{"title":"Reliability-based preventive maintenance scheduling in power generation systems: A lévy flight and chaotic local search-based discrete mayfly algorithm","authors":"Soufiane Belagoune , Konstantinos Zervoudakis , Bousaadia Baadji , Atif Karim , Noureddine Bali","doi":"10.1016/j.compeleceng.2024.109904","DOIUrl":null,"url":null,"abstract":"<div><div>An adequate and reliable precautionary upkeep plan in power generation systems is required to reduce failures, to improve the generator's lifespan, to diminish repair costs and to ensure consistent power supply to consumers with managing the energy flows in power systems. The Generators’ Precautionary Upkeep Planning (GPUP) problem is a complex optimization problem. It is a critical challenge in the power generation industry, involving the optimization of maintenance schedules for power generators to minimize the generation reserve and maximize the reliability. This problem consists of several important restrictions which include the load power demand and the labour force restrictions. In this research paper, a Discrete Chaotic Mayfly Optimization (DCMFO) algorithm which uses Lévy flight random walk for female mayflies and chaotic local search move rule for male ones, is adapted for designing an appropriate precautionary upkeep scheme of a list of generators in power generation systems. The DCMFO algorithm is evaluated using 21-unit test thermal power system. The results indicate that unlike the classical DMFO algorithm, the DCMFO algorithm has proven to have superior optimization capabilities and to surpass all earlier adopted algorithms in performance. This reinforces DCMFO's standing as the current leading optimization algorithm for solving this particular problem, ever since its initial inception. The DCMFO's efficiency and reliability have been demonstrated with different cases through several statistical tests.</div></div>","PeriodicalId":50630,"journal":{"name":"Computers & Electrical Engineering","volume":"121 ","pages":"Article 109904"},"PeriodicalIF":4.0000,"publicationDate":"2024-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Computers & Electrical Engineering","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0045790624008309","RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, HARDWARE & ARCHITECTURE","Score":null,"Total":0}
Reliability-based preventive maintenance scheduling in power generation systems: A lévy flight and chaotic local search-based discrete mayfly algorithm
An adequate and reliable precautionary upkeep plan in power generation systems is required to reduce failures, to improve the generator's lifespan, to diminish repair costs and to ensure consistent power supply to consumers with managing the energy flows in power systems. The Generators’ Precautionary Upkeep Planning (GPUP) problem is a complex optimization problem. It is a critical challenge in the power generation industry, involving the optimization of maintenance schedules for power generators to minimize the generation reserve and maximize the reliability. This problem consists of several important restrictions which include the load power demand and the labour force restrictions. In this research paper, a Discrete Chaotic Mayfly Optimization (DCMFO) algorithm which uses Lévy flight random walk for female mayflies and chaotic local search move rule for male ones, is adapted for designing an appropriate precautionary upkeep scheme of a list of generators in power generation systems. The DCMFO algorithm is evaluated using 21-unit test thermal power system. The results indicate that unlike the classical DMFO algorithm, the DCMFO algorithm has proven to have superior optimization capabilities and to surpass all earlier adopted algorithms in performance. This reinforces DCMFO's standing as the current leading optimization algorithm for solving this particular problem, ever since its initial inception. The DCMFO's efficiency and reliability have been demonstrated with different cases through several statistical tests.
期刊介绍:
The impact of computers has nowhere been more revolutionary than in electrical engineering. The design, analysis, and operation of electrical and electronic systems are now dominated by computers, a transformation that has been motivated by the natural ease of interface between computers and electrical systems, and the promise of spectacular improvements in speed and efficiency.
Published since 1973, Computers & Electrical Engineering provides rapid publication of topical research into the integration of computer technology and computational techniques with electrical and electronic systems. The journal publishes papers featuring novel implementations of computers and computational techniques in areas like signal and image processing, high-performance computing, parallel processing, and communications. Special attention will be paid to papers describing innovative architectures, algorithms, and software tools.