System and method for predicting residual service life of power supply lightning protection device

Abstract

The invention discloses a system and a method for predicting the residual service life of a power supply lightning protection device. The method comprises the following steps: monitoring a plurality of working state parameters of the power supply lightning protection device in real time through a sensor; based on the working state parameters, predicting the current remaining service life of the power supply lightning protection device by using a preset failure mechanism function for describing the degradation behavior of the power supply lightning protection device; calculating a comprehensive health assessment index of the power supply lightning protection device according to the historical data of the working state parameters; and adjusting the current remaining service life by using the comprehensive health assessment index to obtain the adjusted remaining service life. According to the embodiment of the invention, real-time monitoring and intelligent data analysis can be integrated to predict the residual service life of the power supply lightning protection device, so that the safety of a power system is effectively enhanced, and economic loss caused by faults of the lightning protection device is reduced at the same time.

Description

Technical Field

[0001] The invention belongs to the technical field of life prediction, and in particular to a system and method for predicting the remaining service life of a power supply lightning protection device. Background Art

[0002] With the continuous development and modernization of power systems, the safety and reliability of power supply lightning protection devices, as important components for protecting power equipment and systems from lightning strikes, are increasingly valued. Lightning strikes can cause serious damage to power equipment, leading to equipment failures and even power system collapse. Therefore, it is crucial to ensure that power supply lightning protection devices remain in good working condition throughout their life cycle.

[0003] Existing power supply lightning protection devices mostly rely on fixed working life assessment standards, which are usually based on product design specifications and material physical properties. However, due to uncertainties such as environmental factors, power load changes, and equipment aging, the performance and life of lightning protection devices during actual use may deviate significantly from the preset standards. In addition, traditional life prediction methods often use empirical analysis, lack dynamic response to real-time monitoring data, and cannot timely reflect the health status and aging degree of lightning protection devices. Summary of the invention

[0004] The purpose of the present invention is to provide a system and method for predicting the remaining service life of a power supply lightning protection device, so as to address the deficiencies in the prior art. The system and method can integrate real-time monitoring and intelligent data analysis to predict the remaining service life of a power supply lightning protection device, effectively enhance the safety of the power system, and reduce the economic losses caused by failure of the lightning protection device.

[0005] An embodiment of the present application provides a method for predicting the remaining service life of a power supply lightning protection device, the method comprising: Monitor multiple working status parameters of the power supply lightning protection device in real time through sensors; Based on the working state parameters, using a preset failure mechanism function describing the degradation behavior of the power supply lightning protection device, predicting the current remaining service life of the power supply lightning protection device; Calculating a comprehensive health assessment index of the power supply lightning protection device according to the historical data of the working status parameters; The current remaining useful life is adjusted using the comprehensive health assessment index to obtain an adjusted remaining useful life.

[0006] Optionally, the current remaining useful life is predicted in the following manner: , wherein the is the current remaining useful life, is the initial estimated lifespan, is the failure mechanism function related to the j-th working state parameter F_j, is the corresponding weight coefficient, and m is the number of working state parameters.

[0007] Optionally, the failure mechanism function is: , wherein the is the corresponding sensitivity coefficient reflecting the impact of device degradation rate.

[0008] Optionally, calculating the comprehensive health assessment index of the power supply lightning protection device according to the historical data of the working status parameter includes: Calculating the historical failure rate of each working state parameter according to the historical data of the working state parameter; According to the historical failure rate, a comprehensive health assessment index of the power supply lightning protection device is calculated.

[0009] Optionally, the calculation formula of the comprehensive health assessment index is: , wherein the is a comprehensive health assessment index. is the historical failure rate of the jth working state parameter, is the corresponding fault impact coefficient.

[0010] Optionally, using the comprehensive health assessment index to adjust the current remaining service life to obtain an adjusted remaining service life includes: The product of the current remaining useful life and the comprehensive health assessment index is calculated as the adjusted remaining useful life.

