A method for on-line evaluation of the residual service life of an insulated bus duct

Abstract

The application discloses a kind of on-line evaluation methods of remaining service life of insulation bus duct, belong to the technical field of electric power equipment.The method comprises: obtaining the reference thermal life parameters of insulating material, including activation energy and frequency factor;Real-time temperature data in operation is obtained according to sampling period;Characteristic life value under current temperature is calculated based on the temperature data using Arrhenius equation, and the ratio of sampling period length and characteristic life value is taken as the thermal aging loss increment of the period;Loss increment is accumulated to historical cumulative thermal aging loss total value;Whether the cumulative value reaches the life termination threshold is judged, and the remaining service life evaluation result is calculated and output when not reached.The application combines thermal aging model with on-line temperature monitoring, to realize dynamic evaluation under variable temperature condition in the form of discrete time sampling and loss accumulation, solves the problem that offline evaluation cannot reflect actual operation condition cumulative aging, algorithm is simple, deployment cost is low, and is suitable for bus duct on-line monitoring.

Description

Technical Field

[0001] This invention relates to the field of power equipment condition monitoring and life assessment technology, and in particular to an online method for assessing the remaining service life of insulated busbar trunking. Background Technology

[0002] As a type of enclosed power distribution equipment for high-current transmission, the operational safety of insulated busbar trunking highly depends on the performance stability of its insulation materials. During long-term operation, insulation materials gradually deteriorate due to the combined effects of heat, electrical, and mechanical stresses, eventually leading to insulation failure. For a well-designed busbar trunking, the most prominent limiting factor for its service life is the gradual thermal aging of the insulation—for every 10°C increase in operating temperature, the lifespan of the insulation material is reduced by approximately half.

[0003] Currently, in engineering practice, the assessment of the remaining life of busbar insulation mainly relies on two methods: one is the offline assessment method based on accelerated thermal aging tests, which uses the Arrhenius equation to fit the relationship between material life and temperature. However, the data for this method comes from constant high-temperature tests and cannot reflect the temperature and stress conditions caused by load fluctuations in actual busbar operation, resulting in a significant deviation between the assessment results and the actual aging state. The second method is the threshold alarm method based on temperature monitoring, which can only react to instantaneous temperature anomalies and cannot assess the cumulative aging degree. There is also a method that fuses multi-source data for life assessment, but this relies on complex data fusion models, requires high computational resources, and has poor universality for different product models. Summary of the Invention

[0004] Therefore, it is necessary to provide an online assessment method for the remaining service life of insulated busbar trunking, which addresses the technical problem of how to track the cumulative aging degree of insulation materials under real-world variable operating conditions and dynamically output the quantitative results of remaining service life without power interruption or disassembly of the equipment.

[0005] An online assessment method for the remaining service life of an insulated busbar trunking, comprising the following steps:

[0006] Obtain the reference thermal lifetime parameters of the busbar insulation material, wherein the reference thermal lifetime parameters include the material activation energy and frequency factor;

[0007] According to the preset sampling period, real-time temperature data of the location of the insulation material during the operation of the bus trunking is obtained;

[0008] Based on the real-time temperature data obtained in the current sampling period, and using the reference thermal lifetime parameter, the cumulative thermal aging loss increment of the insulating material within the sampling period is calculated using the thermal aging lifetime equation.

[0009] The incremental thermal aging loss is added to the historical cumulative thermal aging loss total value to obtain the updated cumulative thermal aging loss total value.

[0010] Determine whether the updated total value of accumulated thermal aging loss has reached the preset end-of-life threshold.

[0011] If the lifespan end threshold is not reached, the remaining lifespan assessment result is calculated and output based on the updated total accumulated thermal aging loss.

[0012] In one embodiment, the step of calculating the cumulative thermal aging loss increment of the insulating material within the sampling period using the thermal aging lifetime equation includes:

[0013] Based on the reference thermal lifetime parameters and the real-time temperature value of the current sampling period, the characteristic lifetime value Li under this temperature condition is determined using the Arrhenius equation;

[0014] The ratio of the sampling period length Δt to the characteristic lifetime value Li is used as the thermal aging loss increment ΔDi of the insulating material within the sampling period.

[0015] In one embodiment, the remaining useful life assessment result is represented in at least one of the following ways: remaining useful life percentage, estimated remaining runtime, and aging rate trend.

[0016] In one embodiment, the remaining lifetime percentage is Rpercent = (1 - Dtotal) × 100%, where Dtotal is the updated total cumulative thermal aging loss.

[0017] In one embodiment, the lifespan termination threshold is set to 1; when the updated total cumulative thermal aging loss reaches or exceeds 1, a replacement suggestion or warning signal is output.

[0018] In one embodiment, the baseline thermal life parameter is obtained by one of the following methods: by retrieving it from a pre-stored material parameter database based on the busbar type and insulation material type index; or by receiving a baseline parameter calculated by fitting accelerated thermal aging test data.

[0019] In one embodiment, when calculating the cumulative thermal aging loss increment, it is further multiplied by an electrical aging correction factor, which is determined based on the partial discharge amount or the ratio of the operating voltage to the rated voltage.

[0020] In one embodiment, when calculating the cumulative thermal aging loss increment, a load fluctuation correction factor is further multiplied, which is determined based on the load change amplitude and frequency per unit time.

[0021] In one embodiment, the sampling period is adaptively adjusted according to the busbar load change rate: the sampling period is shortened when the load change rate exceeds a preset threshold, and the sampling period is extended when the load change rate is below the preset threshold.

[0022] In one embodiment, the real-time temperature data is collected by a temperature sensor deployed on the surface of the busbar conductor or inside the insulation layer; the method is executed by a data processing device deployed at the busbar site, or by a remote monitoring platform that is communicatively connected to the field acquisition equipment.

[0023] The aforementioned online assessment method for the remaining service life of insulated busbar trunking combines the Arrhenius thermal aging model with online temperature monitoring data. It employs a discretized processing approach of "time-sharing sampling - single-step loss calculation - cumulative summation," transforming the original life formula, applicable only to constant temperature conditions, into an online recursive algorithm suitable for real-time variable temperature conditions. Because the incremental thermal aging loss is calculated in real-time based on the actual operating temperature within each sampling period and accumulated to a historical total, the cumulative thermal aging loss accurately reflects the actual aging degree of the insulation material under actual operating conditions such as load fluctuations and ambient temperature changes. This solves the problem of significant deviations between the assessment results and the actual aging state caused by offline assessment methods that ignore dynamic changes in operating conditions. This method relies only on two benchmark thermal life parameters (activation energy and frequency factor) and real-time acquired operating temperature data. The algorithm structure is simple, without complex data fusion models, machine learning training, or large-scale historical data storage. It consumes low computational resources and is easily deployed on embedded data processing devices or field monitoring terminals, making it suitable for practical engineering applications of busbar trunking. Furthermore, by setting the dimensionless "life consumption rate" index of the total cumulative thermal aging loss, the operating time under different temperature conditions is uniformly converted into the thermal life consumption share. This enables the quantification and comparison of the aging degree of insulation materials under multiple operating conditions and variable loads, providing operation and maintenance personnel with intuitive and clear quantitative indicators and decision-making basis for remaining life, and effectively improving the scientific nature and initiative of busbar operation and maintenance. Attached Figure Description

[0024] Figure 1 and Figure 2 These are schematic diagrams illustrating the overall process of an online assessment method for the remaining service life of an insulated busbar trunking in one embodiment.

