Reliability evaluation method and device of intelligent excitation system and computer equipment

Abstract

The invention relates to a reliability evaluation method and device of an intelligent excitation system and computer equipment. The method comprises the following steps: carrying out reliability analysis on a control module of the excitation system to obtain a reliability analysis result of the control module; adjusting the system redundancy configuration of the excitation system to obtain the system redundancy target configuration of the excitation system; performing failure rate analysis on the components and the whole of the excitation system to obtain a component failure rate distribution curve and a whole failure rate distribution curve of the excitation system; according to the reliability analysis result, the system redundancy target configuration, the component failure rate distribution curve and the overall failure rate distribution curve, constructing a reliability evaluation model of the excitation system; and the reliability evaluation model is used for carrying out reliability evaluation on the excitation system. By adopting the method, the technical problem of high reliability evaluation hysteresis of the excitation system can be solved.

Description

Technical Field

[0001] This application relates to the technical field of equipment monitoring and reliability assessment, and particularly to a reliability assessment method, device, computer equipment, computer-readable storage medium, and computer program product for an intelligent excitation system. Background Art

[0002] The excitation system is an important part of a generator, mainly responsible for controlling the rotor magnetic field of the generator by adjusting the excitation current, thereby stabilizing the output voltage and power of the generator. The reliability of the excitation system is directly related to the safety and stability of the generator and the entire power system. With the increase in the complexity of modern power systems and load fluctuations, the excitation system needs to operate stably for a long time under high loads and complex environments. Therefore, accurate assessment of its reliability is particularly important.

[0003] Traditional reliability assessment methods for excitation systems can usually only conduct reliability assessment after the excitation system is actually put into use. This makes it impossible to correctly evaluate the reliability of the excitation system in advance during the design and planning stages, resulting in frequent problems where the performance indicators of the excitation system cannot meet expectations during actual operation. At this time, rectifying and optimizing the excitation system requires additional time and human resources. Therefore, traditional reliability assessment methods for excitation systems have a strong problem of lag in reliability assessment. Summary of the Invention

[0004] Based on this, it is necessary to provide a reliability assessment method, device, computer equipment, computer-readable storage medium, and computer program product for an intelligent excitation system that can solve the technical problem of strong lag in the reliability assessment of the excitation system.

[0005] In a first aspect, this application provides a reliability assessment method for an intelligent excitation system, including:

[0006] Conduct reliability analysis on the control module of the excitation system to obtain the reliability analysis result of the control module;

[0007] Adjust the system redundancy configuration of the excitation system to obtain the system redundancy target configuration of the excitation system;

[0008] Conduct failure rate analysis on the components and the whole of the excitation system to obtain the component failure rate distribution curve and the overall failure rate distribution curve of the excitation system;

[0009] Construct a reliability assessment model for the excitation system according to the reliability analysis result, the system redundancy target configuration, the component failure rate distribution curve, and the overall failure rate distribution curve; the reliability assessment model is used to conduct reliability assessment on the excitation system.

[0010] In one embodiment, after constructing the reliability evaluation model of the excitation system, the method further includes:

[0011] Performing thermal stress analysis, vibration stress analysis, and life analysis on the whole of the excitation system to obtain the analysis results of the excitation system;

[0012] Inputting the analysis results into the reliability evaluation model to obtain the reliability evaluation results of the excitation system.

[0013] In one embodiment, performing reliability analysis on the control module of the excitation system to obtain the reliability analysis results of the control module includes:

[0014] Performing failure mechanism analysis on the devices on any circuit board in the control module of the excitation system, and analyzing the physical characteristics of the any circuit board to obtain the mechanism analysis results of the any circuit board;

[0015] Obtaining the failure mechanism analysis results of the control module according to the mechanism analysis results and corresponding weights of each circuit board in the control module;

[0016] Performing failure mode analysis on the devices on any circuit board in the control module, and analyzing the physical characteristics of the any circuit board to obtain the mode analysis results of the any circuit board;

[0017] Obtaining the failure mode analysis results of the control module according to the mode analysis results and corresponding weights of each circuit board in the control module;

[0018] Obtaining the reliability analysis results of the control module according to the failure mode analysis results and the failure mechanism analysis results.

[0019] In one embodiment, obtaining the reliability analysis results of the control module according to the failure mode analysis results and the failure mechanism analysis results includes:

[0020] Constructing a failure prediction model of the control module according to the failure mode analysis results and the failure mechanism analysis results; the failure prediction model is used to simulate the operation of the control module and output the probability information of various failures occurring in the control module;

[0021] Inputting the stress analysis results of the control module into the failure prediction model to obtain the failure prediction information for the control module;

[0022] Obtaining the reliability analysis results of the control module according to the failure prediction information.

[0023] In one embodiment, inputting the stress analysis result of the control module into the fault prediction model to obtain the fault prediction information for the control module includes:

[0024] Inputting the thermal stress analysis result and the vibration stress analysis result of the control module into the fault prediction model to obtain the fault prediction information for the control module; the fault prediction information includes the failure time of potential fault components in the control module under continuous action of thermal stress, and the failure time of potential fault components in the control module under continuous action of vibration stress;

[0025] Wherein, the thermal stress analysis result includes the analysis result obtained by performing thermal stress analysis on the control module, and the vibration stress analysis result includes the analysis result obtained by performing vibration stress analysis on the control module.

[0026] In one embodiment, adjusting the system redundancy configuration of the excitation system to obtain the system redundancy target configuration of the excitation system includes:

[0027] Taking the independent operation of the power cabinets in the excitation system as the operation target, and adjusting the data synchronization configuration, fault self-diagnosis configuration, and redundancy switching mechanism configuration of the excitation system during operation based on the operation target to obtain the system redundancy target configuration of the excitation system.

