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Tri-objective generator maintenance scheduling model based on sequential strategy.

Shatha Abdulhadi Muthana1, Ku Ruhana Ku-Mahamud2,3

  • 1General Company of South Electricity Distribution, Ministry of Electricity, Baghdad, Iraq.

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|October 18, 2022
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Summary
This summary is machine-generated.

This study introduces a new tri-objective generator maintenance scheduling (GMS) model using a sequential approach. It optimizes cost, reliability, and violation, outperforming traditional periodic methods for longer generator lifespan.

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Area of Science:

  • Electrical Engineering
  • Operations Research
  • Power Systems Analysis

Background:

  • Traditional generator maintenance scheduling (GMS) uses periodic approaches with fixed windows, limiting generator lifespan and suitability for diverse units.
  • Existing multi-objective GMS models often fail to account for operational hours and start-up times, crucial for realistic scheduling.

Purpose of the Study:

  • To propose a novel tri-objective GMS model employing a sequential system approach, considering operating hours and start-up times.
  • To enhance generator lifespan and system reliability while minimizing operational costs and scheduling violations.
  • To develop a Pareto Ant Colony System (PACS) algorithm for solving the proposed GMS model.

Main Methods:

  • Development of a tri-objective GMS model based on the sequential system approach, optimizing cost, reliability, and violation.
  • Creation of a multi-objective graph model for generating maintenance unit schedules.
  • Implementation of a proposed Pareto Ant Colony System (PACS) algorithm to solve the GMS problem.

Main Results:

  • The proposed tri-objective GMS model demonstrated robust solutions by incorporating initial operational hours of generating units.
  • The sequential approach proved more effective than periodic methods for optimizing maintenance schedules.
  • Evaluation on IEEE RTS 26, 32, and 36-unit systems validated the model's performance.

Conclusions:

  • The sequential GMS model offers a more realistic and effective approach to generator maintenance scheduling.
  • The PACS algorithm provides an efficient method for obtaining optimal solutions for complex GMS problems.
  • This research contributes to improved power system reliability and extended generator operational life.