Data Mining Applied to the Electric Power Industry: Classification of Short-Circuit Faults in Transmission Lines

摘要

Data mining can play a fundamental role in mod- ern power systems. However, the companies in this area still face several difficulties to benefit from data mining. A major problem is to extract useful informa- tion from the currently available non-labeled digitized time series. This work focuses on automatic classifi- cation of faults in transmission lines. These faults are responsible for the majority of the disturbances and cas- cading blackouts. To circumvent the current lack of la- beled data, the Alternative Transients Program (ATP) simulator was used to create a public comprehensive la- beled dataset. Results with different preprocessing (e.g., wavelets) and learning algorithms (e.g., decision trees and neural networks) are presented, which indicate that neural networks outperform the other methods.