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Rapid Fault Analysis by Deep Learning-Based PMU for Smart Grid System

Intelligent Automation & Soft Computing 2023年第2期35卷 1581-1594页

作者： J.Shanmugapriya K.Baskaran Department of EEE Park College of Engineering and TechnologyCoimbatore641659India Department of EEE Alagappa Chettiar Government College of Engineering and TechnologyKaraikudi630004India

Smart Grids(SG)is a power system development concept that has received signiﬁcant attention *** signiﬁes real-time data for speciﬁc communication *** best capabilities for monitoring and controlling the grid are essential to system *** of the most critical needs for smart-grid execution is fast,precise,and economically synchronized measurements,which are made feasible by Phasor Measurement Units(PMU).PMUs can pro-vide synchronized measurements and measure voltages as well as current phasors *** utilize GPS time-stamping at coordinated universal time(UTC)to capture electric phasors with great accuracy and *** research tends to Deep Learning(DL)advances to design a Residual Network(ResNet)model that can accurately identify and classify defects in grid-connected *** part of fault detection and probe,the proposed strategy uses a ResNet-50 tech-nique to evaluate real-time measurement data from geographically scattered *** a result of its excellent signal classiﬁcation efﬁciency and ability to extract high-quality signal features,its fault diagnosis performance is *** results demonstrate that the proposed method is effective in detecting and classifying faults at sufﬁcient *** proposed approaches classify the fault type with a precision of 98.5%and an accuracy of 99.1%.The long-short-term memory(LSTM),Convolutional Neural Network(CNN),and CNN-LSTM algo-rithms are applied to compare the ***-world data tends to evaluate these networks.

关键词： Smart grid phasor measurement units global positioning system coordinated universal time deep learning residual network–50

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