[0011] Another embodiment of the present application provides a system for predicting the remaining service life of a power supply lightning protection device, the system comprising: A monitoring module, used to monitor multiple working state parameters of the power supply lightning protection device in real time through sensors; a prediction module, used to predict the current remaining service life of the power supply lightning protection device based on the working state parameters and using a preset failure mechanism function that describes the degradation behavior of the power supply lightning protection device; A calculation module, used for calculating the comprehensive health assessment index of the power supply lightning protection device according to the historical data of the working status parameters; The adjustment module is used to adjust the current remaining service life by using the comprehensive health assessment index to obtain the adjusted remaining service life.

[0012] Yet another embodiment of the present application provides a storage medium, wherein the storage medium stores a computer program, wherein the computer program is configured to execute any of the above methods when running.

[0013] Yet another embodiment of the present application provides an electronic device, including a memory and a processor, wherein the memory stores a computer program, and the processor is configured to run the computer program to execute any of the methods described above.

[0014] Compared with the prior art, the present invention provides a method for predicting the remaining service life of a power supply lightning protection device, which monitors multiple working state parameters of the power supply lightning protection device in real time through sensors; based on the working state parameters, a preset failure mechanism function describing the degradation behavior of the power supply lightning protection device is used to predict the current remaining service life of the power supply lightning protection device; according to the historical data of the working state parameters, a comprehensive health assessment index of the power supply lightning protection device is calculated; using the comprehensive health assessment index, the current remaining service life is adjusted to obtain the adjusted remaining service life, thereby integrating real-time monitoring and intelligent data analysis to predict the remaining service life of the power supply lightning protection device, effectively enhancing the safety of the power system, while reducing the economic losses caused by lightning protection device failures. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 A hardware structure block diagram of a computer terminal for a method for predicting the remaining service life of a power supply lightning protection device provided by an embodiment of the present invention; Figure 2 A flow chart of a method for predicting the remaining service life of a power supply lightning protection device provided by an embodiment of the present invention; Figure 3 A schematic structural diagram of a system for predicting the remaining service life of a power supply lightning protection device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0016] The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present invention, but should not be construed as limiting the present invention.

[0017] The embodiment of the present invention first provides a method for predicting the remaining service life of a power supply lightning protection device. The method can be applied to electronic equipment, such as a computer terminal, specifically a common computer, etc.

[0018] The following describes it in detail by taking running on a computer terminal as an example. Figure 1 The hardware structure block diagram of a computer terminal for predicting the remaining service life of a power supply lightning protection device provided by an embodiment of the present invention. Figure 1 As shown, the computer terminal may include one or more ( Figure 1Only one is shown in the figure) a processor 102 (the processor 102 may include but is not limited to a processing device such as a microprocessor MCU or a programmable logic device FPGA) and a memory 104 for storing data. Optionally, the computer terminal may also include a transmission device 106 for communication functions and an input and output device 108. It can be understood by those skilled in the art that Figure 1 The structure shown is only for illustration and does not limit the structure of the above-mentioned computer terminal. Figure 1 More or fewer components as shown, or with Figure 1 Different configurations are shown.

[0019] The memory 104 can be used to store software programs and modules of application software, such as the program instructions / modules corresponding to the method for predicting the remaining service life of the power supply lightning protection device in the embodiment of the present application. The processor 102 executes various functional applications and data processing by running the software programs and modules stored in the memory 104, that is, implementing the above method. The memory 104 may include a high-speed random access memory, and may also include a non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some examples, the memory 104 may further include a memory remotely arranged relative to the processor 102, and these remote memories may be connected to the computer terminal via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0020] The transmission device 106 is used to receive or send data via a network. The specific example of the above network may include a wireless network provided by a communication provider of a computer terminal. In one example, the transmission device 106 includes a network adapter (Network Interface Controller, NIC), which can be connected to other network devices through a base station so as to communicate with the Internet. In one example, the transmission device 106 can be a radio frequency (RF) module, which is used to communicate with the Internet wirelessly.