[0025] Figure 3 This is a detailed flowchart illustrating the loss increment calculation process in an online assessment method for the remaining service life of an insulated busbar trunking in one embodiment.

[0026] Figure 4This is a schematic diagram of the evaluation result output interface in an online evaluation method for the remaining service life of an insulated busbar trunking in one embodiment;

[0027] Figure 5 This is a schematic diagram illustrating the logic for calculating the percentage of remaining service life in an online assessment method for the remaining service life of an insulated busbar trunking in one embodiment.

[0028] Figure 6 This is a schematic diagram of the multi-level threshold early warning logic in the online assessment method for the remaining service life of an insulated busbar trunking in one embodiment;

[0029] Figure 7 This is a schematic diagram of the process for obtaining the baseline thermal life parameters in an online assessment method for the remaining service life of an insulated busbar trunking in one embodiment;

[0030] Figure 8 This is a schematic diagram of the loss increment calculation process in the online assessment method for the remaining service life of an insulated busbar trunking in one embodiment, which introduces an electrical aging correction coefficient.

[0031] Figure 9 This is a schematic diagram of the loss increment calculation process in the online assessment method for the remaining service life of an insulated busbar trunking in one embodiment, which introduces a load fluctuation correction coefficient.

[0032] Figure 10 This is a schematic diagram of the sampling period adaptive adjustment logic in the online assessment method for the remaining service life of an insulated busbar trunking in one embodiment;

[0033] Figure 11 and Figure 12 These are schematic diagrams of the system deployment architecture in an online assessment method for the remaining service life of an insulated busbar trunking, as shown in one embodiment. Detailed Implementation

[0034] To make the above-mentioned objects, features, and advantages of the present invention more apparent and understandable, the specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. Many specific details are set forth in the following description to provide a thorough understanding of the present invention. However, the present invention can be practiced in many other ways different from those described herein, and those skilled in the art can make similar modifications without departing from the spirit of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below. In the description of the present invention, it should be understood that the terms "center," "longitudinal," "lateral," "length," "width," "thickness," "upper," "lower," "front," "rear," "left," "right," "vertical," "horizontal," "top," "bottom," "inner," "outer," "clockwise," "counterclockwise," "axial," "radial," and "circumferential," etc., indicating orientation or positional relationships, are based on the orientation or positional relationships shown in the accompanying drawings and are only for the convenience of describing the present invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on the present invention.

[0035] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. In the description of this invention, "a plurality of" means at least two, such as two, three, etc., unless otherwise explicitly specified.

[0036] In this invention, unless otherwise explicitly specified and limited, the terms "installation," "connection," "linking," and "fixing," etc., should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral part; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; they can refer to the internal communication of two components or the interaction between two components, unless otherwise explicitly limited. Those skilled in the art can understand the specific meaning of the above terms in this invention according to the specific circumstances.

[0037] In this invention, unless otherwise explicitly specified and limited, "above" or "below" the second feature can mean that the first feature is in direct contact with the second feature, or that the first feature is in indirect contact with the second feature through an intermediate medium. Furthermore, "above," "over," and "on top" of the second feature can mean that the first feature is directly above or diagonally above the second feature, or simply that the first feature is at a higher horizontal level than the second feature. "Below," "below," and "under" the second feature can mean that the first feature is directly below or diagonally below the second feature, or simply that the first feature is at a lower horizontal level than the second feature.

[0038] It should be noted that when an element is referred to as being "fixed to" or "set on" another element, it can be directly on the other element or there may be an intervening element. When an element is considered to be "connected to" another element, it can be directly connected to the other element or there may be an intervening element. The terms "vertical," "horizontal," "upper," "lower," "left," "right," and similar expressions used herein are for illustrative purposes only and do not represent the only possible implementation.

[0039] An embodiment of the present invention provides an online assessment method 10 for the remaining service life of insulated busbar trunking, the overall process of which is as follows: Figure 1 As shown. Method 10 includes the following steps:

[0040] S101: Obtain the reference thermal life parameters of the busbar insulation material, the reference thermal life parameters including material activation energy and frequency factor;

[0041] S102: According to the preset sampling period, obtain the real-time temperature data of the location of the insulation material during the operation of the bus trunking;

[0042] S103: Based on the real-time temperature data obtained in the current sampling period, and based on the reference thermal lifetime parameter, calculate the cumulative thermal aging loss increment of the insulating material within the sampling period using the thermal aging lifetime equation.

[0043] S104: The incremental thermal aging loss is added to the historical cumulative thermal aging loss to obtain the updated cumulative thermal aging loss.

[0044] S105: Determine whether the updated total value of accumulated thermal aging loss has reached the preset lifespan termination threshold.

[0045] S106: If the lifespan end threshold is not reached, calculate and output the remaining lifespan assessment result based on the updated total accumulated thermal aging loss value.

[0046] The aforementioned online assessment method 10 for the remaining service life of insulated busbar trunking combines the Arrhenius thermal aging model with online temperature monitoring data. It employs a discretized processing approach of "time-sharing sampling - single-step loss calculation - cumulative summation," transforming the original life formula, applicable only to constant temperature conditions, into an online recursive algorithm suitable for real-time variable temperature conditions. Because the incremental thermal aging loss is calculated in real-time based on the actual operating temperature within each sampling period and accumulated to a historical total, the cumulative thermal aging loss accurately reflects the actual aging degree of the insulation material under actual operating conditions such as load fluctuations and ambient temperature changes. This solves the problem of significant deviations between the assessment results and the actual aging state caused by offline assessment methods neglecting dynamic changes in operating conditions. This method relies only on two benchmark thermal life parameters (activation energy and frequency factor) and real-time acquired operating temperature data. The algorithm structure is simple, does not involve complex data fusion models, machine learning training, or large-scale historical data storage, consumes low computational resources, and is easily deployed on embedded data processing devices or field monitoring terminals, making it suitable for practical engineering applications of busbar trunking. Furthermore, by setting the dimensionless "life consumption rate" index of the total cumulative thermal aging loss, the operating time under different temperature conditions is uniformly converted into the thermal life consumption share. This enables the quantification and comparison of the aging degree of insulation materials under multiple operating conditions and variable loads, providing operation and maintenance personnel with intuitive and clear quantitative indicators and decision-making basis for remaining life, and effectively improving the scientific nature and initiative of busbar operation and maintenance.

[0047] It should be noted that the online assessment method for the remaining service life of an insulated busbar trunking provided in this embodiment has the following overall process: Figure 1 and Figure 2 As shown. This method can be executed by a data processing device deployed at the busbar site, such as an ARM-based embedded industrial control unit installed in the busbar's plug-in box or terminal box; or, the method can also be executed by a remote monitoring platform that communicates with the field acquisition equipment, such as a server located in the power distribution monitoring center.

[0048] Specifically, before the busbar trunking is put into operation or during initial power-on initialization, the data processing device first acquires the reference thermal life parameters of the busbar trunking insulation material. These reference thermal life parameters include the material activation energy Ea and the frequency factor A. The acquisition method can be as follows: the device pre-stores a material parameter database, indexed by "busbar trunking model + insulation material type," which associates and stores the activation energy and frequency factor of various commonly used insulation materials. For example, for the Baoshi Electric BSM3 series dense insulated busbar trunking, its insulation material is polyester film (PET), and the database corresponds to an activation energy Ea of approximately 1.2 eV to 1.5 eV and a frequency factor A of approximately 1 × 10^8 hours to 1 × 10^12 hours for this model.

