Reliability evaluation method and apparatus for intelligent excitation system, and computer device

Abstract

The present application relates to a reliability evaluation method and apparatus for an intelligent excitation system, and a computer device. The method comprises: performing reliability analysis on a control module of an excitation system, so as to obtain a reliability analysis result of the control module; adjusting a system redundancy configuration of the excitation system, so as to obtain a target system redundancy configuration of the excitation system; performing failure rate analysis on components of the excitation system and the overall excitation system, so as to obtain a component failure rate distribution curve and an overall failure rate distribution curve of the excitation system; and on the basis of the reliability analysis result, the target system redundancy configuration, the component failure rate distribution curve and the overall failure rate distribution curve, constructing a reliability evaluation model for the excitation system, wherein the reliability evaluation model is used for performing reliability evaluation on the excitation system. By means of using the present method, the technical problem of the reliability evaluation of an excitation system having a significant lag can be solved.

Description

Reliability assessment methods, devices, and computer equipment for intelligent excitation systems Technical Field

[0001] This application relates to the field of equipment monitoring and reliability assessment technology, and in particular to a reliability assessment method, apparatus, computer equipment, computer-readable storage medium and computer program product for an intelligent excitation system. Background Technology

[0002] The excitation system is a crucial component of a generator, primarily responsible for controlling the generator rotor magnetic field by adjusting the excitation current, thereby stabilizing the generator's output voltage and power. The reliability of the excitation system directly impacts the safety and stability of the generator and the entire power system. With the increasing complexity of modern power systems and load fluctuations, the excitation system needs to operate stably for extended periods under high loads and complex environments; therefore, accurate assessment of its reliability is of paramount importance.

[0003] Traditional methods for assessing the reliability of excitation systems typically only allow for reliability evaluation after the system has been put into actual use. This makes it impossible to accurately assess the reliability of the excitation system during the design and planning phases, leading to frequent issues where the excitation system fails to meet expected performance indicators during actual operation. In such cases, rectifying and optimizing the excitation system requires additional time and human resources. Therefore, traditional methods for assessing the reliability of excitation systems suffer from a significant lag in reliability assessment. Summary of the Invention

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

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

[0006] A reliability analysis was performed on the control module of the excitation system, and the reliability analysis results of the control module were obtained.

[0007] The system redundancy configuration of the excitation system is adjusted to obtain the target system redundancy configuration of the excitation system;

[0008] Failure rate analysis is performed on the components and the whole excitation system to obtain the component failure rate distribution curves and the overall failure rate distribution curves of the excitation system.

[0009] Based on the reliability analysis results, the system redundancy target configuration, the component failure rate distribution curve, and the overall failure rate distribution curve, a reliability assessment model for the excitation system is constructed; the reliability assessment model is used to assess the reliability of the excitation system.

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

[0011] Thermal stress analysis, vibration stress analysis, and life analysis are performed on the excitation system as a whole to obtain the analysis results of the excitation system.

[0012] The analysis results are input into the reliability assessment model to obtain the reliability assessment results of the excitation system.

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

[0014] Fault mechanism analysis is performed on any device on any circuit board in the control module of the excitation system, and the physical characteristics of any circuit board are analyzed to obtain the mechanism analysis results of any circuit board.

[0015] Based on the mechanism analysis results and corresponding weights of each circuit board in the control module, the fault mechanism analysis results of the control module are obtained.

[0016] Fault mode analysis is performed on the devices on any circuit board in the control module, and the physical characteristics of any circuit board are analyzed to obtain the fault mode analysis results of any circuit board.

[0017] Based on the mode analysis results and corresponding weights of each circuit board in the control module, the fault mode analysis results of the control module are obtained.

[0018] Based on the failure mode analysis results and the failure mechanism analysis results, the reliability analysis results of the control module are obtained.

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

[0020] Based on the failure mode analysis results and the failure mechanism analysis results, a failure prediction model for the control module is constructed; the failure prediction model is used to simulate the operation of the control module and output the probability information of various failures of the control module.

[0021] The stress analysis results of the control module are input into the fault prediction model to obtain fault prediction information for the control module.

[0022] Based on the fault prediction information, the reliability analysis results of the control module are obtained.

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

[0024] The thermal stress analysis results and vibration stress analysis results of the control module are input into the fault prediction model to obtain fault prediction information for the control module. 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.

[0025] 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.

