NDLI: Machine learning based diagnosis support for ShipBoard Power Systems controls

Content Provider	IEEE Xplore Digital Library
Author	Amgai, R. Jian Shi Santos, R. Abdelwahed, S.
Copyright Year	2013
Description	Author affiliation: Dept. of Electr. & Comput. Eng., Mississippi State Univ., Starkville, MS, USA (Amgai, R.; Jian Shi; Santos, R.; Abdelwahed, S.)
Abstract	In this paper, a machine learning based decision support system for a naval shipboard power management system is proposed considering contingencies and load priority. A probabilistic model based Bayes' classifier is implemented to classify the current operation state of the ShipBoard Power System (SPS), depending upon the power system readiness for critical contingencies. Real power, reactive power, and generator status are taken as input features for the algorithm. Loss of vital/non-vital load is calculated by solving optimal power flow (OPF) to help build the knowledge base. Training data are updated online to increase the accuracy of the proposed approach. The characterization of the operation states helps the shipboard power management system to take the appropriate control action. Initial results from tests are presented and the outcomes from the particular techniques are discussed. Moreover, we also present RTDS based experimental framework towards the ongoing research on overall management system including the diagnosis support. Naïve Bayes' approach has classified the system states with 97.67% accuracy to new instances. Preliminary results show the computation time of this approach is in the order of 25 ms.
Sponsorship	IEEE Power Energy Soc.
Starting Page	405
Ending Page	410
File Size	653510
Page Count	6
File Format	PDF
ISBN	9781467352437
e-ISBN	9781467352451
DOI	10.1109/ESTS.2013.6523768
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-04-22
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Accuracy Computational modeling Naïve Bayes' Diagnosis support Data models Generators Vectors E-Ship Mathematical model Machine Learning Load modeling
Content Type	Text
Resource Type	Article

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