Remaining life reliability assessment methods, apparatus, equipment, and dielectric for magnetically controlled transformers.

Abstract

This application relates to a method, apparatus, computer equipment, and computer-readable storage medium for assessing the remaining life reliability of a magnetically controlled transformer. The method includes: acquiring electrical test data, dissolved gas information in the oil, operation and maintenance records, and accessory operation information of the magnetically controlled transformer; inputting the acquired information into a pre-established transformer operation status assessment model to obtain an operation status assessment result for the magnetically controlled transformer; acquiring the main insulation structure status data and FDS measurement data of the magnetically controlled transformer; inputting the acquired data into a pre-established transformer insulation life assessment model to obtain an insulation life assessment result for the magnetically controlled transformer; and inputting the operation status assessment result and the insulation life assessment result into a pre-established transformer remaining life reliability assessment model to obtain a remaining life reliability assessment result for the magnetically controlled transformer. This method improves the comprehensiveness of the assessment.

Description

Technical Field

[0001] This application relates to the field of transformer reliability assessment technology, and in particular to a method, apparatus, computer equipment, and computer-readable storage medium for assessing the remaining life reliability of a magnetically controlled transformer. Background Technology

[0002] With the application and development of computer technology in power grid systems, the operation and maintenance mode of power equipment has shifted to condition-based maintenance, which involves developing maintenance plans by assessing the health status of magnetically controlled transformers. The core of condition-based maintenance lies in accurately determining the operational reliability of the equipment; therefore, a comprehensive and accurate reliability assessment of magnetically controlled transformers is of significant guiding importance for condition-based maintenance.

[0003] However, the reliability assessment results obtained from the current failure rate model of magnetically controlled transformers are not comprehensive enough. Summary of the Invention

[0004] Therefore, it is necessary to provide a method, apparatus, computer equipment, and computer-readable storage medium for assessing the remaining lifetime reliability of magnetically controlled transformers, which can comprehensively evaluate the remaining lifetime reliability of magnetically controlled transformers, in response to the above-mentioned technical problems.

[0005] In a first aspect, this application provides a method for assessing the remaining lifetime reliability of a magnetically controlled transformer, including:

[0006] Acquire electrical test data, dissolved gas information in oil, operation and maintenance records, and accessory operation information of the magnetically controlled transformer, and input the acquired information into a pre-established transformer operation status assessment model to obtain the operation status assessment results for the magnetically controlled transformer.

[0007] The main insulation structure status data and FDS measurement data of the magnetically controlled transformer are obtained, and the obtained data are input into a pre-established transformer insulation life assessment model to obtain the insulation life assessment result for the magnetically controlled transformer. The pre-established transformer insulation life assessment model is based on an initial model for the magnetically controlled transformer, and the initial model is based on the equivalent circuit of the magnetically controlled transformer.

[0008] The operating status assessment results and the insulation life assessment results are input into the pre-established transformer remaining life reliability assessment model to obtain the remaining life reliability assessment results for the magnetically controlled transformer; the pre-established transformer remaining life reliability assessment model is obtained based on the initial model.

[0009] In one embodiment, the step of obtaining the pre-established transformer remaining life reliability assessment model includes:

[0010] Obtain multiple initial models with different configuration parameters as the initial population;

[0011] The initial population is iteratively optimized using the snake optimization algorithm, and the target initial model is obtained by using the failure rate function corresponding to the initial model as the evaluation target.

[0012] Based on the model parameters and failure rate function corresponding to the target initial model, the initial transformer remaining life reliability assessment model is configured to obtain the transformer remaining life reliability assessment model.

[0013] In one embodiment, the step of iteratively optimizing the initial population using a snake optimization algorithm, with the failure rate function corresponding to the initial model as the evaluation target, to obtain a target initial model, includes:

[0014] Determine the failure rate function corresponding to multiple initial models with different configuration parameters;

[0015] Taking the initial model in the initial population as an individual, the variables of different individuals in the initial population are adjusted based on the constraints corresponding to the failure rate function to obtain the first adjustment result;

[0016] Based on the first adjustment result, the functional fitness of multiple individuals with respect to the failure rate function is determined; wherein, the functional fitness is used to quantify the performance of the individual on the corresponding failure rate function;

[0017] Based on the fitness of the function, multiple individuals are iteratively optimized to obtain the target individual, which serves as the target initial model.

[0018] In one embodiment, the step of iteratively optimizing multiple individuals based on the function fitness to obtain the target individual includes:

[0019] Based on the fitness function, multiple individuals in the initial population are selected to obtain an elite population;

[0020] A subpopulation is obtained by searching multiple individuals in the initial population based on the failure rate function;

[0021] The elite population and subpopulations are iteratively searched, and the target individuals contained in the final population are obtained when the number of iterations exceeds a preset threshold.

[0022] In one embodiment, the iterative search of the elite population and subpopulations, where the number of iterations exceeds a preset iteration threshold, to obtain the target individuals contained in the final population includes:

[0023] Based on the constraints corresponding to the failure rate function, the variables of different individuals in the elite population and subpopulation are adjusted to obtain the second adjustment result;

[0024] Based on the second adjustment result and multiple failure rate functions, the elite population and subpopulation are iteratively searched, and the target individual is obtained when the number of iterations is greater than the preset iteration threshold.

[0025] In one embodiment, the step of screening multiple individuals in the initial population based on the functional fitness to obtain an elite population includes:

[0026] Multiple individuals in the initial population are selected based on the fitness of the function to obtain multiple initial individuals;

[0027] Determine the crowding degree of multiple initial individuals in the failure rate function space corresponding to the failure rate function; wherein, the crowding degree is used to represent the distribution density of multiple initial individuals in the failure rate function space;

[0028] Based on the crowding level, the initial individuals are screened to obtain an elite population.

[0029] In one embodiment, the equivalent circuit of the magnetically controlled transformer includes a power supply, a resistor, an inductor, a first coil, a second coil, a third coil, a fourth coil, a diode, a first thyristor, a second thyristor, a high-voltage coil, and a low-voltage coil; wherein,

[0030] The first terminal of the power supply is connected to the first terminal of the resistor, the second terminal of the resistor is connected to the first terminal of the inductor, the second terminal of the inductor is connected to the first terminal of the high-voltage coil, the second terminal of the high-voltage coil is connected to the first terminal of the first coil and the first terminal of the second coil, the second terminal of the first coil is connected to the first terminal of the fourth coil, the second terminal of the second coil is connected to the first terminal of the third coil, and the second terminal of the power supply is connected to the second terminal of the fourth coil and the second terminal of the third coil. The second terminal of the first coil is connected to the anode of the diode, and the cathode of the diode is connected to the second terminal of the second coil. The first terminal of the first thyristor is connected to the midpoint of the first coil, and the second terminal of the first thyristor is connected to the first terminal of the third coil. The first terminal of the second thyristor is connected to the midpoint of the fourth coil, and the second terminal of the second thyristor is connected to the second terminal of the second coil.