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

[0027] With the independent operation of the power cabinet in the excitation system as the operating goal, the data synchronization configuration, fault self-diagnosis configuration, and redundancy switching mechanism configuration of the excitation system are adjusted based on the operating goal to obtain the system redundancy target configuration of the excitation system.

[0028] Secondly, this application also provides a reliability assessment device for an intelligent excitation system, comprising:

[0029] An evaluation module is used to perform reliability analysis on the control module of the excitation system and obtain the reliability analysis results of the control module.

[0030] The adjustment module is used to adjust the system redundancy configuration of the excitation system to obtain the target system redundancy configuration of the excitation system.

[0031] The analysis module is used to perform failure rate analysis on the components and the whole of the excitation system, and obtain the component failure rate distribution curve and the overall failure rate distribution curve of the excitation system.

[0032] A construction module is used to construct a reliability assessment model for the excitation system based on the reliability analysis results, 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 perform reliability assessment on the excitation system.

[0033] Thirdly, this application also provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps of the above-described method.

[0034] Fourthly, this application also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the above-described method.

[0035] Fifthly, this application also provides a computer program product, including a computer program that, when executed by a processor, implements the steps of the above-described method.

[0036] The aforementioned reliability assessment method, apparatus, computer equipment, computer-readable storage medium, and computer program product for the intelligent excitation system perform reliability analysis on the control module of the excitation system to obtain the reliability analysis results of the control module; adjust the system redundancy configuration of the excitation system to obtain the target system redundancy configuration; perform failure rate analysis on the components and the entire excitation system to obtain the component failure rate distribution curves and the overall failure rate distribution curves; and construct a reliability assessment model for the excitation system based on the reliability analysis results, the target system redundancy configuration, the component failure rate distribution curves, and the overall failure rate distribution curves. This reliability assessment model is used to assess the reliability of the excitation system. Reliability assessment, through comprehensive modeling of component and overall failure rates during the design and planning stages of the excitation system, enables the construction of an accurate reliability assessment model. This allows for the early prediction and optimization of potential problems in the excitation system. Furthermore, it overcomes the lag problem of traditional reliability assessment methods, enabling a comprehensive reliability assessment of the excitation system before actual use. This significantly reduces the probability of failures during operation, improves the overall safety and stability of the system, and reduces the additional time and human resources consumed by fault rectification in the later stages of operation, providing a strong guarantee for the long-term stable operation of complex power systems. Attached Figure Description

[0037] To more clearly illustrate the technical solutions in the embodiments of this application or related technologies, the drawings used in the description of the embodiments of this application or related technologies will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

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

[0039] Figure 2 is a flowchart illustrating a reliability assessment method for an intelligent excitation system in one embodiment.

[0040] Figure 3 is a flowchart illustrating a reliability assessment method for an intelligent excitation system in another embodiment;

[0041] Figure 4 is a structural block diagram of a reliability assessment device for an intelligent excitation system in one embodiment;

[0042] Figure 5 is an internal structure diagram of a computer device in one embodiment. Detailed Implementation

[0043] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0044] The reliability assessment method for the intelligent excitation system provided in this application embodiment can be applied to the application environment shown in Figure 1. The terminal 102 can be connected to the excitation system 100, and can acquire the overall operating data of the excitation system 100 and the operating data of each component. The terminal 102 also communicates with the server 104 via a 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 it can be placed in the cloud or on another network server. The terminal 102 performs reliability analysis on the control module of the excitation system and obtains the reliability analysis results of the control module; the terminal 102 adjusts the system redundancy configuration of the excitation system to obtain the system redundancy target configuration; the terminal 102 performs failure rate analysis on the components and the entire excitation system to obtain the component failure rate distribution curve and the overall failure rate distribution curve; based on the reliability analysis results, the system redundancy target configuration, the component failure rate distribution curve, and the overall failure rate distribution curve, the terminal 102 constructs a reliability assessment model for the excitation system; the reliability assessment model is used to assess the reliability of the excitation system. The terminal 102 can be, but is not limited to, various personal computers, laptops, smartphones, tablets, and IoT devices. The server 104 can be a standalone physical server, 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 assessment model of the excitation system to the server 104, or it can store it locally.

[0045] In an exemplary embodiment, as shown in FIG2, a reliability assessment method for an intelligent excitation system is provided. Taking the application of this method to terminal 102 in FIG1 as an example, the method includes:

[0046] Step S202: Perform a reliability analysis on the control module of the excitation system and obtain the reliability analysis results of the control module.

