A review of generator maintenance scheduling using artificial intelligence techniques

Description

New Artificial Intelligence (AI) approaches such as simulated annealing, genetic algorithms, simulated evolution, neural networks, tabu
search, fuzzy logic and their hybrid techniques have been applied in recent years to solving Generator Maintenance Scheduling (GMS)
problems. This paper presents a review of these AI approaches for the GMS problem. The formulation of problems and the
methodologies of solution are discussed and analysed. A case study is also included which presents the application of a genetic
algorithm to a test system based on a practical power system scenario.

Links & Downloads

1. Dahal, K. P. and McDonald, J. R. (1997) A review of generator maintenance scheduling using artificial intelligence techniques. In: 32nd Universities Power Engineering Conference (UPEC `97), University of Manchester, September 10-12, 1997.
2. http://hdl.handle.net/10454/2452

Tags

Maintenance Scheduling

Power generators

Genetic algorithms

Artificial intelligence

Additional Fields

Identifer	oai:union.ndltd.org:BRADFORD/oai:bradscholars.brad.ac.uk:10454/2452
Date	January 1997
Creators	Dahal, Keshav P., McDonald, J.R.
Source Sets	Bradford Scholars
Language	English
Detected Language	English
Type	Conference paper, Accepted Manuscript
Rights	© 1997 Universities Power Engineering Conference.