@inproceedings{ead110b38c6248f4942815b09e1dedbe,
title = "Naive Bayesian islanding detection technique for distributed generation in modern distribution system",
abstract = "In this paper a new islanding detection technique for grid-mode distributed-generation (DG) is proposed. Twenty one features are extracted from measurement of the voltage and frequency at the point of common coupling (PCC) in order to identify islanding occurrence with high accuracy. An IEEE 34-bus system was used in this paper to generate islanding and non-islanding training cases. Then a Support Vector Machine (SVM) was trained using the training cases in order to discriminate islanding and non-islanding cases. In order to test the accuracy of the Na{\"i}ve Bayesian Classifier, Cross-Validation was used to evaluate the performance of the proposed islanding detection technique. Accuracy of 100\% was achieved using the proposed algorithm.",
keywords = "Bayesian Network, Cross Validation, inverter-based distributed generator, islanding detection, Na{\"i}ve Bayes, power systems, Support Vector Machine, WEKA",
author = "Faqhruldin, \{Omar N.\} and El-Saadany, \{E. F.\} and Zeineldin, \{H. H.\}",
year = "2012",
doi = "10.1109/EPEC.2012.6474982",
language = "British English",
isbn = "9781467320801",
series = "2012 IEEE Electrical Power and Energy Conference, EPEC 2012",
pages = "69--74",
booktitle = "2012 IEEE Electrical Power and Energy Conference, EPEC 2012",
note = "2012 IEEE Electrical Power and Energy Conference, EPEC 2012 ; Conference date: 10-10-2012 Through 12-10-2012",
}