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Title: Research and development of diagnostic algorithms to support fault accommodating control for emerging shipboard power system architectures
Author: Soman, Ruturaj
Awarding Body: University of Strathclyde
Current Institution: University of Strathclyde
Date of Award: 2013
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The U.S. Navy has proposed development of next generation warships utilising an increased amount of power electronics devices to improve flexibility and controllability. The high power density finite inertia network is envisioned to employ automated fault detection and diagnosis to aid timely remedial action. Integration of condition monitoring and fault diagnosis to form an intelligent power distribution system is anticipated to assist decision support for crew while enhancing security and mission availability. This broad research being in the conceptual stage has lack of benchmark systems to learn from. Thorough studies are required to successfully enable realising benefits offered by using increased power electronics and automation. Application of fundamental analysis techniques is necessary to meticulously understand dynamics of a novel system and familiarisation with associated risks and their effects. Additionally, it is vital to find ways of mitigating effects of identified risks. This thesis details the developing of a generalised methodology to help focus research into artificial intelligence (AI) based diagnostic techniques. Failure Mode and Effects Analysis (FMEA) is used in identifying critical parts of the architecture. Sneak Circuit Analysis (SCA) is modified to provide signals that differentiate faults at a component level of a dc-dc step down converter. These reliability analysis techniques combined with an appropriate AI-algorithm offer a potentially robust approach that can potentially be utilised for diagnosing faults within power electronic equipment anticipated to be used onboard the novel SPS. The proposed systematic methodology could be extended to other types of power electronic converters, as well as distinguishing subsystem level faults. The combination of FMEA, SCA with AI could also be used for providing enhanced decision support. This forms part of future research in this specific arena demonstrating the positives brought about by combining reliability analyses techniques with AI for next generation naval SPS.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available