Method, system and device for analyzing correlation between aging prediction parameters of planar transformer

Abstract

The application discloses a planar transformer aging prediction parameter correlation analysis method, system and device, and relates to the technical field of planar transformers. The application receives change data of to-be-analyzed parameters of a planar transformer at different time points; takes data recorded at zero time as a standard value to normalize the rest of the data; processes the normalized data through curve fitting to take parameter data with a slope closest to 1 as a reference sequence; uses a grey correlation degree analysis method to calculate correlation coefficients of the rest of the parameters and the selected reference sequence, and normalizes the correlation coefficients. The application uses the grey correlation degree analysis method to comprehensively apply multiple parameters of the planar transformer, provides a theoretical basis for weight distribution when the influence of the multiple parameters on the prediction process is comprehensively considered, and finally constructs a planar transformer life prediction model according to a mathematical statistics method, thereby improving the accuracy and reliability of planar transformer aging prediction.

Description

Technical Field

[0001] This invention relates to the field of planar transformer technology, specifically to a method, system, and equipment for correlation analysis of aging prediction parameters of planar transformers. Background Technology

[0002] With the rapid development of electric vehicles, new energy power generation, and data centers, power conversion has penetrated all aspects of industrial development. Switching power supply technology plays a crucial role in this, and with the demands of industry development, switching power supply converters are rapidly evolving towards smaller size and weight, and higher power density. Traditional magnetic components have certain limitations in terms of efficiency, size, and weight. The biggest difference between planar magnetic components, such as planar transformers, and traditional transformers is that planar transformers often have a low-profile magnetic core structure and lighter weight, giving them advantages such as small size, low leakage inductance, and high efficiency. These advantages can well meet the development needs of miniaturization in switching power supplies. Therefore, planar magnetic components have become an important research point for the miniaturization and high efficiency of power systems. In the production process of planar magnetic components, aging life prediction methods and stress testing of screening equipment are required. Defective or failed electronic components must be strictly removed to improve the reliability of electronic component applications and the overall quality of equipment.

[0003] In existing engineering practice, the aging of planar transformers is affected by numerous parameters, including primary inductance (P_L), primary resistance (P_R), insulation resistance (PS_R), short-circuit resistance (S_R), leakage inductance (S_L), and insulation capacitance (PS_C). However, existing methods only consider a single parameter or lack corresponding weighting theory analysis, and there is little analysis on the mutual influence between the parameters, resulting in low accuracy and reliability of the prediction results. Therefore, we propose a parameter correlation analysis method, system, and equipment for predicting the aging of planar transformers. Summary of the Invention

[0004] The purpose of this invention is to provide a method, system, and equipment for correlation analysis of parameters in planar transformer aging prediction. By using grey relational analysis, multiple parameters of the planar transformer are comprehensively applied in a correlational manner. While comprehensively considering the influence of multiple parameters on the prediction process, it also provides a theoretical basis for their weight allocation, thereby improving the accuracy and reliability of planar transformer aging prediction.

[0005] According to a first aspect of the present invention, in order to achieve the above-mentioned objective, the present invention provides the following technical solution: a method for correlation analysis of aging prediction parameters of planar transformers, specifically including the following steps:

[0006] Receive data on the changes in the parameters to be analyzed of the planar transformer at different points in time;

[0007] The data recorded at time zero is used as the standard value, and the remaining data is normalized.

[0008] The normalized data is processed by curve fitting, and the parameter data with the slope closest to 1 is taken as the reference sequence.

[0009] The grey relational analysis method is used to calculate the correlation coefficient between the remaining parameters and the selected reference sequence, and the correlation coefficients are normalized so that their sum is 1, which is used as the weight ratio of each parameter.

[0010] The normalized data obtained above is processed according to the weight ratio of each parameter;

[0011] By curve fitting, the time L corresponding to the parameter value dropping to 60% for each device under different temperature T conditions was obtained;

[0012] Using Minitab, the predicted lifetime times at B10 and B20 are obtained through accelerated lifetime testing, and then input into the Arrhenius model to obtain the final lifetime model.

[0013] Furthermore, the data is normalized as follows:

[0014] The data recorded at time zero is the standard value x. ij (0) = 1, and the remaining data are normalized based on this, denoted as x. ij (k), where i is the number of parameters to be investigated, j is the number of experimental transformers, and k is the sequence number of different time points taken in the experiment.