[0028] In a second aspect, the present application also provides a reliability evaluation device for an intelligent excitation system, including:

[0029] An evaluation module, configured to perform reliability analysis on the control module of the excitation system to obtain the reliability analysis result of the control module;

[0030] An adjustment module, configured to adjust the system redundancy configuration of the excitation system to obtain the system redundancy target configuration of the excitation system;

[0031] An analysis module, configured to perform failure rate analysis on the components and the whole of the excitation system to obtain the component failure rate distribution curve and the overall failure rate distribution curve of the excitation system;

[0032] A construction module, configured to construct a reliability evaluation model for the excitation system according to the reliability analysis result, the system redundancy target configuration, the component failure rate distribution curve, and the overall failure rate distribution curve; the reliability evaluation model is used to perform reliability evaluation on the excitation system.

[0033] In a third aspect, the present application further provides a computer device, including a memory and a processor. The memory stores a computer program, and when the processor executes the computer program, the steps of the above method are implemented.

[0034] In a fourth aspect, the present application further provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the steps of the above method are implemented.

[0035] In a fifth aspect, the present application further provides a computer program product, including a computer program. When the computer program is executed by a processor, the steps of the above method are implemented.

[0036] The above reliability evaluation method, device, computer device, computer-readable storage medium and computer program product of the intelligent excitation system perform reliability analysis on the control module of the excitation system to obtain the reliability analysis result of the control module; adjust the system redundancy configuration of the excitation system to obtain the system redundancy target configuration of the excitation system; perform failure rate analysis on the components and the whole of the excitation system to obtain the component failure rate distribution curve and the overall failure rate distribution curve of the excitation system; construct a reliability evaluation model of the excitation system according to the reliability analysis result, the system redundancy target configuration, the component failure rate distribution curve and the overall failure rate distribution curve. The reliability evaluation model is used to evaluate the reliability of the excitation system. It can construct an accurate reliability evaluation model through the reliability analysis of the control module, the adjustment of the system redundancy configuration, and the comprehensive modeling of the component and overall failure rates in the design and planning stage of the excitation system, so as to realize the early prediction and optimization of potential problems of the excitation system; and overcome the lag problem of traditional reliability evaluation methods, enabling a comprehensive reliability evaluation of the excitation system before actual use, significantly reducing the probability of failures during operation, improving the overall safety and stability of the system, and at the same time reducing the additional time and human resource consumption caused by fault rectification in the later operation stage, providing a strong guarantee for the long-term stable operation of complex power systems. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] In order to more clearly illustrate the technical solutions in the embodiments of the present application or related technologies, the following will briefly introduce the drawings required for use in the description of the embodiments of the present application or related technologies. Obviously, the drawings in the following description are only some embodiments of the present application. For those of ordinary skill in the art, other related drawings can be obtained based on these drawings without creative efforts.

[0038] Figure 1 It is an application environment diagram of a reliability evaluation method of an intelligent excitation system in an embodiment;

[0039] Figure 2 Schematic flowchart of a reliability evaluation method for an intelligent excitation system in an embodiment;

[0040] Figure 3 Schematic flowchart of a reliability evaluation method for an intelligent excitation system in another embodiment;

[0041] Figure 4 Block diagram of the structure of a reliability evaluation device for an intelligent excitation system in an embodiment;

[0042] Figure 5 Internal structure diagram of a computer device in an embodiment. Detailed implementation manners

[0043] In order to make the objectives, technical solutions and advantages of the present application clearer and more understandable, the present application will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0044] The reliability evaluation method for the intelligent excitation system provided by the embodiments of the present application can be applied to the application environment as Figure 1 shown. Among them, the terminal 102 can be connected to the excitation system 100, and the terminal 102 can obtain the overall operation data of the excitation system 100 and the operation data of each component. The terminal 102 also communicates with the server 104 through the network. The data storage system can store the data that the server 104 needs to process. The data storage system can be integrated on the server 104, or can be placed in the cloud or other network servers. The terminal 102 performs a reliability analysis on the control module of the excitation system to obtain the reliability analysis result of the control module; the terminal 102 adjusts the system redundancy configuration of the excitation system to obtain the system redundancy target configuration of the excitation system; the terminal 102 performs a failure rate analysis on the components and the whole of the excitation system to obtain the component failure rate distribution curve and the overall failure rate distribution curve of the excitation system; the terminal 102 constructs a reliability evaluation model of the excitation system according to the reliability analysis result, the system redundancy target configuration, the component failure rate distribution curve and the overall failure rate distribution curve; the reliability evaluation model is used to perform a reliability evaluation on the excitation system. Among them, the terminal 102 can be but is not limited to various personal computers, laptop computers, smart phones, tablet computers, and Internet of Things devices. The server 104 can be an independent physical server, or a server cluster or distributed system composed of multiple physical servers, or a cloud server providing cloud computing services. Optionally, the terminal 102 can upload the constructed reliability evaluation model of the excitation system to the server 104, or store it locally.

[0045] In an exemplary embodiment, as Figure 2As shown, a reliability evaluation method for an intelligent excitation system is provided. Taking the application of this method to Figure 1 the terminal 102 in

[0046] Step S202: Conduct a reliability analysis on the control module of the excitation system to obtain the reliability analysis result of the control module.

[0047] It should be noted that the excitation system can be an intelligent excitation system. An intelligent excitation system is a system that precisely controls the excitation current of a generator through digital, automated, and intelligent technologies. It can monitor the operating status of the generator in real time and adaptively adjust the excitation current according to the demands of the power system and changes in operating conditions to maintain the stability of the generator output voltage and power, while improving the safety, stability, and reliability of the system.

[0048] In specific implementation, conducting a reliability analysis on the control module of the excitation system can be to conduct a reliability analysis on the circuit board of the excitation system and the components (such as chips, capacitors, resistors, etc.) on the circuit board, identify potential failure modes, failure causes, and their occurrence probabilities, and obtain the reliability analysis result of the control module. Optionally, the reliability analysis result may include but is not limited to the mean time to failure, failure rate, etc.

[0049] As an example, conducting a reliability analysis on the control module of the excitation system can be based on the failure data of the control module (such as component life distribution, operating environmental conditions), conduct multiple random samplings, and through a large number of simulated operations, calculate the failure probability of the control module under different working conditions to obtain the reliability analysis result of the control module.

