Detecção de falhas em unidades geradoras de usinas hidrelétricas utilizando DCT e simulações Bootstrap

Abstract

Fault Detection in generating units is a matter of utmost importance for maintenance in hydroelectric power plants, as anticipating faults can mean avoiding losses in energy supply to the national system, as well as potential savings through preventive actions or component replacement. The main challenge for fault detection is that not every anomaly data necessarily indicates a fault, so the model to be used must be capable of identifying intrinsic data characteristics and detecting an anomaly only when a specific fault signature exists. In an effort to address this issue, the DCT-Bootstrap model was developed and tested in real scenario of a power plant and on a reference dataset, successfully outperforming state-of-the-art reference models in both situations. This work thus introduces a new model that can be employed in fault detection tasks across industry scope.