[0015] Furthermore, using grey relational analysis, the correlation coefficients between the remaining parameters and the selected reference sequence are calculated, as follows:

[0016] (1) Data preprocessing using the mean method:

[0017]

[0018] In the formula, x i (k) represents the change sequence for the i-th parameter, which is the change sequence x of j experimental transformers under that parameter. ij (k), a new variation sequence obtained through curve fitting, where i represents the number of parameters other than the reference sequence parameters; x' i (k) is the new change sequence after processing with the mean method;

[0019] (2) Calculate the difference between each sequence and the reference sequence:

[0020] Δ i (k)=|y(k)-x i(k)|

[0021] In the formula, y(k) represents x' i The reference sequence in (k) is selected based on the linearity of the data, and the data sequence with the slope closest to -1 is taken as the reference sequence.

[0022] (3) Calculate the correlation coefficient:

[0023]

[0024] In the formula, m is the minimum difference between the two levels, M is the maximum difference between the two levels, and ρ is the resolution coefficient, which is taken as ρ = 0.5.

[0025] Furthermore, the correlation coefficients are normalized so that their sum is 1, and this sum is used as the weighting of each parameter, as follows:

[0026]

[0027] In the formula, ξ i (k) represents the correlation coefficient of the i-th parameter to the reference sequence; n represents the number of all parameters, including the reference parameter.

[0028] Furthermore, the normalized data obtained above is processed according to the weight ratio of each parameter, as follows:

[0029] (1) Allocate the normalized data according to their weights and sum them up:

[0030]

[0031] (2) Use the following formula to convert all data sequences into a downward trend for comparison with the declining lifespan trend:

[0032] x i (k) = 1 - |1 - x i '(k)|

[0033] In the formula, x i (k) represents the change sequence for the i-th parameter, which is a new change sequence obtained by curve fitting from the change sequences of j experimental transformers under that parameter, where i represents the number of parameters; ξ i '(k) represents the weight percentage of the i-th parameter; x i '(k) represents the new change sequence obtained after considering the parameter weight allocation for the i-th parameter; x i (k) means x i The new time series obtained after '(k) turns into a downward trend.

[0034] Furthermore, when calculating the time L corresponding to the parameter values ​​of each device dropping to 60%, considering the influence of temperature during the experiment, the calculated time is corrected according to the following formula:

[0035] L'=L+(T-T0)Δt+t0

[0036] In the formula, T is the current temperature, T0 is the rated temperature, Δt is the temperature coefficient, and t0 is the experimental error at the rated temperature.

[0037] Furthermore, the final lifetime model is expressed as follows:

[0038]

[0039] In the formula, L, L0, T, and T0 represent the actual and rated lifespan and temperature of the planar transformer, and KB is the Boltzmann constant (8.62 × 10⁻⁵ eV / K).

[0040] According to a second aspect of the present invention, the present invention provides a correlation analysis system for predicting aging parameters of a planar transformer, comprising:

[0041] The receiving module is used to receive the change data of the parameters to be analyzed of the planar transformer at different time points;

[0042] The first data processing module is used to normalize the remaining data using the data recorded at time zero as the standard value.

[0043] The reference sequence acquisition module is used to process the normalized data through curve fitting and select the parameter data with the slope closest to 1 as the reference sequence.

[0044] The calculation module is used to calculate the correlation coefficient between the remaining parameters and the selected reference sequence using the grey relational analysis method, and normalize the correlation coefficients so that their sum is 1, which is used as the weight ratio of each parameter.

[0045] The second data processing module is used to process the normalized data obtained above according to the weight ratio of each parameter.

[0046] The time parameter acquisition module is used to obtain the time L corresponding to the parameter value dropping to 60% for each device under different temperature T conditions by curve fitting.

[0047] The model output module is used to obtain the predicted lifetime times at B10 and B20 through accelerated lifetime testing using Minitab, and then input them into the Arrhenius model to obtain the final lifetime model.

[0048] According to a third aspect of the present invention, the present invention provides a terminal device, including a memory, a processor, and a computer program stored in the memory and capable of running on the processor. The memory stores the computer program capable of running on the processor, and when the processor loads and executes the computer program, it employs the above-described planar transformer aging prediction parameter correlation analysis method.

