Optimal structure screening for large-scale multi-state series-parallel systems based on structure ordinal optimization

Abstract

Multi-state series-parallel systems are widely-used for representing engineering systems. In real-life cases, engineers need to select an optimal system structure among many different multi-state series-parallel system structures. Screening of system structures is meaningful and critical. Moreover, to design a reliable structure, reliability evaluation is an indispensable part of the process. Due to the large number of available system structures, the computational burden can be huge when selecting the optimal one. Also, the number of components and possible states of each system can be enormous when the system scale is large, which causes significant complexity in exact reliability evaluation. To effectively select the optimal structure among numerous multi-state series-parallel systems under a reliability constraint, this article proposes an optimal structure screening method called the structure ordinal optimization. The proposed method combines the fuzzy universal generating function technique with an ordinal optimization algorithm. The fuzzy universal generating function technique is applied to reduce the computational time by approximately evaluating the reliability. Based on the approximate reliabilities, ordinal optimization helps to reduce the number of structure options and thus accelerate the screening process. Numerical examples show that the structure ordinal optimization method has advantages in computational efficiency with satisfactory accuracy.

Keywords:

• Multi-state system
• structure screening
• ordinal optimization
• fuzzy universal generating function

Additional information

Funding

This work was supported partly by the National Natural Science Foundation of China (Grant #71871200 and #51537010).

Notes on contributors

Yishuang Hu

Yishuang Hu received a BS degree in electrical engineering from North China Electric Power University, China. She is currently pursuing a PhD degree in electrical engineering from Zhejiang University, Hangzhou, China. Her research interests include reliability analysis and structure optimization of the engineering system.

Yi Ding

Yi Ding received bachelor’s and PhD degrees in electrical engineering from Shanghai Jiaotong University, Shanghai, China, and Nanyang Technological University, Singapore in 2002 and 2007, respectively. He is a professor with the College of Electrical Engineering, Zhejiang University, Hangzhou, China. His current research interests include power systems reliability/performance analysis incorporating renewable energy resources, smart grid performance analysis, and engineering systems reliability modeling and optimization.

Yu Lin

Yu Lin received a Master’s degree in electrical engineering from Zhejiang University, China. His research interests include reliability analysis of power systems.

Ming J. Zuo

Ming J Zuo received a PhD degree in industrial engineering from Iowa State University, Ames, Iowa, USA. He is currently a full professor in the Department of Mechanical Engineering at the University of Alberta, Canada and an adjunct professor at University of Electronic Science and Technology of China. His research interests include system reliability analysis, maintenance modeling and optimization, signal processing, and fault diagnosis.

Donglian Qi

Donglian Qi received a PhD degree in control theory and control engineering from Zhejiang University, Hangzhou, China, in 2002. She is currently a professor at the College of Electrical Engineering, Zhejiang University. Her research interests include analysis and control of nonlinear systems and comprehensive utilization of renewable energy.