An analytical algorithm was presented for the exact computation of the probability distribution of the project completion time in stochastic networks,where the activity durations are mutually independent and continuou...An analytical algorithm was presented for the exact computation of the probability distribution of the project completion time in stochastic networks,where the activity durations are mutually independent and continuously distributed random variables. Firstly,stochastic activity networks were modeled as continuous-time Markov process with a single absorbing state by the well-know method of supplementary variables and the time changed from the initial state to absorbing state is equal to the project completion time.Then,the Markov process was regarded as a special case of Markov skeleton process.By taking advantage of the backward equations of Markov skeleton processes,a backward algorithm was proposed to compute the probability distribution of the project completion time.Finally,a numerical example was solved to demonstrate the performance of the proposed methodology.The results show that the proposed algorithm is capable of computing the exact distribution function of the project completion time,and the expectation and variance are obtained.展开更多
为了解决高比例分布式电源(distributed generation,DG)大规模并网后实时量测数目缺失、传统预测辅助状态估计方法(forecasting-aided state estimation,FASE)估计精度有限等问题,提出了基于改进Crossformer伪量测构建的主动配电网FASE...为了解决高比例分布式电源(distributed generation,DG)大规模并网后实时量测数目缺失、传统预测辅助状态估计方法(forecasting-aided state estimation,FASE)估计精度有限等问题,提出了基于改进Crossformer伪量测构建的主动配电网FASE方法。首先,基于最大信息系数法(maximal information coefficient,MIC)筛选出高相关性的输入特征,提高预测模型的精度;然后,通过全变差正则化技术(total variation regularized,TV)优化鲁棒主成分分析法(robust principal component analysis,RPCA),构建TRPCA层,并将其嵌入到Crossformer中,以填补Crossformer无法有效处理非高斯噪声的空白;最后,利用改进的预测模型进行超短期负荷预测,经潮流计算得到节点伪量测,在量测不足情况下补全缺失数据,并结合扩展卡尔曼滤波器(extended Kalman filter,EKF)进行状态估计。在IEEE 33节点和IEEE 118节点标准配电网上进行仿真测试,结果表明所提方法在估计精度和鲁棒性等方面具有一定优势,可为主动配电网FASE提供参考。展开更多
基金Project(10671212) supported by the National Natural Science Foundation of ChinaProject(20050533036) supported by the Specialized Research Found for the Doctoral Program Foundation of Higher Education of China
文摘An analytical algorithm was presented for the exact computation of the probability distribution of the project completion time in stochastic networks,where the activity durations are mutually independent and continuously distributed random variables. Firstly,stochastic activity networks were modeled as continuous-time Markov process with a single absorbing state by the well-know method of supplementary variables and the time changed from the initial state to absorbing state is equal to the project completion time.Then,the Markov process was regarded as a special case of Markov skeleton process.By taking advantage of the backward equations of Markov skeleton processes,a backward algorithm was proposed to compute the probability distribution of the project completion time.Finally,a numerical example was solved to demonstrate the performance of the proposed methodology.The results show that the proposed algorithm is capable of computing the exact distribution function of the project completion time,and the expectation and variance are obtained.
文摘为了解决高比例分布式电源(distributed generation,DG)大规模并网后实时量测数目缺失、传统预测辅助状态估计方法(forecasting-aided state estimation,FASE)估计精度有限等问题,提出了基于改进Crossformer伪量测构建的主动配电网FASE方法。首先,基于最大信息系数法(maximal information coefficient,MIC)筛选出高相关性的输入特征,提高预测模型的精度;然后,通过全变差正则化技术(total variation regularized,TV)优化鲁棒主成分分析法(robust principal component analysis,RPCA),构建TRPCA层,并将其嵌入到Crossformer中,以填补Crossformer无法有效处理非高斯噪声的空白;最后,利用改进的预测模型进行超短期负荷预测,经潮流计算得到节点伪量测,在量测不足情况下补全缺失数据,并结合扩展卡尔曼滤波器(extended Kalman filter,EKF)进行状态估计。在IEEE 33节点和IEEE 118节点标准配电网上进行仿真测试,结果表明所提方法在估计精度和鲁棒性等方面具有一定优势,可为主动配电网FASE提供参考。
