The problem of distribution system maintenance has a combinatorial explosion of choices. It may not be worthwhile to solve this problem using the analytical method that require too much time and cost. Ordinal optimization is a prime candidate of stochastic optimization problem. Ordinal optimization is used to quickly narrow the search space and find a good enough solution with reasonable confidence. This paper proposes the reliability centered maintenance model of distribution systems, and the solution of this problem through the application of ordinal optimization. A numerical examples are performed to illustrate the efficiency of the proposed ordinal optimization and to find a maintenance strategy of distribution systems.Maintenance has to be performed to reduce outages and maintain system reliability in RCM. Maintenance decisions are determined by considering the effect of equipment failure to the system. We proposed the equipment modeling through modified Markov chain [10] that is suitable for the concept of RCM. This model describes the deterioration process, inspection, and maintenance. Figure 1 shows the equipment model. Figure 1. Basic modified Markov chain model.
This paper presents the application of particle swarm optimization (PSO) technique to find the optimal maintenance strategy of transmission equipment with minimum total expected cost of generation cost, maintenance cost, repair cost and outage cost. Three types of transmission equipment, the overhead line, the underground cable and the insulator are considered. To consider aging, the equipment state model through modified Markov chain is proposed. Simulation is performed on IEEE 9-bus systems. The results obtained are quite encouraging and will be useful in maintenance scheduling.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.