0218] First, calculate the capacitance temperature coefficient η of the capacitor material, and determine the upper temperature limit T according to the chip manual or constant temperature oven baking. max (Capacitor maximum operating temperature) corresponds to Electrostatic capacitance value C at 25℃ 25 Given as standard parameters, ε1 is determined by calculation.

[0219] Step 2: Determine the second correction coefficient ε2.

[0220] Since the parameter compensation of the capacitor material has been performed, it is only necessary to input the basic capacitance value for calculation. The other parameters in the ε2 expression are fixed parameters, and the value is relatively easy to obtain.

[0221] Step 3: Obtain the actual working temperature T h .

[0222] A platinum resistor is designed in the circuit to be close to the lower end of the capacitor, and then an external measuring circuit is connected to obtain the voltage and current values ​​of the platinum resistor. The temperature of the capacitor during operation is determined based on the relationship between the resistance value of the platinum resistor and the temperature.

[0223] Step 4: Get the actual current value I flowing through the capacitor during operation x Since the actual capacitance is widely distributed in the circuit, the current value on the series side is directly collected in real time as the current flowing through the current side.

[0224] Step 5: Get the frequency f in the running circuit. In the relay protection algorithm, attention is paid to high-frequency harmonic interference. Therefore, the current collected in step 4 is directly subjected to discrete Fourier transform, and the highest frequency component is extracted and brought into it. The actual current frequency f flowing through the capacitor is s in the country, 50 Hz is directly input for calculation.

[0225] Step 6: Determine the life L0 of the capacitor at the maximum temperature and the maximum operating temperature T of the capacitor max and the standard allowable current I s .

[0226] The maximum temperature allowed by the capacitor is T max The operating life L0 is generally given in the manual. Or you can bake it in a constant temperature box to specify a preset high temperature. This temperature value should be controlled above the actual operating temperature as much as possible, so that the accuracy of the calibrated model will be higher. s In circuit design, the current value allowed by the capacitor design is usually a fixed value.

[0227] After steps (1)-(6), the model parameter determination method proposed in the present invention can determine or provide a real-time acquisition method.

[0228] According to the specific application scenario, the specific actual model can be determined as above. The present invention not only provides a method for determining the model, but also establishes a corresponding panel life prediction algorithm based on the model. Figure 2 The following is a schematic diagram of the operation of the algorithm.

[0229] STEP 1: Construct a model of board failure related to the working temperature of capacitor components to provide a theoretical basis for calculating the life of boards using capacitor components;

[0230] STPE 2: Measure the real-time operating temperature of the capacitor, bring it into the calculation model for analysis, and obtain the trouble-free working time t2;

[0231] STEP 3: The algorithm calculates the cumulative running time t1. If t1>t2*0.95, the LCD directly outputs that the specific board is about to be in a faulty state. Please replace it in time. Otherwise, the LCD displays the normal running status of the board.

[0232] In order to solve the uncertainty of panel replacement time and assist in smart grid fault prediction and diagnosis, the present invention first evaluates the influencing factors of the evaluation object of the panel to determine the appropriate characteristic quantity; then evaluates whether the characteristic quantity is susceptible to external factors during operation, and determines that the temperature and load voltage determine the state of the characteristic quantity; secondly, the predicted life of the panel and the characteristic quantity, as well as the typical factors affecting the characteristic quantity are given for model fitting, thereby determining the predicted life model of the panel; finally, the prediction result is output in combination with the device display, and provided to the remote manager through a wireless module for non-site monitoring.

[0233] Embodiment 3

[0234] Figure 4 FIG. 1 is a system structure diagram of a board life prediction system according to an embodiment of the present invention. Figure 4 As shown, according to one embodiment of the present invention, a system for predicting the life of a board includes:

[0235] A construction module 10 is used to construct a board life prediction model based on the influencing components that affect the board life;

[0236] The monitoring module 20 is used to monitor the operation data that affects the life of the board, obtain the trouble-free working time t2 based on the board life prediction model, and calculate the accumulated operation time t1 of the board;

[0237] The judging module 30 is used to judge whether t1 and t2 meet the preset conditions; if they do, it is judged that the board is about to be in a fault state and a replacement prompt is given; if they do not meet the conditions, the output board is in a normal operating state;

[0238] The preset condition is: t1>t2*0.95.

[0239] According to one embodiment of the present invention, an electronic device includes a processor, a memory, and a computer program stored in the memory and executable on the processor, wherein the computer program implements any method for predicting the life of a board when executed by the processor.

[0240] According to one embodiment of the present invention, a computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, any method for predicting the life of a board is implemented.