[0021] See also Figure 2 , an embodiment of the present invention provides a method for predicting the remaining service life of a power supply lightning protection device, which may include the following steps: S201, monitoring multiple working status parameters of the power supply lightning protection device in real time through sensors; Real-time monitoring of multiple working state parameters of the power supply lightning protection device through sensors means continuously collecting key data related to its performance, status and environmental conditions in the working environment of the power supply lightning protection device. These working state parameters usually include current (I), voltage (U), temperature (T), frequency (f), humidity (H), and possible overload conditions. These working state parameters can not only reflect the current operating status of the power supply lightning protection device, but also provide important basis for judging whether it has potential faults or is about to fail. Through real-time monitoring, abnormal situations can be discovered in time, so that corresponding measures can be taken to ensure the safety and effectiveness of the lightning protection device.

[0022] Specifically, appropriate sensors can be installed at key parts of the power lightning protection device (such as input, output, and internal circuits). Sensor types include current sensors, voltage sensors, temperature sensors, and humidity sensors. Appropriate sensor models and specifications are selected according to actual needs. A distributed sensor network can be used to connect these sensors to the central monitoring system in an appropriate manner to ensure that the working state parameters of each point can be collected synchronously. Use data acquisition equipment (such as data recorders or embedded systems) to collect sensor signals in real time. Set the frequency of data acquisition (such as once per second) to achieve sufficient time resolution. Filter out noise and interference through signal processing algorithms to ensure the accuracy and effectiveness of data. For example, use moving average method or low-pass filter for data smoothing. Use wireless communication technology (such as Wi-Fi, Zigbee, etc.) or wired network (such as Ethernet, RS-485) to transmit the collected data in real time to the central control system or cloud server for centralized storage and management. The stored data can include timestamps, actual values ​​of various working state parameters, and regular backup to prevent data loss.

[0023] S202, based on the working state parameters, using a preset failure mechanism function describing the degradation behavior of the power supply lightning protection device, predicting the current remaining service life of the power supply lightning protection device; The core of this step is to use the collected working state parameters and the preset failure mechanism function to predict the remaining useful life (RUL) of the power supply lightning protection device. The failure mechanism function is a mathematical model based on the physical and chemical principles of the equipment, engineering experience or historical data, which aims to quantify the relationship between various factors affecting the performance of the lightning protection device and the equipment degradation. By substituting the working state parameters monitored in real time into the failure mechanism function, the current degradation state can be obtained, and the current remaining service life can be calculated. This process usually involves modeling the relationship between the working state parameters and the aging rate of the equipment in order to reveal the true health status of the equipment under specific environmental and operating conditions.

[0024] Specifically, a method for predicting the current remaining useful life may be: This formula is used to calculate the current remaining useful life (RUL) of the power supply lightning protection device by subtracting the cumulative value of the degradation effect caused by each operating state parameter. The structure of the formula reflects the dynamic relationship between the initial estimated life (RUL_0) and the actual degradation.

[0025] Among them, the The remaining service life is the time that the power supply lightning protection device can still operate safely and effectively under the current working conditions. It is the initial estimated life span, which is usually based on theoretical knowledge or historical data and is the expected service time of the power supply lightning protection device under fault-free conditions. is the failure mechanism function related to the jth working state parameter F_j, which is used to quantify the impact of this parameter on equipment degradation. This function reflects the performance loss under different working states. is the corresponding weight coefficient, reflecting the relative importance of the jth working state parameter to the overall degradation, and is used to adjust the influence of the parameter in the failure mechanism. The m is the number of working state parameters.

[0026] Specifically, a failure mechanism function can be: : This formula defines the specific form of the failure mechanism function, and uses an exponential model to describe the relationship between the working state parameters and the degradation rate. Exponential functions are often used to describe the rapid changes in the system over time or conditions, and are very suitable for modeling the degradation of power supply lightning protection devices. is the corresponding sensitivity coefficient reflecting the impact of the device degradation rate, which is used to describe the degree of change in the degradation rate caused by the change of the working state parameter F_j, and characterizes the sensitivity of the impact of this parameter on the device degradation.