[0050] As another example, conducting a reliability analysis on the control module of the excitation system can be to conduct an accelerated life test on the control module. By applying extreme conditions such as high temperature, high humidity, or overload, accelerate the aging process of the components, and based on the acceleration factor and actual operating conditions, estimate the life of the control module under normal operating conditions to obtain the reliability analysis result of the control module.

[0051] As another example, conducting a reliability analysis on the control module of the excitation system can be to establish a fault tree model of the control module, decompose the faults of the module into multiple basic events (such as solder joint cracking, chip overheating, capacitor failure); by analyzing the combination relationship of the underlying events, calculate the total failure rate of the control module to obtain the reliability analysis result of the control module.

[0052] As another example, conducting a reliability analysis on the control module of the excitation system can be to conduct a temperature cycle test, simulate the thermal expansion and contraction process of the control module in an alternating high and low temperature environment, analyze the reliability of solder joints and material connections, and obtain the reliability analysis result of the control module.

[0053] As another example, for the reliability analysis of the control module of the excitation system, key parameters (such as temperature, current, power) during the operation of the control module can be collected, a health index model can be established, and based on the real-time monitoring data, the health status of the module can be dynamically evaluated to obtain the reliability analysis result of the control module.

[0054] Step S204: Adjust the system redundancy configuration of the excitation system to obtain the system redundancy target configuration of the excitation system.

[0055] Among them, the system redundancy configuration refers to reserving spare hardware or control strategies during system operation to ensure that the system can continue to operate stably when some components fail. The terminal can adjust the system redundancy configuration of the excitation system based on data such as the operating status of the excitation system and the results of fault risk assessment to obtain the system redundancy target configuration of the excitation system, improving the reliability and fault tolerance of the excitation system.

[0056] Optionally, the system redundancy configuration can include hardware redundancy configuration, functional redundancy configuration, data redundancy configuration, communication redundancy configuration, etc. Among them, the hardware redundancy configuration includes the configuration of reserving spare hardware resources such as power cabinets, control modules, and storage chips; the functional redundancy configuration includes the configuration where multiple modules have the same function and can take over tasks in case of failure; the data redundancy configuration includes the configuration of real-time synchronizing system operation data; the communication redundancy includes the configuration of multiple communication paths.

[0057] Exemplarily, the terminal can adjust the system redundancy configuration of the excitation system according to the target configuration strategy, and the target configuration strategy can include ensuring that at least one spare power cabinet is always available, increasing the number of functional redundancy modules in the case of a high failure rate of the control module, and increasing the redundancy of communication links according to the reliability of communication paths.

[0058] Step S206: Conduct failure rate analysis on the components and the whole of the excitation system to obtain the component failure rate distribution curve and the overall failure rate distribution curve of the excitation system.

[0059] Among them, the failure rate analysis is to calculate the failure rate of each component and its contribution to the overall system by studying the operating characteristics and historical data of the system and its components. By plotting the failure rate distribution curve, the failure characteristics of each component and the failure trend of the whole system can be intuitively displayed, providing data support for system optimization and reliability improvement.

[0060] In a specific implementation, the terminal can obtain the operation data of the components and the whole of the excitation system (such as current, voltage, temperature, etc.), fault records (such as solder joint cracking, capacitor failure, chip failure, etc.), and environmental data (such as vibration, humidity, temperature difference, etc.), and determine the failure modes and causes of the components and the whole based on these data, and calculate the failure rates of the components and the whole of the excitation system using statistical methods (such as life data analysis, Weibull distribution fitting), obtain the calculation results, and draw a curve of the failure rate changing with time according to the calculation results to obtain the component failure rate distribution curve and the overall failure rate distribution curve of the excitation system.

[0061] The component failure rate distribution curve can be used to reflect the change trend of the failure rate of the components of the excitation system within a certain time range, and shows how the failure rate changes with time during the period from the start of operation of the components to failure. The failure rate is the probability density of a component failing at the next moment on the premise that it is operating normally at a certain time point.

[0062] Similarly, the overall failure rate distribution curve can be used to reflect the change trend of the overall failure rate of the excitation system within a certain time range, and shows how the failure rate changes with time during the period from the start of operation of the whole to failure. The failure rate is the probability density of the whole failing at the next moment on the premise that it is operating normally at a certain time point.

[0063] Step S208, construct a reliability evaluation model for the excitation system according to the reliability analysis results, system redundancy target configuration, component failure rate distribution curve, and overall failure rate distribution curve.

[0064] Among them, the reliability evaluation model is used to evaluate the reliability of the excitation system.

[0065] The reliability evaluation model can integrate information such as reliability analysis results, system redundancy target configuration, component failure rate distribution curve, and overall failure rate distribution curve, and through comprehensive analysis of this information, can predict the reliability performance of the excitation system in various operating environments.

[0066] The terminal can select a suitable reliability modeling method, such as fault tree analysis (FTA), reliability block diagram (RBD), or Markov model, etc., input the reliability analysis results, system redundancy target configuration, component failure rate distribution curve, and overall failure rate distribution curve into the selected model, define the reliability parameters and failure logic of each component, reflect the impact of the redundancy mechanism on the system reliability in the model, establish the overall reliability framework of the system, clarify the relationships and mutual influences between each component, and obtain the final reliability evaluation model.

[0067] The constructed reliability evaluation model can comprehensively reflect the reliability status of the excitation system and improve the accuracy of reliability evaluation.

[0068] In the reliability evaluation method of the above intelligent excitation system, the reliability analysis of the control module of the excitation system is carried out to obtain the reliability analysis result of the control module; the system redundancy configuration of the excitation system is adjusted to obtain the system redundancy target configuration of the excitation system; the failure rate analysis of the components and the whole of the excitation system is carried out to obtain the component failure rate distribution curve and the whole failure rate distribution curve of the excitation system; according to the reliability analysis result, the system redundancy target configuration, the component failure rate distribution curve and the whole failure rate distribution curve, a reliability evaluation model of the excitation system is constructed, wherein the reliability evaluation model is used to evaluate the reliability of the excitation system, and can, in the design and planning stage of the excitation system, through the reliability analysis of the control module, the adjustment of the system redundancy configuration and the comprehensive modeling of the component and whole failure rates, construct an accurate reliability evaluation model, so as to realize the early prediction and optimization of potential problems of the excitation system; and overcome the lag problem of the traditional reliability evaluation method, so that the reliability of the excitation system can be evaluated comprehensively before it is actually put into use, significantly reducing the probability of failures occurring during operation, improving the overall safety and stability of the system, and at the same time reducing the additional time and human resource consumption caused by fault rectification in the later operation stage, providing a strong guarantee for the long-term stable operation of complex power systems.

