Machine Learning-Based Image Pattern Recognition Using Histogram of Oriented Gradient for Islanding Detection

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dc.contributor.authorKumaresh Pal
dc.contributor.authorKumari Namrata
dc.contributor.authorAshok Kumar Akella
dc.contributor.authorManoj Gupta
dc.contributor.authorPannee Suanpang
dc.contributor.authorAziz Nanthaamornphong
dc.contributor.correspondenceM. Gupta; SOS-Engineering and Technology, Guru Ghasidas Vishwavidyalaya, Department of Electrical Engineering, Bilaspur, Chhattisgarh, 495009, India; email: manojgupta35@yahoo.co.in; A. Nanthaamornphong; Prince of Songkla University, Phuket Campus, College of Computing, Phuket, 83120, Thailand; email: aziz.n@phuket.psu.ac.th
dc.date.accessioned2025-07-07T18:16:38Z
dc.date.available2025-07-07T18:16:38Z
dc.date.issued2025
dc.description.abstractA vital issue faced by the distribution network is the occurrence of unintentional islanding. The failure to identify unintentional islanding results in significant implications for both the power system and human lives. In this paper, a novel machine learning islanding detection method (IDM) based on image classification utilizing the histogram of oriented gradient (HOG) feature is proposed. In particular, the set of parameters are utilized, namely total harmonic distortion (THD) of both three phase currents and voltages, and rate of change of negative sequence voltage, are first transformed into time-frequency representations (i.e., spectrograms via the short time Fourier transform, and scalograms through continuous wavelet transform). Then, the HOG features are extracted from these images and used to train the machine learning (ML) algorithms to distinguish between occurrences of islanding and non-islanding events. Performance metrics including F1 score, recall, accuracy, precision and misclassification error are employed in the assessment process. Numerical results show that our image-based detector achieves faster detection times and higher detection accuracy versus state-of-art methods, thus confirming the validity of such approach for identifying islanding events. © 2013 IEEE.
dc.identifier.citationIEEE Access
dc.identifier.doi10.1109/ACCESS.2025.3564145
dc.identifier.issn21693536
dc.identifier.scopus2-s2.0-105003701588
dc.identifier.urihttps://repository.dusit.ac.th/handle/123456789/7315
dc.languageEnglish
dc.publisherInstitute of Electrical and Electronics Engineers Inc.
dc.rights.holderScopus
dc.subjecthistogram of oriented gradient (HOG)
dc.subjectmachine learning (ML) algorithm
dc.subjectscalogram images
dc.subjectspectrogram images
dc.subjectTotal harmonics distortion (THD)
dc.titleMachine Learning-Based Image Pattern Recognition Using Histogram of Oriented Gradient for Islanding Detection
dc.typeArticle
mods.location.urlhttps://www.scopus.com/inward/record.uri?eid=2-s2.0-105003701588&doi=10.1109%2fACCESS.2025.3564145&partnerID=40&md5=752b6de6fa32902dbac467a98292cb92
oaire.citation.endPage74416
oaire.citation.startPage74396
oaire.citation.volume13
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