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Meistaravörn við verkfræðideild - Roddy Akeel

Predicting Weather-related Transmission Line Failures Using Machine Learning

  • 10.5.2019, 9:00 - 10:00

Date and time: 10th of may at 9 in room V104. 

Predicting Weather-related Transmission Line Failures Using Machine Learning By Roddy Akeel

Supervisor:
Ragnar Kristjánsson, Assistant Professor, Reykjavík University, Iceland
Samuel Perkin, Specialist, Landsnet, Iceland

Examiner:
Eyjólfur Ingi Ásgeirsson, Assistant Professor, Reykjavík University, Iceland

Abstract

Power systems are required to reliably deliver electricity to many different consumers. To attempt to meet this constant demand for delivery while also maintaining a level of safety, power systems operators must make informed decisions by considering a number of factors. At present, utilities are developing and implementing smarter methods of reliability assessment to allow for more informed decisions to be made. This thesis presents a machine learning approach for determining and providing a general weather-related transmission line failure probability based on weather forecasts. To develop this informational tool, a number of different feed forward neural network configurations were built, tested, and assessed. In combination with the feed forward neural network, different preprocessing methods were introduced and utilized to allow different information to be extracted from base weather parameters prior to neural network neural network training and testing. In addition to developing a neural network approach to failure probability prediction, a 3-state failure probability method was investigated using a k-means classifier to show the amount of information provided by the neural network compared to another state-of-the-art approach. Finally, three case studies were conducted on transmission lines which experience a low, medium, and high risk of failure using the best performing neural network configurations.



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