[0069] In another embodiment, after constructing the reliability evaluation model of the excitation system, it further includes: carrying out thermal stress analysis, vibration stress analysis and life analysis on the whole of the excitation system to obtain the analysis result of the excitation system; inputting the analysis result into the reliability evaluation model to obtain the reliability evaluation result of the excitation system.

[0070] Among them, the thermal stress analysis can be used to evaluate the performance of the system under different temperature conditions and identify the failure risks caused by temperature changes. For example, by using thermal analysis software to simulate the thermal distribution of key components of the excitation system (such as solder joints, chips, capacitors, etc.), calculate the stress changes caused by thermal expansion and contraction, and predict the failure rate data of the whole excitation system caused by thermal stress.

[0071] Among them, the vibration stress analysis can be used to analyze the influence of the vibration environment on the mechanical structure and electronic components (such as solder joints, connectors, etc.) of the excitation system. For example, record the mechanical stress distribution and failure time when the system is subjected to vibrations with specific frequencies and amplitudes through a vibration test device, and obtain the life and failure rate under vibration stress.

[0072] Among them, the life analysis can be used to predict the normal operation of the excitation system within a certain period of time, and identify and evaluate the factors that may cause system failures or performance degradation. Among them, the life analysis can include analyzing the number of operations of electronic components in the excitation system, that is, the number of operations activated or used within a certain period.

[0073] The reliability evaluation model can be used to simulate the reliability performance of the excitation system under different operating conditions and output the reliability evaluation results of the excitation system, including the mean time between failures (MTBF), failure rate, failure risk, predicted life, and reliability change.

[0074] In the specific implementation, the analysis results are input into the reliability evaluation model to obtain the reliability evaluation results of the excitation system. The analysis results can be used as supplementary input variables for the reliability evaluation model, including the influence of temperature change on the failure rate, the mechanical failure probability caused by vibration, and the failure probability caused by the service life. Or, these analysis results can be embedded into the reliability evaluation model in the form of correction factors, including the thermal stress, vibration stress, and the increase in failure rate caused by life, etc. The reliability evaluation model corrects the reliability of the excitation system by integrating the failure rates of multiple variables, such as through the multi-variable failure probability calculation formula.

[0075] The technical solution of this embodiment can more comprehensively reflect the reliability performance of the excitation system in the actual operating environment by introducing multi-dimensional data such as thermal stress, vibration stress, and life. Through the quantitative analysis of potential high-risk factors such as thermal stress, vibration stress, and life, it helps to early warn of possible failure points, enabling the model to more accurately predict the reliability of the excitation system under complex working conditions.

[0076] In another embodiment, a reliability analysis is performed on the control module of the excitation system to obtain the reliability analysis results of the control module, including: performing a failure mechanism analysis on the devices on any circuit board in the control module of the excitation system, and analyzing the physical characteristics of any circuit board to obtain the mechanism analysis results of any circuit board; obtaining the failure mechanism analysis results of the control module according to the mechanism analysis results and corresponding weights of each circuit board in the control module; performing a failure mode analysis on the devices on any circuit board in the control module, and analyzing the physical characteristics of any circuit board to obtain the mode analysis results of any circuit board; obtaining the failure mode analysis results of the control module according to the mode analysis results and corresponding weights of each circuit board in the control module; and obtaining the reliability analysis results of the control module according to the failure mode analysis results and the failure mechanism analysis results.

[0077] It should be noted that the control module of the excitation system includes multiple circuit boards, and each circuit board may include devices such as chips, capacitors, and resistors.

[0078] Among them, the analysis of the physical characteristics of the circuit board may include the analysis of the material characteristics of the circuit board, the analysis of the electrical characteristics of the circuit board, etc.

[0079] Among them, the failure mechanism analysis can be to analyze the cause of failure by combining the material characteristics of the components on the circuit board in the control module and the usage environment (such as temperature, humidity, vibration). For example, the material characteristics of the components can be analyzed to evaluate their fatigue life under thermal cycling or vibration conditions; the operating state of the components under extreme conditions (such as high temperature, high humidity or vibration) can be simulated to identify the stress factors leading to failure; the electrical performance of the components can be checked, and the failure mechanism caused by overcurrent or overvoltage can be analyzed to obtain the results of mechanism analysis.

[0080] Since different circuit boards correspond to different weights, the failure mechanism analysis results of the control module can be obtained according to the mechanism analysis results of each circuit board in the control module and the corresponding weights. For example, the failure mechanism analysis results of the control module can be obtained by means of weighted summation.

[0081] Among them, the failure mode analysis can be to analyze the operating characteristics of the components on the circuit board in the control module to identify its possible failure modes (such as overheating, vibration damage, circuit aging). For example, the possible failure modes of each component (such as solder joint cracking, capacitor breakdown, chip overheating) can be analyzed to evaluate the impact of the failure mode on the function of the component to obtain the results of mode analysis.

[0082] Since different circuit boards correspond to different weights, the failure mode analysis results of the control module can be obtained according to the mode analysis results of each circuit board in the control module and the corresponding weights. For example, the failure mode analysis results of the control module can be obtained by means of weighted summation.

