Traditional global sensitivity analysis(GSA)neglects the epistemic uncertainties associated with the probabilistic characteristics(i.e.type of distribution type and its parameters)of input rock properties emanating du...Traditional global sensitivity analysis(GSA)neglects the epistemic uncertainties associated with the probabilistic characteristics(i.e.type of distribution type and its parameters)of input rock properties emanating due to the small size of datasets while mapping the relative importance of properties to the model response.This paper proposes an augmented Bayesian multi-model inference(BMMI)coupled with GSA methodology(BMMI-GSA)to address this issue by estimating the imprecision in the momentindependent sensitivity indices of rock structures arising from the small size of input data.The methodology employs BMMI to quantify the epistemic uncertainties associated with model type and parameters of input properties.The estimated uncertainties are propagated in estimating imprecision in moment-independent Borgonovo’s indices by employing a reweighting approach on candidate probabilistic models.The proposed methodology is showcased for a rock slope prone to stress-controlled failure in the Himalayan region of India.The proposed methodology was superior to the conventional GSA(neglects all epistemic uncertainties)and Bayesian coupled GSA(B-GSA)(neglects model uncertainty)due to its capability to incorporate the uncertainties in both model type and parameters of properties.Imprecise Borgonovo’s indices estimated via proposed methodology provide the confidence intervals of the sensitivity indices instead of their fixed-point estimates,which makes the user more informed in the data collection efforts.Analyses performed with the varying sample sizes suggested that the uncertainties in sensitivity indices reduce significantly with the increasing sample sizes.The accurate importance ranking of properties was only possible via samples of large sizes.Further,the impact of the prior knowledge in terms of prior ranges and distributions was significant;hence,any related assumption should be made carefully.展开更多
本文提出一种基于GraphSAGE(graph sample and aggregate)算法的配电网故障定位方法。以对系统侧母线电压进行形态学黑帽运算的结果启动故障定位算法;利用GSA模型自主挖掘网络拓扑和零序电流特征,根据节点特征和标签建立函数映射,评估...本文提出一种基于GraphSAGE(graph sample and aggregate)算法的配电网故障定位方法。以对系统侧母线电压进行形态学黑帽运算的结果启动故障定位算法;利用GSA模型自主挖掘网络拓扑和零序电流特征,根据节点特征和标签建立函数映射,评估线路运行状态从而实现故障定位。基于PSCAD/EMTDC仿真平台搭建IEEE33节点模型,测试结果表明所提配电网故障定位方法可行且有效。并且配电网拓扑变化时,该方法无需重新训练模型即能获得可靠的故障定位结果,验证了方法的鲁棒性和对拓扑变化的适应性。展开更多
文摘Traditional global sensitivity analysis(GSA)neglects the epistemic uncertainties associated with the probabilistic characteristics(i.e.type of distribution type and its parameters)of input rock properties emanating due to the small size of datasets while mapping the relative importance of properties to the model response.This paper proposes an augmented Bayesian multi-model inference(BMMI)coupled with GSA methodology(BMMI-GSA)to address this issue by estimating the imprecision in the momentindependent sensitivity indices of rock structures arising from the small size of input data.The methodology employs BMMI to quantify the epistemic uncertainties associated with model type and parameters of input properties.The estimated uncertainties are propagated in estimating imprecision in moment-independent Borgonovo’s indices by employing a reweighting approach on candidate probabilistic models.The proposed methodology is showcased for a rock slope prone to stress-controlled failure in the Himalayan region of India.The proposed methodology was superior to the conventional GSA(neglects all epistemic uncertainties)and Bayesian coupled GSA(B-GSA)(neglects model uncertainty)due to its capability to incorporate the uncertainties in both model type and parameters of properties.Imprecise Borgonovo’s indices estimated via proposed methodology provide the confidence intervals of the sensitivity indices instead of their fixed-point estimates,which makes the user more informed in the data collection efforts.Analyses performed with the varying sample sizes suggested that the uncertainties in sensitivity indices reduce significantly with the increasing sample sizes.The accurate importance ranking of properties was only possible via samples of large sizes.Further,the impact of the prior knowledge in terms of prior ranges and distributions was significant;hence,any related assumption should be made carefully.
文摘万有引力搜索算法(gravitational search algorithm,GSA)相比于传统的优化算法具有收敛速度快、开拓性能强等特点,但GSA易陷入早熟收敛和局部最优,搜索能力较弱.为此,提出一种基于改进的Tent混沌万有引力搜索算法(gravitational search algorithm based on improved tent chaos,ITC-GSA).首先,改进Tent混沌映射来初始化种群,利用Tent混沌序列随机性、遍历性和规律性的特性使得初始种群随机性和遍历性在可行域内,具有加强算法的全局搜索能力;其次,引入引力常数G的动态调整策略提高算法的收敛速度和收敛精度;再次,设计成熟度指标判断种群成熟度,并使用Tent混沌搜索有效抑制算法早熟收敛,帮助种群跳出局部最优;最后,对10个基准函数进行仿真实验,结果表明所提算法能够有效克服GSA易陷入早熟收敛和局部最优的缺点,提高算法的收敛速度和寻优精度.
文摘本文提出一种基于GraphSAGE(graph sample and aggregate)算法的配电网故障定位方法。以对系统侧母线电压进行形态学黑帽运算的结果启动故障定位算法;利用GSA模型自主挖掘网络拓扑和零序电流特征,根据节点特征和标签建立函数映射,评估线路运行状态从而实现故障定位。基于PSCAD/EMTDC仿真平台搭建IEEE33节点模型,测试结果表明所提配电网故障定位方法可行且有效。并且配电网拓扑变化时,该方法无需重新训练模型即能获得可靠的故障定位结果,验证了方法的鲁棒性和对拓扑变化的适应性。
