Please use this identifier to cite or link to this item: https://doi.org/10.1049/iet-gtd.2009.0172
Title: Optimisation of maintenance schedules and extents for composite power systems using multi-objective evolutionary algorithm
Authors: Yang, F.
Chang, C.S. 
Issue Date: 2009
Source: Yang, F.,Chang, C.S. (2009). Optimisation of maintenance schedules and extents for composite power systems using multi-objective evolutionary algorithm. IET Generation, Transmission and Distribution 3 (10) : 930-940. ScholarBank@NUS Repository. https://doi.org/10.1049/iet-gtd.2009.0172
Abstract: Reducing the overall cost and improving the reliability are the two primary but often conflicting objectives in power system. Preventive-maintenance schedules thus need to be optimised to trade-off among multiple objectives. An integrated methodology with three functional blocks is proposed in this study. The first block models the stochastic deterioration process of individual component with a continuous-time Markov model, of which transition rates are influenced by different maintenance extents and aging of components. The second block evaluates the reliability of a composite power system, taking into account the configuration and failure dependence of the system. Particularly, this block identifies the minimum cut sets with consideration of protection trip and operational switching. The third block employs the Pareto-based multi-objective evolutionary algorithm to find the optimal solutions in a large search space and provide a holistic view of relationships among conflicting multiple objectives. A novel representation of maintenance activities is introduced in this study specifying both the maintenance timings and extents, and is proven to outperform the authors' previous representation, specifying the maintenance frequencies only. Optimisation of the reliability, maintenance failure costs is carried out on the Roy Billinton Test System (RBTS) demonstrating the potential of this approach in handling complex systems. © The Institution of Engineering and Technology.
Source Title: IET Generation, Transmission and Distribution
URI: http://scholarbank.nus.edu.sg/handle/10635/56949
ISSN: 17518687
DOI: 10.1049/iet-gtd.2009.0172
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