[0049] The device automatically retrieves the corresponding baseline parameters from the database based on the busbar model in its configuration information. Alternatively, the device receives a parameter configuration file from a host computer via a communication interface; the parameter values ​​in this file are derived from the fitting calculation results of accelerated thermal aging test data. Regardless of the method used, the baseline parameters only need to be acquired once during the initialization phase or parameter update, after which the online evaluation cycle can begin.

[0050] During normal operation, the device acquires real-time temperature data of the location of the insulation material in the busbar trunking at a preset sampling period. The sampling period can be set according to the load variation characteristics of the busbar trunking, for example, 10 minutes. The temperature data is sourced from temperature sensors deployed on the surface of the busbar trunking conductors or inside the insulation layer. For densely insulated busbar trunking, the temperature sensor is preferably attached to the interface between the copper conductor and the insulation coating material to accurately sense the actual thermal stress borne by the insulation material; for cast-in-place busbar trunking, the temperature sensor can be pre-embedded in the resin layer near the conductor during the casting process. The sensor transmits the temperature measurement value to the data processing device via an RS485 bus or wireless communication module. At the end of each sampling period, the device reads the latest temperature value Ti (unit: Kelvin).

[0051] Next, the device calculates the cumulative thermal aging loss increment of the insulation material within the current sampling period based on the real-time temperature data acquired during that sampling period. Specifically, the device uses the Arrhenius thermal aging lifetime equation to determine the characteristic lifetime value Li under that temperature condition: Li = A × exp(Ea / (k × Ti)), where exp represents an exponential function with the natural constant e as the base, and k is the Boltzmann constant. The physical meaning of this characteristic lifetime value is: assuming the busbar continues to operate at temperature Ti, the operating time required for the insulation material to go from a brand-new state to the end of its lifetime. Then, the device uses the ratio Δt / Li, which is the time length Δt of the sampling period to the characteristic lifetime value Li, as the thermal aging loss increment ΔDi of the insulation material within that sampling period. This ratio ΔDi is the share of lifetime consumed by operating at temperature Ti for Δt time.

[0052] The device then adds the calculated increase in thermal aging loss to the historical cumulative total thermal aging loss value, Dtotal, to obtain the updated cumulative total thermal aging loss value. The initial value of Dtotal is 0, indicating that the brand-new insulation material has not yet generated any thermal aging loss. After each sampling cycle, Dtotal is increased by ΔDi, thereby achieving a cycle-by-cycle cumulative accumulation of aging degree.

[0053] The device determines whether the updated Dtotal has reached the preset end-of-life threshold Dend. Dend is set to 1, based on the principle that when Dtotal reaches 1, it indicates that the insulation material has consumed its entire usable thermal life. If Dtotal < 1, the device calculates and outputs the remaining service life assessment result based on Dtotal. For example, the remaining service life percentage Rpercent = (1 - Dtotal) × 100%, displayed as a percentage on the local screen or uploaded to the monitoring platform. Simultaneously, the device can also calculate the estimated remaining operating time: Rtime = (1 - Dtotal) × Lrated, where Lrated is the design life value of the busbar at the rated operating temperature (e.g., 30 years converted to hours). If Dtotal ≥ 1, the device outputs a warning signal: "Insulation life has expired; replacement is recommended."

[0054] More preferably, when calculating the cumulative thermal aging loss increment, the device can also comprehensively consider the impact of the temperature measurement value selection strategy on the conservatism of the evaluation results. Since the operating temperature of the busbar trunking may fluctuate within a sampling period, the device can read multiple temperature sampling points within that period (e.g., recording once per minute), and then take the highest temperature value within that period as Ti for calculation. Using the highest temperature value for calculation yields a more conservative loss assessment result, meaning the calculated loss increment is larger and the remaining life prediction is shorter. This is a more prudent choice for safety-sensitive applications. Conversely, if the average temperature value within the period is used for calculation, the evaluation result is closer to the average aging trend. The device can automatically select the temperature value selection method according to the user-defined evaluation strategy (conservative or standard).

[0055] Thus, through the above method, this embodiment organically combines the Arrhenius thermal aging model with online temperature monitoring data. Using a discretized recursive processing method of "time-sharing sampling—single-step loss calculation—cumulative summation," it achieves continuous and dynamic tracking of the cumulative thermal aging loss of busbar insulation materials under real variable operating conditions. The evaluation results show a significantly higher degree of agreement with the actual aging state of the equipment than static evaluation methods based on offline accelerated testing. Furthermore, the algorithm is simple, consumes low computational resources, and can be easily deployed in embedded devices in the field.

[0056] To further refine the specific calculation steps for the cumulative thermal aging loss increment, and to provide more optimized calculation details, the refined process is as follows: Figure 3 As shown. Specifically, during the operation of the busbar trunking, the data processing device first obtains the real-time temperature value Ti of the current cycle from the temperature sensor at the arrival of each sampling cycle. As mentioned earlier, Ti can be the highest or average temperature value within that cycle. Then, the device calls upon the acquired reference thermal lifetime parameters, namely the activation energy Ea and the frequency factor A, and combines them with the Boltzmann constant k to determine the characteristic lifetime value Li under that temperature condition using the Arrhenius equation. The specific calculation formula is: Li = A × exp(Ea / (k × Ti)). Here, exp(·) represents exponential operation. The device internally stores the value of the Boltzmann constant k (e.g., 8.617 × 10⁻⁵ eV / K), using standard double-precision floating-point numbers in the calculation to ensure calculation accuracy.

[0057] After obtaining the characteristic lifetime value Li, the device acquires the duration Δt of the current sampling period. Δt is determined by the system clock; for example, if the sampling period is set to 10 minutes, then Δt = 10 / 60 = 0.1667 hours (if Li is in hours). The device calculates the increase in thermal aging loss of the insulation material within this sampling period, ΔDi = Δt / Li. The physical meaning of this ratio is: at temperature Ti, the actual operating time Δt experienced is the proportion of the total lifetime Li under that temperature condition, which is the share of lifetime consumed in this period.

[0058] More preferably, to improve the stability and anti-interference capability of the evaluation results, the device can preprocess the real-time temperature value Ti before calculating the characteristic lifetime value Li. For example, the device can perform median filtering or moving average filtering on multiple continuously acquired raw temperature measurements (such as 10 instantaneous temperature values ​​acquired within 1 minute) to filter out instantaneous peak values ​​caused by sensor noise or electromagnetic interference, and use the filtered temperature value T_filtered as Ti for calculation. This processing method avoids drastic jumps in the loss increment calculation results caused by single-point abnormal temperatures, making the cumulative aging loss curve smoother and more realistically reflecting the macroscopic thermal aging process of the insulating material.

[0059] Furthermore, when ΔDi is added to the historical cumulative thermal aging loss total Dtotal, the device can also record the timestamp of each addition, forming a cumulative loss curve with time as the horizontal axis and Dtotal as the vertical axis. This curve visually displays the thermal life consumption history of the busbar insulation material since its commissioning. By performing real-time or offline analysis on this curve, maintenance personnel can identify high-load periods in the busbar's operating history—the intervals with steeply increasing slopes on the curve correspond to the stages of accelerated thermal aging, thus providing data support for maintenance decisions such as load management and ventilation and heat dissipation upgrades.

