Open AccessIslanding detection approach with negligible non‐detection zone based on feature extraction discrete wavelet transform and artificial neural network
Author(s) -
Hashemi Farid,
Mohammadi Mohammad
Publication year - 2016
Publication title -
international transactions on electrical energy systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.428
H-Index - 42
ISSN - 2050-7038
DOI - 10.1002/etep.2197
Subject(s) - islanding , artificial neural network , pattern recognition (psychology) , computer science , artificial intelligence , discrete wavelet transform , probabilistic neural network , feature extraction , wavelet , feature (linguistics) , wavelet transform , engineering , distributed generation , time delay neural network , linguistics , philosophy , renewable energy , electrical engineering
Summary The paper presents a novel detection method based on feature extraction discrete wavelet transform (DWT) combined with artificial neural network (ANN) for identification of islanding condition in distributed generation (DG) system. Islanding detection methods can be classified into two major categories as active and passive methods. The main disadvantages of the passive methods are determined threshold value and related to their large non‐detection zone. The emphasis of the proposed approach is eliminating the aforementioned drawbacks. The DWT allows revealing various hidden features of the signal. The aim of this paper is to determine the best wavelet basis function in order to identify islanding occurrence with higher accuracy and lower decomposition level. The proposed approach requires the measure rate of change of frequency at the DG's terminal, and various features are extracted from DWT then these features used as input into ANN. Also, the performance of the different structures of ANN such as feed‐forward neural network, radial basis function, and probabilistic neural network are compared for islanding detection purpose. The proposed method is simulated and tested in various operation conditions such as islanding conditions, motor starting, capacitor bank switching, and nonlinear load switching. The test results showed that the proposed method correctly detects the islanding operation and does not mal‐operate in the other situations. Copyright © 2016 John Wiley & Sons, Ltd.