[0031] Secondly, this application also provides a device for assessing the remaining life reliability of a magnetically controlled transformer, comprising:

[0032] The operation status assessment module is used to acquire electrical test data, dissolved gas information in oil, operation and maintenance record information, and accessory operation information of the magnetically controlled transformer, and input the acquired information into a pre-established transformer operation status assessment model to obtain the operation status assessment results for the magnetically controlled transformer.

[0033] The insulation life assessment module is used to acquire the main insulation structure status data and FDS measurement data of the magnetically controlled transformer, and input the acquired data into a pre-established transformer insulation life assessment model to obtain the insulation life assessment result for the magnetically controlled transformer; the pre-established transformer insulation life assessment model is based on an initial model for the magnetically controlled transformer, and the initial model is based on the equivalent circuit of the magnetically controlled transformer;

[0034] The remaining life reliability assessment module is used to input the operating status assessment results and the insulation life assessment results into a pre-established transformer remaining life reliability assessment model to obtain the remaining life reliability assessment results for the magnetically controlled transformer; the pre-established transformer remaining life reliability assessment model is obtained based on the initial model.

[0035] Thirdly, this application also provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps of the method described in the first aspect.

[0036] Fourthly, this application also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the method described in the first aspect.

[0037] The aforementioned method, apparatus, computer equipment, and computer-readable storage medium for assessing the remaining life reliability of magnetically controlled transformers acquire electrical test data, dissolved gas information in oil, operation and maintenance records, and accessory operation information of the magnetically controlled transformer. This acquired information is then input into a pre-established transformer operating status assessment model to obtain the operating status assessment result for the magnetically controlled transformer. The method also acquires the main insulation structure status data and FDS measurement data of the magnetically controlled transformer, inputting this data into a pre-established transformer insulation life assessment model to obtain the insulation life assessment result for the magnetically controlled transformer. This pre-established transformer insulation life assessment model is based on an initial model for the magnetically controlled transformer, which in turn is based on the equivalent circuit of the magnetically controlled transformer. Finally, the operating status assessment result and the insulation life assessment result are input into the pre-established transformer remaining life reliability assessment model to obtain the remaining life reliability assessment result for the magnetically controlled transformer. This pre-established transformer remaining life reliability assessment model is based on the initial model. In this embodiment, by acquiring electrical test data, dissolved gas information in oil, operation and maintenance records, etc., and inputting them into the operation status assessment model, the current operating status of the magnetically controlled transformer can be accurately diagnosed. By monitoring the main insulation structure status data and FDS measurement data, and using the corresponding insulation life assessment model, the remaining service life of the transformer's insulation material can be accurately assessed. Combining the results of the operation status and insulation life assessment, and inputting them into the remaining life reliability assessment model, the overall reliability and safety of the magnetically controlled transformer can be analyzed. The pre-established transformer insulation life assessment model is based on the initial model for the magnetically controlled transformer, and the initial model is based on the equivalent circuit of the magnetically controlled transformer. The pre-established transformer remaining life reliability assessment model is based on the initial model. Such a transformer remaining life reliability assessment model can obtain a more comprehensive remaining life reliability assessment result. Attached Figure Description

[0038] To more clearly illustrate the technical solutions in the embodiments of this application or related technologies, the drawings used in the description of the embodiments of this application or related technologies will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0039] Figure 1 This is a flowchart illustrating a method for assessing the remaining lifetime reliability of a magnetically controlled transformer in one embodiment.

[0040] Figure 2 This is a schematic diagram illustrating the composition of a transformer condition assessment index system in one embodiment;

[0041] Figure 3This is a flowchart illustrating the fuzzy hierarchical analysis method for transformer operating status in one embodiment;

[0042] Figure 4 This is a flowchart illustrating the steps for obtaining a pre-established transformer remaining life reliability assessment model in one embodiment.

[0043] Figure 5 This is a schematic diagram of the equivalent circuit composition of a magnetically controlled transformer in one embodiment;

[0044] Figure 6 This is a schematic diagram of the magnetic circuit structure of a magnetically controlled transformer in one embodiment;

[0045] Figure 7 This is a structural block diagram of a device for assessing the remaining lifetime reliability of a magnetically controlled transformer in one embodiment;

[0046] Figure 8 This is an internal structural diagram of a computer device in one embodiment. Detailed Implementation

[0047] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0048] In one exemplary embodiment, such as Figure 1 As shown, a method for assessing the remaining lifetime reliability of a magnetically controlled transformer is provided. This embodiment illustrates the application of this method to a server. It is understood that this method can also be applied to a terminal, and further to a system including both a terminal and a server, and implemented through interaction between the terminal and the server. In this embodiment, the method includes the following steps:

[0049] Step S202: Obtain electrical test data, dissolved gas information in oil, operation and maintenance record information, and accessory operation information of the magnetically controlled transformer, and input the obtained information into the pre-established transformer operation status assessment model to obtain the operation status assessment results for the magnetically controlled transformer.

[0050] Among them, the pre-established transformer operation status assessment model can be used to assess the operation status of the magnetically controlled transformer based on the input data and then obtain the assessment results.

[0051] The aforementioned magnetically controlled transformer can refer to a device that utilizes magnetic field control technology to achieve voltage conversion and power transmission. The functions of this device are not limited to voltage regulation, energy transfer, and enhancing the electromagnetic compatibility of the system. Depending on the application scenario, magnetically controlled transformers can be classified into various types, such as magnetically controlled tube transformers, magnetically controlled current-type transformers, magnetically controlled voltage-type transformers, and magnetically controlled power-type transformers. The specific type of magnetically controlled transformer used depends on the actual application requirements and is not further limited here.

[0052] For example, in step S202 above, the acquired information is input into a pre-established transformer operating status evaluation model to obtain the operating status evaluation result for the magnetically controlled transformer. The specific process can be as follows:

[0053] Numerous informational features characterize the operating status of transformers, reflecting their condition from different perspectives and to varying degrees. Therefore, to accurately determine the operating status of a transformer, a reasonable and scientific condition assessment index system is necessary. Based on the "Preventive Testing Regulations for Power Equipment" and the "Guidelines for Dissolved Gas Analysis and Judgment in Transformer Oil," the content of characteristic gases (dissolved gases) in the oil, electrical test data, operation and maintenance records, and accessory operation status are used as indicators in the transformer condition assessment system. Figure 2 As shown.

[0054] Transformer operating condition assessment involves statistically analyzing data such as dissolved gas content in transformer oil, electrical test data, operation and maintenance records, and accessories to accurately calculate the transformer's operating condition score and determine its health status, providing decision-making support for equipment maintenance. Scholars have studied the detailed scoring rules and weighting calculations for transformers, comparing the results with the established "Guidelines for Condition Assessment of Oil-Immersed Transformers," and establishing a statistical relationship between health status and operating condition scores.

[0055] The operating condition of transformers exhibits complexity and uncertainty. To more accurately assess their status, a hierarchical processing technique can be employed. This method involves analyzing the transformer's condition layer by layer, from local to global, ultimately calculating a comprehensive score to determine the transformer's overall health level. Based on this need, fuzzy hierarchical analysis (such as...) is used... Figure 3 (As shown) is used for evaluating the operating status of transformers. The comprehensive operating status score of a transformer can be calculated using the following formula:

[0056]

[0057] Among them, W i Xi represents the fault type weight related to the fault type among the transformer characteristic quantities; Xi is the fault type status score.

