High-frequency transformer real-time life prediction and early warning method, system, device and medium

Abstract

This invention discloses a method, system, device, and medium for real-time life prediction and early warning of high-frequency transformers, belonging to the field of power equipment condition monitoring and early warning technology. The method includes: obtaining inter-turn temperature information of the high-frequency transformer based on its operating data, ambient temperature, and thermal resistance parameters; performing real-time life prediction of the high-frequency transformer insulation layer based on the inter-turn temperature information and the transformer's operating parameters, obtaining a real-time life prediction value; obtaining the planned remaining life of the high-frequency transformer based on historical cumulative damage; and performing multi-level life warning of the high-frequency transformer based on the real-time life prediction value and the planned remaining life. This application directly correlates insulation life through multi-physics field coupling and combines a multi-level early warning mechanism to promptly capture anomalies before insulation failure, improving prediction accuracy and early warning reliability.

Description

Technical Field

[0001] This application relates to the field of power equipment condition monitoring and early warning technology, specifically to methods, systems, equipment and media for real-time life prediction and early warning of high-frequency transformers. Background Technology

[0002] High-frequency transformers are indispensable energy conversion and transmission components in modern power modules (such as inverters, frequency converters, and DC-DC converters). They ensure complete electrical isolation between the input side (e.g., high-voltage grids, battery packs) and the output side (e.g., low-voltage loads, motor drive circuits), preventing DC components and dangerous high voltages from entering the low-voltage side and ensuring personal and equipment safety. As power modules evolve towards higher power density, higher efficiency, and higher reliability, internal high-frequency transformers are subjected to extreme stresses from the coupling of multiple physical fields, including electrical, thermal, and mechanical forces. Performance degradation and insulation failure have become critical bottlenecks restricting the overall lifespan of the module. Therefore, accurate lifespan prediction is not only necessary for preventative maintenance and avoiding catastrophic failures but also a core requirement for enhancing product competitiveness and ensuring reliable system operation throughout its entire lifecycle.

[0003] Faults in high-frequency transformers can be categorized into three main types: electrical stress, thermal stress, and core structure problems. Inter-turn voltage represents the most concentrated and drastically changing voltage stress within the transformer. Simultaneously, issues such as copper loss heating, core loss heating, and partial discharge caused by poor design will ultimately manifest as inter-turn insulation breakdown. Inter-turn insulation breakdown is both an independent fault and the final manifestation of other faults.

[0004] Therefore, the lifespan of high-frequency transformers mainly depends on the lifespan of the inter-turn insulation layer. However, the lifespan of the insulation layer cannot be directly measured. In existing technologies, the lifespan prediction of high-frequency transformers typically involves obtaining the transformer's operating parameters, such as load data, operating data, model parameter data, and environmental data for distribution transformers. From these, load characteristics, load fluctuations, and environmental factor-related features are extracted to construct a transformer lifespan prediction model. However, the transformer operating parameters and extracted features used in existing technologies only reflect the external operating conditions and have an indirect correlation with insulation aging. They cannot directly reflect the true degree of aging of the inter-turn insulation, resulting in low prediction accuracy. Furthermore, since the mapping relationship between external data and lifespan may vary for different transformer models and operating conditions, the prediction model has poor generalization ability, leading to unstable prediction results. Summary of the Invention

[0005] The purpose of this application is to address the problems of insufficient accuracy and unreliable early warning in conventional transformer life prediction models, which cannot directly correlate with the degree of inter-turn aging and have poor generalization ability. This application proposes a method, system, equipment, and medium for real-time life prediction and early warning of high-frequency transformers. By acquiring the inter-turn temperature information of the transformer, key characteristics directly related to insulation aging are obtained, thereby reducing short-term prediction errors and improving prediction accuracy. Furthermore, by combining historical cumulative damage to obtain the planned remaining life, timely early warning can be provided before insulation failure, improving the timeliness and reliability of early warning.

[0006] To achieve the above objectives, the technical solutions adopted in the embodiments of this application are as follows:

[0007] In a first aspect, embodiments of this application provide a method for real-time life prediction and early warning of high-frequency transformers, the method comprising:

[0008] The inter-turn temperature information of the high-frequency transformer is obtained based on the operating data, ambient temperature, and thermal resistance parameters of the high-frequency transformer. The real-time lifespan prediction of the insulation layer of the high-frequency transformer is performed based on the inter-turn temperature information and the transformer's operating parameters, and the real-time lifespan prediction value is obtained. The planned remaining lifespan of the high-frequency transformer is obtained based on historical cumulative damage. Multi-level lifespan warning of the high-frequency transformer is performed based on the real-time lifespan prediction value and the planned remaining lifespan.