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

[0048] In practice, reliability analysis of the excitation system's control module can be performed on the excitation system's circuit board and its components (such as chips, capacitors, and resistors) to identify potential failure modes, causes of failure, and their probabilities of occurrence, thereby obtaining the reliability analysis results of the control module. Optionally, the reliability analysis results may include, but are not limited to, mean time to failure (MTBF) and failure rate.

[0049] As an example, reliability analysis of the control module of the excitation system can be performed by conducting multiple random samplings based on the fault data of the control module (such as component life distribution and operating environment conditions), and through a large number of simulations, calculating the failure probability of the control module under different operating conditions, and obtaining the reliability analysis results of the control module.

[0050] As another example, reliability analysis of the control module of the excitation system can be performed by conducting accelerated life testing on the control module. By applying extreme conditions such as high temperature, high humidity or overload, the aging process of the components is accelerated. Based on the acceleration factor and actual operating conditions, the life of the control module under normal operating conditions is estimated, and the reliability analysis results of the control module are obtained.

[0051] As another example, reliability analysis of the control module of the excitation system can be performed by establishing a fault tree model of the control module, decomposing the module's failures into multiple basic events (such as solder joint cracking, chip overheating, and capacitor failure); by analyzing the combination relationship of the underlying events, the total failure rate of the control module can be calculated to obtain the reliability analysis results of the control module.

[0052] As another example, reliability analysis of the control module of the excitation system can be performed by temperature cycling tests to simulate the thermal expansion and contraction process of the control module under alternating high and low temperature environments, analyze the reliability of solder joints and material connections, and obtain the reliability analysis results of the control module.

[0053] As another example, reliability analysis of the control module of the excitation system can be performed by collecting key parameters (such as temperature, current, and power) during the operation of the control module, establishing a health index model, dynamically evaluating the health status of the module based on real-time monitoring data, and obtaining the reliability analysis results of the control module.

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

[0055] System redundancy configuration refers to reserving backup hardware or control strategies during system operation to ensure stable system operation even if certain components fail. The terminal can adjust the system redundancy configuration of the excitation system based on data such as the operating status and fault risk assessment results to obtain the target system redundancy configuration, thereby improving the reliability and fault tolerance of the excitation system.

[0056] Optionally, system redundancy configuration may include hardware redundancy configuration, functional redundancy configuration, data redundancy configuration, and communication redundancy configuration. Hardware redundancy configuration includes reserving spare power cabinets, control modules, storage chips, and other hardware resources; functional redundancy configuration includes configuring multiple modules with the same function, capable of taking over tasks in case of failure; data redundancy configuration includes configuring real-time synchronization of system operating data; and communication redundancy includes configuring multiple communication paths.

[0057] For example, the terminal can adjust the system redundancy configuration of the excitation system according to the target configuration strategy. The target configuration strategy may include ensuring that at least one backup power cabinet is always available, increasing the number of functional redundancy modules when the failure rate of the control module is high, and increasing the redundancy of the communication link according to the reliability of the communication path.

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

[0059] Failure rate analysis involves studying the operational characteristics and historical data of the system and its components to calculate the failure rate of each component and its contribution to the overall system. By plotting failure rate distribution curves, the failure characteristics of each component and the failure trend of the entire system can be visually displayed, providing data support for system optimization and reliability improvement.

[0060] In practice, the terminal can acquire the operating data (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.) of the components and the whole excitation system. Based on this data, it can determine the failure modes and causes of the components and the whole system, and use statistical methods (such as lifetime data analysis, Weibull distribution fitting) to calculate the failure rate of the components and the whole excitation system, obtain the calculation results, and plot the failure rate curve over time based on 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 changing trend of the failure rate of components in an excitation system within a certain time range, showing how the failure rate changes with time from the start of operation to failure. The failure rate is the probability density of a component failing at the next moment, assuming it is operating normally at a certain point in time.

[0062] Similarly, the overall failure rate distribution curve can be used to reflect the changing trend of the overall failure rate of the excitation system within a certain time range, showing how the failure rate changes with time from the start of operation to failure. The failure rate is the probability density of failure occurring at the next moment, assuming the entire system is operating normally at a certain point in time.

[0063] Step S208: Based on the reliability analysis results, system redundancy target configuration, component failure rate distribution curves, and overall failure rate distribution curves, construct a reliability assessment model for the excitation system.

