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AUTHOR(S): Seba Algharaibeh, Mohammad Almomani, Tamadher Almomani, Malik Alkasasbeh, Ali Aldmour
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TITLE PMU-Based Artificial Intelligence Techniques for Islanding Detection |
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ABSTRACT This paper presents an islanding detection method (IdM) based on phasor measuring unit (PMU) technology. The PMUs are used to measure the synchronized data and collect it in real-time. Three PMUs are located in this work to obtain full observability: at distributed generator (DG) side, load location, and point of common coupling (PCC). The measured signals are directly fed to the classifier to identify the islanding events. This algorithm shows that the proposed method can detect the islanding with 100% detection accuracy and zero non-detection zone (NDZ). Both DG types are tested in this paper: inverter-based DG and rotating machine-based DG. The detection time of the remote method is five cycles for the inverter-based test system and seven cycles for the rotating machine test system. |
KEYWORDS Artificial Neural Network (ANN), Decision Tree (DT), Islanding Detection Method (IdM), K-nearest Neighbor Algorithm (KNN), Support Vector Machine (SVM), rotating machine DG, inverter-based DG. |
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Cite this paper Seba Algharaibeh, Mohammad Almomani, Tamadher Almomani, Malik Alkasasbeh, Ali Aldmour. (2022) PMU-Based Artificial Intelligence Techniques for Islanding Detection. International Journal of Power Systems, 6, 1-9 |
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