[0058] Based on the above formula, combined with Figure 3 The process shown can calculate the operating status assessment result for a magnetically controlled transformer when the specific data in the transformer status assessment index system is known. Figure 3 The process shown first involves classifying the transformer condition assessment index system into transformer hierarchical levels, determining the fault type-related state variables and the state variable evaluation matrix r. B Calculate the state variable weights w i Simultaneously, transformer state data is collected, and a unified state score xi is calculated for the state variables. Then, based on the aforementioned steps, the state score Xi for the fault type is calculated. Based on the aforementioned steps, all fault types are traversed to determine the state score for each fault type. Next, the fault type judgment matrix R is determined. B Then calculate the fault type weight W. i The transformer status score S (i.e., operating status score) is then calculated. Finally, it is necessary to determine whether the obtained transformer status score S is normal. In case of an anomaly, the anomaly information needs to be identified before outputting the anomaly information and the score result. In case of a normal condition, the score result (including the operating status score S) is output directly.

[0059] Step S204: Obtain the main insulation structure status data and FDS measurement data of the magnetically controlled transformer, input the obtained data into the pre-established transformer insulation life assessment model, and obtain the insulation life assessment results for the magnetically controlled transformer.

[0060] The pre-established transformer insulation life assessment model is based on an initial model for magnetically controlled transformers, which in turn is based on the equivalent circuit of the magnetically controlled transformer.

[0061] The initial model can refer to a basic model framework established based on theoretical analysis and numerical calculations of the equivalent circuit before the formal design of the magnetically controlled transformer. This model aims to predict the working performance of the magnetically controlled transformer and provide a scientific basis for the subsequent design optimization process.

[0062] Among them, the main insulation structure status data can refer to multiple characteristic indicators considered in the design process of magnetically controlled transformers, such as various insulation performance data and material performance data related to magnetically controlled transformers. These can be determined according to the specific performance of the magnetically controlled transformer, and no specific limitation is made here.

[0063] Among them, FDS measurement data refers to data obtained by frequency domain spectroscopy (FDS).

[0064] For example, the state of the internal oil-paper insulation of an oil-immersed magnetically controlled transformer directly determines the transformer's remaining lifespan. The electrical and mechanical properties of the oil-paper gradually decline under the combined effects of stresses such as electric field, temperature, mechanical vibration, and moisture. Moisture content and aging are key factors affecting the insulation state. Increased moisture content in the insulating paperboard leads to increased dielectric loss and a decrease in breakdown voltage, damaging the oil-paper insulation structure. With increasing aging, the insulation paper deteriorates further, significantly reducing the breakdown voltage and mechanical properties of the insulation system, ultimately causing the main insulation system of the magnetically controlled transformer to fail completely. Therefore, assessing the state of the oil-paper insulation in a magnetically controlled transformer is crucial for reliability evaluation. Since the insulation lifespan of a magnetically controlled transformer is significantly affected by moisture, temperature, and the current degree of polymerization, obtaining accurate polymerization and moisture content allows for further research into the transformer's insulation lifespan. This example establishes a comprehensive insulation lifespan assessment model for magnetically controlled transformers, taking into account the effects of moisture content, aging, and temperature on the insulation paperboard's lifespan.

[0065] The accelerated thermal aging process of oil-paper insulation is described using a cellulose degradation kinetic equation. By improving the traditional kinetic equation, a kinetic equation for the cumulative loss of fiber polymerization degree is proposed:

[0066]

[0067] In the formula, DP0 represents the initial degree of aggregation, ω DPt This represents the degree of polymerization after thermal aging for time t, DPt represents the cumulative loss rate of degree of polymerization, ω*DP represents the ability to store and degrade the degree of polymerization, and k DP The rate of degradation of the degree of polymerization.

[0068] In accelerated thermal aging experiments in the laboratory, the temperature of the insulating paperboard is much higher than the actual operating temperature of the transformer. To ensure consistency with the variation pattern of the insulating performance of the insulating paperboard in actual operation, a time-temperature superposition method is used to extrapolate the experimental data of high-temperature thermal aging to a low-temperature environment. Combining the time-temperature superposition method with the aforementioned kinetic equations, the temperature shift factor α is obtained. T :

[0069]

[0070] In the formula, the Arrhenius activation energy Ea = 91.4 kJ / mol, the gas constant R = 8.314 J / mol / K, and T ref The reference temperature is the lowest temperature tested, and T is the hot spot temperature of the transformer.

[0071] In addition to temperature, the effect of moisture on thermal aging must also be considered. Based on the superposition of time and temperature, the curve was shifted to obtain the moisture shift factor α.M This allows accelerated thermal aging test data to be extrapolated to the actual moisture content of the insulating paper.

[0072]

[0073] In the formula, the moisture shift factor parameter b = 0.773, and the reference moisture content M ref =0.5%, where M is the moisture content in the insulating paper. At this point, we can obtain the time-temperature-moisture shift factor α. T，M :

[0074]

[0075] Therefore, at any temperature T and moisture content M, the degree of polymerization decreases from DP0 to DP. t Required time t T，M The extrapolation formula, i.e., the transformer insulation life assessment model, is shown below:

[0076]

[0077] In the formula, the degree of polymerization and degradation storage capacity are represented by the following: =0.699, cellulose degree of polymerization degradation rate =2.04*10 -3 From the above formula, it can be seen that, given the initial degree of polymerization DP0, water content M, and hot spot temperature T of the transformer, the degree of polymerization can be calculated from DP0 to any degree of polymerization DP0. t The required time. For the convenience of further research, we simplify the formula to the following: the running time is related to the degree of polymerization, water content, and temperature.

[0078]

[0079] Step S206: Input the operating status assessment results and insulation life assessment results into the pre-established transformer remaining life reliability assessment model to obtain the remaining life reliability assessment results for the magnetically controlled transformer.

[0080] The pre-established transformer remaining life reliability assessment model is based on the initial model.

[0081] For example, after calculating the operational status assessment result for the magnetically controlled transformer in step S202, the calculation result is compared with the established "Guidelines for Status Assessment of Oil-Immersed Transformers" to determine the transformer's health status. Studies have shown that the transformer's health status is directly related to its probability of failure. The "Guidelines for Risk Assessment of Power Transmission and Transformation Equipment" provides a failure rate model based on equipment health status, the mathematical expression of which is:

[0082]

[0083] Where K represents the scaling parameter and C represents the curvature parameter.

[0084] However, since the operating status of a transformer is based on assessments derived from a large amount of statistical data, it may not fully reflect the individual differences of each transformer. Therefore, comprehensively considering both the transformer's operating status and the condition of its main insulation will help improve the accuracy of transformer reliability assessments.

[0085] The aging process of transformers is often described by the Weibull distribution, and its failure rate function is shown in the following equation:

[0086]

[0087] Where β represents the shape parameter and η represents the characteristic lifetime parameter.

