Dissertation/Thesis Abstract

Classification of Faults in Microgrids Using Deep Learning
by Karan, Sainesh, M.S., California State University, Long Beach, 2020, 74; 27834650
Abstract (Summary)

A microgrid is a network consisting of one or several loads and distributed generation (DG) sources that operate as a single aggregate load or source. They have become an increasingly popular method to manage DG sources at the distribution level. As such, fault detection and protection of microgrids is a topic of current research. In this work, we study the application of deep learning based techniques such as Long Short Term Memory Networks (LSTM) to classify faults, since accurate real time fault classification can support superior grid operation and prevent relays from closing non-faulted phases. Here, we used Matlab/Simulink to model a simple microgrid and simulate various types of faults to generate training data for the neural network model. Additive White Gaussian Noise (AWGN) and Additive Impulsive Gaussian Noise (AIGN) are added to the signal to mimic real world data. We used Discrete Wavelet Transform (DWT) and Multi-Resolution Analysis (MRA) to process the simulated signals, which are subsequently used to train a LSTM to classify eleven types of faults. We investigate the application of Convolutional Neural Networks (CNN) for fault classification by transforming the simulated signals into images, which are used to train the CNN to classify the same faults. The investigation showed that the LSTM out-performed the CNN and achieved high accuracy in classifying the faults using half the data.

Indexing (document details)
Advisor: Yeh, Hen-Geul
Commitee: Talebi, Mohammed, Kwon, Seok-Chul
School: California State University, Long Beach
Department: Electrical Engineering
School Location: United States -- California
Source: MAI 82/4(E), Masters Abstracts International
Subjects: Electrical engineering, Artificial intelligence, Systems science
Keywords: Classification, Convolutional Neural Networks, Deep Learning, LSTM Neural Networks, Microgrids, Power System Faults
Publication Number: 27834650
ISBN: 9798684652363
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