[0009] This solution calculates the inter-turn temperature of the transformer through multi-parameter fusion, accurately capturing the core characteristics affecting insulation aging. It transforms the inability to directly measure inter-turn temperature into a core indicator directly related to insulation aging, providing precise data support for real-time life prediction of high-frequency transformers. By combining inter-turn temperature information with transformer operating parameters, it takes into account the synergistic effects of thermal and electrical stresses, directly establishing a causal relationship with insulation life. This avoids prediction deviations caused by indirect parameter correlations, achieving real-time and accurate life prediction. It is also adaptable to different operating conditions and transformer models. By combining real-time predicted life with historical accumulated losses to obtain the planned remaining life, it comprehensively considers the effects of loss aging and long-term accumulated damage, making the remaining life prediction more closely aligned with the actual operating conditions of the equipment. Furthermore, based on the real-time predicted life and the user's remaining life, multi-level early warnings are provided, timely warnings are issued before insulation failure, avoiding the risk of missed or false alarms from a single early warning mechanism, and improving the reliability and comprehensiveness of the early warning system.

[0010] Preferably, the acquisition of inter-turn temperature information of the high-frequency transformer based on its operating condition data, ambient temperature, and thermal resistance parameters includes:

[0011] Real-time acquisition of operating data of high-frequency transformers, including at least voltage, frequency, and heat source loss power;

[0012] The temperature signal at the top of the high-frequency transformer is acquired, and cold junction compensation and linearization are performed to obtain the top temperature value.

[0013] Based on the preset thermal resistance parameters, the inter-turn temperature of the high-frequency transformer is calculated by combining the top temperature value and the heat source loss power, and the inter-turn temperature information is obtained.

[0014] Preferably, the preset thermal resistance parameters include at least a first thermal resistance between the top of the high-frequency transformer and the environment, and a second thermal resistance between the top of the high-frequency transformer and the inter-turn hot spot.

[0015] Preferably, the heat source loss power is calculated based on a loss index and a corresponding index coefficient, wherein:

[0016] Loss metrics include load loss and no-load loss;

[0017] The load loss index coefficient is determined based on the proportion of copper loss located on the side adjacent to the hot spot of the high-frequency transformer.

[0018] The index coefficient of no-load loss is determined based on the proportion of iron loss in the target area at the top of the high-frequency transformer.

[0019] Preferably, the step of predicting the real-time lifespan of the high-frequency transformer insulation layer based on inter-turn temperature information and transformer operating parameters, and obtaining the real-time lifespan prediction value, includes:

[0020] Based on the correlation between the lifetime of the inter-turn insulation layer of a high-frequency transformer and frequency, a lifetime model of the inter-turn insulation layer of a high-frequency transformer is constructed by combining the voltage and thermal coupling relationship.

[0021] Based on the failure characteristics of transformers, model correction coefficients are determined to adjust the influence of transformer frequency, voltage and thermal coupling on insulation life.

[0022] The inter-turn temperature information, the real-time frequency of the transformer, and the voltage are fused together as inputs to the high-frequency transformer inter-turn insulation layer lifetime model. The real-time lifetime prediction value of the transformer inter-turn insulation layer is obtained based on the model correction coefficient.

[0023] Preferably, the step of obtaining the planned remaining life of the high-frequency transformer based on historical cumulative damage includes:

[0024] The cumulative damage is calculated based on the real-time lifetime prediction value at the current moment and the historical cumulative damage at an hourly cumulative step size, and the minimum time accumulation segment when the sum of the historical cumulative damage and the cumulative damage is greater than or equal to 1 is obtained.

[0025] The time length from the current moment to the failure moment of the inter-turn insulation layer of the high-frequency transformer is determined based on the minimum time accumulation segment, thereby obtaining the planned remaining life.

[0026] Preferably, the multi-level lifespan early warning system for high-frequency transformers based on real-time lifespan predictions and planned remaining lifespan includes:

[0027] The instantaneous damage rate of the high-frequency transformer is calculated based on the real-time life prediction value. When the instantaneous damage rate of the high-frequency transformer is greater than the preset instantaneous damage threshold and the duration exceeds the preset time threshold, an early warning signal is generated and an early warning prompt is triggered.

[0028] The planned remaining lifespan is compared with a preset remaining lifespan threshold. If the planned remaining lifespan is less than the remaining lifespan threshold, an early warning signal is generated and an early warning prompt is triggered; or, when the accumulated damage is greater than or equal to the accumulated damage threshold, an early warning prompt is triggered.

[0029] Secondly, embodiments of this application provide a real-time life prediction and early warning system for high-frequency transformers, including:

[0030] The inter-turn temperature acquisition module is used to acquire the inter-turn temperature information of the high-frequency transformer based on the operating condition data, ambient temperature and thermal resistance parameters of the high-frequency transformer.

[0031] The inter-turn life prediction module is used to predict the real-time life of the insulation layer of a high-frequency transformer based on the inter-turn temperature information and the transformer's operating parameters, and to obtain the real-time life prediction value.

