Transformer differential protection with wavelet transform and difference function

Abstract

The Transformer Differential Protection (TDP) algorithm instantly compares the target transformer’s terminal currents for each phase. The differential current is not expected to appear during regular operation. However, nonlinear characteristics of the core material, such as the hysteresis curve, result in significant variation in the differential current, known as magnetizing inrush current. This inrush current lasts for a while before disappearing, causing significant variation in the differential current. TDP algorithm is supposed to remain silent during this transient time (selectivity). In addition, one of the most difficult tasks for protection systems is detecting inter-turn faults in their early stages. This fault type typically begins at low levels due to moisture, high temperature, and so on, and gradually spreads to other turns. It is vital to detect inter-turn faults before they expand more than 10% of total windings [RW-2-1]. This paper presents a transformer differential protection algorithm that distinguishes between inter-turn, low-level internal faults, and inrush current. Maximum Overlapped Discrete Wavelet Transform (MODWT) energy and difference function are used for feature extraction, and the traditional 87T TDP method has been updated. Performance is evaluated using data collected from a laboratory-based experimental rig. The results demonstrate that the suggested approach performs very well in a range of low-level, inter-turn fault, and transient scenarios, including internal fault, inrush current, and sympathetic inrush current. These results are confirmed by the identified indices for Accuracy (AC), Dependability (DP), and Selectivity (SE).