[0083] In some embodiments, it is also possible to perform board error analysis on any circuit board, and combine the results of failure mode analysis of devices and the results of analysis of the physical characteristics of the circuit board to obtain the mode analysis result of any circuit board. Among them, board error analysis can be used to analyze the impact of the deviation and cumulative error of input and output signals of the boards in the excitation system during long-term operation on reliability. In board error analysis, it is necessary to evaluate the acquisition error and output error of the board. For example, when the input AC voltage is 10V, the actually measured voltage may be 10.1V, with a certain deviation, and this deviation will gradually increase with the increase of the usage time. After 3 years of operation, the measured voltage amplitude may deviate to 10.2V. In addition, for AC signals, it is necessary to analyze the errors of amplitude, phase angle and period, and assign different weights to different types of signals. For DC signals, the deviation of amplitude is mainly analyzed. In the evaluation of board output error, the accuracy of signal output also needs to be concerned, including possible output delay problems. As the core control component in the intelligent excitation system, the acquisition and output accuracy of the board is directly related to the overall reliability and performance of the system. For example, board error analysis can be to compare the input signal with the actual measured value, record the change of error over time, and analyze the delay and accuracy of the output signal of the board, and combine the board health index to calculate the reliability loss caused by the error.

[0084] As an example, according to the results of failure mode analysis and failure mechanism analysis, the severity, occurrence frequency and detection difficulty of each potential failure mode can be quantitatively scored, the risk priority number can be calculated, and the reliability analysis result of the control module can be determined according to the risk priority number.

[0085] As another example, a mathematical model or physical model (such as Weibull distribution, lognormal distribution model) of the control module can be established according to the results of failure mode analysis and failure mechanism analysis, which is used to simulate the operating state of the control module under specific working conditions. By inputting the temperature change curve and vibration frequency of the control module into the established mathematical model or physical model, the failure data such as the failure rate and failure time of the control module under different stress conditions can be predicted, and the reliability analysis result of the control module can be obtained.

[0086] The technical solution of this embodiment deeply explores the failure reasons of the control module and quantifies its reliability level through failure mode analysis and mechanism analysis, improving the accuracy of reliability assessment.

[0087] In another embodiment, according to the results of failure mode analysis and failure mechanism analysis, the reliability analysis results of the control module are obtained, including: constructing a failure prediction model of the control module according to the results of failure mode analysis and failure mechanism analysis; the failure prediction model is used to simulate the operation of the control module and output the probability information of various failures occurring in the control module; inputting the stress analysis results of the control module into the failure prediction model to obtain the failure prediction information for the control module; and obtaining the reliability analysis results of the control module according to the failure prediction information.

[0088] Among them, the failure prediction model can be a mathematical model or a physical model that describes the failure behavior during the operation of the control module based on the failure mode and failure mechanism. Optionally, the failure prediction model can be a Weibull distribution model (describing the change of the failure rate of components over time), a thermo-mechanical coupling model (simulating the combined action of thermal stress and mechanical stress), or a Monte Carlo simulation model (stochastically simulating failure events to generate a failure probability distribution). The failure prediction model can be used to simulate the operation state of the control module under different stress conditions and output the occurrence probabilities of various failure modes.

[0089] Among them, the stress analysis results can include thermal stress analysis results, vibration stress analysis results, and electrical stress analysis results, etc. Among them, thermal stress analysis is to simulate the operation status of the control module at different temperatures; vibration stress analysis is to apply vibrations with specific frequencies and amplitudes to observe the fatigue behavior of components; and electrical stress analysis is to simulate the impact of voltage and current overload on the control module.

[0090] In specific implementation, the stress analysis results (such as temperature, vibration amplitude, stress intensity) are used as parameters to input into the failure prediction model, and the failure probabilities of each component in the control module under these stress conditions are calculated through the failure prediction model to obtain the failure prediction information.

[0091] Among them, the failure prediction information can include the failure probability of the control module and the life prediction data calculated based on the failure prediction model and stress analysis. Optionally, the failure prediction information can include the failure probability, the remaining life, high-risk components, etc. Among them, the failure probability is the probability of a specific failure occurring in the control module within a given operation time; the remaining life is the expected life of the key components in the control module under specific stresses; and the high-risk components are the components with the highest failure probability identified in the control module.

[0092] In a specific implementation, according to the fault prediction information, the reliability analysis results of the control module are obtained. The reliability analysis results may include the mean time between failures (MTBF), reliability, failure rate, failure risk report, etc. Among them, the mean time between failures can be the reliable working time of the control module under average conditions; the reliability can be the probability that the control module does not fail within a certain time period; the failure rate can be the failure frequency of the control module at a certain time point; the failure risk report can be to determine the comprehensive impact of the failure mode, failure mechanism and stress factors on the system reliability.

[0093] The technical solution of this embodiment comprehensively evaluates the reliability of the control module through failure mode analysis, failure mechanism analysis, construction of a fault prediction model, and combination with stress analysis. This method can accurately identify potential failure risks, provide a scientific basis for system design improvement and maintenance strategies, and ultimately improve the reliability and stability of the excitation system.

[0094] In another embodiment, the stress analysis results of the control module are input into the fault prediction model to obtain the fault prediction information for the control module, including: inputting the thermal stress analysis results of the control module and the vibration stress analysis results of the control module into the fault prediction model to obtain the fault prediction information for the control module; among them, the thermal stress analysis results include the analysis results obtained by performing thermal stress analysis on the control module, and the vibration stress analysis results include the analysis results obtained by performing vibration stress analysis on the control module.

[0095] Among them, thermal stress analysis is used to evaluate the impact of temperature changes on the components in the control module and predict the failure time caused by thermal stress; vibration stress analysis is used to evaluate the impact of mechanical vibration on the components in the control module and predict the failure time caused by vibration stress.

[0096] The thermal stress analysis results may include the stress values and failure lifetimes of the components at different temperatures; the vibration stress analysis results may include the fatigue lifetimes of the components at different vibration frequencies and amplitudes.

[0097] Among them, the fault prediction information includes the failure time of potential faulty components in the control module under continuous thermal stress and the failure time of potential faulty components in the control module under continuous vibration stress. For example, under thermal stress: "The solder joint is expected to fail after 3000 hours"; under vibration stress: "The capacitor may fail after 2000 hours".

[0098] Optionally, the fault prediction information may also include the component failure probability. For example, at a 95% confidence level, the failure probability of the solder joint within 4000 hours is 80%; the fault prediction information may also include a list of high-risk components, such as the components with the shortest identified failure time or the highest failure probability.