[0032] The cumulative damage analysis module is used to obtain the planned remaining life of high-frequency transformers based on historical cumulative damage.

[0033] The lifespan warning module is used to provide multi-level lifespan warnings for high-frequency transformers based on real-time lifespan predictions and planned remaining lifespan.

[0034] Thirdly, embodiments of this application provide a computer device, including: a processor, a communication interface, a memory, and a communication bus, wherein the processor, the communication interface, and the memory communicate with each other through the communication bus; the memory is used to store computer programs; and the processor is used to implement the steps of the method described in the first aspect above when executing the program stored in the memory.

[0035] Fourthly, embodiments of this application provide a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the steps of the method described in the first aspect above.

[0036] The beneficial effects of this application are:

[0037] 1. To address the issue that conventional life prediction technologies cannot directly monitor inter-turn aging, thus failing to directly correlate with core transformer life factors, this application transforms the unmeasurable inter-turn temperature into a calculable parameter, establishing monitoring indicators directly related to insulation aging. This achieves state penetration from the outside in, accurately quantifying internal heating intensity. Furthermore, it integrates inter-turn temperature, voltage, and frequency to calculate real-time life prediction, taking into account the synergistic effects of thermal and electrical stresses, comprehensively covering the main influencing factors of insulation aging. In particular, the high-frequency transformer inter-turn insulation layer life model is constructed using the degradation effects of temperature and electrical stress on the insulation layer, rather than relying solely on statistical correlations of historical data, improving the accuracy of the mechanism and thus enhancing the precision of life prediction.

[0038] 2. To ensure an accurate reflection of the true aging degree of the inter-turn insulation, this application determines the model correction coefficient by means of the transformer failure characteristics (i.e., failure manifestation: inter-turn insulation breakdown) when constructing the inter-turn insulation layer life model of the high-frequency transformer. This is used to adjust the influence of transformer frequency, voltage and temperature on the insulation layer life, thereby flexibly adapting to complex working conditions such as load changes and environmental fluctuations or the life prediction needs of different transformer models.

[0039] 3. To address the early warning errors caused by long-term aging accumulation, the remaining lifespan is assessed through real-time loss and historical accumulation to avoid misjudgments of remaining lifespan due to failure to consider long-term aging. Furthermore, the instantaneous damage rate is obtained, and multi-dimensional early warning is performed by combining accumulated damage or remaining lifespan to adapt to timely anomaly detection under complex operating conditions such as load changes and voltage fluctuations. This avoids the risk of missed or false alarms from a single early warning mechanism and improves the reliability and comprehensiveness of the early warning. Attached Figure Description

[0040] Other features, objects, and advantages of this application will become more apparent from the following detailed description of non-limiting embodiments with reference to the accompanying drawings. The drawings are for illustrative purposes only and are not intended to limit the scope of this application. Furthermore, the same reference numerals denote the same parts throughout the drawings.

[0041] Figure 1 A flowchart of the high-frequency transformer real-time life prediction and early warning method provided in the embodiments of this application.

[0042] Figure 2 This is a schematic diagram of a high-frequency transformer real-time life prediction and early warning system module provided in an embodiment of this application.

[0043] Figure 3 This is a schematic diagram of another lifetime prediction and early warning process provided in an embodiment of this application.

[0044] Figure 4This is a schematic diagram illustrating the change in the aging damage rate of transformer inter-turn insulation, provided as an embodiment of this application.

[0045] Figure 5 A schematic diagram of the structure of a computer device provided in an embodiment of this application. Detailed Implementation

[0046] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description of this application is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely one preferred embodiment of this application and are only used to explain this application. They do not limit the scope of protection of this application. All other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0047] Example 1: As Figure 1 As shown, a method for real-time life prediction and early warning of high-frequency transformers includes steps S1-S4, wherein:

[0048] S1. Obtain the inter-turn temperature information of the high-frequency transformer based on the operating data, ambient temperature and thermal resistance parameters of the high-frequency transformer.

[0049] As an optional implementation, step S1 includes:

[0050] Real-time acquisition of operating data of high-frequency transformers, including at least voltage, frequency, and heat source loss power;

[0051] The temperature signal at the top of the high-frequency transformer is acquired, and cold junction compensation and linearization are performed to obtain the top temperature value.

[0052] Based on the preset thermal resistance parameters, the inter-turn temperature of the high-frequency transformer is calculated by combining the top temperature value and the heat source loss power, and the inter-turn temperature information is obtained.

[0053] As an optional implementation, the preset thermal resistance parameters include at least a first thermal resistance between the top of the high-frequency transformer and the environment, and a second thermal resistance between the top of the high-frequency transformer and the inter-turn hot spot.

[0054] As an optional implementation, the heat source loss power is calculated based on a loss index and a corresponding index coefficient, wherein:

[0055] Loss metrics include load loss and no-load loss;

[0056] The load loss index coefficient is determined based on the proportion of copper loss located on the side adjacent to the hot spot of the high-frequency transformer.

