Resumo:
High-voltage circuit breakers play a vital role in a substation, facilitating the connection and
disconnection of loads, as well as isolating the system during fault conditions. Among the problems
related to circuit breakers operating with capacitor banks, one of the main issues is related to
the closing operation, which can generate high inrush current transients associated with high
frequencies. These transients may cause damage and premature wear to the circuit breakers
and the grid equipment connected to them. Thus, monitoring the main parameters of circuit
breakers becomes essential to anticipate failures and estimate their useful life, resulting in economic,
operational, and strategic gains. In this context, this work presents an approach based on signal
processing and Artificial Intelligence (AI) to identify the instants of insertion of the pre-insertion
resistor and the main contact during the operation of a high-voltage SF6 circuit breaker. To this
end, current and voltage signals from a real Brazilian substation are used as inputs to the AI-based
models, resulting in a feature vector with 600 rows and 82 columns, considering the noise and
interference common in this type of environment. Thus, the proposed modeling considers signal preprocessing
steps with techniques such as discrete derivative, integral, frequency-domain transforms,
and wavelets, to reduce noise and highlight relevant features for feature extraction, the generation
of the dataset for model training, the use of different machine learning techniques, and the use of
two machine learning models for evaluation: the Multilayer Perceptron (MLP) and the AdaBoost
classifier, both trained to automatically identify the critical points in the signal and, finally, the
identification of the best moments for controlled switching of the circuit breakers. The results
showed that the MLP achieved 94% accuracy, while the AdaBoost reached 95% accuracy. The
conclusions indicate that the proposed methodologies are robust for use in real-world environments,
allowing for more reliable fault prediction, optimized maintenance, and reduced operational costs.
Thus, the work directly contributes to improving the closing synchronism in high-voltage circuit
breakers and mitigating critical transients in the electrical system.
