现有安全稳定控制系统(简称稳控系统)的可靠性评估方法本质上属于静态建模,由于未能体现系统内各装置老化和检修等动态过程,在一定程度上影响了评估结果的准确性。为此,文中提出一种基于马尔可夫链蒙特卡洛(Markov chain Monte Carlo,MC...现有安全稳定控制系统(简称稳控系统)的可靠性评估方法本质上属于静态建模,由于未能体现系统内各装置老化和检修等动态过程,在一定程度上影响了评估结果的准确性。为此,文中提出一种基于马尔可夫链蒙特卡洛(Markov chain Monte Carlo,MCMC)的稳控系统动态可靠性评估方法。首先针对失效过程,构建四状态非齐次马尔可夫模型来模拟装置老化过程,并给出各状态评判方法;其次针对修复过程,分析不同检修策略对装置状态转移的影响以体现状态检修的差异性;最后考虑稳控装置状态转移过程的时序或条件相关性,对稳控系统可靠性进行动态建模。以实际稳控系统为例,仿真对比不同检修策略下的可靠性,并对模型参数进行灵敏度分析。评估结果表明,该方法可以求解稳控系统的时变可用度,用于指导稳控装置现场合理检修。展开更多
Statistical biases may be introduced by imprecisely quantifying background radiation reference levels. It is, therefore, imperative to devise a simple, adaptable approach for precisely describing the reference backgro...Statistical biases may be introduced by imprecisely quantifying background radiation reference levels. It is, therefore, imperative to devise a simple, adaptable approach for precisely describing the reference background levels of naturally occurring radionuclides (NOR) in mining sites. As a substitute statistical method, we suggest using Bayesian modeling in this work to examine the spatial distribution of NOR. For naturally occurring gamma-induced radionuclides like 232Th, 40K, and 238U, statistical parameters are inferred using the Markov Chain Monte Carlo (MCMC) method. After obtaining an accurate subsample using bootstrapping, we exclude any possible outliers that fall outside of the Highest Density Interval (HDI). We use MCMC to build a Bayesian model with the resampled data and make predictions about the posterior distribution of radionuclides produced by gamma irradiation. This method offers a strong and dependable way to describe NOR reference background values, which is important for managing and evaluating radiation risks in mining contexts.展开更多
准确、合理地构建间歇性电源的发电功率模型对于电力系统的仿真分析与计算具有重要意义。提出了一种风光发电功率时间序列模拟的单变量与多变量马尔科夫链蒙特卡罗(Markov chain Monte Carlo,MCMC)仿真方法。该模型针对风电场与光伏电...准确、合理地构建间歇性电源的发电功率模型对于电力系统的仿真分析与计算具有重要意义。提出了一种风光发电功率时间序列模拟的单变量与多变量马尔科夫链蒙特卡罗(Markov chain Monte Carlo,MCMC)仿真方法。该模型针对风电场与光伏电站等多种类型的间歇性电源,构建发电功率时间序列的马尔科夫链,采用Gibbs抽样技术实现了单变量或多变量的时间序列模拟。不仅全面地分析了不同类型间歇性电源马尔科夫过程的特征与影响因素,并且在MCMC方法中考虑了多变量之间的相互联系,使模型能够适应多组间歇性电源彼此间存在相关性的情形。对德国2家电力公司控制区域内的风电场、光伏电站进行仿真模拟,通过统计特征参数的对比分析,验证了所提模型的有效性。展开更多
文摘现有安全稳定控制系统(简称稳控系统)的可靠性评估方法本质上属于静态建模,由于未能体现系统内各装置老化和检修等动态过程,在一定程度上影响了评估结果的准确性。为此,文中提出一种基于马尔可夫链蒙特卡洛(Markov chain Monte Carlo,MCMC)的稳控系统动态可靠性评估方法。首先针对失效过程,构建四状态非齐次马尔可夫模型来模拟装置老化过程,并给出各状态评判方法;其次针对修复过程,分析不同检修策略对装置状态转移的影响以体现状态检修的差异性;最后考虑稳控装置状态转移过程的时序或条件相关性,对稳控系统可靠性进行动态建模。以实际稳控系统为例,仿真对比不同检修策略下的可靠性,并对模型参数进行灵敏度分析。评估结果表明,该方法可以求解稳控系统的时变可用度,用于指导稳控装置现场合理检修。
文摘Statistical biases may be introduced by imprecisely quantifying background radiation reference levels. It is, therefore, imperative to devise a simple, adaptable approach for precisely describing the reference background levels of naturally occurring radionuclides (NOR) in mining sites. As a substitute statistical method, we suggest using Bayesian modeling in this work to examine the spatial distribution of NOR. For naturally occurring gamma-induced radionuclides like 232Th, 40K, and 238U, statistical parameters are inferred using the Markov Chain Monte Carlo (MCMC) method. After obtaining an accurate subsample using bootstrapping, we exclude any possible outliers that fall outside of the Highest Density Interval (HDI). We use MCMC to build a Bayesian model with the resampled data and make predictions about the posterior distribution of radionuclides produced by gamma irradiation. This method offers a strong and dependable way to describe NOR reference background values, which is important for managing and evaluating radiation risks in mining contexts.
文摘准确、合理地构建间歇性电源的发电功率模型对于电力系统的仿真分析与计算具有重要意义。提出了一种风光发电功率时间序列模拟的单变量与多变量马尔科夫链蒙特卡罗(Markov chain Monte Carlo,MCMC)仿真方法。该模型针对风电场与光伏电站等多种类型的间歇性电源,构建发电功率时间序列的马尔科夫链,采用Gibbs抽样技术实现了单变量或多变量的时间序列模拟。不仅全面地分析了不同类型间歇性电源马尔科夫过程的特征与影响因素,并且在MCMC方法中考虑了多变量之间的相互联系,使模型能够适应多组间歇性电源彼此间存在相关性的情形。对德国2家电力公司控制区域内的风电场、光伏电站进行仿真模拟,通过统计特征参数的对比分析,验证了所提模型的有效性。