[0057] The index coefficient of no-load loss is determined based on the proportion of iron loss in the target area at the top of the high-frequency transformer.

[0058] Specifically, the inter-turn temperature of the high-frequency transformer is calculated based on a thermal network model, and the calculation formula is as follows:

[0059] ;

[0060] In the formula, Inter-turn temperature, The temperature at the top of the high-frequency transformer is measured by a thermocouple. T a For ambient temperature, The first thermal resistance, This is the second thermal resistance.

[0061] Specifically, the formula for calculating the power loss of the heat source is as follows:

[0062] ;

[0063] In the formula, This indicates the heat source power acting on the "region near the top of the high-frequency transformer," which mainly originates from copper losses and core losses. For copper loss, For core loss, ∈[0,1] represents the proportion of copper loss falling on the "hotspot proximity" side. This indicates the proportion of iron loss that is closer to the top.

[0064] It should be noted that the inter-turn hot spot temperature is a major influencing factor on the aging of insulation materials, and insulation layer aging is a key factor affecting the health status of high-frequency transformers. Since the inter-turn area is located deep inside the transformer windings and cannot be directly monitored by sensors, this embodiment uses measurable parameters such as thermal resistance, heat source power, ambient temperature, and transformer top temperature, combined with a thermal network model, to indirectly calculate and thus achieve accurate quantification of the unmeasurable inter-turn temperature.

[0065] In this embodiment, by accurately calculating the inter-turn temperature, the substitution error of top oil temperature or ambient temperature can be avoided, thereby reducing the calculation error of insulation aging rate, ensuring that the subsequent life prediction results are more in line with reality, and enabling timely detection of hidden risks such as overheating of the inter-turns even when the top temperature has not exceeded the standard. This avoids the early warning lag caused by relying on surface temperature, enhances the timeliness and reliability of early warning, and solves the problem of not being able to directly correlate with the core life factor of the transformer.

[0066] Furthermore, the inter-turn temperature calculation in this embodiment is based on the physical mechanism of heat conduction rather than statistical correlation. Therefore, it can adapt to complex operating conditions such as sudden load changes and ambient temperature fluctuations. Even if the operating conditions exceed the range of historical data, it can still be accurately calculated through dynamic adjustment of thermal resistance parameters and heat source power, ensuring the accuracy of real-time life prediction of high-frequency transformers and the continuity of reliable early warning.

[0067] S2. Based on the inter-turn temperature information and the transformer's operating parameters, perform real-time lifetime prediction of the high-frequency transformer insulation layer and obtain the real-time lifetime prediction value.

[0068] As an optional implementation, step S2 includes:

[0069] Based on the correlation between the lifetime of the inter-turn insulation layer of a high-frequency transformer and frequency, a lifetime model of the inter-turn insulation layer of a high-frequency transformer is constructed by combining the voltage and thermal coupling relationship.

[0070] Based on the failure characteristics of transformers, model correction coefficients are determined to adjust the influence of transformer frequency, voltage and thermal coupling on insulation life.

[0071] The inter-turn temperature information, the real-time frequency of the transformer, and the voltage are fused together as inputs to the high-frequency transformer inter-turn insulation layer lifetime model. The real-time lifetime prediction value of the transformer inter-turn insulation layer is obtained based on the model correction coefficient.

[0072] Specifically, the life model of the inter-turn insulation layer of a high-frequency transformer is expressed as follows:

[0073] ;

[0074] In the formula, L Indicates lifespan. f For switching frequency, V Indicates voltage. T Indicates temperature. nf Represents the coefficients in the frequency lifetime model. For reference switching frequency, For reference voltage, For reference temperature, for , as well as The reference life is below. , , , These are the model correction coefficients, used to adjust the degree of influence of the interaction between temperature, frequency, and voltage stresses on the thermal and electrical lifespan.

[0075] Furthermore, based on the current moment, assuming that the operating conditions (voltage, frequency, and inter-turn temperature) remain constant from the current operating condition, the real-time lifetime prediction value at the current moment is calculated using the above-mentioned high-frequency transformer inter-turn insulation layer lifetime model, and is taken as the instantaneous constant remaining lifetime, as follows:

[0076] ;

[0077] in, Indicates under constant operating conditions Real-time lifetime prediction values.

[0078] It should be noted that frequency, inter-turn temperature (i.e., heat), and voltage are the three core factors affecting the aging of the insulation layer of high-frequency transformers. Therefore, in this embodiment, the frequency model is used as the basis to integrate the temperature-dominated Arrhenius thermal aging model and the voltage-dominated voltage aging model to construct a high-frequency transformer inter-turn insulation layer life model, so as to avoid the one-sidedness of prediction caused by a single-factor model.

