Islanding detection method based on image classification technique using histogram of oriented gradient features. Issue 14 (21st May 2020)

• Record Type: Journal Article
• Authors: Manikonda, Santhosh K.G.; Gaonkar, Dattatraya N.
• Is Part Of: IET generation, transmission & distribution. Volume 14:Issue 14(2020)
• Page Start: 2790
• Page End: 2799
• Publication Date: 2020-05-21
• Subjects: power distribution faults -- image classification -- distributed power generation -- feature extraction -- support vector machines -- power engineering computing -- gradient methods; islanding detection method -- image classification technique -- histogram of oriented gradient feature -- nonislanding -- islanding event -- input feature vector -- early islanding detection -- detection accuracy -- multiple support vector machine classifier testing; Electric power production -- Periodicals; Electric power transmission -- Periodicals; Electric power distribution -- Periodicals; 621.3105
• DOI: 10.1049/iet-gtd.2019.1824
• Languages: English
• ISSNs: 1751-8687

Abstract

Abstract : A new islanding detection method based on image classification with support vector machine is proposed in this study. Histogram of oriented gradient features is extracted from the image for classifying non‐islanding and islanding events. In the proposed technique, the time‐series signal acquired from the point of common coupling is first converted into an image. Histogram of oriented gradient features is extracted from the image, which is used as an input feature vector for training and testing multiple support vector machine classifiers. Parameters such as voltage, rate of change of voltage, and rate of change of negative sequence voltage are used. Furthermore, a feature for early islanding detection is also presented to detect an islanding event even before it has occurred. The detection accuracy of the proposed method is tested with different kernels. The performance of all the classifiers is tested with 5‐fold cross‐validation. The classification results show that islanding detection with image classification based on the histogram of oriented gradient feature and multiple support vector machine classifiers can achieve excellent results.