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

[0065] The reliability assessment model can integrate information such as reliability analysis results, system redundancy target configuration, component failure rate distribution curves and overall failure rate distribution curves. Through comprehensive analysis of this information, it can predict the reliability performance of the excitation system under various operating environments.

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

[0067] The constructed reliability assessment model can comprehensively reflect the reliability status of the excitation system, thus improving the accuracy of reliability assessment.

[0068] The aforementioned reliability assessment method for intelligent excitation systems involves: performing reliability analysis on the control module of the excitation system to obtain its reliability analysis results; adjusting the system redundancy configuration of the excitation system to obtain the target redundancy configuration; analyzing the failure rates of the components and the entire excitation system to obtain component failure rate distribution curves and overall failure rate distribution curves; and constructing a reliability assessment model for the excitation system based on the reliability analysis results, target redundancy configuration, component failure rate distribution curves, and overall failure rate distribution curves. This reliability assessment model is used to assess the reliability of the excitation system. It enables the construction of an accurate reliability assessment model during the design and planning stages of the excitation system through comprehensive modeling of the control module's reliability analysis, system redundancy configuration adjustment, and component and overall failure rates. This allows for early prediction and optimization of potential problems in the excitation system. Furthermore, it overcomes the lag problem of traditional reliability assessment methods, enabling comprehensive reliability assessment of the excitation system before actual use. This significantly reduces the probability of failures during operation, improves the overall safety and stability of the system, and reduces the additional time and human resource consumption caused by fault rectification in the later stages of operation, providing a strong guarantee for the long-term stable operation of complex power systems.

[0069] In another embodiment, 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 the analysis results of the excitation system; and inputting the analysis results into the reliability assessment model to obtain the reliability assessment results of the excitation system.

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

[0071] Vibration stress analysis can be used to analyze the impact of vibration environment on the mechanical structure and electronic components (such as solder joints, connectors, etc.) of excitation system. For example, it can record the mechanical stress distribution and failure time when the system is subjected to vibration of a specific frequency and amplitude by vibration test equipment, and obtain the life and failure rate under vibration stress.

[0072] Lifetime analysis can be used to predict the normal operation of an excitation system within a certain period, and to identify and assess factors that may lead to system failure or performance degradation. Specifically, lifetime analysis may include analyzing the number of times electronic components in the excitation system are activated or used within a certain period.

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

[0074] In practice, the analysis results are input into the reliability assessment model to obtain the reliability assessment results of the excitation system. This can be achieved by using the analysis results as supplementary input variables to the reliability assessment model, including the impact of temperature changes on the failure rate, the probability of mechanical failure caused by vibration, and the probability of failure caused by service life. Alternatively, these analysis results can be embedded into the reliability assessment model as correction factors, including thermal stress, vibration stress, and the failure rate increment caused by service life. The reliability assessment model corrects the reliability of the excitation system by integrating the failure rates of multiple variables, such as through a multivariate failure probability calculation formula.

[0075] The technical solution of this embodiment, by introducing multi-dimensional data such as thermal stress, vibration stress, and lifespan, can more comprehensively reflect the reliability performance of the excitation system in actual operating environments. Through quantitative analysis of potential high-risk factors such as thermal stress, vibration stress, and lifespan, it helps to provide early warning of possible failure points, enabling the model to more accurately predict the reliability of the excitation system under complex operating 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. This includes: performing fault 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 fault mechanism analysis results of the control module based on the mechanism analysis results and corresponding weights of each circuit board in the control module; performing fault 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 fault mode analysis results of the control module based on 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 based on the fault mode analysis results and the fault 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 components such as chips, capacitors, and resistors.

[0078] The analysis of the physical characteristics of the circuit board can include the analysis of the material properties of the circuit board and the analysis of the electrical characteristics of the circuit board.

[0079] Fault mechanism analysis can involve combining the material properties of the components on the circuit board in the control module with the operating environment (such as temperature, humidity, and vibration) to analyze the causes of failure. For example, it can analyze the material properties of the components to assess their fatigue life under thermal cycling or vibration conditions; simulate the operating state of the components under extreme conditions (such as high temperature, high humidity, or vibration) to identify stress factors that lead to failure; and examine the electrical performance of the components to analyze the failure mechanisms caused by overcurrent or overvoltage, thus obtaining the mechanism analysis results.