[0079] Furthermore, insulation aging is not an independent effect of temperature and voltage. High temperatures amplify the breakdown effect of voltage on the insulation layer. Therefore, this embodiment uses a correction coefficient to quantify the coupled influence of thermal stress and electrical stress, achieving accurate calculation of the insulation layer aging effect, rather than simply superimposing the effects of a single factor. The model is constructed by integrating classical aging mechanisms, not through pure statistical fitting, but by simultaneously inputting dynamic data of inter-turn temperature, real-time voltage, and frequency. This ensures that the prediction results conform to physical laws while also influencing real-time changes in operating conditions.

[0080] In this embodiment, the high-frequency transformer inter-turn insulation layer lifetime model constructed through multi-element fusion can adapt to complex scenarios such as load abrupt changes, ambient temperature fluctuations, and voltage sags / droops, and achieve aging state prediction covering different operating stages of high-frequency transformers. Furthermore, by adjusting the correction coefficients, the intensity of the synergistic effect of heat and voltage can be adjusted according to the transformer model and insulation material type, thereby improving the adaptability and flexibility of the prediction model. This solves the problem of low prediction accuracy caused by the inability to directly correlate with the core life factors of the transformer, and significantly improves the prediction accuracy.

[0081] S3. Obtain the planned remaining life of the high-frequency transformer based on historical cumulative damage.

[0082] As an optional implementation, step S3 includes:

[0083] The cumulative damage is calculated based on the real-time lifetime prediction value at the current moment and the historical cumulative damage at an hourly cumulative step size, and the minimum time accumulation segment when the sum of the historical cumulative damage and the cumulative damage is greater than or equal to 1 is obtained.

[0084] The time length from the current moment to the failure moment of the inter-turn insulation layer of the high-frequency transformer is determined based on the minimum time accumulation segment, thereby obtaining the planned remaining life.

[0085] Specifically, the planned remaining lifetime refers to the remaining lifetime at the current moment, assuming all future operating conditions are known and the project is executed strictly according to the schedule. The planned remaining lifetime is calculated by combining known historical accumulated damage with the real-time predicted lifetime at the current moment. Damage is gradually accumulated according to the schedule until the sum of the gradually accumulated damage and the historical accumulated damage exceeds 1 (including 1) at the minimum accumulation time. This time corresponds to the failure time. The planned remaining lifetime is then obtained based on the time length between the failure time and the current moment.

[0086] In some embodiments, progressively accumulating damage is represented as:

[0087] ;

[0088] in, This represents the damage at time i. The predicted lifetime is the sum of time i. Indicates the time step;

[0089] Accumulate until found The minimum value of j at time, where j represents the accumulated time stage (i.e., the time period corresponding to the failure time). Represents the current moment, representing the total cumulative damage value. When the accumulated damage reaches or exceeds 1, the point at which time stage it has accumulated is determined, and this time stage is used to identify the failure point. ;

[0090] Once j is determined, the current time can be calculated. until the time of failure The length of time at which cumulative damage equals 1 is used to obtain the planned remaining lifetime, as shown below:

[0091] ;

[0092] in, For the current moment The remaining lifespan of the plan.

[0093] It should be noted that the purpose of calculating the planned remaining life based on historical cumulative damage is to simulate the aging trajectory of the entire life cycle from the past to the future. It takes into account that the prediction of the complete planned operating conditions can reduce the prediction error. Specifically, it seamlessly connects the historical cumulative damage with the expected damage under the future planned operating conditions, accurately calculates the time window from the present to failure, and achieves accurate damage accumulation. In this way, it transforms the abstract degree of insulation aging into a specific calculable remaining life time and damage accumulation value, overcoming the prediction defects of relying solely on historical data or single operating condition assumptions.

[0094] S4. Perform multi-level life warning for high-frequency transformers based on real-time life prediction values ​​and planned remaining life.

[0095] As an optional implementation, step S4 includes:

[0096] The instantaneous damage rate of the high-frequency transformer is calculated based on the real-time life prediction value. When the instantaneous damage rate of the high-frequency transformer is greater than the preset instantaneous damage threshold and the duration exceeds the preset time threshold, an early warning signal is generated and an early warning prompt is triggered.

[0097] The planned remaining lifespan is compared with a preset remaining lifespan threshold. If the planned remaining lifespan is less than the remaining lifespan threshold, a warning signal is generated and a warning notification is triggered; or,

[0098] An early warning is triggered when the cumulative damage is greater than or equal to the cumulative damage threshold.

[0099] Specifically, the formula for calculating the instantaneous damage rate of a high-frequency transformer is as follows:

[0100] ;

[0101] in, This represents the instantaneous damage rate. An alert is triggered when the instantaneous damage rate exceeds the instantaneous damage threshold and the duration exceeds 10 minutes.