[0049] According to a fourth aspect of the present invention, the present invention provides a storage medium containing computer-executable instructions, wherein the computer-executable instructions, when executed by a computer processor, are used to perform the above-described method for correlation analysis of aging prediction parameters of planar transformers.

[0050] The present invention has at least the following beneficial effects:

[0051] This invention uses grey relational analysis to comprehensively apply the correlation of multiple parameters of a planar transformer. While comprehensively considering the influence of multiple parameters on the prediction process, it also provides a theoretical basis for their weight allocation. Finally, a planar transformer life prediction model is constructed based on mathematical statistics, which improves the accuracy and reliability of planar transformer aging prediction.

[0052] Of course, any product implementing this invention does not necessarily need to achieve all of the advantages described above at the same time. Attached Figure Description

[0053] Figure 1 This is a schematic diagram of the method flow described in this invention.

[0054] Figure 2 This is a fitting graph showing the changing trends of various parameters at 180℃ according to the present invention.

[0055] Figure 3 This is a time series trend diagram of the planar transformer of the present invention after considering the correlation between various parameters. Detailed Implementation

[0056] The technical solutions of the embodiments of this disclosure will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this disclosure, and not all embodiments. Based on the embodiments of this disclosure, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this disclosure.

[0057] Example 1:

[0058] Please see Figure 1-3 This invention provides a technical solution: a method for correlation analysis of aging prediction parameters of planar transformers, specifically including the following steps:

[0059] S1. Receive the change data of the parameters to be analyzed of the planar transformer at different time points;

[0060] It should be noted that the parameters to be analyzed include primary inductance (P_L), primary resistance (P_R), insulation resistance (PS_R), short-circuit resistance (S_R), leakage inductance (S_L), and insulation capacitance (PS_C).

[0061] S2. Using the data recorded at time zero as the standard value, normalize the remaining data as follows:

[0062] The data recorded at time zero is the standard value x. ij (0) = 1, and the remaining data are normalized based on this, denoted as x. ij (k), where i is the number of parameters to be examined, j is the number of experimental transformers, and k is the sequence number of different time points taken in the experiment;

[0063] S3. The normalized data is processed by curve fitting, and the parameter data with the slope closest to 1 is taken as the reference sequence;

[0064] S4. Using grey relational analysis, calculate the correlation coefficients between the remaining parameters and the selected reference sequence, and normalize the correlation coefficients so that their sum is 1, which is used as the weight ratio of each parameter.

[0065] Furthermore, using grey relational analysis, the correlation coefficients between the remaining parameters and the selected reference sequence are calculated, as follows:

[0066] S4.1 Data preprocessing using the mean method:

[0067]

[0068] In the formula, x i (k) represents the change sequence for the i-th parameter, which is the change sequence x of j experimental transformers under that parameter. ij (k), a new variation sequence obtained through curve fitting, where i represents the number of parameters other than the reference sequence parameters; x' i (k) is the new change sequence after processing with the mean method;

[0069] S4.2 Calculate the difference between each sequence and the reference sequence:

[0070] Δ i (k)=|y(k)-x i (k)|

[0071] In the formula, y(k) represents x' iThe reference sequence in (k) is selected based on the linearity of the data, and the data sequence with the slope closest to -1 is taken as the reference sequence.

[0072] S4.3 Calculate the correlation coefficient:

[0073]

[0074] In the formula, m is the minimum difference between the two levels, M is the maximum difference between the two levels, and ρ is the resolution coefficient, which is generally taken as ρ = 0.5;

[0075] Furthermore, the correlation coefficients are normalized so that their sum is 1, and this sum is used as the weighting of each parameter, as follows:

[0076]

[0077] In the formula, ξ i (k) represents the correlation coefficient of the i-th parameter with respect to the reference sequence; n represents the number of all parameters, including the reference parameter.

