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

Abstract

Smart Grids (SG) is a power system development concept that has received significant attention nationally. SG signifies real-time data for specific communication requirements. The best capabilities for monitoring and controlling the grid are essential to system stability. One 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 provide synchronized measurements and measure voltages as well as current phasors dynamically. PMUs utilize GPS time-stamping at Coordinated Universal Time (UTC) to capture electric phasors with great accuracy and precision. This research tends to Deep Learning (DL) advances to design a Residual Network (ResNet) model that can accurately identify and classify defects in grid-connected systems. As part of fault detection and probe, the proposed strategy uses a ResNet-50 technique to evaluate real-time measurement data from geographically scattered PMUs. As a result of its excellent signal classification efficiency and ability to extract high-quality signal features, its fault diagnosis performance is excellent. Our results demonstrate that the proposed method is effective in detecting and classifying faults at sufficient time. The 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 algorithms are applied to compare the networks. Real-world data tends to evaluate these networks.