[0088] Transformers are affected by load fluctuations during actual operation, leading to temperature changes. Considering the impact of temperature on the Weibull distribution, the concept of "equivalent operating time" is introduced, and a "hotspot temperature calculation model" is used to convert the actual service time into a reference hotspot temperature θ. H The equivalent operating time under these conditions was determined. Based on the Arrhenius reaction principle, a temperature-dependent transformer aging failure model was established. However, besides temperature, moisture and polymerization degree in the oil-paper insulation are also important factors accelerating transformer aging; therefore, a transformer insulation life model based solely on temperature is not entirely accurate. Therefore, by introducing bulk parameters (i.e., model parameters corresponding to the initial model), a new failure rate formula was obtained:

[0089]

[0090] In the model corresponding to the new failure rate formula, to explore the relationship between operating state and expected lifespan in depth, we first define the parameter η as the expected lifespan of the transformer, L(S,DP). Exponential functions are more flexible and adaptable than logarithmic or quadratic functions, and can simultaneously encompass the characteristics of linear, concave, and convex functions. Therefore, drawing on the transformer failure rate model based on operating state (i.e., the failure rate model based on equipment health state), we assume that L(S,DP) and S satisfy the following exponential relationship:

[0091]

[0092] Where n, m, and K are coefficients in the exponential relationship expression, and the running status score S is the independent variable.

[0093] Based on the basic working principle and operational analysis of transformers, it is known that as the operating condition deteriorates, the expected lifespan also decreases monotonically; when the operating condition score drops to 0, the lifespan also decreases to 0; however, when the operating condition score reaches 1, indicating that the transformer is under ideal operating conditions and external environmental conditions, the expected lifespan of the transformer depends solely on its insulation aging state, denoted as L0. Assuming the degree of aggregation at the end of the transformer's lifespan is 250, the calculated t... T,M The value is L0. Therefore, the function L(S,DP) must satisfy the following condition:

[0094] (1) L decreases monotonically with S.

[0095] (2) When S=0, L=0.

[0096] (3) When S=1, L=L0.

[0097] Substituting the above conditions, we get:

[0098]

[0099] Substituting the new expected lifetime function L(S,DP|m) into the aging failure model yields a new failure rate model function (i.e., the transformer remaining lifetime reliability assessment model):

[0100]

[0101] In this model, it is assumed that parameters β and m remain constant with temperature changes and can be determined using a nonlinear least squares method based on historical data. Operating time t (i.e., remaining insulation life in the insulation life assessment result) and operating status score S (i.e., status score S in the operating status assessment result) are independent variables. Different β and m parameters can be derived from historical data of different transformers, thus avoiding the influence of individual differences on the failure rate results. On the other hand, to further reduce inter-individual influences, the calculation of operating time t fully considers the impact of temperature, moisture, and DP differences during the operation of different transformers on the measurement results. The remaining life reliability assessment results for magnetically controlled transformers can be obtained through the above-mentioned new failure rate model function.

[0102] In the aforementioned method for assessing the remaining life reliability of magnetically controlled transformers, by acquiring electrical test data, dissolved gas information in oil, and operation and maintenance records, and inputting them into the operating status assessment model, the current operating status of the magnetically controlled transformer can be accurately diagnosed. By monitoring the main insulation structure status data and FDS measurement data, and using the corresponding insulation life assessment model, the remaining service life of the transformer's insulation materials can be accurately assessed. Combining the results of the operating status and insulation life assessment, and inputting them into the remaining life reliability assessment model, the overall reliability and safety of the magnetically controlled transformer can be analyzed. The pre-established transformer insulation life assessment model is based on an initial model for the magnetically controlled transformer, which in turn is based on the equivalent circuit of the magnetically controlled transformer. The pre-established transformer remaining life reliability assessment model is based on this initial model, resulting in a more comprehensive remaining life reliability assessment.

[0103] In one exemplary embodiment, such as Figure 4 As shown, the steps for obtaining the pre-established transformer remaining life reliability assessment model may include:

[0104] Step S302: Obtain multiple initial models with different configuration parameters as the initial population.

[0105] Step S304: The initial population is iteratively optimized using the snake optimization algorithm, and the target initial model is obtained by using the failure rate function corresponding to the initial model as the evaluation target.

[0106] Step S306: Configure the initial transformer remaining life reliability assessment model according to the model parameters and the corresponding failure rate function of the target initial model to obtain the transformer remaining life reliability assessment model.

[0107] The initial model can refer to a model built based on the known characteristics and configuration parameters of the transformer. Model parameters can be variables used to define the characteristics of these initial models; different initial models correspond to different model parameters. These model parameters may include, but are not limited to, input variables, algorithm parameters, and environmental settings. The specific types of model parameters will be determined according to the actual needs of the initial model, and are not further limited here. The snake optimization algorithm can be used to iteratively optimize the initial model to minimize the failure rate function. The failure rate function can be a mathematical function used to evaluate the probability of failure during transformer use. The initial transformer remaining life reliability assessment model can be used to evaluate transformer reliability based on the equipment's operating conditions, historical data, and failure rate. Using the failure rate function corresponding to the initial model as the evaluation target can mean using the failure rate function as the evaluation standard, and by continuously adjusting the parameters, making the output of the failure rate function reach the expected optimal point, such as making the failure rate output by the failure rate function less than a preset lower limit failure rate, or making the failure rate output by the failure rate function meet a preset failure rate range.

[0108] In this embodiment, an initial population consisting of multiple initial models is created, where each initial model corresponds to a specific parameter configuration representing the output failure rate of the magnetically controlled transformer. After generating the initial population, iterative updates are performed to continuously optimize the parameter configuration of the transformer remaining life reliability assessment model corresponding to the failure rate, which can improve the assessment accuracy of the transformer remaining life reliability assessment model.

[0109] In an exemplary embodiment, step S304, which involves iteratively optimizing the initial population using a snake optimization algorithm and using the failure rate function corresponding to the initial model as the evaluation target to obtain the target initial model, may specifically include:

[0110] Step S11: Determine the failure rate function corresponding to multiple initial models with different configuration parameters.

[0111] Step S12: Take the initial model in the initial population as an individual, and adjust the variables of different individuals in the initial population based on the constraints corresponding to the failure rate function to obtain the first adjustment result.

[0112] Step S13: Determine the function fitness of multiple individuals for the failure rate function based on the first adjustment result; wherein, the function fitness is used to quantify the performance of an individual on the corresponding failure rate function.

[0113] Step S14: Iteratively optimize multiple individuals based on function fitness to obtain the target individual, which serves as the initial target model.

[0114] In this context, an individual can refer to an element in the initial population, that is, a specific instance of the initial model, whose parameter configuration represents a possible operating state. Constraints can refer to conditions or rules that limit parameter changes during the optimization process. Function fitness can be a metric used to quantify an individual's performance on the failure rate function. Iterative optimization refers to the process of gradually improving and optimizing the individual model through repeated methods. In each iteration, the variables of different individuals, i.e., the individual's parameters, are adjusted based on the previously evaluated function fitness in order to find the optimal solution. The target individual can be the individual that exhibits the best function fitness after iterative optimization, meaning that this individual has the best performance characteristics under a given failure rate function, and is ultimately determined as the target initial model.