[0060] More preferably, when calculating ΔDi, the device can further consider the correction of the temperature measurement location. Since the temperature sensor is usually installed on the conductor surface or at a specific depth of the insulation layer, there may be a systematic deviation between its measured value T_sensor and the equivalent thermal stress temperature T_eff borne by the insulation material as a whole. To address this, a temperature correction coefficient α can be preset during device initialization, such that Ti = T_sensor + α in the calculation. α can be obtained through thermal field simulation or actual measurement calibration. For example, for the Baoshi Electric BSM3 busbar trunking, when the sensor is installed on the conductor surface, α can be taken as -2℃ to +2℃; when the sensor is installed on the inner wall of the casing, α may need to be taken as +5℃ to +10℃ to compensate for the internal temperature gradient of the insulation material. After introducing the temperature correction coefficient, the formula for calculating the characteristic lifetime value becomes: Li = A × exp(Ea / (k × (T_sensor + α))), thus making the loss assessment result closer to the actual aging state of the insulation material itself.

[0061] Thus, through the refined calculation method of this embodiment, the calculation steps for the cumulative thermal aging loss increment are clear and highly operable. Furthermore, by introducing optimization processing methods such as temperature filtering and temperature correction, the accuracy, robustness, and engineering practicality of the evaluation method are further improved.

[0062] To enrich and refine the output representation of remaining useful life assessment results, and to provide more display formats suitable for operation and maintenance scenarios, the output representation is illustrated as follows: Figure 4 As shown.

[0063] Specifically, after the data processing device determines that the total accumulated thermal aging loss Dtotal has not yet reached the end-of-life threshold (Dtotal<1), the device calculates and outputs the remaining service life assessment result based on Dtotal. In this embodiment, the assessment result is represented in at least one of the following three forms or any combination thereof.

[0064] The first method of representation is the remaining lifespan percentage. The device calculates Rpercent = (1 - Dtotal) × 100% and displays this percentage on the local LCD screen, or uploads it to the monitoring system interface via a communication protocol (such as Modbus TCP). The remaining lifespan percentage is presented intuitively in the form of "%", making it easy for maintenance personnel to quickly grasp the overall aging progress of the insulation material. For example, when "Remaining Lifespan: 82.5%" is displayed, maintenance personnel can intuitively understand that the insulation material still has about 80% of its lifespan remaining.

[0065] The second representation method is the estimated remaining runtime. The device selects a reference lifespan value Lref based on the current operating conditions or the design rated operating conditions, and calculates Rtime = (1 - Dtotal) × Lref. There are several strategies for selecting Lref: one is to take the characteristic lifespan value Li under the current operating temperature conditions, i.e., Rtime = (1 - Dtotal) × Li. This method gives the remaining runtime based on the assumption that the system will continue to operate at the current temperature in the future. Another method is to take the nominal lifespan value Lrated under the design rated operating conditions of the busbar trunking (e.g., 30 years, or 262,800 hours). This method focuses more on comparing the remaining runtime with the design lifespan. The device can display the estimated remaining runtime in "years" or "hours". More preferably, the device can simultaneously calculate and display the remaining runtime corresponding to both Lref values, labeled as "Remaining runtime based on current operating conditions" and "Remaining runtime based on rated operating conditions," respectively, providing maintenance personnel with a more comprehensive decision-making reference.

[0066] The third representation method is the aging rate trend. The device records the wear increment ΔDi data for multiple consecutive sampling periods (e.g., 144 10-minute periods in the last 24 hours) and calculates its trend. For example, the device can compare the average wear increment of the last hour with the average wear increment of the last 24 hours. If the former is significantly greater than the latter, it determines that the aging rate is accelerating and outputs a "Changing Aging Rate" prompt; conversely, if the former is less than the latter, it outputs a "Changing Aging Rate" prompt; if the two are close, it outputs a "Stable Aging Rate" prompt. The aging rate trend can be presented as a text prompt or a simple arrow icon (↑ for acceleration, ↓ for deceleration, → for stability).

[0067] More preferably, the device can also integrate the above three representation methods into a single comprehensive evaluation interface. For example, the device drives a graphical display interface where a progress bar in the center shows the remaining lifetime percentage; the progress bar's fill color gradually changes from green to yellow and then to red as the remaining lifetime decreases; the estimated remaining runtime is displayed below the progress bar; and an aging rate trend icon is displayed in the corner of the interface. Furthermore, the interface can also plot a trend curve of the cumulative thermal aging loss total value Dtotal over time, such as... Figure 3 As shown, the horizontal axis represents running time, and the vertical axis represents Dtotal (from 0 to 1). The current running point is marked on the curve. By observing the changes in the slope of the curve, maintenance personnel can intuitively judge the speed of the aging process.

[0068] More preferably, the device can also generate maintenance recommendations based on the remaining life assessment results. For example, when Rpercent drops below 30%, the device outputs a prompt "It is recommended to arrange a preventive maintenance plan"; when Rpercent drops below 10%, it outputs a warning "It is recommended to replace the insulation components as soon as possible". These prompts can be triggered by dry contact signals to activate alarm indicator lights, or pushed to maintenance personnel via SMS, email, or other means.

[0069] Thus, through the diverse result representation methods in this embodiment, the remaining service life assessment result is no longer an abstract numerical value, but is transformed into multi-dimensional, visualized operation and maintenance guidance information, which greatly enhances the practical value of the assessment method and the user experience.

[0070] To further clarify the specific calculation method for the percentage of remaining useful life, and based on this, several variant calculation methods and accuracy improvement measures are introduced, the calculation logic is illustrated as follows: Figure 5 As shown.

[0071] As can be seen from the above embodiments, the total accumulated thermal aging loss, Dtotal, is defined as the sum of the thermal lifetime consumed by the insulation material since the busbar trunking was put into operation. When Dtotal < 1, the remaining lifetime percentage, Rpercent, is calculated according to the formula Rpercent = (1 - Dtotal) × 100%. This formula assumes that the accumulation of thermal aging loss is linearly additive, and that the lifetime endpoint corresponds to Dtotal = 1. This calculation method is intuitive and simple, with minimal computational overhead, making it particularly suitable for resource-constrained embedded devices.

[0072] More preferably, to improve the accuracy and warning effect of the remaining lifespan percentage indication near the end of lifespan, the device can perform non-linear mapping processing on the calculation result of Rpercent. For example, when Dtotal exceeds a certain warning threshold (e.g., 0.7), the device can use an accelerated decay mapping function, so that the same Dtotal increment corresponds to a larger decrease in Rpercent, thereby conveying a stronger sense of urgency to maintenance personnel when the remaining lifespan is low. Specifically, the device can pre-store a segmented mapping table: when Dtotal is in the range [0, 0.7], Rpercent = (1 - Dtotal) × 100%; when Dtotal is in the range (0.7, 1], Rpercent = (1 - Dtotal) × 100% × β, where β is an amplification factor greater than 1, for example, β is 1.5, which makes the displayed remaining percentage value decrease faster. Although this method changes the displayed value, it does not change the underlying actual accumulation logic of Dtotal, and only serves as a warning enhancement means at the human-computer interaction level.