[0027] S203, calculating a comprehensive health assessment index of the power supply lightning protection device according to the historical data of the working status parameters; This step aims to calculate a comprehensive health assessment index (HI) by analyzing the historical data of the working status parameters of the power supply lightning protection device. The index is used to quantify the current health status of the device. Through historical data, especially the trend of changes in working status parameters and the occurrence rate of failures, the performance of the device in the past operation cycle or potential risks is evaluated. This process usually includes collecting and analyzing the historical records of each working status parameter, evaluating its impact on the overall health of the equipment, and establishing a comprehensive indicator to provide an intuitive overview of the health status.

[0028] Specifically, the historical failure rate of each working state parameter may be calculated according to the historical data of the working state parameter; The purpose of this step is to analyze the working state parameters of the power supply lightning protection device, record and calculate the historical failure rate of each state parameter within a certain time range. The failure rate refers to the probability of failure under a certain working state parameter within a specific time period. This calculation can be based on the frequency of historical failure events. This calculation involves classifying and counting the data of all collected working state parameters to identify which parameters have the greatest impact on the health of the device.

[0029] According to the historical failure rate, a comprehensive health assessment index of the power supply lightning protection device is calculated.

[0030] After calculating the historical failure rate of each working state parameter, this step will comprehensively derive the comprehensive health assessment index (HI) of the power supply lightning protection device based on these failure rates. This calculation process will take into account the failure rate of each working state parameter and its impact on the overall performance of the equipment.

[0031] Specifically, a calculation formula for a comprehensive health assessment index can be: : This formula uses an exponential decay model to calculate the comprehensive health assessment index (HI), which reflects the negative impact of the failure rate on the health status of the equipment. By combining the failure rate (G_j) of each working state parameter and its corresponding failure impact coefficient (p_j), the contribution of these parameters to the overall health assessment is quantified. Here, the form of the exponential function makes the increase in the failure rate exponentially reduce the comprehensive health assessment index, reflecting the actual situation that the health status of the equipment deteriorates rapidly when the failure rate is high.

[0032] Among them, the It is a comprehensive health assessment index, which indicates the overall health status of the power supply lightning protection device. The higher the HI value, the better the equipment status and the lower the risk of failure; conversely, it means that the equipment status is poor and the risk of failure is high. is the historical failure rate of the jth working state parameter, is the corresponding fault impact coefficient, which reflects the impact of the fault of the jth working state parameter on the overall health of the equipment. By setting a suitable coefficient, the fault type with significant impact can be highlighted, making the calculation result of HI more representative.

[0033] S204: Using the comprehensive health assessment index, adjust the current remaining useful life to obtain an adjusted remaining useful life.

[0034] The core of this step is to combine the calculated comprehensive health assessment index (HI) with the currently predicted remaining useful life (RUL) to obtain a more accurate and realistic adjusted remaining useful life. Specifically, the comprehensive health assessment index reflects the current health status of the power supply lightning protection device, which directly affects the service life of the equipment. By multiplying the HI with the current RUL or adjusting it in a certain proportion, the impact of the equipment health status on the remaining useful life can be effectively considered, so that the calculated adjusted remaining useful life is more in line with the actual situation. This process is usually intended to correct the underestimation or overestimation of the RUL due to equipment aging or degradation.

[0035] Specifically, the product of the current remaining useful life and the comprehensive health assessment index may be calculated as the adjusted remaining useful life.

[0036] In this step, the adjusted remaining useful life is obtained by multiplying the current remaining useful life (RUL) with the comprehensive health assessment index (HI). This product reflects the actual useful life considering the health status of the equipment, providing managers with a more accurate reference. In the calculation process, HI is used as a weighting factor to correct the RUL, so that the adjusted remaining useful life obtained is more practical. This step emphasizes the actual impact of the current equipment operating status, ensuring that when formulating maintenance and usage plans, corresponding management can be carried out based on a more realistic equipment status.