[0080] Since different circuit boards have different weights, the failure mechanism analysis results of the control module can be obtained based on the mechanism analysis results of each circuit board in the control module and their corresponding weights. For example, the failure mechanism analysis results of the control module can be obtained by weighted summation.

[0081] Failure mode analysis (FMA) involves analyzing the operating characteristics of components on a circuit board within a control module to identify potential failure modes (such as overheating, vibration damage, and circuit aging). For example, it can analyze the possible failure modes of each component (such as solder joint cracking, capacitor breakdown, and chip overheating), assess the impact of these failure modes on component functionality, and obtain the FMA results.

[0082] Since different circuit boards have different weights, the failure mode analysis results of the control module can be obtained based on the mode analysis results of each circuit board in the control module and their corresponding weights. For example, the failure mode analysis results of the control module can be obtained by weighted summation.

[0083] In some embodiments, board error analysis can be performed on any circuit board, and the results of failure mode analysis of the devices and analysis of the physical characteristics of the circuit board can be combined to obtain the mode analysis results of any circuit board. Board error analysis can be used to analyze the impact of input and output signal deviations and cumulative errors on the reliability of the excitation system's circuit board during long-term operation. In board error analysis, the acquisition and output errors of the circuit board need to be evaluated. For example, when the input AC voltage is 10V, the actual measured voltage may be 10.1V, indicating a certain deviation, and this deviation will gradually increase with the increase of usage time. After 3 years of operation, the measured voltage amplitude may deviate to 10.2V. Furthermore, for AC signals, it is necessary to analyze the errors in amplitude, phase angle, and period, and assign different weights to different types of signals. For DC signals, the amplitude deviation is mainly analyzed. In the evaluation of the circuit board's output error, the accuracy of the signal output also needs to be considered, including potential output delay issues. As the core control component of the intelligent excitation system, the acquisition and output accuracy of the circuit board directly affects the overall reliability and performance of the system. For example, board error analysis can involve comparing the input signal with the actual measured value, recording the change of error over time, analyzing the delay and accuracy of the board's output signal, and calculating the reliability loss caused by the error in conjunction with the board health index.

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

[0085] As another example, a mathematical or physical model (such as a Weibull distribution or a log-normal distribution model) of the control module can be established based on the results of failure mode analysis and failure mechanism analysis. This model can be 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 or physical model, the failure rate, failure time, and other failure data of the control module under different stress conditions can be predicted, thereby obtaining the reliability analysis results of the control module.

[0086] The technical solution in this embodiment, through failure mode analysis and mechanism analysis, delves into the causes of control module failure and quantifies its reliability level, thereby improving the accuracy of reliability assessment.

[0087] In another embodiment, the reliability analysis results of the control module are obtained based on the failure mode analysis results and the failure mechanism analysis results, including: constructing a failure prediction model of the control module based on 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 of the control module; the stress analysis results of the control module are input into the failure prediction model to obtain failure prediction information for the control module; and the reliability analysis results of the control module are obtained based on the failure prediction information.

[0088] The fault prediction model can be a mathematical or physical model that describes the failure behavior of the control module during operation, based on failure modes and mechanisms. Optionally, the fault prediction model can be a Weibull distribution model (describing the change in component failure rate over time), a thermo-mechanical coupling model (simulating the combined effects of thermal and mechanical stresses), or a Monte Carlo simulation model (randomly simulating failure events to generate a failure probability distribution). The fault prediction model can be used to simulate the operating state of the control module under different stress conditions and output the probability of occurrence of various failure modes.

[0089] The stress analysis results can include thermal stress analysis results, vibration stress analysis results, and electrical stress analysis results. Thermal stress analysis simulates the operation of the control module at different temperatures; vibration stress analysis applies vibrations of specific frequencies and amplitudes to observe the fatigue behavior of components; and electrical stress analysis simulates the effects of voltage and current overloads on the control module.

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

[0091] The fault prediction information may include control module failure probability and life prediction data calculated based on fault prediction models and stress analysis. Optionally, the fault prediction information may include failure probability, high-risk components, etc., where failure probability is the probability that the control module will experience a specific failure within a given operating time; remaining life is the expected life of key components in the control module under specific stress; and high-risk components are the components in the control module identified as having the highest failure probability.

