Unsupervised Clustering of Power Quality Events with Transformer

This project focuses on developing an unsupervised learning approach for classifying power quality events using Transformer networks. The goal is to leverage the powerful sequence modeling capabilities of Transformers to accurately identify and categorize various power quality disturbances without the need for labeled training data. By doing so, the project aims to enhance the efficiency and reliability of power quality monitoring systems, enabling more effective management of electrical networks