[0073] More preferably, the device can also calculate the confidence interval or uncertainty of the remaining lifetime percentage while calculating Rpercent. Since the baseline thermal lifetime parameters Ea and A are derived from statistical fitting of accelerated thermal aging tests, they have certain confidence intervals (e.g., 95% upper and lower confidence limits). During initialization, the device obtains not only the nominal values ​​of Ea and A, but also their upper and lower confidence limits. During online evaluation, in addition to calculating the nominal remaining lifetime percentage using the nominal values, the device also calculates the pessimistic and optimistic remaining lifetime percentages using combinations of the upper and lower limits of the parameters, and displays or uploads all three together. For example, the interface displays "Remaining lifetime: 82.5% (pessimistic 75.2% ~ optimistic 88.9%)". This approach provides a quantitative basis for risk assessment in operation and maintenance decisions, and is particularly suitable for scenarios with extremely high power supply reliability requirements, such as data centers and hospitals.

[0074] More preferably, the device can also use historical Dtotal growth data to predict future changes in Rpercent. For example, if the device calculates the average daily growth rate v_D (in units of 1 / day) of Dtotal over the past week, the estimated remaining days are approximately (1-Dtotal) / v_D. This estimated remaining days can serve as another expression for the estimated remaining runtime, complementing Rpercent.

[0075] Thus, through this embodiment, the calculation of the remaining lifespan percentage is not limited to a simple linear formula, but integrates a variety of optimized processing methods such as nonlinear warning mapping, confidence interval display, and remaining days prediction, further enriching the information content and decision support capabilities of the assessment results.

[0076] To further explain the setting and early warning mechanism of the lifespan termination threshold, and to expand on preferred solutions such as multi-level thresholds and adaptive thresholds, the early warning logic is illustrated as follows: Figure 6 As shown.

[0077] Specifically, in this invention, the preset end-of-life threshold Dend is set to 1. When the total accumulated thermal aging loss Dtotal reaches or exceeds 1 through periodic accumulation, it indicates that the insulating material has theoretically consumed its entire usable thermal life and reached the end of its life. At this time, the data processing device outputs a replacement suggestion or warning signal. The warning signal may take the form of: illuminating a red LED indicator on the device panel, driving a relay to output a dry contact signal to trigger an external audible and visual alarm, or sending an alarm message to the monitoring platform, etc.

[0078] More preferably, in addition to the end-of-life threshold Dend=1, the device can also set one or more warning thresholds to issue maintenance reminders in advance before the insulation material enters the final stage of aging. For example, the device can set a first warning threshold D_warn1=0.7 and a second warning threshold D_warn2=0.85. When Dtotal first reaches 0.7, the device outputs a prompt message "Insulation life has been consumed by 70%, it is recommended to include it in the maintenance observation plan"; when Dtotal reaches 0.85, the device outputs a prompt message "Insulation life has been consumed by 85%, it is recommended to prepare spare parts or arrange maintenance windows in advance"; when Dtotal reaches 1, it outputs an emergency alarm "Insulation life has expired, please replace it immediately". This multi-level warning mechanism provides ample response time for operation and maintenance management, avoiding the passive situation caused by sudden alarms at the end of the life.

[0079] More preferably, the device can adaptively adjust the warning threshold setting according to the importance level of the load carried by the busbar trunking. For ordinary industrial loads, the warning threshold can be set relatively leniently (e.g., 0.8, 0.9); for primary loads or critical data center loads, the warning threshold should be set more conservatively (e.g., 0.6, 0.75) to ensure that replacement is arranged before significant degradation of insulation performance, minimizing the risk of unexpected power outages. The load importance level parameter can be set by the user during device initialization via DIP switches or configuration software.

[0080] More preferably, the determination of end-of-life depends not only on whether Dtotal reaches 1, but also on a comprehensive judgment in conjunction with other auxiliary criteria. For example, the device can monitor the operating temperature rise trend of the busbar trunking in real time. If, within a certain period of time, the temperature rise of the busbar trunking conductor shows an abnormal and continuous increase (e.g., more than 15% higher than the historical average for the same period), while the load current remains basically unchanged, this may indicate an increase in the thermal resistance of the insulation material and deterioration in heat dissipation, which is an external manifestation of severe insulation aging. In this case, even if Dtotal has not yet reached 1, the device can issue a warning of "abnormal insulation performance, inspection recommended." This auxiliary criterion, together with the Dtotal threshold criterion, forms an "AND" or "OR" logical combination, which can more comprehensively capture the precursors of insulation failure.

[0081] More preferably, when Dtotal reaches 1 and triggers a lifespan end alarm, the device does not stop the evaluation process but continues to calculate Dtotal according to the sampling period, allowing its value to exceed 1 and continue to increase. The excess can be used to quantify the risk level of "overdue service". For example, Dtotal=1.2 indicates that the insulation material has exceeded its service life by aging equivalent to 20% of its full lifespan, indicating a higher risk level. Maintenance personnel can then decide whether to immediately shut down the power and replace the insulation.

[0082] Thus, through the design of multi-level thresholds, adaptive thresholds, and auxiliary criteria in this embodiment, the determination of lifespan termination is no longer a simple binary logic, but a hierarchical, multi-source integrated decision-making process, which significantly improves the timeliness, accuracy, and practicality of early warning.

[0083] To further clarify the methods for obtaining baseline thermal life parameters, and to present several preferred schemes for flexible acquisition and dynamic updating, the parameter acquisition process is illustrated below. Figure 7 As shown.

[0084] The baseline thermal lifetime parameters—including the material activation energy Ea and the frequency factor A—are the fundamental input data for the method of this invention. In this embodiment, the parameters are obtained through methods including, but not limited to, the following two approaches, and these two approaches can be used in combination or as backups for each other.

[0085] The first approach is to retrieve the material parameters from a pre-stored material parameter database within the device, indexed by busbar type and insulation material type. The material parameter database structure, as shown in Table 1, is stored in the device's non-volatile memory (such as Flash or EEPROM).

[0086]

[0087] Table 1 Example of a material parameter database

[0088] Thus, during initial power-on initialization, the device reads its own model identification code (e.g., a device ID set via a DIP switch or embedded in the firmware), and then uses this model as an index to retrieve the corresponding insulation material type and its Ea and A values ​​from the database. If the search is successful, the device loads the parameters into its running memory for subsequent calculations. If the search fails (e.g., the model is not pre-stored in the database), the device can prompt the user to manually enter the parameters or obtain them through a second method.

[0089] The second approach involves receiving the base parameters calculated from accelerated thermal aging test data. In practical engineering, for newly developed insulation materials or specially customized busbar trunking, the accurate Ea and A values ​​may need to be determined through laboratory accelerated thermal aging tests. Test personnel age material samples at different temperature points according to IEC 60216 or GB / T 11026 standards, record the end-of-life time, and then use the Arrhenius equation lnL = Ea / (kT) + lnA to perform least-squares fitting to obtain Ea and A. Afterwards, test personnel can manually input the fitted parameter values ​​into the device and save them via the device's human-machine interface (such as buttons and a display screen) or remote configuration software. The device receives the input, stores it in non-volatile memory, and optionally updates the material parameter database to enrich its content.

[0090] More preferably, the device can support a combination of the two methods mentioned above: the device retrieves parameters from the database index as initial values ​​by default, and also provides a "parameter calibration" function, allowing maintenance personnel to fine-tune the parameters based on actual operating experience or the results of specific tests. The fine-tuned parameters override the database default values ​​and are marked with a "user calibration" tag.

