Hierarchical fuzzy logic system for implementing maintenance schedules of offshore power systems

• Main Authors: CHANG, C. S., WANG, Zhaoxia, YANG, Fan, TAN, W. W.
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2012
• Subjects: Adaptive maintenance advisor; hierarchical fuzzy logic; multiobjective evolutionary algorithm; offshore substation; smart grid; system maintenance optimizer; Numerical Analysis and Scientific Computing; Operations Research, Systems Engineering and Industrial Engineering
• Online Access: https://ink.library.smu.edu.sg/sis_research/5562; https://ink.library.smu.edu.sg/context/sis_research/article/6565/viewcontent/2012_HierarchicalFuzzyLogicSystem_MaintenanceSchedulesofOffshorePowerSystems_av.pdf
• Institution: Singapore Management University

Smart grid provides the technology for modernizing electricity delivery systems by using distributed and computer-based remote sensing, control and automation, and two-way communications. Potential benefits of the technology are that the smart grid's central control will now be able to control and operate many remote power plant, optimize the overall asset utilization and operational efficiently. In this paper, we propose an innovative approach for the smart grid to handle uncertainties arising from condition monitoring and maintenance of power plant. The approach uses an adaptive maintenance advisor and a system-maintenance optimizer for designing/implementing optimized condition-based maintenance activities, and collectively handles operational variations occurring in each substation. The system-maintenance optimizer generates the initial maintenance plans for each substation with multiobjective optimization by considering only the design or average operational conditions. During operation, the substation will experience aging, control shifts, changing weather and load factors, and uncertain measurements. Residing on each host substation, the maintenance advisor will assess the adequacy of initial maintenance plans; and estimate the reliability changes caused by operational variations on the substation using a hierarchical fuzzy system. The advisor will also alert the maintenance optimizer on whether a reoptimization of its maintenance activities should be initiated for meeting the overall grid-reliability requirement. Three scenarios will be studied in this paper, which will demonstrate the ability of the proposed approach for handling operational variations occurring in an offshore substation with manageable computational complexity.