[0241] According to one embodiment of the present invention, a computer program product includes a computer program, and when the computer program is executed by a processor, the steps of any method for predicting the life of a board are implemented.

[0242] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the above-described systems, devices, media and computer program products can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0243] The above description is only a preferred embodiment of the present application and an explanation of the technical principles used. Those skilled in the art should understand that the scope of the invention involved in the present application is not limited to the technical solution formed by a specific combination of the above technical features, but should also cover other technical solutions formed by any combination of the above technical features or their equivalent features without departing from the inventive concept. For example, the above features are replaced with (but not limited to) technical features with similar functions disclosed in the present application.

[0244] It should be understood that the size of the serial numbers of each step in the content of the invention and the embodiments of the present invention does not absolutely mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

Claims

1. A method for predicting the life of a board, characterized in that: The following steps are involved: Construct a board life prediction model based on the influencing components that affect the board life; Monitor the operation data that affects the life of the board, obtain the trouble-free working time t2 based on the board life prediction model, and calculate the accumulated operation time t1 of the board; Determine whether t1 and t2 meet the preset conditions; If the conditions are met, it is determined that the board is about to fail, and a replacement prompt is given; If not, output that the board is in normal operation; The preset condition is: t1>t2*0.

95.

2. The method for predicting the life of a board according to claim 1, characterized in that: The board life prediction model is: Among them, L op Indicates the predicted life of the board capacitor; ε1 and ε2 are the first correction coefficient and the second correction coefficient respectively; L o is the life of the capacitor at the maximum temperature; T max is the maximum operating temperature of the capacitor; T h is the actual working temperature of the capacitor; I x Is the actual current value flowing through the capacitor during operation; Is is the standard allowable current; f x is the actual current frequency flowing through the capacitor, f s It is the standard industrial electrical frequency.

3. The method for predicting the life of a board according to claim 2, characterized in that: In the board life prediction model: ε1=e η ε2=e -2πC Where C is the nominal capacity of the capacitor at room temperature; η is the temperature coefficient of electrostatic capacity, which is determined by the following formula: in, C is the electrostatic capacitance value corresponding to the upper limit temperature of the capacitor material; 25 is the electrostatic capacitance value at 25°C; T c-max is the upper limit temperature of the capacitor material.

4. The method for predicting the life of a board according to claim 3, characterized in that: Capacitor actual operating temperature T h The acquisition method is as follows: a platinum resistor is designed in the circuit to be close to the lower end of the capacitor, and then an external measuring circuit is connected to obtain the voltage and current values ​​of the platinum resistor, and the operating temperature of the capacitor is determined according to the relationship between the resistance value of the platinum resistor and the temperature; The actual current value I flowing through the capacitor during operation x The acquisition method is: real-time acquisition of the series side current value as the current flowing through the current side.

5. The method for predicting the life of a board according to claim 1, characterized in that: The process of building a board life prediction model is as follows: Obtain the influencing components that affect the life of the board; Determine the impact factor of each influencing device; An influencing component characterizing the life of the board is determined based on the influencing factor.

6. The method for predicting the life of a board according to claim 5, characterized in that: The influencing devices include chips, capacitors, resistors, inductors, and diodes; Determine the influencing factors of each influencing device. The specific steps are: Obtain the membership of each influencing factor; the membership is a 1x5 vector, representing the influence of each influencing device under each influencing factor; Based on the membership degree of each influencing factor, the Euclidean distance between each influencing device and the reference centroid point is calculated; The influence factor of each influencing component is calculated based on the Euclidean distance of each influencing component from the reference centroid point.

7. A system for predicting the life of a board, characterized in that: include: A building module, used for building a board life prediction model based on influencing components that affect the board life; A monitoring module, used to monitor the operating data that affects the life of the board, obtain the trouble-free working time t2 based on the board life prediction model, and calculate the accumulated running time t1 of the board; A judgment module, used to judge whether t1 and t2 meet the preset conditions; If the conditions are met, it is determined that the board is about to fail, and a replacement prompt is given; If not, output that the board is in normal operation; The preset condition is: t1>t2*0.

95.

8. An electronic device, characterized in that: The invention comprises a processor, a memory and a computer program stored in the memory and executable on the processor, wherein when the computer program is executed by the processor, the method for predicting the life of a board as claimed in any one of claims 1 to 6 is implemented.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the method for predicting the life of a board as claimed in any one of claims 1 to 6 is implemented.

10. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of the method for predicting the life of a board as described in any one of claims 1 to 6 are implemented.

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