{"title":"Reliability analysis and optimization of multi-state tree-structured systems with performance sharing mechanism","authors":"Liudong Gu , Guanjun Wang , Yifan Zhou","doi":"10.1016/j.ress.2025.110990","DOIUrl":null,"url":null,"abstract":"<div><div>Existing studies on reliability modeling of performance sharing systems (PSSs) have primarily focused on common bus or series structure. However, in some practical PSSs, units are organized in a tree structure. This paper addresses the research gap in reliability optimization of tree-structured PSSs. In such systems, units with random performance and demand are arranged in different layers. The surplus performance of each unit can be shared by the connected units located in adjacent layers. The system fails if there exists performance deficiency. A recursive method is proposed to determine the state of connected units. Additionally, a reliability evaluation algorithm is developed based on universal generating function. We further investigate the optimization of transmission capacity allocation to maximize system reliability. To streamline the search for the optimal solution, a strategy space reduction approach is introduced to derive the more appropriate value range of each decision variables, thereby simplifying the optimization process. Genetic algorithm (GA) is employed to identify the optimal solution within the optimized strategy space. Validation through two power systems demonstrates that the proposed reliability evaluation method accurately evaluates the system reliability, and the improved GA efficiently finds a superior transmission capacity allocation strategy compared to the conventional GA.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":"260 ","pages":"Article 110990"},"PeriodicalIF":9.4000,"publicationDate":"2025-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Reliability Engineering & System Safety","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0951832025001930","RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENGINEERING, INDUSTRIAL","Score":null,"Total":0}
引用次数: 0
Abstract
Existing studies on reliability modeling of performance sharing systems (PSSs) have primarily focused on common bus or series structure. However, in some practical PSSs, units are organized in a tree structure. This paper addresses the research gap in reliability optimization of tree-structured PSSs. In such systems, units with random performance and demand are arranged in different layers. The surplus performance of each unit can be shared by the connected units located in adjacent layers. The system fails if there exists performance deficiency. A recursive method is proposed to determine the state of connected units. Additionally, a reliability evaluation algorithm is developed based on universal generating function. We further investigate the optimization of transmission capacity allocation to maximize system reliability. To streamline the search for the optimal solution, a strategy space reduction approach is introduced to derive the more appropriate value range of each decision variables, thereby simplifying the optimization process. Genetic algorithm (GA) is employed to identify the optimal solution within the optimized strategy space. Validation through two power systems demonstrates that the proposed reliability evaluation method accurately evaluates the system reliability, and the improved GA efficiently finds a superior transmission capacity allocation strategy compared to the conventional GA.
期刊介绍:
Elsevier publishes Reliability Engineering & System Safety in association with the European Safety and Reliability Association and the Safety Engineering and Risk Analysis Division. The international journal is devoted to developing and applying methods to enhance the safety and reliability of complex technological systems, like nuclear power plants, chemical plants, hazardous waste facilities, space systems, offshore and maritime systems, transportation systems, constructed infrastructure, and manufacturing plants. The journal normally publishes only articles that involve the analysis of substantive problems related to the reliability of complex systems or present techniques and/or theoretical results that have a discernable relationship to the solution of such problems. An important aim is to balance academic material and practical applications.