DocumentCode :
535593
Title :
A new hybrid intelligent based approach to islanding detection in distributed generation
Author :
Ghazi, R. ; Lotfi, N.
Author_Institution :
Ferdowsi Univ. of Mashhad, Mashhad, Iran
fYear :
2010
fDate :
Aug. 31 2010-Sept. 3 2010
Firstpage :
1
Lastpage :
5
Abstract :
This paper presents a new hybrid intelligent based approach for detecting islanding in distributed generation (DG). In this proposed method the passive and active techniques are combined to get a better reliability. So this hybrid method can secure the detection of islanding for different network topology and various operating conditions of synchronous machine based DG. Hence a better reliability is provided. This approach utilizes the artificial neural network (ANN) as a machine learning technology for processing and analyzing the large data sets provided from network simulations using PSCAD/EMTD software. The technique is tested on two typical distribution networks. The results obtained from one case study are compared with results of one of references to show the validity of the proposed method. The results of both studied cases indicate that the developed method can successfully detect islanding situations.
Keywords :
CAD; distributed power generation; learning (artificial intelligence); network topology; neural nets; power distribution reliability; power engineering computing; PSCAD-EMTD software; active techniques; artificial neural network; distributed generation; hybrid intelligent based approach; islanding detection; machine learning technology; network topology; passive techniques; Accuracy; Artificial intelligence; Artificial neural networks; Distributed power generation; Loading; Reactive power; Sensitivity; Artificial Neural Network (ANN); Distributed Generation (DG); Islanding Detection;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Universities Power Engineering Conference (UPEC), 2010 45th International
Conference_Location :
Cardiff, Wales
Print_ISBN :
978-1-4244-7667-1
Type :
conf
Filename :
5649170
Link To Document :
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