Please use this identifier to cite or link to this item: https://doi.org/10.1109/ICSMC.2009.5346899
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dc.titleAdaptive type-2 fuzzy maintenance advisor for offshore power systems
dc.contributor.authorWang, Z.-X.
dc.contributor.authorChang, C.S.
dc.contributor.authorYang, F.
dc.contributor.authorTan, W.W.
dc.date.accessioned2014-06-19T02:58:19Z
dc.date.available2014-06-19T02:58:19Z
dc.date.issued2009
dc.identifier.citationWang, Z.-X., Chang, C.S., Yang, F., Tan, W.W. (2009). Adaptive type-2 fuzzy maintenance advisor for offshore power systems. Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics : 4520-4526. ScholarBank@NUS Repository. https://doi.org/10.1109/ICSMC.2009.5346899
dc.identifier.isbn9781424427949
dc.identifier.issn1062922X
dc.identifier.urihttp://scholarbank.nus.edu.sg/handle/10635/69231
dc.description.abstractProper maintenance strategies are very desirable for minimizing the operational and maintenance costs of power systems without sacrificing reliability. Condition-based maintenance has largely replaced time-based maintenance because of the former's potential economic benefits. As offshore substations are often remotely located, they experience more adverse environments, higher failures, and therefore need more powerful analytical tools than their onshore counterpart. As reliability information collected during operation of an offshore substation can rarely avoid uncertainties, it is essential to obtain consistent estimates of reliability measures under changing environmental and operating conditions. Some attempts with type-1 fuzzy logic were made with limited success in handling uncertainties occurring in onshore power-system maintenance. An adaptive maintenance advisor using type-2 fuzzy logic is proposed here for handling operational variations and uncertainties for condition-based maintenance of an offshore substation. The maintenance advisor receives maintenance plans for its key components from a system maintenance optimizer, which is optimizing all the maintenance activities in the entire connected grid by considering only major system variables and the overall system performance. During operation, the offshore substation will experience continuing ageing and shifts in control, set-point, weather and load factors, measurement and humanjudgment detected from the connected grid and all other equipments; which will certainly contain a lot of uncertainties. The advisor implements the system-optimized maintenance plan within its offshore substation, and estimates the change of loadpoint reliability due to operational variations and uncertainties of its key components. The maintenance advisor will report any drastic deterioration of load-point reliability within each substation, which may lead to re-optimization of the substation's maintenance activities for meeting its desired reliability during operation. The reliability of an offshore substation connected to a medium-sized onshore grid will be studied here using minimum cut set method. The relative merits between type-2 & type-1 fuzzy logic will also be studied in terms of their versatility, efficiency and ability for reliability modelling of operational variations and uncertainties. ©2009 IEEE.
dc.description.urihttp://libproxy1.nus.edu.sg/login?url=http://dx.doi.org/10.1109/ICSMC.2009.5346899
dc.sourceScopus
dc.subjectAdaptive maintenance advisor
dc.subjectHidden Markov model
dc.subjectLoad-point reliability
dc.subjectMinimum cut set
dc.subjectOffshore substation
dc.subjectType-2 fuzzy sets
dc.typeConference Paper
dc.contributor.departmentELECTRICAL & COMPUTER ENGINEERING
dc.description.doi10.1109/ICSMC.2009.5346899
dc.description.sourcetitleConference Proceedings - IEEE International Conference on Systems, Man and Cybernetics
dc.description.page4520-4526
dc.description.codenPICYE
dc.identifier.isiut000279574602172
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