Open AccessFast reliability evaluation method for composite power system based on the improved EDA and double cross linked list
Author(s) -
Liu Wenxia,
Cheng Rui,
Xu Yahui,
Liu Zongqi
Publication year - 2017
Publication title -
iet generation, transmission and distribution
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.92
H-Index - 110
eISSN - 1751-8695
pISSN - 1751-8687
DOI - 10.1049/iet-gtd.2016.1927
Subject(s) - reliability (semiconductor) , monte carlo method , population , computer science , computation , sampling (signal processing) , limit (mathematics) , estimation of distribution algorithm , process (computing) , algorithm , power (physics) , reliability engineering , state (computer science) , mathematical optimization , mathematics , engineering , statistics , physics , quantum mechanics , mathematical analysis , demography , filter (signal processing) , sociology , computer vision , operating system
To improve the computational efficiency of Monte Carlo simulation in composite power systems reliability evaluation, this study presents a method based on the improved estimation of distribution algorithm (EDA) and double cross linked list. Compared to traditional techniques, this method is comprehensively improved in the stage of both sampling and state evaluation. In the sampling stage, the population‐based incremental learning algorithm is presented, where the probability vector is updated based on the distribution characteristics of excellent samples in population of previous generations. Meanwhile, setting a limit to the probabilities of elements in normal state and mutation strategy are introduced, which improves the excellent characteristics of the population. In the state evaluation stage, the state search and match process is speed up by utilising the intelligent storage technology based on the double across linked list. It avoids calling the optimal power flow for the same state repeatedly. Finally, the proposed method is tested in IEEE RTS 79. As the result shows, compared with other methods ever used in reliability evaluation, this method is not only more efficient in computation but also more accurate. Thus, the proposed method is proved to be reliable and effective.