[0092] In practice, based on fault prediction information, the reliability analysis results of the control module are obtained. The reliability analysis results may include 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; reliability can be the probability that the control module will not fail within a certain period of time; failure rate can be the failure frequency of the control module at a certain point in time; failure risk report can determine the comprehensive impact of failure mode, failure mechanism and stress factors on system reliability.

[0093] The technical solution in this embodiment comprehensively evaluates the reliability of the control module by constructing fault mode analysis, fault mechanism analysis, and fault prediction models, combined with stress analysis. This method can accurately identify potential failure risks, providing a scientific basis for system design improvement and maintenance strategies, and ultimately improving 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 fault prediction information for the control module, including: inputting the thermal stress analysis results and the vibration stress analysis results of the control module into the fault prediction model to obtain fault prediction information for the control module; 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.

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

[0096] Thermal stress analysis results can include the stress values ​​and failure life of components at different temperatures; vibration stress analysis results can include the fatigue life of components at different vibration frequencies and amplitudes.

[0097] The fault prediction information includes the failure time of potential faulty components in the control module under continuous thermal stress and 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 failure prediction information may also include the component failure probability, such as an 80% probability of solder joint failure within 4000 hours at a 95% confidence level; the failure prediction information may also include a list of high-risk components, such as the components identified with the shortest failure time or the highest failure probability.

[0099] The technical solution of this embodiment predicts the potential failure time of key components under continuous thermal 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 failures in 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. This includes: taking the independent operation of the power cabinet in the excitation system as the operating target, adjusting the data synchronization configuration, fault self-diagnosis configuration, and redundancy switching mechanism configuration of the excitation system during operation based on the operating target to obtain the system redundancy target configuration of the excitation system.

[0101] In practice, by configuring data synchronization, fault self-diagnosis, and redundancy switching mechanisms, it can be ensured that the power cabinet of the excitation system can undertake the operation and control tasks of the excitation system in independent operation mode, and that the excitation system can quickly switch to backup equipment under fault conditions, thus ensuring the continuous and stable operation of the system.

[0102] Data synchronization configuration can be used to synchronize critical operating data in real time among multiple power cabinets, ensuring that the backup power cabinet can immediately take over operation control in the event of a failure. For example, a real-time data synchronization mechanism can be configured to copy the operating data (such as voltage, current, and excitation current values) of the primary power cabinet to the backup power cabinet in real time; and an efficient data synchronization protocol (such as CAN bus or Ethernet communication) can be used to ensure that data latency is minimized.

[0103] The fault self-diagnosis configuration can be used to configure the power cabinet's self-diagnostic function to detect system faults in real time and determine the severity and scope of the faults. For example, it can configure fault detection algorithms, such as self-test programs and health monitoring mechanisms, to periodically diagnose the hardware and software status of the power cabinet; identify common fault types, such as abnormal voltage, overheating, and communication interruption, and automatically classify the faults.

[0104] The redundancy switching mechanism configuration can be used to configure an automatic switching mechanism between the primary power cabinet and the backup power cabinet to ensure that the system can quickly resume operation in the event of a fault. For example, the automatic switching logic can be set so that when the primary power cabinet detects a fault, it immediately switches to the backup power cabinet in a healthy state; switching judgment conditions can be configured such as abnormal voltage, communication loss, hardware failure, etc. to trigger the switching; and the switching process can be ensured to be seamless and the switching time can be minimized.

[0105] Among them, the system redundancy target configuration is the optimal redundancy design scheme obtained by optimizing and adjusting the 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. This ensures that, under independent operation, the system can quickly switch to a healthy backup power cabinet in the event of a fault, significantly improving the system's stability and fault tolerance in the face of faults. In reliability assessment, it can significantly improve the system's stability and fault tolerance in the face of faults. Through redundancy design, the system will not fail as a whole due to the failure of a single component. Furthermore, the fault self-diagnosis configuration in the system redundancy target configuration can accurately identify high-risk components and assess the frequency and location of faults in reliability assessment. Information from 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 redundancy configuration, more fault switching scenarios can be added to the reliability assessment model to simulate the system's reliability performance under different redundancy states. Moreover, considering factors such as redundancy switching time and the health status of the backup power cabinet, the system's failure rate and reliability calculation can be dynamically adjusted.

[0107] In another embodiment, as shown in FIG3, a reliability assessment method for an intelligent excitation system is provided. Taking the application of this method to terminal 102 in FIG1 as an example, the method includes the following steps:

[0108] Step S302: Perform fault mechanism analysis on any device 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 results of any circuit board.

