Open AccessGenerator Maintenance Scheduling Models for Electrical Power Systems: A Review
Author(s) -
Shatha Abdulhadi Muthana,
Ku Ruhana Ku-Mahamud
Publication year - 2021
Publication title -
international journal of electrical and electronic engineering and telecommunications
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.171
H-Index - 6
ISSN - 2319-2518
DOI - 10.18178/ijeetc.10.5.307-318
Subject(s) - reliability engineering , electric power system , scheduling (production processes) , computer science , schedule , operations research , pareto principle , preventive maintenance , mathematical optimization , industrial engineering , engineering , power (physics) , operations management , mathematics , physics , quantum mechanics , operating system
Interest in Generator Maintenance Scheduling (GMS) has increased due to the advent of demand-related expansion in size for modern power systems. Timely maintenance plays a significant role in minimizing failures and helps in averting cost incurred as a result of production shutdowns. The GMS problem is a complex and nonlinear optimization problem that specifies the schedule for carrying out planned preventive maintenance on power generation units. There is no clear concept to GMS model types and choosing the appropriate maintenance scheduling type. Thus, this paper presented a comprehensive review on GMS models in electrical power systems that covers the maintenance strategies, main elements of GMS models, and optimization methods used in solving GMS models. The list of references comprised related works from the years 2000 until 2020, which were classified into three based on the objectives. A new type of objective function for the GMS models was among the suggestions provided. A numerical example which focuses on a multi-objective GMS model and a proposed multi-objective Pareto ant colony system algorithm are also presented. The results of this review will not only enable researchers to gain a good overview of the existing GMS models for electrical power systems but also provide a source of references in choosing an appropriate maintenance scheduling strategy that is suitable with the type of generating unit and existing operating conditions.