摘要
针对电力系统抗震可靠性评估中蒙特卡罗方法误差收敛相对较慢的特点,将以低偏差序列抽样的拟蒙特卡罗方法应用于可靠性评估中,并结合了在求解传递闭包中能够减少计算量的三角形算法,建立了结合低偏差序列抽样与三角形算法的抗震可靠性计算模型.基于川北地区110 k V发电站与变电站的可靠性分析,分别进行了三种算法下的标准蒙特卡罗方法模拟和Sobol序列拟蒙特卡罗方法模拟.模拟结果表明:在电力系统抗震可靠性求解中,与伪随机数序列相比,Sobol序列的解算结果具有更高的收敛速度.当抽样次数为5000次时,拟蒙特卡罗(QMC)方法的计算结果为0.6689,误差不超过0.1%,而蒙特卡罗(MC)方法的计算结果为0.6659,误差为0.389%;在相同抽样次数下,三角型算法相对于其他算法具有更高的运算效率,将三角形算法与拟蒙特卡罗方法结合既提高了精确度又提高了运算速度.
Aiming at the characteristic of relatively slow error convergence of Monte Carlo method in seismic reliability assessment of power systems,the quasi-Monte Carlo method sampled by low discrepancy sequence was applied in reliability evaluation.In addition,triangle algorithm,which can reduce computation amount in solving transitive closure,was applied to establish a postearthquake connectivity reliability calculation model combining low-deviation sequence sampling and triangle algorithm.Based on the reliability analysis of 110 k V power station and substation in north sichuan,the standard Monte Carlo simulation and Sobol sequence quasi-Monte Carlo simulation were carried out respectively.The simulation results show that compared with the pseudorandom number sequence,the solution result of Sobol sequence has a higher convergence speed in the power system seismic reliability solution.When the number of samplings is 5000,the calculation result of the quasi-Monte Carlo method is 0.6689,and the error does not exceed 0.1%,while the calculation method of the Monte Carlo method is 0.6659,and the error is 0.389%.At the same sampling number,the triangular algorithm has higher operation efficiency,combining triangle algorithm with quasi-Monte Carlo method not only improves the accuracy but also the operation speed.
作者
刘晓航
贺金川
郑山锁
汪靖
LIU Xiaohang;HE Jinchuan;ZHENG Shansuo;WANG Jing(School of Civil Engineering,Xi'an University of Architecture and Technology,Xi'an 710055,China;Key Lab of Structural Engineering and Earthquake Resistance,Ministry of Education,Xi'an University of Architecture and Technology,Xi'an 710055,China;Architectural Design and Research Institute,Xi'an University of Architecture and Technology,Xi'an 710055,China)
出处
《华中科技大学学报（自然科学版）》
EI
CAS
CSCD
北大核心
2020年第9期119-125,共7页
Journal of Huazhong University of Science and Technology(Natural Science Edition)
基金
国家自然科学基金资助项目(51678475)
国家重点研发计划资助项目(2019YFC1509302)
西安市科技计划资助项目(2019113813CXSF016SF026)
陕西省教育厅产业化项目(18JC020)。
