Studies on applications of Artificial Intelligence in medium and high voltage circuit breakers

Artificial Intelligence (AI) has played an important role in several engineering applications, including preventive maintenance and fault detection in high-voltage electrical equipment. Among them, medium and high-voltage circuit breakers stand out, which are strategic components, used not only in maneuvers, but also in protection against overcurrents and short circuits in electrical power systems. Failures in these equipment can lead to significant interruptions in the power supply, sometimes causing great economic and social losses, as well as risks to the safety of facilities. In this sense, the objective of this work is to present different scientific studies on AI applied to medium and high-voltage circuit breakers, aiming at the analysis and comparisons between them. The justification for these studies is evidenced by the need to identify mechanical and electrical faults early, minimizing unplanned downtime and costs associated with the corrective maintenance of these equipment. The methodology adopted is based on available scientific studies with selection and analysis of cases on the application of AI in diagnosing incipient faults of medium and high-voltage circuit breakers. The results demonstrate the efficiency of integrating AI algorithms. They present different methods, such as signal processing techniques, for example: Wavelet Transform and Improved Empirical Mode Decomposition Energy Entropy; Machine Learning, namely: Principal Component Analysis (PCA), K-means , Random Forest and Support Vector Machine (SVM); and Deep Learning, such as: AlexNet Network and Autoencoder , to extract relevant features from the vibration and voltage signals of these equipment. Therefore, this work highlights the importance of applying Artificial Intelligence aiming at innovations in the area of ​​Maintenance Engineering. Given the challenges and perspectives in the area, we propose complements with studies that use methods that deal well with little data and can be used for more constant monitoring of the operating status of medium and high voltage circuit breakers. Furthermore, these tools must be able to identify when the equipment has undergone intervention and whether its condition has improved, as well as present failure predictions based on its history, since the application of AI techniques shows promise in the early detection of failures, preventive maintenance and improvement of the operational efficiency of this important equipment for the electrical power system.