Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/105624
Title: Comparative study in determining features extraction for islanding detection using data mining technique
Authors: Aziah Khamis
Xu, Yan
Azah Mohamed
Keywords: Distributed Generation
Engineering::Electrical and electronic engineering
Islanding Detection
Issue Date: 2017
Source: Aziah Khamis., Xu, Y., & Azah Mohamed. (2017). Comparative study in determining features extraction for islanding detection using data mining technique. International Journal of Electrical and Computer Engineering, 7(3), 1112-1124. doi:10.11591/ijece.v7i3.pp1112-1124
Series/Report no.: International Journal of Electrical and Computer Engineering
Abstract: A comprehensive comparison study on the data mining based approaches for detecting islanding events in a power distribution system with inverter-based distributed generations is presented. The important features for each phase in the island detection scheme are investigated in detail. These features are extracted from the time-varying measurements of voltage, frequency and total harmonic distortion (THD) of current and voltage at the point of common coupling. Numerical studies were conducted on the IEEE 34-bus system considering various scenarios of islanding and non-islanding conditions. The features obtained are then used to train several data mining techniques such as decision tree, support vector machine, neural network, bagging and random forest (RF). The simulation results showed that the important feature parameters can be evaluated based on the correlation between the extracted features. From the results, the four important features that give accurate islanding detection are the fundamental voltage THD, fundamental current THD, rate of change of voltage magnitude and voltage deviation. Comparison studies demonstrated the effectiveness of the RF method in achieving high accuracy for islanding detection.
URI: https://hdl.handle.net/10356/105624
http://hdl.handle.net/10220/50251
DOI: 10.11591/ijece.v7i3.pp1112-1124
Rights: © 2017 Institute of Advanced Engineering and Science. This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:EEE Journal Articles

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