[0115] In this embodiment, by accurately evaluating the failure rate function of multiple initial models with different configurations, it can be ensured that the evaluation model is more in line with the actual situation and its prediction accuracy can be improved.

[0116] In an exemplary embodiment, the iterative optimization of multiple individuals based on function fitness performed in step S14 above to obtain the target individual may include the following specific implementation steps:

[0117] Step S21: Based on the function fitness, multiple individuals in the initial population are screened to obtain an elite population.

[0118] Step S22: Search for multiple individuals in the initial population based on the failure rate function to obtain a subpopulation.

[0119] Step S23: Perform iterative search on the elite population and subpopulations. If the number of iterations is greater than the preset iteration threshold, obtain the target individuals contained in the final population.

[0120] In this process, the elite population can be a subset formed by selecting individuals with the best fitness. These individuals are chosen to preserve superior traits for further optimization in subsequent iterations. The subpopulation can be a set of individuals obtained through a search based on a failure rate function. It may contain individuals that perform worse than the elite population, but the search mechanism identifies potentially superior traits or parameter configurations. Iterative search can be an iterative process that involves selecting, crossovering, and mutating individuals within the population to obtain better individuals. This process continuously adjusts individuals based on their fitness.

[0121] In this embodiment, by screening the elite population, individuals with the best performance can be effectively retained, thereby further utilizing the superior characteristics of these individuals in subsequent iterations to improve the performance of the entire population and enhance the selection accuracy of the target individuals included in the final population.

[0122] In an exemplary embodiment, the iterative search of the elite population and subpopulation performed in step S23 above, where the number of iterations exceeds a preset iteration threshold, yields the target individuals included in the final population, including:

[0123] Step S31: Adjust the variables of different individuals in the elite population and subpopulation based on the constraints corresponding to the failure rate function to obtain the second adjustment result;

[0124] Step S32: Based on the second adjustment result and multiple failure rate functions, perform iterative search on the elite population and subpopulations. If the number of iterations is greater than the preset iteration threshold, the target individual is obtained.

[0125] The second adjustment result can be the output obtained after adjusting the individual variables in the elite population and subpopulation according to the constraints of the failure rate function. This result reflects the optimization of the individual variables to further improve their performance in terms of failure rate. Iterative search can be a process of gradually improving and optimizing the population in each iteration by continuously adjusting the parameters of individuals, evaluating fitness, selecting high-performance individuals, and performing operations such as crossover and mutation. Constraints can refer to a series of rules that limit the range of model parameters and variable adjustments. These conditions are used to ensure that the adjustments during the optimization process do not exceed the physical or operational limitations of the equipment, thereby maintaining its reliability and safety.

[0126] In this embodiment, by adjusting the variables of the elite and subpopulation based on the constraints corresponding to the failure rate function, the model can not only meet the theoretical optimal solution, but also adapt to the actual use environment and conditions, thereby improving the application effectiveness of the model.

[0127] In one exemplary embodiment, multiple individuals in the initial population are screened based on functional fitness to obtain an elite population, including:

[0128] Step S41: Based on the function fitness, multiple individuals in the initial population are screened to obtain multiple initial individuals.

[0129] Step S42: Determine the crowding degree of multiple initial individuals in the failure rate function space corresponding to the failure rate function; wherein, the crowding degree is used to represent the distribution density of multiple initial individuals in the failure rate function space.

[0130] Step S43: Based on the crowding level, multiple initial individuals are screened to obtain an elite population.

[0131] The failure rate function space can be a multi-dimensional space representing the failure rate corresponding to different parameter combinations. Each dimension represents a parameter, and each point in the space corresponds to a specific failure rate value. Crowding can be an indicator representing the distribution density of multiple initial individuals in the failure rate function space. It measures the number of other individuals around a given individual; high crowding indicates that individuals are concentrated in the region, while low crowding indicates relative dispersion.

[0132] In this embodiment, by screening based on crowding, it can be ensured that the elite population not only retains individuals with high fitness, but also avoids concentration in overly narrow areas, promoting the diversity of model parameters. By screening out elite individuals that are well distributed in the failure rate function space, the optimization process can focus on high-potential areas more quickly, improving the overall search efficiency.

[0133] Steps S302-S306 above, and the specific steps related to each of the above steps, can be calculated using the following snake optimization algorithm:

[0134] The Snake Optimizer (SO) algorithm is used to simulate the foraging and reproductive behaviors of snakes. Compared with traditional optimization algorithms, it has the significant advantages of novel approach, speed, and efficiency. Its mathematical model is as follows:

[0135] (1) Snake population initialization.

[0136] The mathematical description of snake population initialization is as follows:

[0137]

[0138] Among them, X i Let X be the position of the i-th snake, and rand is a random number between 0 and 1. max and X min It represents the upper and lower boundaries of the independent variable.

[0139] (2) The snake population is divided into two groups according to the sex of male and female.

[0140] Assuming the number of male and female snakes is 50% each, and they are divided into two groups: a male group and a female group, the population is divided as follows:

[0141]

[0142] Where N is the snake population size, N m N represents the number of male snakes. f This refers to the number of female snakes.

[0143] (3) Assess the temperature and food availability for each group of snakes.

[0144] The temperature of each snake group can be defined by the formula:

[0145]

[0146] Among them, t is the current iteration number, and Tmax is the maximum iteration number.

[0147] The food quantity of each snake group can be defined by the formula:

[0148]

[0149] Among them, C1 is a constant, and here it is taken as 0.5.

[0150] (4) Exploration stage of the snake group in the state of food shortage.

[0151] If Q < Td (Td is the threshold), the snakes search for food by choosing any random position and update their positions. To simulate the exploration stage, as follows:

[0152]

[0153] Among them, X i,m is the position of the male; X rand,f is the position of a randomly selected female; rand is a random number within the range of [0, 1].

[0154] The ability of the male to search for food is shown in the following formula:

[0155]

[0156] Among them, f rand,f is the fitness value of the position X rand,f of a randomly selected female; f i,f is the fitness value of the female position X i,f

[0157] (5) The exploitation stage when the snake group has abundant food is:

[0158]

[0159]

[0160] Among them, (a) the combat mode of the snake group is:

[0161]

[0162]

[0163]

[0164]

[0165] Among them, (b) the mating mode of the snake group is:​

[0166]

[0167]

[0168]

[0169]

[0170]

[0171]

[0172] The snake optimization algorithm described above can be used to optimize the evaluation of magnetically controlled transformers by outputting a failure rate function and based on this single optimization objective, thereby ensuring the accuracy of the remaining lifetime reliability assessment of magnetically controlled transformers.