[0109] Step S304: Based on the mechanism analysis results and corresponding weights of each circuit board in the control module, obtain the fault mechanism analysis results of the control module.

[0110] Step S306: Perform fault mode analysis on any device on any circuit board in the control module, and analyze the physical characteristics of any circuit board to obtain the fault mode analysis results of any circuit board.

[0111] Step S308: Based on the mode analysis results and corresponding weights of each circuit board in the control module, obtain the fault mode analysis results of the control module.

[0112] Step S310: Based on the failure mode analysis results and failure mechanism analysis results, obtain the reliability analysis results of the control module.

[0113] In one embodiment, the reliability analysis results of the control module are obtained based on the failure mode analysis results and the failure mechanism analysis results, including: constructing a failure prediction model of the control module based on 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 of the control module; the stress analysis results of the control module are input into the failure prediction model to obtain failure prediction information for the control module; and the reliability analysis results of the control module are obtained based on the failure prediction information.

[0114] In one embodiment, the stress analysis results of the control module are input into the fault prediction model to obtain fault prediction information for the control module. This includes: inputting the thermal stress analysis results and the vibration stress analysis results 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 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. 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.

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

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

[0117] Step S316: Based on the reliability analysis results, system redundancy target configuration, component failure rate distribution curves, and overall failure rate distribution curves, construct a reliability assessment model for the excitation system.

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

[0119] Step S318: Perform thermal stress analysis, vibration stress analysis, and life analysis on the entire excitation system to obtain the analysis results of the excitation system.

[0120] Step S320: Input the analysis results into the reliability assessment model to obtain the reliability assessment results of the excitation system.

[0121] It should be noted that the specific limitations of the above steps can be found in the specific limitations of the reliability assessment method for an intelligent excitation system described above.

[0122] It should be understood that although the steps in the flowcharts of the embodiments described above are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the embodiments described above may include multiple steps or multiple stages. These steps or stages are not necessarily completed 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 performed alternately or in turn with other steps or at least some of the steps or stages of other steps.

[0123] Based on the same inventive concept, this application also provides a reliability assessment device for an intelligent excitation system to implement the reliability assessment method for the intelligent excitation system described above. The solution provided by this device is similar to the implementation described in the above method. Therefore, the specific limitations in one or more embodiments of the reliability assessment device for intelligent excitation systems provided below can be found in the limitations of the reliability assessment method for intelligent excitation systems described above, and will not be repeated here.

[0124] In an exemplary embodiment, as shown in FIG4, a reliability assessment device for an intelligent excitation system is provided, comprising:

[0125] Evaluation module 410 is used to perform reliability analysis on the control module of the excitation system and obtain the reliability analysis results of the control module.

[0126] The adjustment module 420 is used to adjust the system redundancy configuration of the excitation system to obtain the target system redundancy configuration of the excitation system.

[0127] Analysis module 430 is used 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.

[0128] The construction module 440 is used to construct a reliability assessment model for the excitation system based on the reliability analysis results, 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 perform reliability assessment on the excitation system.

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

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

[0131] In one embodiment, the evaluation module 410 is specifically used to construct a fault prediction model for the control module based on the fault mode analysis results and the fault mechanism analysis results; 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; the stress analysis results of the control module are input into the fault prediction model to obtain fault prediction information for the control module; and the reliability analysis results of the control module are obtained based on the fault prediction information.

[0132] In one embodiment, the evaluation module 410 is specifically used to input the thermal stress analysis results and the vibration stress analysis results 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 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; 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 used to adjust the data synchronization configuration, fault self-diagnosis configuration, and redundancy switching mechanism configuration of the excitation system based on the independent operation of the power cabinet in the excitation system as the operating target, so as to obtain the system redundancy target configuration of the excitation system.

[0134] Each module in the reliability assessment device of the aforementioned intelligent excitation system can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in a computer device, or stored in the memory of a computer device as software, so that the processor can call and execute the corresponding operations of each module.