[0078] S5. Process the normalized data obtained above according to the weight ratio of each parameter, as follows:

[0079] S5.1 assigns weighted proportions to the normalized data and adds them together:

[0080]

[0081] S5.2 uses the following formula to convert all data sequences into a downward trend for comparison with the declining lifespan trend:

[0082] x i (k) = 1 - |1 - x i '(k)|

[0083] In the formula, x i (k) represents the change sequence for the i-th parameter, which is a new change sequence obtained by curve fitting from the change sequences of j experimental transformers under that parameter, where i represents the number of parameters; ξ i '(k) represents the weight percentage of the i-th parameter; x i '(k) represents the new change sequence obtained after considering the parameter weight allocation for the i-th parameter; x i (k) means x i The new time series obtained after '(k) turns into a downward trend;

[0084] S6. By curve fitting, the time L corresponding to the parameter value dropping to 60% for each device under different temperature T conditions was obtained. Considering the influence of temperature during the experiment, the obtained time was corrected according to the following formula:

[0085] L'=L+(T-T0)Δt+t0

[0086] In the formula, T is the current temperature, T0 is the rated temperature, and Δ t Here, t is the temperature coefficient, and t0 is the experimental error at the rated temperature.

[0087] S7. Using Minitab, the predicted lifetime times at B10 and B20 are obtained through accelerated lifetime testing. These are then input into the Arrhenius model to obtain the final lifetime model, as shown in the following expression:

[0088]

[0089] In the formula, L, L0, T, and T0 represent the actual and rated lifespan and temperature of the planar transformer, and KB is the Boltzmann constant (8.62 × 10⁻⁵ eV / K).

[0090] It should be noted that the technical solution of this embodiment can be applied not only to the analysis of planar transformers, but also to various transformers and inductors such as power transformers, high-frequency transformers, and wound transformers. This embodiment is not limited here and can be used according to the actual situation.

[0091] The technical solution of the present invention will be further described in detail below with reference to specific embodiments:

[0092] Taking the changes in primary inductance (P_L), primary resistance (P_R), insulation resistance (PS_R), short-circuit resistance (S_R), leakage inductance (S_L), and insulation capacitance (PS_C) of a small planar transformer obtained through degradation experiments at 180℃ and 200℃ at different time points as an example, the process of obtaining the lifetime model and the comparison results with the model obtained by the traditional method are as follows.

[0093] Step 1: Obtain the changes in primary inductance (P_L), primary resistance (P_R), insulation resistance (PS_R), short-circuit resistance (S_R), leakage inductance (S_L), and insulation capacitance (PS_C) of 12 planar transformers at 180℃ and 200℃ at different time points;

[0094] Step 2: Normalize the obtained data, and take the data recorded at time zero as the standard value x. ij (0) = 1, and the remaining data are normalized based on this, denoted as x. ij(k), where i is the number of parameters to be investigated, i = 1, 2, ..., 6, j is the number of experimental transformers, j = 1, 2, ..., 12, and k is the sequence number of different time points taken in the experiment. In this example, at 180℃, k = 1, 2, ..., 9, and at 200℃, k = 1, 2, ..., 7;

[0095] Step 3: Taking 180℃ as an example, for the i-th parameter, take the change sequence x of this parameter for 12 experimental devices respectively. ij (k), j = 1, 2, ..., 12, k = 1, 2, ..., 9; the changing trend of these 12 sequences is represented by a curve through curve fitting, denoted as x. i (k), the fitting results are as follows Figure 2 As shown, to examine the linearity of the data, the parameter data with the slope closest to 1 is taken as the reference sequence. For the technical solution of this embodiment, PS_C is taken as the reference sequence.

[0096] Step 4: Use grey relational analysis to calculate the correlation coefficients between the remaining parameters and PS_C. The specific steps are as follows:

[0097] (1) Data preprocessing using the mean method

[0098]

[0099]

[0100] (2) Calculate the difference between each sequence and the reference sequence:

[0101] Δ i (k)=|y(k)-x i (k)|

[0102] (3) Calculate the correlation coefficient:

[0103]

[0104] In the formula, m is the minimum difference between the two levels, M is the maximum difference between the two levels, and ρ is the resolution coefficient, which is generally taken as ρ = 0.5. The obtained correlation coefficients are shown in Table 1:

[0105] Table 1. Correlation coefficients between various parameters and PS_C at 180℃

[0106]

[0107] Step 5: Calculate the weight of each parameter in the aging process of the planar transformer, and process the normalized data obtained in Step 2 according to the weight:

[0108] (1) The obtained correlation coefficients are normalized according to the following formula, so that their sum is 1. The normalized correlation coefficients are used as the weight ratio of each parameter, as shown in Table 2:

