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Asset Risk Management of Electric Power Grids

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Asset Risk Management of Electric Power Grids

Youssefi, Niloufar (2015) Asset Risk Management of Electric Power Grids. Masters thesis, Concordia University.

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Abstract

ABSTRACT
Asset Risk Management for Electric Power Grids
Niloufar Youssefi
Civil Infrastructure is essential for the quality of life in developed and developing countries. Since electric power supply is needed for the operation of other vital infrastructure, it is ranked as the highest critical infrastructure. There are substantial adverse impacts on society when power grids fail, resulting in interruption and/or degradation of services. Such failure can cause heavy traffic congestions resulting from nonfunctioning traffic lights, and disturbances for other critical infrastructure elements such as water and sewage treatment plants.
In order to ensure reliability of the bulk power system (BPS) in North America, the North American Electric Reliability Corporation (NERC) requires that power companies submit reports when sufficiently enormous instabilities happen within their territories in order to share the experiences and lessons learned, and to suggest solutions that utilities can apply to their procedures during unusual situations. To simplify and organize information, the NERC has divided the BPS of North America into eight zones, three of which consist of both US states and Canadian provinces. The research presented here focuses on the Canadian part of NPCC zone which covers Quebec, Ontario, New Brunswick and Nova Scotia.
The main purpose of this research is to identify factors affecting power outages in the eastern Canada and develop a model for predicting the likelihood of power outage occurrences based on weather forecasted data. For this reason, System Disturbances Reports from 1992 to 2009 have been scrutinized to determine the conditions in which an attack on power grids can likely happen. According to these reports, various reasons were found to trigger power outages, including equipment failure, voltage reduction, human error, etc. However, weather conditions are the paramount cause of unavailability of power service in the northeastern district. Weather conditions variables such as wind speed, temperature, humidity, precipitation and lightning are obtained for those same periods from the Environment Canada database. In addition, in two other variables (i.e. electric consumption index and electric network size) are considered as the factors that are likely to impact power outage incidents indirectly.
Based on historical data gathered for weather conditions and power outages, different types of Artificial Neural Network models (i.e. BPNN, GRNN, and PNN) were studied and developed to predict the likely occurrence of power outage utilizing weather forecasted data for four eastern Canadian provinces. Two types of datasets are used for training the models: Dataset I considers the extreme values for all the weather variables, and Dataset II, which consists the extreme value for wind speed (the most critical factor affecting the power grids) plus the values of the other weather variables at the same time that the wind speed reached its maximum value. The results indicate that the best performing model is PNN that was trained with Dataset I for it provides more accurate results. The model is also trained using Quebec dataset, which indicates that data for a specific location is expected to lead to better results. Social cost for electric power outage are then estimated four sectors; residential, commercial, industrial and agriculture.
As a result, once the average duration of power outage is recognized as well as its likelihood of occurrence, the social cost of that power failure could be estimated in the four sectors. The present research helps power companies to predict the likelihood of electric power outage based on weather forecasting data. Furthermore, they are able to estimate the social cost of electric power failure in advance. This will provide useful information for further actions in risk mitigation, and will aide professionalisms in the process of creating choices to improve opportunities and to lessen threats.

Divisions:Concordia University > Gina Cody School of Engineering and Computer Science > Building, Civil and Environmental Engineering
Item Type:Thesis (Masters)
Authors:Youssefi, Niloufar
Institution:Concordia University
Degree Name:M.A. Sc.
Program:Building Engineering
Date:9 November 2015
Thesis Supervisor(s):Moselhi, Osama
ID Code:981173
Deposited By: NILOUFAR YOUSSEFI
Deposited On:15 Jun 2016 13:30
Last Modified:18 Jan 2018 17:52
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