Circuitos auxiliares de disjuntores a SF6 por meio de lógica fuzzy

Abstract

High-voltage circuit breakers are critical components in the operation and protection of electrical power systems, ensuring the safe performance of the grid. The integrity of their auxiliary circuits, particularly the opening and closing coils, is essential for reliable equipment operation. Failures in these components — such as material wear, triggering faults, internal short circuits, or variations in energization time — can compromise breaker performance, resulting in unsuccessful current interruption, unintended tripping, or system unavailability. This work proposes a diagnostic method based on serial Fuzzy Inference Systems to evaluate the integrity of these components by calculating a Risk Degree from the analysis of coil current signals in an SF6 circuit breaker, without the need to interrupt equipment operation. The results demonstrated classification behavior consistent with expectations, with variations in the Risk Degree associated with the accumulation or reduction of the Recurrence Factor. Differences in the quantity and diversity of samples affected the stability of the classifications: HD4-model breakers showed greater exploration in the variation of classification intervals, while AP3-model breakers revealed higher magnitude and lower diversity of values. The analysis of fault signals was not suitable for preventive calibration, reinforcing the importance of pre-fault data. Future work should focus on expanding the number of samples in both controlled and field environments, as well as integrating other operational variables to increase the robustness and applicability of the model to more complex electrical systems. Despite these limitations, the proposed method contributes to the modernization of diagnostics in power systems, promoting greater availability and reducing operational costs.