[0109]

[0110] Table 2. Weighting of each parameter at 180℃

[0111]

[0112] (2) The normalized data are allocated and summed according to their weights using the following formula. For the j-th device, the time series obtained after considering the correlation between the parameters is x. j (k);

[0113]

[0114] (3) Since only the trend of data change is considered, and the known trend of lifespan change is downward, the following formula is used to transform all data sequences into a downward sequence:

[0115] x j '(k)=1-|1-x j (k)|;

[0116] Step 6: Plot the descending sequence x using curve fitting. j The changing trend of (k)', such as Figure 3 As shown. Under a temperature condition of 180℃, calculate the time L corresponding to the parameter value of each device dropping to 60%. 180 Considering the influence of temperature during the experiment, the calculated time is corrected according to the following formula:

[0117] L 180 '=L 180 +(T-T0)Δt+t0

[0118] Where T is the current temperature, T0 is the rated temperature, Δt is the temperature coefficient, and t0 is the experimental error at the rated temperature, all determined by specific experimental data. In this example, Δt = 6℃, t0 = 30℃;

[0119] Step 7: Repeat steps 3 to 6 at 200℃ to generate a new time series L. 200 Using the minitab tool, with L... 180 'and L 200 Two sets of data were used in accelerated life tests to obtain the predicted lifetime times at B10 and B20, as shown in Table 3:

[0120] Table 3. Lifespan Prediction Table for Planar Transformers B10 and B20

[0121]

[0122] Step 8: Input the data into the Arrhenius model to calculate the activation energy, thus obtaining the final lifetime model as follows.

[0123] B10:

[0124]

[0125] B20:

[0126]

[0127] Where L, L0, T, and T0 represent the actual and rated lifespan and temperature of the planar transformer, and K... B It is the Boltzmann constant (8.62×10⁻⁵ eV / K).

[0128] In summary, this invention uses grey relational analysis algorithm to analyze and optimize the parameters of planar transformers in the life prediction process. Combined with the influence of experimental temperature, it establishes the relationship between various parameters related to the aging process of planar transformers, including primary inductance (P_L), primary resistance (P_R), insulation resistance (PS_R), short-circuit resistance (S_R), leakage inductance (S_L), and insulation capacitance (PS_C), providing a scientific basis for weight allocation in multi-parameter life prediction of planar transformers.

[0129] Example 2:

[0130] This invention provides a correlation analysis system for predicting aging parameters of planar transformers, comprising:

[0131] The receiving module is used to receive the change data of the parameters to be analyzed of the planar transformer at different time points;

[0132] The first data processing module is used to normalize the remaining data using the data recorded at time zero as the standard value.

[0133] The reference sequence acquisition module is used to process the normalized data through curve fitting and select the parameter data with the slope closest to 1 as the reference sequence.

[0134] The calculation module is used to calculate the correlation coefficient between the remaining parameters and the selected reference sequence using the grey relational analysis method, and normalize the correlation coefficients so that their sum is 1, which is used as the weight ratio of each parameter.

[0135] The second data processing module is used to process the normalized data obtained above according to the weight ratio of each parameter.

[0136] The time parameter acquisition module is used to obtain the time L corresponding to the parameter value dropping to 60% for each device under different temperature T conditions by curve fitting.

[0137] The model output module is used to obtain the predicted lifetime times at B10 and B20 through accelerated lifetime testing using Minitab, and then input them into the Arrhenius model to obtain the final lifetime model.

[0138] Specifically, the aforementioned receiving module, first data processing module, reference sequence acquisition module, calculation module, and second data processing module can be embedded into a computer processing system. The computer, based on the aforementioned planar transformer aging prediction parameter correlation analysis method, calls the aforementioned modules to complete the task of equivalent modeling. The aforementioned receiving module, first data processing module, reference sequence acquisition module, calculation module, and second data processing module can perform operations according to the specific steps given in the aforementioned planar transformer aging prediction parameter correlation analysis method.

