Ferroresonance fault detection in electric power networks by artificial neural networks

dc.contributor.advisor Akıncı, Tahir Çetin
dc.contributor.author Kulaklı, Gizem
dc.contributor.authorID 650079
dc.contributor.department Department of Electrical Engineering
dc.date.accessioned 2022-06-09T12:01:54Z
dc.date.available 2022-06-09T12:01:54Z
dc.date.issued 2020-07
dc.description Thesis (M.Sc.) -- İstanbul Technical University, Institute of Science and Technology, 2020
dc.description.abstract Ferroresonance is a complicated nonlinear waving which can appear in electrical circuits with a series or parallel connection of nonlinear inductance and capacitance. Cause of the current of ferroresonance on the transmission line elements such as cables or transformers can be partially or completely damaged. This destruction not only creates huge material losses on the system but also creates unjust suffering. It is important for the sustainability of the system that a devastating error such as ferroresonance can be detected. If ferroresonance can detecting in advance prevent the loss of time and money for the user by destroying the elements such as power transformer and cables used in the system Ferroresonance is nonlinear situation and learning in artificial neural networks has advantages such as working with missing or uncertain data, processing real conditions, handling nonlinear situations, being more successful than traditional methods, fault tolerance. Artificial neural networks are referred to by this name because they are based on learning of the human neural cell in principle. One nerve cell receives information from other cells from the dendrites department, which corresponds to input in artificial neural networks, while axon in human nerve cells corresponds to output in artificial neural networks. Artificial neural networks mainly consist of three layers. There are hidden tabs determined by the number of layers between the input and the output. The learning process is multiplied by the randomly assigned weight value of the input value, and the NET value is created, and if it is determined, the bias others are summed and output from the cell where this total value is found according to the activation function. This output value is the input of the next hidden layer and continues until the same process reaches the output value. The output value gives the result of the learning operation according to the specified value ranges. The activation function is important in solving the problem used. Various activation functions are mentioned in the thesis. A successful algorithm was investigated by using an artificial neural network method to detect ferroresonance error. In this study, four different ferroresonance data emerging with different scenarios in the transmission line which used energy transmission line modeling from western Anatolia Turkey Seydisehir-Oymapınar transmission line has 380 kV were used as input values. Work steps; literature search on the subject, detection of the moment when ferroresonance starts in voltage outputs, creating input, training and example data from ferroresonance data, to create the appropriate algorithm for nonlinear ferroresonance.
dc.description.degree M.Sc.
dc.identifier.uri http://hdl.handle.net/11527/20129
dc.language.iso en
dc.publisher Institute of Science and Technology
dc.sdg.type Goal 9: Industry, Innovation and Infrastructure
dc.subject ferroresonance
dc.subject electrical circuits
dc.subject artificial neural networks
dc.subject o detect ferroresonance error
dc.title Ferroresonance fault detection in electric power networks by artificial neural networks
dc.title.alternative Elektrik güç hatlarında ferrorezonans arızasının yapay sinir ağları ile belirlenmesi
dc.type Master Thesis
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