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author:

Jin, Tao (Jin, Tao.) [1] | Li, Hongnan (Li, Hongnan.) [2]

Indexed by:

EI

Abstract:

By researching models of hybrid cable-overhead multiple branch distribution lines with distributed generators, a novel fault location method is proposed which combines binary particle swarm optimisation (BPSO) and a genetic algorithm. These two methods are mixed using a double population evolution strategy and information sharing technology. The proposed fault location method consists of section line location and distance detection. On the basis of the mutation direction differences of the transient zero sequence current and voltage of each section line when a fault occurs, the authors propose a new coding criterion and method capable of section line location. Distance detection equations are built through double-end steady-state positive sequence voltage distribution regulations along the transmission lines and are then solved by the proposed hybrid BPSO genetic algorithm. Through simulation and experiment, the proposed hybrid algorithm is shown to have good performance. The effectiveness of the proposed fault location method is proven to be accurate and reliable and it is insensitive to impact factors such as fault distance, fault angle, and ground resistance. © The Institution of Engineering and Technology 2016.

Keyword:

Distributed power generation Electric fault currents Genetic algorithms Location Particle swarm optimization (PSO)

Community:

  • [ 1 ] [Jin, Tao]Department of Electrical Engineering, Fuzhou University, Fuzhou, China
  • [ 2 ] [Li, Hongnan]Department of Electrical Engineering, Fuzhou University, Fuzhou, China

Reprint 's Address:

  • [jin, tao]department of electrical engineering, fuzhou university, fuzhou, china

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Related Keywords:

Source :

IET Generation, Transmission and Distribution

ISSN: 1751-8687

Year: 2016

Issue: 10

Volume: 10

Page: 2454-2463

2 . 2 1 3

JCR@2016

2 . 0 0 0

JCR@2023

ESI HC Threshold:177

JCR Journal Grade:2

CAS Journal Grade:3

Cited Count:

WoS CC Cited Count:

SCOPUS Cited Count: 23

ESI Highly Cited Papers on the List: 0 Unfold All

WanFang Cited Count:

Chinese Cited Count:

30 Days PV: 0

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