Optimizing the maximum filling level of perfect storage in system with imperfect production unit-Reference-Cited by-同舟云学术

Optimizing the maximum filling level of perfect storage in system with imperfect production unit

Published:2022-09 Issue: Volume:225 Page:108629
ISSN:0951-8320
Container-title:Reliability Engineering & System Safety
language:en
Short-container-title:Reliability Engineering & System Safety

Author:

Levitin Gregory^ORCID,Xing Liudong,Dai Yuanshun

Publisher

Elsevier BV

Subject

Industrial and Manufacturing Engineering,Safety, Risk, Reliability and Quality

Reference34 articles.

1. Power system reliability impact of energy storage integration with intelligent operation strategy;Xu;IEEE Trans Smart Grid,2013

2. Optimal storage scheduling using Markov decision processes;Grillo;IEEE Trans Sustain Energy,2016

3. Semi-Markov processes with semi-regenerative states for the availability analysis of chemical process plants with storage units;Fink;Proc Inst Mech Eng Part O,2013

4. An integrated transmission expansion planning and battery storage systems placement - a security and reliability perspective;Kazemi;Int J Electr Power Energy Syst,2022

5. Reliability assessment of multi-state phased mission systems with common bus performance sharing considering transmission loss and performance storage;Cheng;Reliab Eng Syst Saf,2020

Cited by 9 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Corrective maintenance minimizing expected mission losses in shock exposed multistate production-storage systems;Reliability Engineering & System Safety;2024-12

2. Intelligent spare ordering and replacement optimisation leveraging adaptive prediction information;Reliability Engineering & System Safety;2024-12

3. Reliability analysis for a generalized sparse connection multi-state consecutive-k-out-of-n linear system;Reliability Engineering & System Safety;2024-06

4. Optimizing corrective maintenance for multistate systems with storage;Reliability Engineering & System Safety;2024-04

5. Benefit of warm-up period, maintenance, reworking, and fuzzy learning in a cleaner production system;AIP Conference Proceedings;2024