[0102] In this embodiment, the instantaneous damage threshold, remaining lifetime threshold, and cumulative damage threshold are set according to the different properties of the solid-state transformer (such as operating voltage, size, etc.). By monitoring the remaining lifetime threshold, damage rate threshold, and cumulative damage threshold in three dimensions, it is possible to adapt to early warning of complex operating conditions such as load changes and voltage fluctuations, thereby enhancing the reliability of early warning.

[0103] Example 2, as Figure 2 As shown, the high-frequency transformer real-time life prediction and early warning system includes:

[0104] The inter-turn temperature acquisition module is used to acquire the inter-turn temperature information of the high-frequency transformer based on the operating condition data, ambient temperature and thermal resistance parameters of the high-frequency transformer.

[0105] The inter-turn life prediction module is used to predict the real-time life of the insulation layer of a high-frequency transformer based on the inter-turn temperature information and the transformer's operating parameters, and to obtain the real-time life prediction value.

[0106] The cumulative damage analysis module is used to obtain the planned remaining life of high-frequency transformers based on historical cumulative damage.

[0107] The lifespan warning module is used to provide multi-level lifespan warnings for high-frequency transformers based on real-time lifespan predictions and planned remaining lifespan.

[0108] As an optional implementation, the inter-turn temperature acquisition module is specifically used for: acquiring real-time operating data of the high-frequency transformer, including at least voltage, frequency, and heat source loss power. The heat source loss power is calculated based on loss indicators and corresponding indicator coefficients, wherein: the loss indicators include load loss and no-load loss; the indicator coefficient of load loss is determined based on the proportion of copper loss located near the hot spot of the high-frequency transformer; the indicator coefficient of no-load loss is determined based on the proportion of iron loss located in the target area at the top of the high-frequency transformer; acquiring the temperature signal at the top of the high-frequency transformer, and performing cold-end compensation and linearization to obtain the top temperature value; and calculating the inter-turn temperature of the high-frequency transformer based on preset thermal resistance parameters, combined with the top temperature value, voltage, frequency, and heat source loss power, to obtain inter-turn temperature information, wherein: the preset thermal resistance parameters include at least a first thermal resistance between the top of the high-frequency transformer and the environment, and a second thermal resistance between the top of the high-frequency transformer and the inter-turn hot spot.

[0109] As an optional implementation, the inter-turn life prediction module is specifically used to: construct a high-frequency transformer inter-turn insulation layer life model based on the correlation between the life of the high-frequency transformer inter-turn insulation layer and frequency, combined with the voltage and thermal coupling relationship; determine the model correction coefficients based on the transformer failure characteristics to adjust the influence of transformer frequency, voltage, and thermal coupling on insulation layer life; and use the inter-turn temperature information, the transformer's real-time frequency, and voltage as input to the high-frequency transformer inter-turn insulation layer life model, and obtain the real-time life prediction value of the transformer inter-turn insulation layer according to the model correction coefficients.

[0110] As an optional implementation, the cumulative damage analysis module is specifically used to: calculate the cumulative damage based on the real-time lifetime prediction value at the current moment and the historical cumulative damage at an hourly cumulative step size, and obtain the minimum time accumulation segment when the sum of the historical cumulative damage and the cumulative damage is greater than or equal to 1; determine the time length from the current moment to the failure moment of the inter-turn insulation layer of the high-frequency transformer according to the minimum time accumulation segment, thereby obtaining the planned remaining lifetime.

[0111] As an optional implementation, the lifespan warning module is specifically used to: calculate the instantaneous damage rate of the high-frequency transformer based on the real-time lifespan prediction value; when the instantaneous damage rate of the high-frequency transformer is greater than a preset instantaneous damage threshold and the duration exceeds a preset time threshold, generate a warning signal and trigger a warning prompt; compare the planned remaining lifespan with a preset remaining lifespan threshold; if the planned remaining lifespan is less than the remaining lifespan threshold, generate a warning signal and trigger a warning prompt; or, when the accumulated damage is greater than or equal to the accumulated damage threshold, trigger a warning prompt.

[0112] In some embodiments, such as Figure 3 As shown, the system also includes a data acquisition module for collecting operating condition data of the high-frequency transformer. The system's prediction process specifically includes:

[0113] (1) The primary winding of the voltage transformer is connected in series in the high-voltage line of the high-frequency transformer, and the secondary winding outputs a proportionally reduced AC voltage signal that is completely in phase with the input voltage. This signal is used to measure the input and output waveforms, voltage, and frequency of the high-frequency transformer. The waveform transmitted from the voltage transformer is sampled by a high-speed analog-to-digital converter (ADC), and the effective voltage value is obtained in real time through software algorithms. The frequency is obtained by zero-crossing detection or fast Fourier transform. Simultaneously, the top temperature signal is collected by the thermocouple at the top of the transformer, and cold junction compensation and linearization are performed to obtain an accurate temperature value. The processed voltage, frequency, and temperature are packaged into a data packet, stored in the data acquisition unit (MCU), and sent to the edge computing gateway via CAN bus or Ethernet. At the same time, the raw or pre-processed data can also be directly uploaded to the cloud via Wi-Fi or 4G.