[0173] In one exemplary embodiment, such as Figure 5 As shown, the equivalent circuit of the above-mentioned magnetically controlled transformer includes a power supply U, a resistor R, an inductor L, a first coil Q1, a second coil Q2, a third coil Q3, a fourth coil Q4, a diode D, a first thyristor TH1, a second thyristor TH2, a high-voltage coil LH, and a low-voltage coil LL; wherein:

[0174] The first terminal of the power supply U is connected to the first terminal of the resistor R. The second terminal of the resistor R is connected to the first terminal of the inductor L. The second terminal of the inductor L is connected to the first terminal of the high-voltage coil LH. The second terminal of the high-voltage coil LH is connected to the first terminals of the first coil Q1 and the second coil Q2, respectively. The second terminal of the first coil Q1 is connected to the first terminal of the fourth coil Q4. The second terminal of the second coil Q2 is connected to the first terminal of the third coil Q3. The second terminal of the power supply U is connected to the second terminals of the fourth coil Q4 and the third coil Q3, respectively. The second terminal of the first coil Q1 is connected to the anode of the diode D. The cathode of the diode D is connected to the second terminal of the second coil Q2. The first terminal of the first thyristor TH1 is connected to the midpoint of the first coil Q1. The second terminal of the first thyristor TH1 is connected to the first terminal of the third coil Q3. The first terminal of the second thyristor TH2 is connected to the midpoint of the fourth coil Q4. The second terminal of the second thyristor TH2 is connected to the second terminal of the second coil Q2. Figure 5 In this context, r represents the load connected to the magnetically controlled transformer.

[0175] In one exemplary embodiment, such as Figure 6As shown, a magnetic circuit diagram of a magnetically controlled transformer is provided. The equivalent circuit of the magnetically controlled transformer can be obtained from this diagram. In the magnetic circuit diagram, the magnetically controlled transformer includes four core pillars, one diode, two thyristors, a high-voltage coil, a low-voltage coil, a sinusoidal AC voltage Us, and a load voltage U2. The four core pillars are core pillar 1, core pillar 2, core pillar 3, and core pillar 4. The two thyristors are thyristor TH1 and thyristor TH2. In the magnetic circuit diagram, the magnetically controlled transformer is composed of a magnetically saturated reactor and a transformer connected together. An N / 2-turn coil is wound around the upper and lower portions of core pillar 1 and core pillar 2. The upper coil of core pillar 1 has a tap, which is connected to the beginning of the lower coil via thyristor TH1. Similarly, the lower coil of core pillar 2 also has a tap, which is connected to the end of the upper coil via thyristor TH2. The upper and lower coils of core column 1 and core column 2 are cross-connected. A diode D is connected between the upper coils of core column 1 and core column 2 to allow continuous current flow. Core column 1, core column 2, and their corresponding coils constitute part of a magnetically saturated reactor. Core column 3 has N turns of coil wound on its upper and lower sides to form the transformer part. The upper coil of core column 3 is a low-voltage coil, with the load connected to both ends, so the voltage between the two ends is the load voltage. The lower coil is a high-voltage coil, with one end connected to the first end of the upper coil of core column 1 and core column 2, and the other end connected to the sinusoidal AC power supply of the power grid, so the voltage between the two ends is a sinusoidal AC voltage.

[0176] It should be understood that, although the various steps in the flowcharts involved in the various embodiments described above are displayed in sequence according to the instructions of the arrows, these steps are not necessarily executed in sequence in the order indicated by the arrows. Unless otherwise specified herein, there is no strict order restriction on the execution of these steps, and these steps can be executed in other orders. Moreover, at least a portion of the steps in the flowcharts involved in the various embodiments described above can include multiple steps or multiple stages, and these steps or stages are not necessarily executed and completed at the same time, but can be executed at different times, and the execution order of these steps or stages is not necessarily to be carried out in sequence, but can be executed in turn or alternately with other steps or at least a portion of steps or stages in other steps.

[0177] Based on the same inventive concept, this application also provides a device for assessing the remaining lifetime reliability of a magnetically controlled transformer, used to implement the aforementioned method for assessing the remaining lifetime reliability of a magnetically controlled transformer. The solution provided by this device is similar to the solution described in the above method. Therefore, the specific limitations of one or more embodiments of the device for assessing the remaining lifetime reliability of a magnetically controlled transformer provided below can be found in the limitations of the method for assessing the remaining lifetime reliability of a magnetically controlled transformer described above, and will not be repeated here.

[0178] In one exemplary embodiment, such as Figure 7 As shown, a remaining life reliability assessment device 900 for a magnetically controlled transformer is provided, comprising: an operating status assessment module 901, an insulation life assessment module 902, and a remaining life reliability assessment module 903, wherein:

[0179] The operation status assessment module 901 is used to acquire electrical test data, dissolved gas information in oil, operation and maintenance record information, and accessory operation information of the magnetically controlled transformer, and input the acquired information into the pre-established transformer operation status assessment model to obtain the operation status assessment results for the magnetically controlled transformer.

[0180] The insulation life assessment module 902 is used to acquire the main insulation structure status data and FDS measurement data of the magnetically controlled transformer, and input the acquired data into the pre-established transformer insulation life assessment model to obtain the insulation life assessment result for the magnetically controlled transformer; the pre-established transformer insulation life assessment model is based on the initial model for the magnetically controlled transformer, and the initial model is based on the equivalent circuit of the magnetically controlled transformer;

[0181] The remaining life reliability assessment module 903 is used to input the operating status assessment results and insulation life assessment results into the pre-established transformer remaining life reliability assessment model to obtain the remaining life reliability assessment results for the magnetically controlled transformer; the pre-established transformer remaining life reliability assessment model is obtained based on the initial model.

[0182] In an exemplary embodiment, the remaining life reliability assessment device for the magnetically controlled transformer further includes a model acquisition module, which is used to acquire multiple initial models with different configuration parameters as an initial population; to iteratively optimize the initial population using a snake optimization algorithm, and to obtain a target initial model by using the failure rate function corresponding to the initial model as the evaluation target; and to configure the initial transformer remaining life reliability assessment model according to the model parameters and the corresponding failure rate function of the target initial model, thereby obtaining the transformer remaining life reliability assessment model.

[0183] In an exemplary embodiment, the model acquisition module is further configured to determine the failure rate function corresponding to multiple initial models with different configuration parameters; take the initial models in the initial population as individuals, adjust the variables of different individuals in the initial population based on the constraints corresponding to the failure rate function, and obtain a first adjustment result; determine the function fitness of multiple individuals for the failure rate function based on the first adjustment result; wherein the function fitness is used to quantify the performance of the individuals on the corresponding failure rate function; and iteratively optimize the multiple individuals based on the function fitness to obtain a target individual, which is used as the target initial model.

[0184] In an exemplary embodiment, the model acquisition module is further configured to: filter multiple individuals in the initial population based on the fitness function to obtain an elite population; search multiple individuals in the initial population based on the failure rate function to obtain a subpopulation; and perform iterative search on the elite population and the subpopulation, obtaining the target individuals contained in the final population when the number of iterations is greater than a preset iteration threshold.

