Student:	Jose Eduardo URREA CABUS
Supervisor:	Prof. Dr. İsmail Hakkı ALTAŞ
Department:	Elektrik-Elektronik Müh.
Institution:	Graduate School of Natural and Applied Sciences
University:	Karadeniz Technical University Turkey

Title of the Thesis:	MACHINE LEARNING FOR FAULT DETECTION IN DISTRIBUTED NETWORKS
Level:	M.Sc.
Acceptance Date:	28/7/2021
Number of Pages:	101
Registration Number:	i3926

Summary:

This research includes applications of machine learning algorithms for the detection,

classification, and analysis of disturbances, emphasizing symmetric and asymmetric short-circuit

faults in electrical networks. The required triphasic voltage and current values were obtained by

simulation with DIgSILENT software according to different short-circuit states, and the analyzes

were performed through Python software. First, the preliminary data processing is done by applying

the discrete wavelet transform, where a model is developed to select the mother wavelet and level

of decomposition by applying the minimum entropy decomposition and Support Vector Machine

algorithm. Additionally, unsupervised dimensionality reduction techniques were applied to improve

machine learning models performance during the training step. Finally, features considered are

minimized through feature extraction and, by considering fewer features, prevent model data sets

from being overfitting and underfitting; hence, the performance of the algorithms can be enhanced.

The algorithms and approaches developed can also be applied to different fault problems to obtain

more reliable protection methods.