[0139] It should be noted that the division of the various modules in the above system is merely a division of logical functions. In actual implementation, they can be fully or partially integrated into a single physical entity, or they can be physically separated. These modules can be implemented entirely in software through processing element calls; they can be fully implemented in hardware; or some modules can be implemented by processing element calls to software, while others are implemented in hardware. For example, the receiving module can be a separate processing element or integrated into a chip in the aforementioned device. Alternatively, it can be stored as program code in the memory of the aforementioned device, and called and executed by a processing element of the device. The implementation of other modules is similar. Furthermore, these modules can be fully or partially integrated together or implemented independently. The processing element mentioned here can be an integrated circuit with signal processing capabilities. During implementation, each step of the above method or each of the above modules can be completed through integrated logic circuits in the hardware of the processor element or through software instructions.

[0140] For example, these modules can be one or more integrated circuits configured to implement the above methods, such as one or more Application Specific Integrated Circuits (ASICs), one or more Digital Signal Processors (DSPs), or one or more Field Programmable Gate Arrays (FPGAs). As another example, when a module is implemented using processing element scheduler code, the processing element can be a general-purpose processor, such as a Central Processing Unit (CPU) or other processor capable of calling program code. Furthermore, these modules can be integrated together to form a system-on-a-chip (SOC).

[0141] Example 3:

[0142] The present invention provides a terminal device, including a memory, a processor, and a computer program stored in the memory and capable of running on the processor. The memory stores the computer program capable of running on the processor. When the processor loads and executes the computer program, it employs the above-mentioned correlation analysis method for aging prediction parameters of planar transformers.

[0143] It should be noted that the terminal device can be a computer device such as a desktop computer, a laptop computer, or a cloud server, and the terminal device includes, but is not limited to, a processor and a memory. For example, the terminal device may also include input / output devices, network access devices, and buses.

[0144] Furthermore, the processor can be a central processing unit (CPU). Of course, depending on the actual use, other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), off-the-shelf programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. can also be used. The general-purpose processor can be a microprocessor or any conventional processor, etc., and this application does not limit it in this regard.

[0145] Example 4:

[0146] The present invention provides a storage medium containing computer-executable instructions, characterized in that the computer-executable instructions, when executed by a computer processor, are used to perform the above-described method for correlation analysis of aging prediction parameters of planar transformers.

[0147] The computer program can be stored in a computer-readable medium. The computer program includes computer program code, which can be in the form of source code, object code, executable file, or certain middleware. The computer-readable medium includes any entity or device capable of carrying computer program code, recording media, USB flash drive, portable hard drive, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signals, telecommunication signals, and software distribution media, etc. It should be noted that the computer-readable medium includes, but is not limited to, the above-mentioned components.

[0148] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

[0149] For those skilled in the art, the specific meaning of the above terms in this invention can be understood according to the specific circumstances. When an element is referred to as being "assembled on," "mounted on," "fixed to," or "set on" another element, it may be directly on the other element or there may be an intermediate element present. When an element is considered to be "connected to" another element, it may be directly connected to the other element or there may be an intermediate element present. The terms "vertical," "horizontal," "upper," "lower," "left," "right," and similar expressions used herein are for illustrative purposes only and do not represent the only possible embodiments.

[0150] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

[0151] In the description of this specification, references to terms such as "an embodiment," "example," "specific example," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of this disclosure. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

Claims

1. A method for correlation analysis of aging prediction parameters of planar transformers, characterized in that, Specifically, the following steps are included: Receive data on the changes in the parameters to be analyzed of the planar transformer at different points in time; The data recorded at time zero is used as the standard value, and the remaining data is normalized. The normalized data is processed by curve fitting, and the parameter data with the slope closest to 1 is taken as the reference sequence. The grey relational analysis method is used to calculate the correlation coefficient between the remaining parameters and the selected reference sequence, and the correlation coefficients are normalized so that their sum is 1, which is used as the weight ratio of each parameter. The normalized data obtained above is processed according to the weight ratio of each parameter; By curve fitting, the time L corresponding to the parameter value dropping to 60% for each device under different temperature T conditions was obtained; Using Minitab, the predicted lifetime times at B10 and B20 are obtained through accelerated lifetime testing, and then substituted into the Arrhenius model to obtain the final lifetime model. The data is normalized as follows: The data recorded at time zero is the standard value. =1, and the remaining data are normalized based on this, denoted as . ,in, i This is the number of parameters to be examined. j This refers to the number of experimental transformers. k These are the sequence numbers of the different time points used in the experiment; The grey relational analysis method was used to calculate the correlation coefficients between the remaining parameters and the selected reference sequence, as follows: (1) Data preprocessing using the mean method: In the formula, This represents the sequence of changes for the i-th parameter, which is formed by... j A sequence of changes in the parameters for an experimental transformer. A new variation sequence was obtained through curve fitting, in which i Indicates the number of parameters other than the reference sequence parameters; This is the new change sequence after processing with the mean method; (2) Calculate the difference between each sequence and the reference sequence: In the formula, express The reference sequence is selected based on the linearity of the data, with the data sequence whose slope is closest to -1 being chosen as the reference sequence. (3) Calculate the correlation coefficient: In the formula, m is the minimum difference between the two levels, and M is the maximum difference between the two levels. To determine the resolution coefficient, take... =0.5; The correlation coefficients are normalized so that their sum is 1, and these coefficients are used as the weighting percentages for each parameter, as follows: In the formula, Indicates the first i The correlation coefficients of each parameter with respect to the reference sequence; n This indicates the number of all parameters, including the reference parameter.