[0185] In an exemplary embodiment, the model acquisition module is further configured to adjust the variables of different individuals in the elite population and subpopulation based on the constraints corresponding to the failure rate function to obtain a second adjustment result; and to perform iterative search on the elite population and subpopulation based on the second adjustment result and multiple failure rate functions to obtain the target individual when the number of iterations is greater than a preset iteration threshold.

[0186] In an exemplary embodiment, the model acquisition module is further configured to screen multiple individuals in the initial population based on the fitness of the function to obtain multiple initial individuals; determine the crowding degree of the multiple initial individuals in the failure rate function space corresponding to the failure rate function; wherein the crowding degree is used to represent the distribution density of the multiple initial individuals in the failure rate function space; and screen the multiple initial individuals based on the crowding degree to obtain an elite population.

[0187] Each module in the aforementioned magnetically controlled transformer remaining life reliability assessment device can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in a computer device in hardware form, or stored in the memory of a computer device in software form, so that the processor can call and execute the corresponding operations of each module.

[0188] In one exemplary embodiment, a computer device is provided, which may be a server, and its internal structure diagram may be as follows: Figure 8As shown, this computer device includes a processor, memory, input / output (I / O) interfaces, and a communication interface. The processor, memory, and I / O interfaces are connected via a system bus, and the communication interface is also connected to the system bus via the I / O interfaces. The processor provides computational and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system, computer programs, and databases. The internal memory provides the environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The I / O interfaces are used for exchanging information between the processor and external devices. The communication interface is used for communicating with external terminals via a network connection. When the computer program is executed by the processor, it implements a method for assessing the remaining life reliability of a magnetically controlled transformer.

[0189] Those skilled in the art will understand that Figure 8 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0190] In one embodiment, a computer device is also provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps in the above method embodiments.

[0191] In one embodiment, a computer-readable storage medium is provided having a computer program stored thereon that, when executed by a processor, implements the steps in the above method embodiments.

[0192] In one embodiment, a computer program product is provided, including a computer program that, when executed by a processor, implements the steps in the above method embodiments.

[0193] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile memory and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM). The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, artificial intelligence (AI) processors, etc., and are not limited to these.

[0194] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this application.

[0195] The above-described embodiments merely represent several implementation methods of the present application. While the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the present application. It should be noted that a person of ordinary skill in the art may make various modifications and improvements without departing from the spirit of the present application, and these modifications and improvements fall within the scope of protection of the present application. Therefore, the scope of protection of the present application shall be determined by the appended claims.

Claims

1. A method for assessing the remaining life reliability of a magnetically controlled transformer, characterized in that, The method includes: The following parameters are obtained for the magnetically controlled transformer: winding dielectric loss factor, winding DC resistance, core grounding current, and winding absorption ratio, as electrical test data; H2 content, CH4 content, C2H2 content, C2H4 content, C2H6 content, CO content, and CO2 content, as dissolved gas information in the oil; operating status information of the cooling test system, test device, protection device, and on-load tap changer, as accessory operating status information; and operation and maintenance records of the magnetically controlled transformer. The acquired information is input into a pre-established transformer operating status assessment model to obtain the operating status assessment results for the magnetically controlled transformer. The process includes: The dissolved gas information in the oil, the electrical test data, the operation and maintenance records, and the operating status of the accessories are used as the transformer condition assessment index system. The transformer condition assessment index system is then hierarchically divided to determine the fault type-related state variables and the state variable evaluation matrix r. B Calculate the state variable weights w i Collect transformer state quantity data, calculate the unified state score xi for the state quantity, calculate the state score Xi for the fault type, traverse all fault types, determine the state score for each fault type, and determine the fault type judgment matrix R. B Calculate the fault type weight W i The operating status score S is calculated using the following formula: Among them, W i Xi represents the fault type weight among the transformer characteristic quantities related to the fault type; Xi is the fault type status score. Determine whether the obtained transformer operating status score S is normal. If abnormal, identify the abnormal information before outputting the abnormal information and the operating status score S; if normal, directly output the operating status score S. The main insulation structure status data and FDS measurement data of the magnetically controlled transformer are obtained, and the obtained data are input into a pre-established transformer insulation life assessment model to obtain the insulation life assessment result for the magnetically controlled transformer. The pre-established transformer insulation life assessment model is based on an initial model for the magnetically controlled transformer, and the initial model is based on the equivalent circuit of the magnetically controlled transformer. The equivalent circuit of the magnetically controlled transformer includes a power supply, a resistor, an inductor, a first coil, a second coil, a third coil, a fourth coil, a diode, a first thyristor, a second thyristor, a high-voltage coil, and a low-voltage coil; wherein, The first terminal of the power supply is connected to the first terminal of the resistor; the second terminal of the resistor is connected to the first terminal of the inductor; the second terminal of the inductor is connected to the first terminal of the high-voltage coil; the second terminal of the high-voltage coil is connected to the first terminals of the first coil and the second coil, respectively; the second terminal of the first coil is connected to the first terminal of the fourth coil; the second terminal of the second coil is connected to the first terminal of the third coil; the second terminal of the power supply is connected to the second terminals of the fourth coil and the third coil, respectively; the second terminal of the first coil is connected to the anode of the diode; the cathode of the diode is connected to the second terminal of the second coil; the first terminal of the first thyristor is connected to the midpoint of the first coil; the second terminal of the first thyristor is connected to the first terminal of the third coil; the first terminal of the second thyristor is connected to the midpoint of the fourth coil; and the second terminal of the second thyristor is connected to the second terminal of the second coil. The operating status assessment results and the insulation life assessment results are input into the pre-established transformer remaining life reliability assessment model to obtain the remaining life reliability assessment results for the magnetically controlled transformer. The steps for obtaining the pre-established transformer remaining life reliability assessment model include: Obtain multiple initial models with different configuration parameters as the initial population; The initial population is iteratively optimized using the snake optimization algorithm, and the target initial model is obtained by using the failure rate function corresponding to the initial model as the evaluation target. Based on the model parameters and failure rate function corresponding to the target initial model, the initial transformer remaining life reliability assessment model is configured to obtain the transformer remaining life reliability assessment model.

2. The method according to claim 1, characterized in that, The initial population is iteratively optimized using the snake optimization algorithm, with the failure rate function corresponding to the initial model as the evaluation target, to obtain the target initial model, including: Determine the failure rate function corresponding to multiple initial models with different configuration parameters; Taking the initial model in the initial population as an individual, the variables of different individuals in the initial population are adjusted based on the constraints corresponding to the failure rate function to obtain the first adjustment result; Based on the first adjustment result, the functional fitness of multiple individuals with respect to the failure rate function is determined; wherein, the functional fitness is used to quantify the performance of the individual on the corresponding failure rate function; Based on the fitness of the function, multiple individuals are iteratively optimized to obtain the target individual, which serves as the target initial model.

3. The method according to claim 2, characterized in that, The iterative optimization of multiple individuals based on the functional fitness to obtain the target individual includes: Based on the fitness function, multiple individuals in the initial population are selected to obtain an elite population; A subpopulation is obtained by searching multiple individuals in the initial population based on the failure rate function; The elite population and subpopulations are iteratively searched, and the target individuals contained in the final population are obtained when the number of iterations exceeds a preset threshold.