[0091] More preferably, the device can also connect to a remote parameter server via a network to periodically check for parameter updates specific to this model of busbar trunking. The thermal aging parameters of the insulation material may be more accurately calibrated with improvements in manufacturing processes or the accumulation of long-term operational data. When a new version of the parameters is detected on the parameter server, the device automatically downloads and updates the local parameters, ensuring continuous optimization of the evaluation benchmark.

[0092] More preferably, for older busbar trunking where accurate Ea and A values ​​are not yet available, the device can employ a conservative parameter estimation method. For example, based on the heat resistance grade of the insulation material (e.g., Grade B, Grade F, Grade H) and empirical data, the lower limit activation energy value and a conservative frequency factor value of that grade of material are selected as temporary parameters, and the evaluation results are marked "based on conservative parameter estimation." This method allows the evaluation method of the present invention to still provide a remaining life estimation result with certain reference value even when parameters are incomplete.

[0093] Thus, through the flexible and diverse parameter acquisition methods of this embodiment, the method of the present invention can adapt to the parameter requirements of different product lines, different insulation material systems and different application scenarios of Baoshi Electric, and has good engineering adaptability and scalability.

[0094] To better reflect the accelerating effect of electrical stress on the aging process of insulating materials, an electrical aging correction factor ke is introduced in one embodiment, and its correction calculation process is illustrated as follows: Figure 8 As shown, the correction calculation process is as follows: Obtain the real-time temperature Ti, and calculate the base loss increment ΔDi_base = Δt / Li. If partial discharge monitoring is enabled, obtain the partial discharge quantity Q, and then determine the electrical aging correction coefficient ke based on the Q level. If partial discharge monitoring is not enabled, obtain the operating voltage ratio, and then determine the electrical aging correction coefficient ke based on the voltage ratio. Finally, calculate the corrected loss increment ΔDi = ke × ΔDi_base, and output ΔDi to the accumulation module.

[0095] It should be noted that in actual operation, insulated busbar trunking is subjected to both thermal and electric field stresses. Partial discharges may occur at busbar joints, near air gaps or impurities within the insulation material. The high-energy electrons and reactive chemicals generated by these partial discharges can corrode the insulation material, synergistically accelerating thermal aging. If only thermal aging is considered while ignoring electrical aging factors, the assessment results may be overly optimistic, underestimating the actual aging rate.

[0096] Therefore, in this embodiment, when calculating the incremental increase in cumulative thermal aging loss, an electrical aging correction factor ke is further multiplied, i.e.: ΔDi = ke × (Δt / Li). Here, ke is a coefficient greater than or equal to 1. When there is no significant partial discharge or electric field stress concentration in the busbar operating environment, ke is taken as 1, and the assessment degenerates into pure thermal aging. When there is an accelerated electrical aging effect, the value of ke is increased accordingly to reflect a faster rate of lifespan consumption.

[0097] The value of the electrical aging correction factor ke is determined based on the partial discharge quantity or the ratio of the operating voltage to the rated voltage. A specific method for assigning this value is as follows: Partial discharge sensors are deployed at key locations in the busbar trunking (such as near joints) to monitor the partial discharge quantity Q (in pC) in real time. The device determines ke according to a preset rule based on the magnitude of Q. For example: when Q < 50 pC, ke = 1.0; when 50 ≤ Q < 200 pC, ke = 1.5; when Q ≥ 200 pC, ke = 2.0.

[0098] If no partial discharge sensor is deployed on-site, ke can be conservatively estimated based on the ratio of operating voltage to rated voltage. When the busbar trunking operates near the rated voltage for an extended period, the electric field stress is comparable to the design value, and ke can be taken as 1.0~1.2. If the busbar trunking is under overvoltage for an extended period due to system reasons (e.g., +10% of rated voltage), the electric field stress increases, and both the probability of partial discharge initiation and the discharge intensity increase; ke can be taken as 1.3~1.8. The specific voltage-correction coefficient mapping relationship can be calibrated using accelerated electro-thermal combined aging test data and pre-set in the device.

[0099] More preferably, the device can perform statistical analysis on the partial discharge signal, calculate the average discharge amount or maximum discharge amount per unit time, and dynamically update ke based on the statistical value. For example, the device calculates the average discharge amount for that hour every hour and updates ke according to the above segmentation rules, so that the correction coefficient can be adaptively adjusted to follow changes in operating conditions.

[0100] More preferably, considering that the synergistic effect between electrical aging and thermal aging is not a simple linear multiplication, the device can also employ a more refined modified model. For example, an electro-thermal joint aging lifetime model can be used: 1 / L_total = 1 / L_thermal + 1 / L_electrical, where L_thermal is the pure thermal aging lifetime (i.e., the aforementioned Li), and L_electrical is the pure electrical aging lifetime (obtainable through the inverse power law V^nt = constant). The device calculates the combined characteristic lifetime value L_total at the current temperature Ti and voltage Vi based on this joint model, and then directly calculates the loss increment using ΔDi = Δt / L_total. This method is more rigorous in its model mechanism, but requires additional electrical aging parameters (such as the voltage tolerance index n). The device can choose this refined model when hardware resources permit to obtain higher evaluation accuracy.

[0101] Thus, by introducing an electrical aging correction factor, the shortcomings of simple thermal aging assessment are effectively compensated, enabling the remaining life assessment results to more comprehensively reflect the actual deterioration process of insulation materials under multi-stress coupling, which is especially suitable for bus trunking application scenarios with high voltage levels or prominent partial discharge risks.

[0102] To better reflect the additional effects of mechanical stress and thermal cycling on the aging of insulation materials, one approach is to introduce a load fluctuation correction factor km in the embodiments, the calculation process of which is illustrated in the diagram below. Figure 9 As shown. The specific calculation process is as follows: Obtain the real-time temperature Ti, calculate the base loss increment ΔDi_base = Δt / Li. Obtain load current monitoring data. Calculate the current change amplitude ΔI_rms and fluctuation frequency N_cross per unit time. Look up or calculate the load fluctuation correction coefficient km based on ΔI_rms and N_cross. Calculate the corrected loss increment ΔDi = km × ΔDi_base. Output ΔDi to the accumulation module.

[0103] It should be noted that in actual operation, the load current of busbar trunking is not constant but fluctuates frequently with the start-up and shutdown of electrical equipment and changes in production rhythm. Load fluctuations cause conductor temperature to fluctuate accordingly, and the insulation material undergoes repeated thermal expansion and contraction, generating alternating thermomechanical stresses within the material and at the conductor-insulator interface. This stress cycle leads to the initiation and propagation of microcracks in the insulation material, delamination, and accelerated insulation degradation. Therefore, in scenarios with severe load fluctuations, calculating thermal aging losses solely based on average temperature will underestimate the actual aging rate.

[0104] Therefore, in this embodiment, when calculating the incremental increase in cumulative thermal aging loss, a load fluctuation correction factor km is further multiplied, i.e.: ΔDi=km×(Δt / Li). Wherein, km is a factor greater than or equal to 1, and its value is determined according to the load change amplitude and frequency per unit time.

[0105] Specifically, the device monitors the load current of the busbar trunking in real time using current transformers and records the effective current value at regular intervals (e.g., every minute). The device continuously calculates load fluctuation characteristic parameters within a unit of time (e.g., 1 hour), such as: the root mean square value of the current change amplitude ΔI_rms; and the number of times the current crosses a preset change threshold N_cross (i.e., fluctuation frequency). The value of km can be calculated using a table or formula based on a combination of ΔI_rms and N_cross. A simple table lookup rule is shown in Table 2.

