Voltage-sag source detection: Developing supervised methods and proposing a new unsupervised learning

Exploring foci of: Sustainable Energy Grids and Networks • Vol 32 Voltage-sag source detection: Developing supervised methods and proposing a new unsupervised learning July 2022 • Younes Mohammadi, Seyed Mahdi Miraftabzadeh, Math Bollen, Michela Longo Recognition and analysis of voltage sags (dips) allow network operators to predict and prevent problems in real-life applications. Clearing the voltage sag source by direction detection methods is the most effective way to solve and improve the voltage sags and their related problems. However, the existing analytical methods use single or two input features as phasor-based (PB) or instantaneous-based (IB) values. Hence, their limited maximum accuracy is given at 93% and 84% when using PB features for noiseless and… Open Article Page

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