[0114] (2) Construct a high-frequency transformer inter-turn insulation layer lifetime model in the edge computing gateway and predict the real-time lifetime of the high-frequency transformer, as well as determine the planned remaining lifetime, thereby generating early warning signals and displaying relevant information on the charging pile screen and issuing early warning notifications.

[0115] In some examples, the transformer's inter-turn insulation life model parameters are assumed to be as follows: =1825 days =2.5、 =10、 =20, =1.5577、 =0.5761、 nf =0.9060、 =640.8800、 =54.0160. The real-time data obtained from the high-frequency transformer test are shown in Table 1 below:

[0116] Table 1. Transformer operating parameters collected during the operation of the charging module.

[0117]

[0118] As shown in Table 1, the high-frequency transformer in the power module maintains a constant copper loss during operation. =12W, iron loss =3W, first thermal resistance from top to environment =1.5 K / W, second thermal resistance from the top to the inter-turn hot spot =0.8 K / W; Assuming the instantaneous damage rate warning value of the high-frequency transformer is... The warning time is 5 minutes. During the entire operation, the transformer inter-turn insulation loss rate is as follows: Figure 4 As shown, the vertical axis represents the insulation aging damage rate, and the horizontal axis represents time. It can be seen that the maximum loss rate is still lower than that of the previous year. Furthermore, the cumulative loss rate is less than 0.75, so no warning is issued during operation.

[0119] The beneficial effects of the embodiment are as follows: By transforming the unmeasurable inter-turn temperature into a calculable parameter, a monitoring index directly related to insulation aging is established, achieving state penetration from the outside to the inside and accurately quantifying the internal heating intensity; furthermore, by integrating inter-turn temperature, voltage, and frequency to calculate real-time lifetime prediction, it takes into account the synergistic effect of thermal stress and electrical stress, comprehensively covering the main influencing factors of insulation aging. In particular, the lifetime model of the high-frequency transformer inter-turn insulation layer is constructed using the degradation effect of temperature and electrical stress on the insulation layer, rather than relying solely on the statistical correlation of historical data, improving the accuracy of the mechanism and thus enhancing the accuracy of lifetime prediction; in constructing the high-frequency transformer inter-turn insulation layer... In the life model, the model correction coefficient is determined by the transformer failure characteristics (i.e., failure manifestation: inter-turn insulation breakdown) to adjust the influence of transformer frequency, voltage and temperature on the insulation life, thereby flexibly adapting to complex operating conditions such as load changes and environmental fluctuations or the life prediction needs of different transformer models. By assessing the remaining life through real-time loss and historical accumulation, the model avoids misjudgment of the remaining life due to failure to consider long-term aging. Furthermore, the model obtains the instantaneous damage rate and combines it with accumulated damage or remaining life for multi-dimensional early warning, enabling timely detection of anomalies before insulation failure. This avoids the risk of missed or false alarms from a single early warning mechanism and improves the reliability and comprehensiveness of the early warning.

[0120] This application also provides a computer device, such as... Figure 5 As shown, it includes a processor, a communication interface, a memory, and a communication bus, wherein the processor, the communication interface, and the memory communicate with each other through the communication bus;

[0121] Memory, used to store computer programs;

[0122] The processor, when executing programs stored in memory, implements a method for real-time life prediction and early warning of high-frequency transformers.

[0123] The communication bus mentioned in the above computer equipment can be a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus, etc. This communication bus can be divided into address bus, data bus, control bus, etc. For ease of illustration, only one thick line is used to represent it in the diagram, but this does not indicate that there is only one bus or one type of bus.

[0124] The communication interface is used for communication between the aforementioned computer equipment and other devices.

[0125] The memory may include random access memory (RAM) or non-volatile memory (NVM), such as at least one disk storage device. Optionally, the memory may also be at least one storage device located remotely from the aforementioned processor.

[0126] The processors mentioned above can be general-purpose processors, including central processing units (CPUs), network processors (NPs), etc.; they can also be digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components.

[0127] This application also provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements a method for real-time life prediction and early warning of high-frequency transformers.

[0128] The above-described embodiments are preferred embodiments of this application and are not intended to limit the specific scope of this application. The scope of this application includes but is not limited to the specific embodiments described above. All equivalent changes made in accordance with the shape, structure, and method of this application are within the protection scope of this application.