[0106]

[0107] Table 2 Examples of Load Fluctuation Correction Factors (km)

[0108] The device updates the km value every hour and applies the km value within that hour to the loss increment calculation for each sampling period within that hour. When the load fluctuation is gentle, km is close to 1, having little impact on the loss assessment result; when the load fluctuation is severe, km increases significantly, causing the accumulation rate of Dtotal to accelerate, and the remaining life assessment result to be shortened accordingly, which is closer to the actual aging state.

[0109] More preferably, the device can adjust the value of km according to the structural characteristics of the busbar trunking (such as whether elastic insulation material is used, conductor fixing method, etc.). For example, for Baoshi Electric's dense busbar trunking using polyester film winding + air additional insulation, the value of km can be appropriately reduced because there is a small relative sliding space between the film layers, which has a certain buffering capacity for thermomechanical stress; for cast-in-place busbar trunking, the conductor is completely wrapped by rigid resin, and the thermomechanical stress is difficult to release, so the value of km should be appropriately increased. These correction factors can be set through device configuration parameters.

[0110] More preferably, the device can also employ a more refined model based on fatigue cumulative damage theory instead of a simple multiplicative coefficient. For example, the device uses the rainflow counting method to perform cyclic counting of the load current-time history, counts the number of cycles at different amplitudes, and then calculates the damage caused by each cycle based on the fatigue curve (SN curve) of the insulation material, before adding it to the total damage. This method is theoretically more accurate, but requires more computation and is suitable for implementation on remote monitoring platforms with ample computing resources.

[0111] Thus, by introducing a load fluctuation correction coefficient, this embodiment effectively takes into account the thermo-mechanical coupling aging effect, enabling the evaluation method to adapt to industrial scenarios with frequent load changes, such as welding production lines, crane power supply, and rolling mill power supply, thereby further improving the accuracy and scenario adaptability of the evaluation results.

[0112] To further illustrate the adaptive adjustment mechanism of the sampling period, in one embodiment, as follows: Figure 10 The diagram illustrates the adaptive adjustment logic. Specifically, load current monitoring data is acquired. The load change rate index CV is calculated. The system checks if "CV ≤ CV_low?". If yes, the sampling period is extended to T_max (e.g., 60 minutes); otherwise, it checks if "CV ≥ CV_high?". If yes, the sampling period is shortened to T_min (e.g., 1 minute); otherwise, the default sampling period T_default (e.g., 10 minutes) is maintained. Then, the next round of sampling is performed according to the adjusted sampling period. Preferably, it checks if dT / dt exceeds a threshold; if so, the sampling period is shortened. Preferably, it checks if the system is battery powered; if so, the lower limit for shortening the sampling period is further restricted.

[0113] It can be understood that the sampling period Δt is a preset fixed value (such as 10 minutes). However, the operating conditions of the busway are dynamically changing: during the period of stable load, the temperature changes slowly, and a longer sampling period can meet the requirements of evaluation accuracy; during the period of drastic load fluctuations (such as equipment start-stop, production peak), the temperature changes rapidly. If a longer sampling period is still used, high-temperature spikes may be missed, resulting in a smaller calculation of the loss increment and an overly optimistic assessment of the remaining life. On the contrary, if a shorter sampling period is always used, although the evaluation accuracy can be guaranteed, it will increase the computing load and power consumption of the device, and cause unnecessary resource waste for the scenario of long-term stable operation.

[0114] For this reason, this embodiment provides a method for adaptive adjustment of the sampling period. The device monitors the change rate of the busway load in real time and dynamically adjusts the sampling period according to the change rate: when the change rate of the load exceeds the preset threshold, the sampling period is shortened; when the change rate of the load is lower than the preset threshold, the sampling period is extended.

[0115] Specifically, the device obtains the load current signal through a current transformer and calculates the load change rate index. A simple index is the ratio of the standard deviation to the average value of the current values of the nearest N sampling points (such as N = 6, corresponding to 6 10-minute sampling points in the past 1 hour), that is, the coefficient of variation CV. Set two thresholds: the stable threshold CV_low (such as 0.05) and the drastic threshold CV_high (such as 0.20). The adjustment rule of the sampling period is as follows: when CV ≤ CV_low, the load is stable, and the sampling period is extended to the preset upper limit value (such as 60 minutes); when CV_low < CV < CV_high, the load has medium fluctuations, and the sampling period remains the default value (such as 10 minutes); when CV ≥ CV_high, the load has drastic fluctuations, and the sampling period is shortened to the preset lower limit value (such as 1 minute).

[0116] To avoid the discontinuity of the evaluation results caused by the frequent switching of the sampling period, the device can set a minimum maintenance time, that is, after each adjustment, it should run at least for a certain duration (such as 30 minutes) with this sampling period before allowing another adjustment.

[0117] More preferably, the device can also consider the direct monitoring of the temperature change rate. When the sampling frequency of the temperature sensor is high enough, the device can directly calculate the slope (dT / dt) of the temperature change curve in the recent period. When dT / dt exceeds the preset threshold, even if the load change index has not been triggered, the device can actively shorten the sampling period to capture the rapid temperature rise event. This triggering mechanism based on the temperature change rate is also effective for the rapid temperature change caused by non-load factors such as ventilation failure and sudden environmental temperature change.

[0118] More preferably, the adaptive adjustment of the sampling period can be combined with the device's energy management strategy. When the device is powered by a battery or supercapacitor (e.g., in situations where monitoring needs to be maintained after a power outage), a longer sampling period can be prioritized to reduce power consumption and extend backup time; when the device is powered normally by an external power source, a shorter sampling period can be prioritized to obtain more accurate evaluation results. The power status can be detected by the device's internal power management module.

[0119] More preferably, the device can also adjust the sampling period according to the stage of the cumulative thermal aging loss value Dtotal. When Dtotal is low (e.g., below 0.5), the insulation is in the early stage of aging, and the requirement for sampling accuracy is relatively relaxed, so a longer sampling period can be used. When Dtotal is close to 1 (e.g., above 0.8), the insulation is in the late stage of aging, and even small temperature fluctuations can significantly affect the remaining life assessment. In this case, a shorter sampling period should be used to increase the monitoring density. This strategy of "the closer to the end of life, the more intensive the sampling" is consistent with engineering intuition and can effectively balance assessment accuracy and computational resource consumption.

[0120] Thus, through the adaptive sampling mechanism of this embodiment, the method of the present invention can significantly reduce the average computational load and power consumption of the device while ensuring the evaluation accuracy, thereby improving the engineering practicality and long-term operating economy of the method.

[0121] To further detail the deployment of the actuator and temperature sensor, and to provide preferred solutions for various system architectures and deployment topologies, one embodiment illustrates the system deployment as follows: Figure 11 and 12 As shown in the figure, T1, T2, and T3 represent temperature sensors, and the data acquisition unit (including the MCU) and remote monitoring platform are used to execute the evaluation method. The execution entity in this embodiment can be a data processing device deployed at the busbar trunking site or a remote monitoring platform; both methods have their applicable scenarios.

