Zhang, Z. (2014) Data Mining Approaches for Intelligent Condition-Based Maintenance—A Framework of Intelligent Fault Diagnosis and Prognosis System (IFDPS). Norwegian University of Science and Technology.

has been cited by the following article:

• TITLE: Multi-ACO Application in Routing and Scheduling Optimization of Maintenance Fleet (RSOMF) Based on Conditions for Offshore Wind Farms

  AUTHORS: Zhenyou Zhang

  KEYWORDS: Multi-Ant Colony Optimization, Offshore Wind Farm, Fleeting Scheduling and Routing, Operation and Maintenance

  JOURNAL NAME: Journal of Power and Energy Engineering, Vol.6 No.10, October 23, 2018

  ABSTRACT: Reducing the operation and maintenance (O & M) cost is one of the potential actions that could reduce the cost of energy produced by offshore wind farms. This article attempts to reduce O & M cost by improving the utilization of the maintenance resources, specifically the efficient scheduling and routing of the maintenance fleet. Scheduling and routing of maintenance fleet is a non-linear optimization problem with high complexity and a number of constraints. A heuristic algorithm, Ant Colony Optimization (ACO), was modified as Multi-ACO to be used to find the optimal scheduling and routing of maintenance fleet. The numerical studies showed that the proposed methodology was effective and robust enough to find the optimal solution even if the number of offshore wind turbine increases. The suggested approaches are helpful to avoid a time-consuming process of manually planning the scheduling and routing with a presumably suboptimal outcome.