Title :
Machine learning techniques for diagnosing and locating faults through the automated monitoring of power electronic components in shipboard power systems
Author :
Mair, A.J. ; Davidson, E.M. ; McArthur, S.D.J. ; Srivastava, S.K. ; Schoder, K. ; Cartes, D.A.
Author_Institution :
Inst. for Energy & Environ., Univ. of Strathclyde, Glasgow
Abstract :
The management and control of shipboard medium voltage AC (MVAC) and medium voltage DC (MVDC) power system architectures under fault conditions present a number of challenges. The use and resulting interaction of multiple power electronic components in mesh-like power distribution architectures possibly result in the effects of faults being detectable throughout the system, for example, line-to-hull faults on DC systems with high resistive grounding.
Keywords :
belief networks; decision trees; fault location; learning (artificial intelligence); power electronics; power engineering computing; ships; support vector machines; Bayesian networks; automated monitoring; decision trees; fault diagnosis; fault location; machine learning; medium voltage DC power system architectures; nearest neighbour classifiers; power electronic components; radial basis function networks; shipboard medium voltage AC; shipboard power systems; support vector machines; Computerized monitoring; Condition monitoring; Control systems; Energy management; Machine learning; Medium voltage; Power electronics; Power system faults; Power system management; Voltage control;
Conference_Titel :
Electric Ship Technologies Symposium, 2009. ESTS 2009. IEEE
Conference_Location :
Baltimore, MD
Print_ISBN :
978-1-4244-3438-1
Electronic_ISBN :
978-1-4244-3439-8
DOI :
10.1109/ESTS.2009.4906553
