Deep Learning for Power Quality Disturbances Classification According to Oscillogram Data

Student: Lazarev Dmitrij

Educational Programme: Master of Data Science (Master)

Year of Graduation: 2024

In the thesis, oscillograms from a real power grid are utilized to obtain a dataset to train several models based on neural networks (a multilayer perceptron model, a convolution-based model and a gated recurrent unit model). The evaluation focused on the task of multi-class classification of electrical power disturbances. The evaluation clearly demonstrated the superiority of the gated recurrent units over a CatBoost classifier (baseline) and other types of the architectures considered, namely: multilayer perceptron and convolutional neural network.

Student Theses at HSE must be completed in accordance with the University Rules and regulations specified by each educational programme.

Summaries of all theses must be published and made freely available on the HSE website.

The full text of a thesis can be published in open access on the HSE website only if the authoring student (copyright holder) agrees, or, if the thesis was written by a team of students, if all the co-authors (copyright holders) agree. After a thesis is published on the HSE website, it obtains the status of an online publication.

Student theses are objects of copyright and their use is subject to limitations in accordance with the Russian Federation’s law on intellectual property.

In the event that a thesis is quoted or otherwise used, reference to the author’s name and the source of quotation is required.

Search all student theses