[0099] In the technical solution of this embodiment, the predictive control module predicts the potential failure time of key components under the continuous action of thermal stress and vibration stress, identifies high-risk components in advance, and provides a basis for system design optimization and maintenance; it identifies potential faults during the design stage or before operation, avoids sudden faults during actual operation, and improves the reliability and stability of the system.

[0100] In another embodiment, the system redundancy configuration of the excitation system is adjusted to obtain the system redundancy target configuration of the excitation system, including: taking the independent operation of the power cabinets in the excitation system as the operation target, adjusting the data synchronization configuration, fault self-diagnosis configuration, and redundancy switching mechanism configuration during the operation of the excitation system based on the operation target, to obtain the system redundancy target configuration of the excitation system.

[0101] In specific implementation, by configuring data synchronization, fault self-diagnosis, and redundancy switching mechanisms, it can be ensured that the power cabinets of the excitation system undertake the operation control tasks of the excitation system in the independent operation state, and ensure that the excitation system can quickly switch to the backup device under fault conditions, guaranteeing the continuous and stable operation of the system.

[0102] Among them, the data synchronization configuration can be used to synchronize key operation data in real time between multiple power cabinets to ensure that the standby power cabinet can immediately take over the operation control when a fault occurs. For example, configure a real-time data synchronization mechanism to copy the operation data (such as voltage, current, excitation current value, etc.) of the main power cabinet to the standby power cabinet in real time; adopt an efficient data synchronization protocol (such as CAN bus, Ethernet communication) to minimize data delay.

[0103] Among them, the fault self-diagnosis configuration can be used to configure the self-diagnosis function of the power cabinet to detect system faults in real time and judge the severity and scope of the faults. For example, configure fault detection algorithms, such as self-check programs and health monitoring mechanisms, to regularly diagnose the hardware and software status of the power cabinet; identify common fault types, such as abnormal voltage, overheating, communication interruption, etc., and automatically classify the faults.

[0104] Among them, the redundancy switching mechanism configuration can be used to configure the automatic switching mechanism between the main power cabinet and the standby power cabinet to ensure that the system can quickly resume operation when a fault occurs. For example, set the automatic switching logic: when the main power cabinet detects a fault, immediately switch to the healthy standby power cabinet; configure the switching determination conditions: such as abnormal voltage, communication loss, hardware failure, etc. to trigger the switching; ensure that the switching process is seamless and the switching time is minimized.

[0105] Among them, the system redundancy target configuration is the best redundancy design scheme obtained through optimized adjustment of data synchronization, fault self-diagnosis, and redundancy switching mechanisms.

[0106] The technical solution of this embodiment obtains the system redundancy target configuration by adjusting the data synchronization configuration, fault self-diagnosis configuration, and redundancy switching mechanism configuration of the excitation system, ensuring that when the power cabinet operates independently, the system can quickly switch to a healthy standby power cabinet in the event of a fault, significantly improving the stability and fault tolerance of the system in the face of faults. In the reliability assessment, the stability and fault tolerance of the system in the face of faults can be significantly improved. Through redundant design, the system will not cause overall failure due to the failure of a single component; moreover, the fault self-diagnosis configuration in the system redundancy target configuration can accurately identify high-risk components in the reliability assessment, and evaluate the frequency and location of fault occurrence; the information of the redundancy target configuration (such as data synchronization frequency, switching time, self-diagnosis cycle, etc.) can be used as input parameters to optimize and improve the reliability assessment model. Through redundant configuration, more fault switching scenarios can be added to the reliability assessment model to simulate the reliability performance of the system in different redundant states; furthermore, considering factors such as redundant switching time and the health status of the standby power cabinet, the failure rate and reliability calculation of the system can be dynamically adjusted.

[0107] In another embodiment, as Figure 3 shown, a reliability assessment method for an intelligent excitation system is provided. Taking the method applied to the Figure 1 terminal 102 in

[0108] Step S302: Conduct a fault mechanism analysis on the devices on any circuit board in the control module of the excitation system, and analyze the physical characteristics of any circuit board to obtain the mechanism analysis result of any circuit board.

[0109] Step S304: Obtain the fault mechanism analysis result of the control module according to the mechanism analysis results and corresponding weights of each circuit board in the control module.

[0110] Step S306: Conduct a fault mode analysis on the devices on any circuit board in the control module, and analyze the physical characteristics of any circuit board to obtain the mode analysis result of any circuit board.

[0111] Step S308: Obtain the fault mode analysis result of the control module according to the mode analysis results and corresponding weights of each circuit board in the control module.

[0112] Step S310: Obtain the reliability analysis result of the control module according to the fault mode analysis result and the fault mechanism analysis result.

[0113] In one embodiment, according to the fault mode analysis result and the fault mechanism analysis result, the reliability analysis result of the control module is obtained, including: constructing a fault prediction model of the control module according to the fault mode analysis result and the fault mechanism analysis result; the fault prediction model is used to simulate the operation of the control module and output the probability information of various faults occurring in the control module; inputting the stress analysis result of the control module into the fault prediction model to obtain the fault prediction information for the control module; and obtaining the reliability analysis result of the control module according to the fault prediction information.

[0114] In one embodiment, inputting the stress analysis result of the control module into the fault prediction model to obtain the fault prediction information for the control module, including: inputting the thermal stress analysis result and the vibration stress analysis result of the control module into the fault prediction model to obtain the fault prediction information for the control module; the fault prediction information includes the failure time of potential fault components in the control module under continuous action of thermal stress and the failure time of potential fault components in the control module under continuous action of vibration stress; wherein, the thermal stress analysis result includes the analysis result obtained by performing thermal stress analysis on the control module, and the vibration stress analysis result includes the analysis result obtained by performing vibration stress analysis on the control module.

[0115] Step S312: Taking the independent operation of the power cabinet in the excitation system as the operation target, adjusting the data synchronization configuration, fault self-diagnosis configuration, and redundancy switching mechanism configuration during the operation of the excitation system based on the operation target to obtain the system redundancy target configuration of the excitation system.