[0122] Method 1: Executed by on-site data processing device. For example... Figure 11As shown, temperature sensors are deployed at key nodes of the busbar trunking (such as the middle of each straight section, the inlet side of each plug-in box, and both sides of each connector). The temperature sensors preferably use platinum resistance thermometers (PT100) or digital temperature chips (such as DS18B20), directly attached to the surface of the copper conductor and covered with thermal grease to ensure good thermal contact. The sensor leads are led out to the data acquisition unit through high-temperature resistant insulating sleeves. The data acquisition unit includes multiple analog / digital input channels, a microcontroller (MCU), and a communication interface. The MCU executes the evaluation method described in this invention and transmits the evaluation results to a local display screen or a higher-level monitoring system via RS485 bus or wirelessly. The advantages of this method are that the evaluation does not rely on network communication, it has high real-time performance, and it is suitable for field environments with poor network conditions.

[0123] Method 2: Executed via a remote monitoring platform. For example... Figure 12 As shown, only temperature sensors and data acquisition / communication gateways are deployed at the busbar trunking site. The gateway uploads the collected temperature data to a remote monitoring platform (such as a cloud-based server or enterprise data center) via 4G / 5G, Ethernet, or Wi-Fi according to the sampling period. The remote monitoring platform runs evaluation algorithm software to centrally evaluate multiple busbar trunking loops and pushes the evaluation results to maintenance personnel via a web interface or mobile app. The advantages of this approach are convenient centralized management, long-term data storage, and advanced analysis. Obviously, this method is suitable for large industrial parks or commercial complexes with numerous busbar trunking loops.

[0124] More preferably, the deployment location and number of temperature sensors can be optimized according to the model and structural characteristics of the busbar trunking. For densely insulated busbar trunking (such as the Baoshi Electric BSM3 series), the conductor is tightly wrapped by multiple layers of polyester film, and the internal temperature gradient is small. It is sufficient to deploy one sensor in the middle of each straight segment to represent the average thermal state of the insulation of that segment. For cast-in-place busbar trunking (such as the BSM10 series), the conductor is completely embedded in epoxy resin with poor thermal conductivity, and there is a significant temperature gradient between the conductor and the outer shell. In this case, it is advisable to deploy multiple sensors at the same cross-section (such as the conductor surface, the middle of the resin layer, and the inner wall of the outer shell), and take the highest temperature value or calculate the equivalent temperature of the insulation material according to the thermal network model for calculation.

[0125] More preferably, the device or platform can also receive data from other auxiliary sensors to enhance the evaluation function. For example, an ambient temperature and humidity sensor can be installed on the busbar casing to correct for the effects of environmental factors on insulation aging; a vibration sensor can be installed to detect abnormal vibrations caused by mechanical loosening, serving as an auxiliary criterion for mechanical stress aging.

[0126] More preferably, for older busbar trunking already in operation, if it is not possible to install sensors on the conductor surface, a non-contact infrared temperature measurement method can be used. The infrared temperature probe is aligned with the conductor through an observation window or pre-drilled hole on the casing to obtain the conductor surface temperature. In this case, an emissivity correction coefficient needs to be preset in the device to compensate for errors in the infrared temperature measurement.

[0127] More preferably, the software architecture of the device or platform can be designed as a modular structure, which includes: a data acquisition module, a parameter management module, a loss calculation module, an accumulation and judgment module, and a result output module. The modules interact through standard interfaces, facilitating functional expansion and algorithm upgrades. For example, if a more accurate insulation aging model is developed later, the upgrade can be completed simply by replacing the loss calculation module, without affecting other modules.

[0128] Thus, through the detailed description of the execution subject and sensor deployment in this embodiment, the method of the present invention can flexibly adapt to various deployment requirements from single-machine local monitoring to centralized management on a cloud platform, covering both newly installed busbar trunking and old busbar trunking already in operation, and has broad engineering applicability.

[0129] 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 specification.

[0130] The embodiments described above are merely illustrative of several implementations of the present invention, and while the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the invention patent. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of the present invention, and these all fall within the protection scope of the present invention. Therefore, the protection scope of this invention patent should be determined by the appended claims.

Claims

1. A method for online assessment of the remaining service life of an insulated busbar trunking, characterized in that, Includes the following steps: Obtain the reference thermal lifetime parameters of the busbar insulation material, wherein the reference thermal lifetime parameters include the material activation energy and frequency factor; According to the preset sampling period, real-time temperature data of the location of the insulation material during the operation of the bus trunking is obtained; Based on the real-time temperature data obtained in the current sampling period, and using the reference thermal lifetime parameter, the cumulative thermal aging loss increment of the insulating material within the sampling period is calculated using the thermal aging lifetime equation. The incremental thermal aging loss is added to the historical cumulative thermal aging loss total value to obtain the updated cumulative thermal aging loss total value. Determine whether the updated total value of accumulated thermal aging loss has reached the preset end-of-life threshold. If the lifespan end threshold is not reached, the remaining lifespan assessment result is calculated and output based on the updated total accumulated thermal aging loss.

2. The method for online assessment of the remaining service life of insulated busbar trunking according to claim 1, characterized in that, The step of calculating the cumulative thermal aging loss increment of the insulating material within the sampling period using the thermal aging life equation includes: Based on the reference thermal lifetime parameters and the real-time temperature value of the current sampling period, the characteristic lifetime value Li under this temperature condition is determined using the Arrhenius equation; The ratio of the sampling period length Δt to the characteristic lifetime value Li is used as the thermal aging loss increment ΔDi of the insulating material within the sampling period.

3. The method for online assessment of the remaining service life of insulated busbar trunking according to claim 1, characterized in that, The remaining useful life assessment results are presented in at least one of the following ways: remaining useful life percentage, estimated remaining runtime, and aging rate trend.

4. The online assessment method for the remaining service life of insulated busbar trunking according to claim 3, characterized in that, The remaining lifetime percentage is Rpercent = (1 - Dtotal) × 100%, where Dtotal is the updated total cumulative thermal aging loss.

5. The method for online assessment of the remaining service life of insulated busbar trunking according to claim 1, characterized in that, The lifespan termination threshold is set to 1; when the updated total cumulative thermal aging loss reaches or exceeds 1, a replacement suggestion or warning signal is output.

6. The method for online assessment of the remaining service life of insulated busbar trunking according to claim 1, characterized in that, The baseline thermal life parameters are obtained in one of the following ways: by indexing the busbar type and insulation material type from a pre-stored material parameter database; or by receiving basic parameters calculated by fitting accelerated thermal aging test data.

7. The method for online assessment of the remaining service life of insulated busbar trunking according to claim 1, characterized in that, When calculating the cumulative thermal aging loss increment, it is further multiplied by an electrical aging correction factor, which is determined based on the partial discharge amount or the ratio of operating voltage to rated voltage.

8. The method for online assessment of the remaining service life of insulated busbar trunking according to claim 1, characterized in that, When calculating the cumulative thermal aging loss increment, it is further multiplied by a load fluctuation correction factor, which is determined based on the load change amplitude and frequency per unit time.

9. The method for online assessment of the remaining service life of insulated busbar trunking according to claim 1, characterized in that, The sampling period is adaptively adjusted according to the busbar load change rate: when the load change rate exceeds a preset threshold, the sampling period is shortened; when the load change rate is below the preset threshold, the sampling period is extended.

10. The method for online assessment of the remaining service life of insulated busbar trunking according to claim 1, characterized in that, The real-time temperature data is collected by temperature sensors deployed on the surface of the busbar conductor or inside the insulation layer; the method is executed by a data processing device deployed at the busbar site, or by a remote monitoring platform that is connected to the field acquisition equipment.

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