In practical engineering, many uncertain factors in loading or degradation of material properties may vary with time. Stochastic process modeling constitutes a suitable approach for describing these time-dependent unc...In practical engineering, many uncertain factors in loading or degradation of material properties may vary with time. Stochastic process modeling constitutes a suitable approach for describing these time-dependent uncertainties. By adopting this approach, however, the timedependent reliability calculation is a great challenge owing to the complexity and the huge computational burden. This paper presents a new instantaneous response surface method t-IRS for time-dependent reliability analysis. Different from the adaptive extreme response surface approach, the proposed method does not need to build and update surrogate models separately at each time node. It first uses the expansion optimal linear estimation method to discretize the stochastic processes into a set of independent standard normal variables together with some deterministic functions of time. Time is then treated as an independent one-dimensional variable. Next, initial samples are generated by Latin hypercube sampling, and the corresponding response values are calculated and utilized to construct an instantaneous response surrogate model of the Kriging type. The active learning method is applied to update the Kriging surrogate model until satisfactory accuracy is achieved. Finally, the instantaneous response surrogate model is used to compute the time-dependent reliability via Monte Carlo simulation. Four case studies are utilized to demonstrate the effectiveness of the ^-IRS method for time-dependent reliability analysis.展开更多
高比例分布式电源的不确定性给孤岛配电网的稳定运行带来了的巨大的挑战。针对基于传统分布模型的源荷短期预测存在尖峰和重尾的缺点,采用双向长短时记忆(bidirectional long and short-term memory,BiLSTM)神经网络与非参数核密度法(ke...高比例分布式电源的不确定性给孤岛配电网的稳定运行带来了的巨大的挑战。针对基于传统分布模型的源荷短期预测存在尖峰和重尾的缺点,采用双向长短时记忆(bidirectional long and short-term memory,BiLSTM)神经网络与非参数核密度法(kernel density method,KDE)结合的方法,构建了多场景及不同时间尺度下源荷预测误差的分布模型;并在此基础上,系统多时段运行调控过程中,考虑短时气象的不确定性波动,采用混合整数二阶锥规划(mixed-integer second-order cone programming,MISOCP)对潮流模型进行松弛,并由随机响应面(stochastic response surface,SRSM)得到系统的概率潮流;基于随机响应面法改进Sobol’法,建立计及源荷不确定性的孤岛配电网运行风险的全局灵敏度分析模型。基于此提出一种基于Bi LSTM-SRSM法的风险实时风险评估及调控策略。最后,采用IEEE33节点的辐射型配电网系统验证了所提方法的可行性。展开更多
基金supported by the National Natural Science Foundation of China (Nos.11572134 and 11832013).
文摘In practical engineering, many uncertain factors in loading or degradation of material properties may vary with time. Stochastic process modeling constitutes a suitable approach for describing these time-dependent uncertainties. By adopting this approach, however, the timedependent reliability calculation is a great challenge owing to the complexity and the huge computational burden. This paper presents a new instantaneous response surface method t-IRS for time-dependent reliability analysis. Different from the adaptive extreme response surface approach, the proposed method does not need to build and update surrogate models separately at each time node. It first uses the expansion optimal linear estimation method to discretize the stochastic processes into a set of independent standard normal variables together with some deterministic functions of time. Time is then treated as an independent one-dimensional variable. Next, initial samples are generated by Latin hypercube sampling, and the corresponding response values are calculated and utilized to construct an instantaneous response surrogate model of the Kriging type. The active learning method is applied to update the Kriging surrogate model until satisfactory accuracy is achieved. Finally, the instantaneous response surrogate model is used to compute the time-dependent reliability via Monte Carlo simulation. Four case studies are utilized to demonstrate the effectiveness of the ^-IRS method for time-dependent reliability analysis.
文摘高比例分布式电源的不确定性给孤岛配电网的稳定运行带来了的巨大的挑战。针对基于传统分布模型的源荷短期预测存在尖峰和重尾的缺点,采用双向长短时记忆(bidirectional long and short-term memory,BiLSTM)神经网络与非参数核密度法(kernel density method,KDE)结合的方法,构建了多场景及不同时间尺度下源荷预测误差的分布模型;并在此基础上,系统多时段运行调控过程中,考虑短时气象的不确定性波动,采用混合整数二阶锥规划(mixed-integer second-order cone programming,MISOCP)对潮流模型进行松弛,并由随机响应面(stochastic response surface,SRSM)得到系统的概率潮流;基于随机响应面法改进Sobol’法,建立计及源荷不确定性的孤岛配电网运行风险的全局灵敏度分析模型。基于此提出一种基于Bi LSTM-SRSM法的风险实时风险评估及调控策略。最后,采用IEEE33节点的辐射型配电网系统验证了所提方法的可行性。
