A New Approach Based on Wavelet Design and Machine Learning for Islanding Detection of Distributed Generation

Author

Alshareef, Sami ; Talwar, Shilpa ; Morsi, Walid G.

Author_Institution

Dept. of Electr. Comput. & Software Eng., Univ. of Ontario Inst. of Technol., Oshawa, ON, Canada

Volume

5

Issue

4

fYear

2014

fDate

Jul-14

Firstpage

1575

Lastpage

1583

Abstract

This paper presents a new approach based on wavelet design and machine learning applied to passive islanding detection of distributed generation. Procrustes analysis is used to determine the filter coefficients of a newly designed wavelet. To automate the classification process, machine learning algorithms are used to develop appropriate models. The IEEE 13-bus standard test distribution system simulated in PSCAD/EMTDC is used as a test bed to assess the performance of the proposed approach. The numerical results demonstrating the effectiveness of the proposed approach are discussed and conclusions are drawn.

Keywords

distributed power generation; learning (artificial intelligence); power distribution faults; power engineering computing; wavelet transforms; IEEE 13-bus standard test distribution system; PSCAD-EMTDC; classification process; distributed generation; machine learning; passive islanding detection; wavelet design; Indexes; Shape; Support vector machines; Switches; Training; Voltage measurement; Wavelet transforms; Discrete wavelet; Procrustes analysis; distributed generation; islanding detection; machine learning;

fLanguage

English

Journal_Title

Smart Grid, IEEE Transactions on

Publisher

ieee

ISSN

1949-3053

Type

jour

DOI

10.1109/TSG.2013.2296598

Filename

6786488