[0135] In an exemplary embodiment, a computer device is provided, which may be a terminal, and its internal structure diagram is shown in Figure 5. The computer device includes a processor, memory, input / output interface, communication interface, display unit, and input device. The processor, memory, and input / output interface are connected via a system bus, and the communication interface, display unit, and input device are also connected to the system bus via the input / output interface. The processor of the computer device provides computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and internal memory. The non-volatile storage medium stores an operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage medium. The input / output interface of the computer device is used for exchanging information between the processor and external devices. The communication interface of the computer device is used for wired or wireless communication with external terminals; wireless communication can be achieved through Wi-Fi, mobile cellular networks, Near Field Communication (NFC), or other technologies. When the computer program is executed by the processor, it implements a reliability assessment method for an intelligent excitation system. The display unit of the computer device is used to form a visually visible image and may be a display screen, a projection device, or a virtual reality imaging device. The display screen can be an LCD screen or an e-ink screen. The input device of the computer device can be a touch layer covering the display screen, or buttons, trackballs, or touchpads set on the casing of the computer device, or external keyboards, touchpads, or mice, etc.

[0136] Those skilled in the art will understand that the structure shown in Figure 5 is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or may combine certain components, or may have different component arrangements.

[0137] In one exemplary embodiment, a computer device is provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps in the above-described method embodiments.

[0138] In one embodiment, a computer-readable storage medium is provided having a computer program stored thereon, which, when executed by a processor, implements the steps in the above method embodiments.

[0139] In one embodiment, a computer program product is provided, including a computer program that, when executed by a processor, implements the steps in the above method embodiments.

[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 used for analysis, data stored, data displayed, 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 the relevant data must comply with relevant regulations.

[0141] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile memory and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM). The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, artificial intelligence (AI) processors, etc., and are not limited to these.

[0142] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this application.

[0143] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of this application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this application should be determined by the appended claims.

Claims

1. A reliability assessment method for an intelligent excitation system, characterized in that, The method includes: A reliability analysis was performed on the control module of the excitation system, and the reliability analysis results of the control module were obtained. The system redundancy configuration of the excitation system is adjusted to obtain the target system redundancy configuration of the excitation system; Failure rate analysis is performed on the components and the whole excitation system to obtain the component failure rate distribution curves and the overall failure rate distribution curves of the excitation system. Based on the reliability analysis results, the system redundancy target configuration, the component failure rate distribution curve, and the overall failure rate distribution curve, a reliability assessment model for the excitation system is constructed; the reliability assessment model is used to assess the reliability of 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: Thermal stress analysis, vibration stress analysis, and life analysis are performed on the excitation system as a whole to obtain the analysis results of the excitation system. The analysis results are input into the reliability assessment model to obtain the reliability assessment results of the excitation system.

3. The method according to claim 1, characterized in that, The reliability analysis of the control module of the excitation system, and the resulting reliability analysis, includes: Fault mechanism analysis is performed on any device on any circuit board in the control module of the excitation system, and the physical characteristics of any circuit board are analyzed to obtain the mechanism analysis results of any circuit board. Based on the mechanism analysis results and corresponding weights of each circuit board in the control module, the fault mechanism analysis results of the control module are obtained. Fault mode analysis is performed on the devices on any circuit board in the control module, and the physical characteristics of any circuit board are analyzed to obtain the fault mode analysis results of any circuit board. Based on the mode analysis results and corresponding weights of each circuit board in the control module, the fault mode analysis results of the control module are obtained. Based on the failure mode analysis results and the failure mechanism analysis results, the reliability analysis results of the control module are obtained.

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

5. The method according to claim 4, characterized in that, The step of inputting the stress analysis results of the control module into the fault prediction model to obtain fault prediction information for the control module includes: The thermal stress analysis results and vibration stress analysis results of the control module are input into the fault prediction model to obtain fault prediction information for the control module. 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. 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.

6. The method according to claim 1, characterized in that, The step of adjusting the system redundancy configuration of the excitation system to obtain the target system redundancy configuration of the excitation system includes: With the independent operation of the power cabinet in the excitation system as the operating goal, the data synchronization configuration, fault self-diagnosis configuration, and redundancy switching mechanism configuration of the excitation system are adjusted based on the operating goal 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 includes: An evaluation module is used to perform reliability analysis on the control module of the excitation system and obtain the reliability analysis results of the control module. The adjustment module is used to adjust the system redundancy configuration of the excitation system to obtain the target system redundancy configuration of the excitation system. The analysis module is used to perform failure rate analysis on the components and the whole of the excitation system, and obtain the component failure rate distribution curve and the overall failure rate distribution curve of the excitation system. A construction module is used to construct a reliability assessment model for the excitation system based on the reliability analysis results, 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 perform reliability assessment on the excitation system.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 6.

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

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

Images