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Energies 2017, 10(12), 1963; https://doi.org/10.3390/en10121963

A Novel Fault Early Warning Model Based on Fault Gene Table for Smart Distribution Grids

Key Laboratory of Industrial Internet of Things & Networked Control, Chongqing University of Posts and Telecommunications, Chongqing 400065, China
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Received: 10 October 2017 / Revised: 16 November 2017 / Accepted: 17 November 2017 / Published: 24 November 2017
(This article belongs to the Section Electrical Power and Energy System)
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Abstract

Since a smart distribution grid has a diversity of components and complicated topology; it is very hard to achieve fault early warning for each part. A fault early warning model for smart distribution grid combining a back propagation (BP) neural network with a gene sequence alignment algorithm is proposed. Firstly; the operational state of smart distribution grid is divided into four states; and a BP neural network is adopted to explore the operational state from the historical fault data of the smart distribution grid. This obtains the relationship between each state transition time sequence and corresponding fault, and is used to construct the fault gene table. Then; a state transition time sequence is obtained online periodically, which is matched with each gene in fault gene table by an improved Smith–Waterman algorithm. If the maximum match score exceeds the given threshold, the relevant fault will be detected early. Finally, plenty of time domain simulation is performed on the proposed fault early warning model to IEEE-14 bus. The simulation results show that the proposed model can achieve efficient early fault warning of smart distribution grids. View Full-Text
Keywords: fault gene table; smart distribution grid; fault early warning; gene sequence alignment; Smith-Waterman algorithm fault gene table; smart distribution grid; fault early warning; gene sequence alignment; Smith-Waterman algorithm
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Xiang, M.; Min, J.; Wang, Z.; Gao, P. A Novel Fault Early Warning Model Based on Fault Gene Table for Smart Distribution Grids. Energies 2017, 10, 1963.

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