[0037] It can be seen that multiple working state parameters of the power supply lightning protection device are monitored in real time by sensors; based on the working state parameters, the current remaining service life of the power supply lightning protection device is predicted using a preset failure mechanism function that describes the degradation behavior of the power supply lightning protection device; according to the historical data of the working state parameters, the comprehensive health assessment index of the power supply lightning protection device is calculated; using the comprehensive health assessment index, the current remaining service life is adjusted to obtain the adjusted remaining service life, so that real-time monitoring and intelligent data analysis can be integrated to predict the remaining service life of the power supply lightning protection device, effectively enhancing the safety of the power system while reducing the economic losses caused by failures of the lightning protection device.

[0038] Another embodiment of the present invention provides a system for predicting the remaining service life of a power supply lightning protection device. Figure 3 , the system may include: Monitoring module 301, used to monitor multiple working state parameters of the power supply lightning protection device in real time through sensors; A prediction module 302 is used to predict the current remaining service life of the power supply lightning protection device based on the working state parameters and using a preset failure mechanism function that describes the degradation behavior of the power supply lightning protection device; A calculation module 303, used to calculate the comprehensive health assessment index of the power supply lightning protection device according to the historical data of the working status parameters; The adjustment module 304 is used to adjust the current remaining useful life by using the comprehensive health assessment index to obtain an adjusted remaining useful life.

[0039] It can be seen that multiple working state parameters of the power supply lightning protection device are monitored in real time by sensors; based on the working state parameters, the current remaining service life of the power supply lightning protection device is predicted using a preset failure mechanism function that describes the degradation behavior of the power supply lightning protection device; according to the historical data of the working state parameters, the comprehensive health assessment index of the power supply lightning protection device is calculated; using the comprehensive health assessment index, the current remaining service life is adjusted to obtain the adjusted remaining service life, so that real-time monitoring and intelligent data analysis can be integrated to predict the remaining service life of the power supply lightning protection device, effectively enhancing the safety of the power system while reducing the economic losses caused by failures of the lightning protection device.

[0040] An embodiment of the present invention further provides a storage medium, in which a computer program is stored, wherein the computer program is configured to execute the steps of any of the above method embodiments when running.

[0041] Specifically, in this embodiment, the above storage medium may be configured to store a computer program for performing the following steps: S201, monitoring multiple working status parameters of the power supply lightning protection device in real time through sensors; S202, based on the working state parameters, using a preset failure mechanism function describing the degradation behavior of the power supply lightning protection device, predicting the current remaining service life of the power supply lightning protection device; S203, calculating a comprehensive health assessment index of the power supply lightning protection device according to the historical data of the working status parameters; S204: Using the comprehensive health assessment index, adjust the current remaining useful life to obtain an adjusted remaining useful life.

[0042] It can be seen that multiple working state parameters of the power supply lightning protection device are monitored in real time by sensors; based on the working state parameters, the current remaining service life of the power supply lightning protection device is predicted using a preset failure mechanism function that describes the degradation behavior of the power supply lightning protection device; according to the historical data of the working state parameters, the comprehensive health assessment index of the power supply lightning protection device is calculated; using the comprehensive health assessment index, the current remaining service life is adjusted to obtain the adjusted remaining service life, so that real-time monitoring and intelligent data analysis can be integrated to predict the remaining service life of the power supply lightning protection device, effectively enhancing the safety of the power system while reducing the economic losses caused by failures of the lightning protection device.

[0043] An embodiment of the present invention further provides an electronic device, including a memory and a processor, wherein the memory stores a computer program, and the processor is configured to run the computer program to execute the steps in any one of the above method embodiments.

[0044] Specifically, the electronic device may further include a transmission device and an input / output device, wherein the transmission device is connected to the processor, and the input / output device is connected to the processor.

[0045] Specifically, in this embodiment, the processor may be configured to perform the following steps through a computer program: S201, monitoring multiple working status parameters of the power supply lightning protection device in real time through sensors; S202, based on the working state parameters, using a preset failure mechanism function describing the degradation behavior of the power supply lightning protection device, predicting the current remaining service life of the power supply lightning protection device; S203, calculating a comprehensive health assessment index of the power supply lightning protection device according to the historical data of the working status parameters; S204: Using the comprehensive health assessment index, adjust the current remaining useful life to obtain an adjusted remaining useful life.

