Evidence theory-based identification of aging for capacitive voltage transformers

In this study, an aging identification approach is proposed for capacitive voltage transformers (CVTs). Aging is an unavoidable phenomenon which emerges and spreads in equipment structure and mainly affects the insulation parts. The aging may affect transformer output in its primary stages. If it grows, it may cause serious damages to the equipment, substation and the staff. Accordingly, it is necessary to diagnose the aging of CVTs in its primary steps in order to implement the required maintenance and repair. The proposed aging identification method is an efficient method which collects data from different sources in digital substations to diagnose aging in CVT structures. Moreover, the possible external disturbances are also taken into account. A modified method is proposed based on the distance between the evidences. In addition, the algorithm is capable to identify the CVTs providing bad data arising from their aging.