4. The method according to claim 3, characterized in that, The iterative search of the elite population and subpopulations, where the number of iterations exceeds a preset threshold, yields the target individuals included in the final population, comprising: Based on the constraints corresponding to the failure rate function, the variables of different individuals in the elite population and subpopulation are adjusted to obtain the second adjustment result; Based on the second adjustment result and multiple failure rate functions, the elite population and subpopulation are iteratively searched, and the target individual is obtained when the number of iterations is greater than the preset iteration threshold.

5. The method according to claim 3, characterized in that, The process of selecting multiple individuals from the initial population based on the functional fitness to obtain an elite population includes: Multiple individuals in the initial population are selected based on the fitness of the function to obtain multiple initial individuals; Determine the crowding degree of multiple initial individuals in the failure rate function space corresponding to the failure rate function; wherein, the crowding degree is used to represent the distribution density of multiple initial individuals in the failure rate function space; Based on the crowding level, the initial individuals are screened to obtain an elite population.

6. The method according to claim 1, characterized in that, The process of obtaining the transformer insulation life assessment model includes: The accelerated thermal aging process of the internal oil-paper insulation of an oil-immersed magnetically controlled transformer is described using a cellulose degradation kinetic equation. A kinetic equation for the cumulative loss of fiber polymerization degree is obtained by improving the traditional kinetic equation. In the formula, DP0 represents the initial degree of aggregation, ω DPt This represents the degree of polymerization after thermal aging for time t, DPt represents the cumulative loss rate of degree of polymerization, ω*DP represents the ability to store and degrade the degree of polymerization, and k DP The rate of degradation of the degree of polymerization; The experimental data of high-temperature thermal aging were extrapolated to a low-temperature environment using a time-temperature superposition method to obtain the temperature shift factor αT: In the formula, the Arrhenius activation energy Ea = 91.4 kJ / mol, the gas constant R = 8.314 J / mol / K, Tref is the reference temperature (the lowest temperature in the experiment), and T is the hot spot temperature of the transformer; Based on the time-temperature superposition, the curve is shifted to obtain the moisture shift factor αM: In the formula, the moisture shift factor parameter b = 0.773, and the reference moisture content M ref =0.5%, where M is the moisture content in the insulating paper; at this point, the time-temperature-moisture shift factor α is obtained. T，M : α T,M ＝α T α M The degree of aggregation decreases from DP0 to DP t Required time t T，M The extrapolation formula yields the following transformer insulation life assessment model: In the formula, the degree of polymerization and degradation storage capacity are represented by the following: Cellulose degree of polymerization and degradation rate Simplifying this formula yields: t=g(Dp0,Dp t ,M,T) Where DP0 represents the initial degree of polymerization of the transformer; M is the water content; T is the hot spot temperature; and DPt is any degree of polymerization.

7. The method according to claim 1, characterized in that, The failure rate function is: Where β represents the shape parameter; m is the coefficient in the exponential relationship expression; parameters β and m remain constant with temperature changes and are determined using the nonlinear least squares method based on historical transformer data; the remaining insulation life t in the insulation life assessment results and the operating status score S in the operating status assessment results are independent variables; L0 represents the expected life of the transformer under ideal operating conditions and external environmental conditions; where... t=g(Dp0,Dp t ,M,T) Where DP0 represents the initial degree of polymerization of the transformer; M is the water content; T is the hot spot temperature; DP t For any degree of aggregation.

8. A device for assessing the remaining life reliability of a magnetically controlled transformer, characterized in that, The device includes: The operational status assessment module is used to acquire the winding dielectric loss factor, winding DC resistance, core grounding current, and winding absorption ratio of the magnetically controlled transformer as electrical test data; acquire the H2 content, CH4 content, C2H2 content, C2H4 content, C2H6 content, CO content, and CO2 content as dissolved gas information in the oil; acquire the operational status information of the cooling test system, test device, protection device, and on-load tap changer as accessory operational status information; and acquire the operation and maintenance records of the magnetically controlled transformer. The acquired information is input into a pre-established transformer operational status assessment model to obtain the operational status assessment results for the magnetically controlled transformer. The process includes: using the dissolved gas information in the oil, the electrical test data, the operation and maintenance records, and the operating status of the accessories as a transformer condition assessment index system; dividing the transformer condition assessment index system into transformer hierarchical categories; determining the fault type-related condition variables and the condition variable evaluation matrix rB; calculating the condition variable weights wi; collecting transformer condition variable data; calculating the unified condition score xi for the condition variables; calculating the condition score Xi for the fault type; traversing all fault types; determining the condition score for each fault type; determining the fault type judgment matrix RB; and calculating the fault type weights Wi and the operating condition score S. The operating condition score S is calculated using the following formula: Among them, W i Xi represents the fault type weight related to the fault type in the transformer characteristic quantities; Xi represents the fault type status score; it is determined whether the obtained transformer operating status score S is normal. If abnormal, the abnormal information is determined before the abnormal information and the operating status score S are output; if normal, the operating status score S is output directly. An insulation life assessment module is used to acquire the main insulation structure status data and FDS measurement data of the magnetically controlled transformer, and input the acquired data into a pre-established transformer insulation life assessment model to obtain the insulation life assessment result for the magnetically controlled transformer. The pre-established transformer insulation life assessment model is based on an initial model for the magnetically controlled transformer, and the initial model is based on the equivalent circuit of the magnetically controlled transformer. The equivalent circuit of the magnetically controlled transformer includes a power supply, a resistor, an inductor, a first coil, a second coil, a third coil, a fourth coil, a diode, a first thyristor, a second thyristor, a high-voltage coil, and a low-voltage coil. The first terminal of the power supply is connected to the first terminal of the resistor, the second terminal of the resistor is connected to the first terminal of the inductor, and the second terminal of the inductor is connected to the... The first end of the high-voltage coil is connected to the first end of the first coil and the first end of the second coil, respectively. The second end of the first coil is connected to the first end of the fourth coil, and the second end of the second coil is connected to the first end of the third coil. The second end of the power supply is connected to the second end of the fourth coil and the second end of the third coil, respectively. The second end of the first coil is connected to the anode of the diode, and the cathode of the diode is connected to the second end of the second coil. The first end of the first thyristor is connected to the midpoint of the first coil, and the second end of the first thyristor is connected to the first end of the third coil. The first end of the second thyristor is connected to the midpoint of the fourth coil, and the second end of the second thyristor is connected to the second end of the second coil. The remaining life reliability assessment module is used to input the operating status assessment results and the insulation life assessment results into a pre-established transformer remaining life reliability assessment model to obtain the remaining life reliability assessment results for the magnetically controlled transformer. The model acquisition module is used to acquire multiple initial models with different configuration parameters as an initial population; the initial population is iteratively optimized using the snake optimization algorithm, and the failure rate function corresponding to the initial model is used as the evaluation target to obtain the target initial model; the initial transformer remaining life reliability assessment model is configured according to the model parameters and the corresponding failure rate function of the target initial model to obtain the transformer remaining life reliability assessment model.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 7.

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