[0046] It can be seen that multiple working state parameters of the power supply lightning protection device are monitored in real time by sensors; based on the working state parameters, the current remaining service life of the power supply lightning protection device is predicted using a preset failure mechanism function that describes the degradation behavior of the power supply lightning protection device; according to the historical data of the working state parameters, the comprehensive health assessment index of the power supply lightning protection device is calculated; using the comprehensive health assessment index, the current remaining service life is adjusted to obtain the adjusted remaining service life, so that real-time monitoring and intelligent data analysis can be integrated to predict the remaining service life of the power supply lightning protection device, effectively enhancing the safety of the power system while reducing the economic losses caused by failures of the lightning protection device.

[0047] The above describes in detail the structure, features and effects of the present invention based on the embodiments shown in the drawings. The above is only a preferred embodiment of the present invention, but the present invention is not limited to the scope of implementation shown in the drawings. Any changes made according to the concept of the present invention, or modifications to equivalent embodiments with equivalent changes, which still do not exceed the spirit covered by the description and drawings, should be within the protection scope of the present invention.

Claims

1. A method for predicting the remaining service life of a power supply lightning protection device, characterized in that: The method comprises: Monitor multiple working status parameters of the power supply lightning protection device in real time through sensors; Based on the working state parameters, using a preset failure mechanism function describing the degradation behavior of the power supply lightning protection device, predicting the current remaining service life of the power supply lightning protection device; Calculating a comprehensive health assessment index of the power supply lightning protection device according to the historical data of the working status parameters; The current remaining useful life is adjusted using the comprehensive health assessment index to obtain an adjusted remaining useful life.

2. The method according to claim 1, characterized in that The current remaining useful life is predicted as follows: , wherein the is the current remaining useful life, is the initial estimated lifespan, is the failure mechanism function related to the j-th working state parameter F_j, is the corresponding weight coefficient, and m is the number of working state parameters.

3. The method according to claim 2, characterized in that The failure mechanism function is: , wherein the is the corresponding sensitivity coefficient reflecting the impact of device degradation rate.

4. The method according to claim 3, characterized in that The calculating the comprehensive health assessment index of the power supply lightning protection device according to the historical data of the working status parameters includes: Calculating the historical failure rate of each working state parameter according to the historical data of the working state parameter; According to the historical failure rate, a comprehensive health assessment index of the power supply lightning protection device is calculated.

5. The method according to claim 4, characterized in that The calculation formula of the comprehensive health assessment index is: , wherein the is a comprehensive health assessment index. is the historical failure rate of the jth working state parameter, is the corresponding fault impact coefficient.

6. The method according to claim 5, characterized in that The using of the comprehensive health assessment index to adjust the current remaining service life to obtain the adjusted remaining service life includes: The product of the current remaining useful life and the comprehensive health assessment index is calculated as the adjusted remaining useful life.

7. A system for predicting the remaining service life of a power supply lightning protection device, characterized in that: The system comprises: A monitoring module, used to monitor multiple working status parameters of the power supply lightning protection device in real time through sensors; A prediction module, configured to predict the current remaining service life of the power supply lightning protection device based on the working state parameters and using a preset failure mechanism function describing the degradation behavior of the power supply lightning protection device; A calculation module, used for calculating the comprehensive health assessment index of the power supply lightning protection device according to the historical data of the working status parameters; The adjustment module is used to adjust the current remaining service life by using the comprehensive health assessment index to obtain the adjusted remaining service life.

8. The system according to claim 7, characterized in that The current remaining useful life is predicted as follows: , wherein the is the current remaining useful life, is the initial estimated lifespan, is the failure mechanism function related to the j-th working state parameter F_j, is the corresponding weight coefficient, and m is the number of working state parameters.

9. A storage medium, characterized in that: The storage medium stores a computer program, wherein the computer program is configured to execute the method according to any one of claims 1 to 6 when executed.

10. An electronic device comprising a memory and a processor, characterized in that: A computer program is stored in the memory, and the processor is configured to run the computer program to perform the method according to any one of claims 1 to 6.

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