Please use this identifier to cite or link to this item: http://scholarbank.nus.edu.sg/handle/10635/25838
Title: Optimization and Implementation of Maintenance Schedule of Power Systems
Authors: YANG FAN
Keywords: Optimization, Implementation, Maintenance Scheduling, Artificial Intelligence, Substation, Power Systems
Issue Date: 17-Jan-2011
Source: YANG FAN (2011-01-17). Optimization and Implementation of Maintenance Schedule of Power Systems. ScholarBank@NUS Repository.
Abstract: The growing economic pressure and complexity of power systems has necessitated the development of intelligent tools to seek a cost-effective maintenance strategy to keep substations operating both reliably and economically. This thesis investigates the application of multi-objective evolutionary algorithms and fuzzy logic techniques for optimization and implementation of preventive maintenance scheduling. The overall objective is the development of an adaptive condition-based maintenance scheme to achieve a balance between the reliability benefits and costs of preventive maintenance in the presence of uncertainty and constraints. Preventive maintenance is performed to extend component lifetime in power systems, and at the same time, the maintenance cost is one of the main expenditure items. In order to evaluate and optimize preventive maintenance schedules, a two-level model for establishing a quantitative relationship between maintenance and reliability at the component level and overall system level has been developed. The strength of this reliability model lies in its ability to easily incorporate various failure modes, protection actions, and constraints in complex system. Based on prediction of reliability, Pareto-optimal maintenance schedules are obtained using multi-objective evolutionary algorithms. This powerful technique identifies the existence of several objectives, operational cost, expected energy not served, and failure cost, all of which are mutually exclusive. A holistic view of relationship between the conflicting objectives of substations has been provided by Pareto front, and the most compromised schedule for achieving certain requirements has been identified for the decision maker. In cooperation with the two-level reliability model, an integrated maintenance optimizer suitable for substations and their connected power grid has been developed. It has been tested on different basic substation configurations and medium-size power system (Roy Billinton Reliability Test System and IEEE Reliability Test System) and impressive results were obtained. Implementation of maintenance schedules according to actual operational variations and uncertainties is crucial for offshore substation because it is often remotely located and the information collected during implementation can rarely avoid uncertainties. Updating the reliability indices of key elements in offshore substations requires re-establish the Pareto-optimal maintenance schedules. A hierarchical fuzzy logic has been developed for effectively handling the operational variations and uncertainties. This approach avoids complex inference process, and it significantly reduces the computational complexity and rule base than conventional Type-1 fuzzy logic. The adaptive condition-based maintenance scheme described in this thesis provides an explicit framework for analyzing system reliability and costs under different maintenance strategies, and produces the optimal maintenance schedules for power systems. Simulation carried on an offshore substation shows that this approach is effective in re-establishing the optimal maintenance schedules in presence of continually updated operational variations during implementation.
URI: http://scholarbank.nus.edu.sg/handle/10635/25838
Appears in Collections:Ph.D Theses (Open)

Show full item record
Files in This Item:
File Description SizeFormatAccess SettingsVersion 
YangF.pdf5.11 MBAdobe PDF

OPEN

NoneView/Download

Page view(s)

382
checked on Dec 11, 2017

Download(s)

999
checked on Dec 11, 2017

Google ScholarTM

Check


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.