[0116] Step S314: Performing failure rate analysis on the components and the whole of the excitation system to obtain the component failure rate distribution curve and the overall failure rate distribution curve of the excitation system.

[0117] Step S316: Constructing a reliability evaluation model of the excitation system according to the reliability analysis result, the system redundancy target configuration, the component failure rate distribution curve, and the overall failure rate distribution curve.

[0118] Among them, the reliability evaluation model is used to evaluate the reliability of the excitation system.

[0119] Step S318: Performing thermal stress analysis, vibration stress analysis, and life analysis on the whole of the excitation system to obtain the analysis result of the excitation system.

[0120] Step S320: Inputting the analysis result into the reliability evaluation model to obtain the reliability evaluation result of the excitation system.

[0121] It should be noted that the specific limitations of the above steps can refer to the specific limitations of a reliability evaluation method for an intelligent excitation system described above.

[0122] It should be understood that although the steps in the flowcharts involved in the above-described embodiments are shown in sequence according to the indications of the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless there is a clear indication in this article, there is no strict order restriction for the execution of these steps, and these steps can be executed in other orders. Moreover, at least a part of the steps in the flowcharts involved in the above-described embodiments may include multiple steps or multiple stages. These steps or stages are not necessarily executed at the same time, but can be executed at different times. The execution order of these steps or stages is not necessarily sequential, but can be executed alternately or in turn with at least a part of other steps or steps or stages in other steps.

[0123] Based on the same inventive concept, an embodiment of the present application further provides a reliability evaluation device for an intelligent excitation system for implementing the reliability evaluation method of the intelligent excitation system involved above. The solution provided by this device to solve the problem is similar to the solution described in the above method. Therefore, the specific limitations in one or more embodiments of the reliability evaluation device for the intelligent excitation system provided below can refer to the limitations on the reliability evaluation method of the intelligent excitation system in the above text, and will not be repeated here.

[0124] In an exemplary embodiment, as Figure 4 shown, a reliability evaluation device for an intelligent excitation system is provided, including:

[0125] An evaluation module 410, configured to perform a reliability analysis on the control module of the excitation system to obtain a reliability analysis result of the control module.

[0126] An adjustment module 420, configured to adjust the system redundancy configuration of the excitation system to obtain a system redundancy target configuration of the excitation system.

[0127] An analysis module 430, configured to perform a failure rate analysis on the components and the whole of the excitation system to obtain a component failure rate distribution curve and a whole failure rate distribution curve of the excitation system.

[0128] A construction module 440, configured to construct a reliability evaluation model of the excitation system according to the reliability analysis result, the system redundancy target configuration, the component failure rate distribution curve, and the whole failure rate distribution curve; the reliability evaluation model is used to perform a reliability evaluation on the excitation system.

[0129] In one embodiment, the reliability evaluation device of the intelligent excitation system further includes an input module, which is specifically configured to perform thermal stress analysis, vibration stress analysis, and life analysis on the overall excitation system to obtain the analysis results of the excitation system; and input the analysis results into the reliability evaluation model to obtain the reliability evaluation results of the excitation system.

[0130] In one embodiment, the evaluation module 410 is specifically configured to perform failure mechanism analysis on the components on any circuit board in the control module of the excitation system, and analyze the physical characteristics of the any circuit board to obtain the mechanism analysis results of the any circuit board; obtain the failure mechanism analysis results of the control module according to the mechanism analysis results and corresponding weights of each circuit board in the control module; perform failure mode analysis on the components on any circuit board in the control module, and analyze the physical characteristics of the any circuit board to obtain the mode analysis results of the any circuit board; obtain the failure mode analysis results of the control module according to the mode analysis results and corresponding weights of each circuit board in the control module; and obtain the reliability analysis results of the control module according to the failure mode analysis results and the failure mechanism analysis results.

[0131] In one embodiment, the evaluation module 410 is specifically configured to construct a failure prediction model of the control module according to the failure mode analysis results and the failure mechanism analysis results; the failure prediction model is used to simulate the operation of the control module and output the probability information of various failures occurring in the control module; input the stress analysis results of the control module into the failure prediction model to obtain the failure prediction information for the control module; and obtain the reliability analysis results of the control module according to the failure prediction information.

[0132] In one embodiment, the evaluation module 410 is specifically configured to input the thermal stress analysis results and the vibration stress analysis results of the control module into the failure prediction model to obtain the failure prediction information for the control module; the failure prediction information includes the failure time of potential failure components in the control module under continuous action of thermal stress and the failure time of potential failure components in the control module under continuous action of vibration stress; wherein, the thermal stress analysis results include the analysis results obtained by performing thermal stress analysis on the control module, and the vibration stress analysis results include the analysis results obtained by performing vibration stress analysis on the control module.

[0133] In one embodiment, the adjustment module 420 is specifically configured to take the independent operation of the power cabinets in the excitation system as the operation target, and adjust the data synchronization configuration, fault self-diagnosis configuration, and redundant switching mechanism configuration in the operation process of the excitation system based on the operation target, so as to obtain the system redundancy target configuration of the excitation system.

[0134] Each module in the reliability evaluation device of the intelligent excitation system described above can be implemented in whole or in part by software, hardware, and their combination. Each of the above modules can be embedded in or independent of the processor in the computer device in the form of hardware, or stored in the memory of the computer device in the form of software, so that the processor can call and execute the operations corresponding to each of the above modules.

[0135] In an exemplary embodiment, a computer device is provided. The computer device may be a terminal, and its internal structure diagram may be as Figure 5 shown. The computer device includes a processor, a memory, an input / output interface, a communication interface, a display unit, and an input device. Among them, the processor, the memory, and the input / output interface are connected through a system bus, and the communication interface, the display unit, and the input device are connected to the system bus through the input / output interface. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The input / output interface of the computer device is used to exchange information between the processor and external devices. The communication interface of the computer device is used to communicate with external terminals in a wired or wireless manner. The wireless manner can be implemented through WIFI, a mobile cellular network, near field communication (NFC), or other technologies. When the computer program is executed by the processor, it implements a method for evaluating the reliability of an intelligent excitation system. The display unit of the computer device is used to form a visually visible picture, which can be a display screen, a projection device, or a virtual reality imaging device. The display screen can be a liquid crystal display screen or an electronic ink display screen. The input device of the computer device can be a touch layer covered on the display screen, or a button, a trackball, or a touchpad provided on the housing of the computer device, or an external keyboard, a touchpad, or a mouse, etc.

