07-07-2012, 10:01 AM
Hierarchical Fuzzy Logic System for Implementing Maintenance Schedules of Offshore Power Systems
Maintenance Schedules of Offshore Power Systems[.pdf (Size: 1.35 MB / Downloads: 43)
Abstract
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.
INTRODUCTION
DEEP integration of condition-based maintenance within
the smart grid is very desirable for extending component
lifetime in power and energy system and achieving high operational
efficiency in the overall power system [1]. Unlike traditional
maintenance optimization methodologies that only consider
the equipment lifetime distribution [2]–[6], an adaptive
condition-based maintenance scheme is proposed in this paper.
The key difference is that other operation-related variations are
also considered. This feature is particularly useful for offshore
power systems because they are remotely located and difficult
to access for data acquisition, inspection, and maintenance.
APPENDIX MAINTENANCE OPTIMIZER
The task of system maintenance optimizer is to optimize the
maintenance schedules for an offshore substation and its connected
grid for providing the best trade-off between multiobjective
functions such as its reliability and costs of maintenance.
Having the main advantage of computing the entire Pareto
Front in one single rather than many algorithm runs, evolutionary
algorithms have been widely used for solving
multiobjective optimization. Their other advantage is simplicity
in terms of formulation and implementation for solving
problems especially with noncontinuous objective functions
in a large-scale search space. Comparing with many conventional
optimization approaches, evolutionary algorithms do
not require any gradient information of their objective and
other functions during computation. Non-dominated Sorting
Genetic Algorithm-II (NSGA-II) is one of standard approaches
of evolutionary algorithms.