Generator maintenance scheduling in power systems using metaheuristic-based hybrid approaches
The effective maintenance scheduling of power system generators is very important for the economical and reliable operation of a power system. This represents a tough scheduling problem which continues to present a challenge for efficient optimization solution techniques. This paper presents the app...
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| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
2014
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| Online Access: | http://www.scopus.com/inward/record.url?eid=2-s2.0-33947162394&partnerID=40&md5=8fb8384378752a75839a78a430be54f8 http://cmuir.cmu.ac.th/handle/6653943832/951 |
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| Institution: | Chiang Mai University |
| Language: | English |
| Summary: | The effective maintenance scheduling of power system generators is very important for the economical and reliable operation of a power system. This represents a tough scheduling problem which continues to present a challenge for efficient optimization solution techniques. This paper presents the application of metaheuristic approaches, such as a genetic algorithm (GA), simulated annealing (SA) and their hybrid for generator maintenance scheduling (GMS) in power systems using an integer representation. This paper mainly focuses on the application of GA/SA and GA/SA/heuristic hybrid approaches. GA/SA hybrid uses the probabilistic acceptance criterion of SA within the GA framework. GA/SA/heuristic hybrid combines heuristic approaches within the GA/SA hybrid to seed the initial population. A case study is formulated in this paper as an integer programming problem using a reliability-based objective function and typical problem constraints. The implementation and performance of the metaheuristic approaches and their hybrid for the test case study are discussed. The results obtained are promising and show that the hybrid approaches are less sensitive to the variations of technique parameters and offer an effective alternative for solving the generator maintenance scheduling problem. © 2006 Elsevier B.V. All rights reserved. |
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