[0136] Those skilled in the art can understand that Figure 5 the structure shown in

[0137] In an exemplary embodiment, a computer device is provided, including a memory and a processor. A computer program is stored in the memory, and when the processor executes the computer program, the steps in the above method embodiments are implemented.

[0138] In an embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the steps in the above method embodiments are implemented.

[0139] In an embodiment, a computer program product is provided, including a computer program. When the computer program is executed by a processor, the steps in the above method embodiments are implemented.

[0140] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use, and processing of relevant data need to comply with relevant regulations.

[0141] Those of ordinary skill in the art can understand that all or part of the processes in the methods of the above embodiments can be completed by instructing relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above methods. Among them, any reference to a memory, database, or other medium used in the embodiments provided in this application can include at least one of non-volatile memory and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetoresistive random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM), etc. The databases involved in the embodiments provided in this application can include at least one of relational databases and non-relational databases. Non-relational databases can include distributed databases based on blockchain, etc., without limitation. The processors involved in the embodiments provided in this application can be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, data processing logics based on quantum computing, artificial intelligence (AI) processors, etc., without limitation.

[0142] The technical features of the above embodiments can be combined arbitrarily. For the sake of concise description, 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, it should be considered as the scope recorded in this application.

[0143] The above-described embodiments merely represent several implementation manners of the present application. The description thereof is relatively specific and detailed, but it should not be construed as a limitation on the patent scope of the present application. It should be noted that for those of ordinary skill in the art, without departing from the concept of the present application, several modifications and improvements can still be made, and these all fall within the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the appended claims.

Claims

1. A reliability assessment method for an intelligent excitation system, characterized in that: The method comprises: Performing reliability analysis on a control module of an excitation system to obtain a reliability analysis result of the control module; Adjusting the system redundancy configuration of the excitation system to obtain a system redundancy target configuration of the excitation system; Performing failure rate analysis on components and the entire excitation system to obtain component failure rate distribution curves and an overall failure rate distribution curve of the excitation system; A reliability evaluation model of the excitation system is constructed according to the reliability analysis result, the system redundancy target configuration, the component failure rate distribution curve and the overall failure rate distribution curve; the reliability evaluation model is used to perform reliability evaluation on the excitation system.

2. The method according to claim 1, characterized in that After constructing the reliability assessment model of the excitation system, the method further includes: Performing thermal stress analysis, vibration stress analysis and life analysis on the entire excitation system to obtain analysis results of the excitation system; The analysis result is input into the reliability evaluation model to obtain a reliability evaluation result of the excitation system.

3. The method according to claim 1, characterized in that The performing reliability analysis on the control module of the excitation system to obtain the reliability analysis result of the control module includes: Performing a fault mechanism analysis on a device on any circuit board in a control module of the excitation system, and analyzing the physical characteristics of any circuit board, to obtain a mechanism analysis result of any circuit board; Obtaining a fault mechanism analysis result of the control module according to the mechanism analysis results and corresponding weights of each circuit board in the control module; Performing a failure mode analysis on a device on any circuit board in the control module and analyzing the physical characteristics of any circuit board to obtain a mode analysis result of any circuit board; Obtaining a failure mode analysis result of the control module according to the mode analysis results and corresponding weights of each circuit board in the control module; A reliability analysis result of the control module is obtained according to the failure mode analysis result and the failure mechanism analysis result.

4. The method according to claim 3, characterized in that: The obtaining of the reliability analysis result of the control module according to the failure mode analysis result and the failure mechanism analysis result includes: According to the failure mode analysis result and the failure mechanism analysis result, a failure prediction model of the control module is constructed; the failure prediction model is used to simulate the operation of the control module and output probability information of various failures of the control module; Inputting the stress analysis result of the control module into the fault prediction model to obtain fault prediction information for the control module; A reliability analysis result of the control module is obtained according to the fault prediction information.

5. The method according to claim 4, characterized in that The step of inputting the stress analysis result of the control module into the fault prediction model to obtain fault prediction information for the control module includes: Inputting the thermal stress analysis result of the control module and the vibration stress analysis result of the control module into the fault prediction model to obtain fault prediction information for the control module; the fault prediction information includes the failure time of potential fault components in the control module under the continuous action of thermal stress and the failure time of potential fault components in the control module under the continuous action of vibration stress; The thermal stress analysis result includes an analysis result obtained by performing a thermal stress analysis on the control module, and the vibration stress analysis result includes an analysis result obtained by performing a vibration stress analysis on the control module.

6. The method according to claim 1, characterized in that The adjusting the system redundancy configuration of the excitation system to obtain the system redundancy target configuration of the excitation system includes: Taking the independent operation of the power cabinet in the excitation system as the operation target, the data synchronization configuration, fault self-diagnosis configuration, and redundant switching mechanism configuration of the excitation system are adjusted during operation based on the operation target to obtain the system redundancy target configuration of the excitation system.

7. A reliability assessment device for an intelligent excitation system, characterized in that: The device comprises: An evaluation module, used for performing reliability analysis on a control module of an excitation system and obtaining a reliability analysis result of the control module; An adjustment module, used for adjusting the system redundancy configuration of the excitation system to obtain a system redundancy target configuration of the excitation system; An analysis module, used for performing failure rate analysis on the components and the whole of the excitation system to obtain a component failure rate distribution curve and a whole failure rate distribution curve of the excitation system; A construction module is used to construct a reliability evaluation model of the excitation system according to the reliability analysis result, the system redundancy target configuration, the component failure rate distribution curve and the overall failure rate distribution curve; the reliability evaluation model is used to perform reliability evaluation on the excitation system.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 6 are implemented.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

10. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

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