2. The method for correlation analysis of aging prediction parameters of planar transformers according to claim 1, characterized in that, The normalized data obtained above is processed according to the weight ratio of each parameter, as follows: (1) Allocate the normalized data according to their weights and sum them up: ； (2) Use the following formula to convert all data sequences into a downward trend for comparison with the downward trend in lifespan: x i ”(k)=1-|1-x i ’(k)| In the formula, Indicates that for the first i The sequence of changes in each parameter is composed of... j The variation sequence of an experimental transformer under this parameter is used to obtain a new variation sequence through curve fitting, where i represents the number of parameters; ξ i '(k) represents the first (k) i The weighting percentage of each parameter; x i '(k) represents the new change sequence obtained after considering the parameter weight allocation for the i-th parameter; x i (k) means x i The new time series obtained after '(k) turns into a downward trend.

3. The method for correlation analysis of aging prediction parameters of planar transformers according to claim 2, characterized in that, When calculating the time L corresponding to the parameter values ​​of each device dropping to 60%, considering the influence of temperature during the experiment, the calculated time is corrected according to the following formula: In the formula, T is the current temperature, and T0 is the rated temperature. t is the temperature coefficient, and t0 is the experimental error at the rated temperature.

4. The method for correlation analysis of aging prediction parameters of planar transformers according to claim 3, characterized in that, The final lifetime model is expressed as follows: In the formula, L, L0, T, and T0 represent the actual and rated lifespan and temperature of the planar transformer, and KB represents the Boltzmann constant (8.62 × 10⁻⁵ eV / K).

5. A system for analyzing the correlation of aging prediction parameters for planar transformers, used to implement the method for analyzing the correlation of aging prediction parameters for planar transformers according to any one of claims 1 to 4, characterized in that, include: The receiving module is used to receive the change data of the parameters to be analyzed of the planar transformer at different time points; The first data processing module is used to normalize the remaining data using the data recorded at time zero as the standard value. The reference sequence acquisition module is used to process the normalized data through curve fitting and select the parameter data with the slope closest to 1 as the reference sequence. The calculation module is used to calculate the correlation coefficient between the remaining parameters and the selected reference sequence using the grey relational analysis method, and normalize the correlation coefficients so that their sum is 1, which is used as the weight ratio of each parameter. The second data processing module is used to process the normalized data obtained above according to the weight ratio of each parameter. The time parameter acquisition module is used to obtain the time L corresponding to the parameter value dropping to 60% for each device under different temperature T conditions by curve fitting. The model output module is used to obtain the predicted lifetime times at B10 and B20 through accelerated lifetime testing using Minitab, and then input them into the Arrhenius model to obtain the final lifetime model.

6. A terminal device, comprising a memory, a processor, and a computer program stored in the memory and capable of running on the processor, characterized in that, The memory stores a computer program that can run on a processor. When the processor loads and executes the computer program, it employs the correlation analysis method for the aging prediction parameters of the planar transformer as described in any one of claims 1 to 4.

7. A storage medium containing computer-executable instructions, characterized in that, The computer-executable instructions, when executed by a computer processor, are used to perform the correlation analysis method for predicting the aging parameters of a planar transformer as described in any one of claims 1 to 4.

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