Transformer Internal and Inrush Current Fault Detection Using Machine Learning

Abstract

Preventive maintenance in the transformer is performed through a differential relay protection system, and it protects the transformer from internal and external faults. However, the Current Transformer (CT) in the differential protection system mal-operates during inrush currents. CT saturates due to magnetizing inrush currents and causes false tripping of the differential relays. Moreover, identification of tripping in protection relay either due to inrush current or internal faults needs to be diagnosed. For the above problem, continuous monitoring of transformer breather and CT terminals with thermal camera helps detect the tripping in relay due to inrush or internal fault. The transformer's internal fault leads to high breathing process in the transformer breather, never for inrush currents. During inrush currents, CT temperature is increased. Continuous monitoring of breather and CT of the transformer through thermal imaging and radiometric pixels detect the causes of CT saturation and differentiates maloperation. Hybrid wavelet threshold image analytics (HWT-IA) based radiometric pixels analysis of the transformer breather and CT after de-noising provides an accurate result of about 95% for identification of the false tripping of differential protection system of transformer.

Research on Intelligent Identification of Magnetizing Inrush Current based on Empirical Modal Decomposition

“…Second, from the voltage side [7] , the analysis is performed by the different changes in its voltage when the fault current occurs, such as the voltage ratio method and the voltage harmonic braking principle. Third, from the perspective of machine learning methods, Literature [8][9] provides a new direction for transformer relay protection operation, such as support vector machinebased, convolutional neural networks, and deep neural networks. Intelligent methods have superiority in learning and deep mining of data.…”