摘要
为进一步加快蒙特卡洛模拟法的收敛速度,文中结合重要抽样法与控制变量法的思想,提出了一种应用于电力系统可靠性评估的重要控制算法。该方法利用元件重要度分析识别出系统中对可靠性影响较大的元件,并基于系统重要元件的故障后果以及系统状态分析的特点构造出满足各项要求的系统重要控制变量,从而实现对系统重要状态函数的重构。文中从理论上对该方法进行了推导,证明了其在不增加每次抽样计算量的同时,能够有效降低抽样方差。最后通过对IEEERTS79系统以及修改后的测试系统的评估验证了算法的正确性、高效性。
In order to further accelerate the convergence speed of Monte Carlo method, an importance-control method （ICM） for power system reliability evaluation is proposed by referring to the importance sampling method and variable control method. ICM makes use of component importance analysis to identify the components with a greater influence on system reliability. Based on the failure effect of the above important components and the characteristics of system state analysis, ICM can construct importance control variables that meet the requirements for calculation. With the importance control variables constructed, the reconfiguration of system important state function can be realized. It is shown by theoretical inference from the method that it is capable of effectively reducing the sampling variance without increasing the sampling calculation workload per time. The accuracy and efficiency of ICM is verified using an IEEE-RTS79 system and the modified system. This work is supported by National Key Technology R＆D Program of China （No. 2013BAA01B02）.
出处
《电力系统自动化》
EI
CSCD
北大核心
2015年第5期69-74,共6页
Automation of Electric Power Systems
基金
国家科技支撑计划资助项目(2013BAA01B02)~~
关键词
可靠性评估
蒙特卡洛法
重要抽样
控制变量
重要度分析
reliability evaluation
Monte Carlo method
importance sampling
control variable
importance analysis
