A comparative analysis of intelligent classifiers for passive islanding detection in microgrids

Author

Azim, Riyasat ; Kai Sun ; Fangxing Li ; Yongli Zhu ; Saleem, Hira Amna ; Di Shi ; Sharma, Ratnesh

Author_Institution

Dept. of Electr. Eng. & Comput. Sci., Univ. of Tennessee, Knoxville, TN, USA

fYear

2015

fDate

June 29 2015-July 2 2015

Firstpage

1

Lastpage

6

Abstract

This paper proposes a passive islanding detection technique for distributed generations in grid-connected microgrids and presents a comprehensive comparative analysis of intelligent classifiers for passive islanding detection application. The proposed method utilizes pattern recognition techniques in classification of underlying signatures of wide variety of system events on critical system parameters for islanding detection. Case study on a grid-connected microgrid model with different types of distributed generations is performed to evaluate the proposed method and compare the classifier performances. Test results demonstrate the effectiveness of the proposed method in detection of islanding events.

Keywords

distributed power generation; knowledge based systems; pattern classification; power engineering computing; distributed generations; grid-connected microgrids; intelligent classifiers; passive islanding detection; pattern classification; pattern recognition techniques; Accuracy; Indexes; Islanding; Mathematical model; Microgrids; Nickel; Training; Decision trees; islanding detection; microgrids; naïve-Bayes; neural networks; support vector machines;

fLanguage

English

Publisher

ieee

Conference_Titel

PowerTech, 2015 IEEE Eindhoven

Conference_Location

Eindhoven

Type

conf

DOI

10.1109/PTC.2015.7232369

Filename

7232369