Resumo:
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.
