AI-Driven Signal Processing for SF6 Circuit Breaker Performance Optimization

Abstract

This work presents an approach based on signal processing and artificial intelligence (AI) to identify the pre-insertion resistor (PIR) and main contact instants during the operation of high-voltage SF6 circuit breakers to help improve the settings of controlled switching and attenuate transients. For this, the current and voltage signals of a real Brazilian substation are used as AI inputs, considering the noise and interferences common in this type of environment. Thus, the proposed modeling considers the signal preprocessing steps for feature extraction, the generation of the dataset for model training, the use of different machine learning techniques to automatically find the desired points, and, finally, the identification of the best moments for controlled switching of the circuit breakers. As a result, the models evaluated obtained good performance in the identification of operation points above 93%, considering precision and accuracy. In addition, valuable statistical notes related to the controlled switching condition are obtained from the circuit breakers evaluated in this research.

Keywords

• high-voltage circuit breakers
• artificial intelligence
• substation capacitor bank
• controlled switching