Research Area:  Machine Learning

Abstract:

This paper presents a denial constraint (DC) discovery approach for detecting faults in utility companies electric transmission lines. Transmission lines rely on a protection system that continually streams and stores waveform data with three-phase current and voltage information. Considering that those data are stored in a relational database, we use the high expressive power of DCs to capture the expected behavior of a transmission line, as they are ideal for representing rules in databases. Since defining DCs in our scenario requires expensive domain expertise and, worse, is an error-prone task, we use a state-of-the-art algorithm to discover reliable DCs. Unfortunately, the amount of data in the studied scenario makes state-of-the-art DC discovery algorithms impractical due to the long execution times. In response, we propose a novel DC discovery approach using streaming windows to address this issue. Our hypothesis is that DCs discovered in pre-fault windows significantly differ from those in post-fault windows and can be used as a fault detection approach. We use this intuition to detect faults without human intervention (i.e., an unsupervised method). The extensive experimental evaluation on a dataset with diversified fault events shows that our approach can detect faults with 100% accuracy.

Keywords:  
Fault Diagnosis
Intelligent Systems
Power Transmission
Denial Constraint
Data Dependency Violation

Author(s) Name:  Nicolas Tamalu ,Leandro Augusto Ensina , Eduardo Cunha de Almeida , Eduardo Henrique Monteiro Pena , Luiz Eduardo Soares de Oliveira

Publisher name:  SBC

DOI:  10.5753/jidm.2025.4293

Volume Information:  Volume: 16, (2025)

Paper Link:   https://journals-sol.sbc.org.br/index.php/jidm/article/view/4293