Fast reliability evaluation method for composite power system based on the improved EDA and double cross linked list

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 comprehe...

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Veröffentlicht in:IET generation, transmission & distribution Jg. 11; H. 15; S. 3835 - 3842
Hauptverfasser: Liu, Wenxia, Cheng, Rui, Xu, Yahui, Liu, Zongqi
Format: Journal Article
Sprache:Englisch
Veröffentlicht: The Institution of Engineering and Technology 01.10.2017
Wiley
Schlagworte:
composite power systems reliability evaluation
double cross linked list
EDA
estimation of distribution algorithm
fast reliability evaluation method
intelligent storage technology
match process
Monte Carlo methods
Monte Carlo simulation
mutation strategy
optimal power flow
population‐based incremental learning algorithm
power system reliability
probability vector
Research Article
state evaluation stage
state search
vectors
EDA
estimation of distribution algorithm
optimal power flow
population-based incremental learning algorithm
state search
mutation strategy
state evaluation stage
Monte Carlo methods
vectors
intelligent storage technology
match process
power system reliability
double cross linked list
composite power systems reliability evaluation
fast reliability evaluation method
Monte Carlo simulation
probability vector
ISSN:1751-8687, 1751-8695
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Beschreibung
Zusammenfassung: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.
ISSN:1751-8687
1751-8695
DOI:10.1049/iet-gtd.2016.1927