Please use this identifier to cite or link to this item:
Author(s): Pedro Emanuel Almeida Cardoso
Title: Deep Learning Applied to PMU Data in Power Systems
Issue Date: 2017-07-13
Abstract: With the advent of Wide Area Measurement Systems and the consequent proliferation of digital measurement devices such as PMUs, control centers are being flooded with growing amounts of data. Therefore, operators are craving for efficient techniques to digest the incoming data, enhancing grid operations by making use of knowledge extraction. Driven by the volumes of data involved, innovative methods in the field of Artificial Intelligence are emerging for harnessing information without declaring complex analytical models. In fact, learning to recognize patterns seems to be the answer to overcome the challenges imposed by processing the huge volumes of raw data involved in PMU-based WAMS. Hence, Deep Learning Frameworks are applied as computational learning techniques so as to extract features from electrical frequency records collected by the Brazillian Medfasee BT Project. More specifically, the work developed proposes a classifier of dynamic events such as generation loss, load shedding, etc., based on frequency change.
Subject: Engenharia electrotécnica, electrónica e informática
Electrical engineering, Electronic engineering, Information engineering
Scientific areas: Ciências da engenharia e tecnologias::Engenharia electrotécnica, electrónica e informática
Engineering and technology::Electrical engineering, Electronic engineering, Information engineering
TID identifier: 201802139
Document Type: Dissertação
Rights: openAccess
Appears in Collections:FEUP - Dissertação

Files in This Item:
File Description SizeFormat 
204483.pdfDeep Learning Applied to PMU Data in Power Systems3.13 MBAdobe PDFThumbnail

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.