Claims

1. A real-time life prediction and early warning method for high-frequency transformers, characterized in that: Includes the following steps: Inter-turn temperature information of a high-frequency transformer is obtained based on its operating data, ambient temperature, and thermal resistance parameters. Specifically: real-time acquisition of operating data, including at least voltage, frequency, and heat source loss power; acquisition of the transformer top-end temperature signal, followed by cold-end compensation and linearization to obtain the top-end temperature value; and calculation of the inter-turn temperature based on preset thermal resistance parameters, combined with the top-end temperature value and heat source loss power. The thermal resistance parameters include at least a first thermal resistance between the transformer top-end and the environment, and a second thermal resistance between the transformer top-end and the inter-turn hot spot. Based on inter-turn temperature information and transformer operating parameters, real-time lifetime prediction of the high-frequency transformer insulation layer is performed to obtain the predicted real-time lifetime value. Specifically, based on the correlation between the lifetime of the high-frequency transformer inter-turn insulation layer and frequency, and combined with the voltage-thermal coupling relationship, a high-frequency transformer inter-turn insulation layer lifetime model is constructed. The high-frequency transformer inter-turn insulation layer lifetime model is expressed as follows: ； Where L represents lifetime, f is the switching frequency, V represents voltage, T represents temperature, and nf represents a coefficient in the frequency lifetime model. For reference switching frequency, For reference voltage, For reference temperature, for , as well as The reference life is below. , , , These are the model's correction coefficients, used to adjust the degree of influence of the interaction between temperature, frequency, and voltage stresses on the thermal and electrical lifespan. Based on the failure characteristics of the transformer, model correction coefficients are determined to adjust the influence of transformer frequency, voltage and thermal coupling on the insulation layer life. The inter-turn temperature information, the real-time frequency and voltage of the transformer are fused as input to the high-frequency transformer inter-turn insulation layer life model, and the real-time life prediction value of the transformer inter-turn insulation layer is obtained according to the model correction coefficients. The planned remaining life of a high-frequency transformer is determined based on historical cumulative damage. Multi-level lifespan early warning for high-frequency transformers is based on real-time lifespan predictions and planned remaining lifespan.

2. The real-time life prediction and early warning method of high-frequency transformer according to claim 1, characterized in that: The heat source loss power is calculated based on the loss index and the corresponding index coefficient, wherein: Loss metrics include load loss and no-load loss; The load loss index coefficient is determined based on the proportion of copper loss located on the side adjacent to the hot spot of the high-frequency transformer. The index coefficient of no-load loss is determined based on the proportion of iron loss in the target area at the top of the high-frequency transformer.

3. The real-time life prediction and early warning method of high-frequency transformer according to claim 1, characterized in that: The method for obtaining the planned remaining life of a high-frequency transformer based on historical cumulative damage includes: The cumulative damage is calculated based on the real-time lifetime prediction value at the current moment and the historical cumulative damage at an hourly cumulative step size, and the minimum time accumulation segment when the sum of the historical cumulative damage and the cumulative damage is greater than or equal to 1 is obtained. The time length from the current moment to the failure moment of the inter-turn insulation layer of the high-frequency transformer is determined based on the minimum time accumulation segment, thereby obtaining the planned remaining life.

4. The real-time life prediction and early warning method of high-frequency transformer according to claim 3, characterized in that: The multi-level lifespan early warning system for high-frequency transformers based on real-time lifespan predictions and planned remaining lifespan includes: The instantaneous damage rate of the high-frequency transformer is calculated based on the real-time life prediction value. When the instantaneous damage rate of the high-frequency transformer is greater than the preset instantaneous damage threshold and the duration exceeds the preset time threshold, an early warning signal is generated and an early warning prompt is triggered. The planned remaining lifespan is compared with a preset remaining lifespan threshold. If the planned remaining lifespan is less than the remaining lifespan threshold, a warning signal is generated and a warning notification is triggered; or, An early warning is triggered when the cumulative damage is greater than or equal to the cumulative damage threshold.

5. The real-time life prediction and early warning system for high-frequency transformer, which is applicable to the real-time life prediction and early warning method for high-frequency transformer as claimed in any one of claims 1-4, characterized in that: include: The inter-turn temperature acquisition module is used to acquire the inter-turn temperature information of the high-frequency transformer based on the operating condition data, ambient temperature and thermal resistance parameters of the high-frequency transformer. The inter-turn life prediction module is used to predict the real-time life of the insulation layer of a high-frequency transformer based on the inter-turn temperature information and the transformer's operating parameters, and to obtain the real-time life prediction value. The cumulative damage analysis module is used to obtain the planned remaining life of high-frequency transformers based on historical cumulative damage. The lifespan warning module is used to provide multi-level lifespan warnings for high-frequency transformers based on real-time lifespan predictions and planned remaining lifespan.

6. A computer device, comprising: include: The system includes a processor, a communication interface, a memory, and a communication bus. The processor, communication interface, and memory communicate with each other via the communication bus. The memory is used to store computer programs. When the processor executes the program stored in the memory, it implements the steps of the high-frequency transformer real-time life prediction and early warning method as described in any one of claims 1-4.

7. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, which, when executed by a processor, implements the steps of the high-frequency transformer real-time life prediction and early warning method as described in any one of claims 1-4.

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