高维模型表达(high dimensional model representation,HDMR)在描述系统输出量关于多输入量之间关系方面具有独特的性能,而电网潮流状态量与网络多个节点源流注入量间正好符合HDMR的相关属性。基于此,将HDMR应用于电网潮流概率评估与调...高维模型表达(high dimensional model representation,HDMR)在描述系统输出量关于多输入量之间关系方面具有独特的性能,而电网潮流状态量与网络多个节点源流注入量间正好符合HDMR的相关属性。基于此,将HDMR应用于电网潮流概率评估与调控问题:通过典型代表性的样本构建关键支路上传输的功率与电源和负荷间的HDMR关系,并替换传统潮流计算方式承担潮流概率评估过程中大规模的潮流计算任务,以极大地提高关键支路潮流累积概率分布生成及其相关特征求取的效率;对关键支路潮流阻塞问题,设计了一种利用HDMR提供的全局灵敏度信息并兼顾节能减排性能指标的概率调控策略。算例表明,HDMR的应用可显著提高电网潮流概率评估的计算效率和关键支路潮流阻塞概率调控的性能。展开更多
A new hybrid method is proposed to estimate the failure probability of a structure subject to random parameters. The high dimensional model representation(HDMR) combined with artificial neural network(ANN) is used to ...A new hybrid method is proposed to estimate the failure probability of a structure subject to random parameters. The high dimensional model representation(HDMR) combined with artificial neural network(ANN) is used to approximate implicit limit state functions in structural reliability analysis. HDMR facilitates the lower dimensional approximation of the original limit states function. For evaluating the failure probability, a first-order HDMR approximation is constructed by deploying sampling points along each random variable axis and hence obtaining the structural responses. To reduce the computational effort of the evaluation of limit state function, an ANN surrogate is trained based on the sampling points from HDMR. The component of the approximated function in HDMR can be regarded as the input of the ANN and the response of limit state function can be regarded as the target for training an ANN surrogate. This trained ANN surrogate is used to obtain structural outputs instead of directly calling the numerical model of a structure. After generating the ANN surrogate, Monte Carlo simulation(MCS) is performed to obtain the failure probability, based on the trained ANN surrogate. Three numerical examples are used to illustrate the accuracy and efficiency of the proposed method.展开更多
文摘高维模型表达(high dimensional model representation,HDMR)在描述系统输出量关于多输入量之间关系方面具有独特的性能,而电网潮流状态量与网络多个节点源流注入量间正好符合HDMR的相关属性。基于此,将HDMR应用于电网潮流概率评估与调控问题:通过典型代表性的样本构建关键支路上传输的功率与电源和负荷间的HDMR关系,并替换传统潮流计算方式承担潮流概率评估过程中大规模的潮流计算任务,以极大地提高关键支路潮流累积概率分布生成及其相关特征求取的效率;对关键支路潮流阻塞问题,设计了一种利用HDMR提供的全局灵敏度信息并兼顾节能减排性能指标的概率调控策略。算例表明,HDMR的应用可显著提高电网潮流概率评估的计算效率和关键支路潮流阻塞概率调控的性能。
基金Project(U1533109)supported by the National Natural Science Foundation,ChinaProject supported by the Priority Academic Program Development of Jiangsu Higher Education Institutions,China
文摘A new hybrid method is proposed to estimate the failure probability of a structure subject to random parameters. The high dimensional model representation(HDMR) combined with artificial neural network(ANN) is used to approximate implicit limit state functions in structural reliability analysis. HDMR facilitates the lower dimensional approximation of the original limit states function. For evaluating the failure probability, a first-order HDMR approximation is constructed by deploying sampling points along each random variable axis and hence obtaining the structural responses. To reduce the computational effort of the evaluation of limit state function, an ANN surrogate is trained based on the sampling points from HDMR. The component of the approximated function in HDMR can be regarded as the input of the ANN and the response of limit state function can be regarded as the target for training an ANN surrogate. This trained ANN surrogate is used to obtain structural outputs instead of directly calling the numerical model of a structure. After generating the ANN surrogate, Monte Carlo simulation(MCS) is performed to obtain the failure probability, based on the trained ANN surrogate. Three numerical examples are used to illustrate the accuracy and efficiency of the proposed method.
