Transformer differential protection scheme based on self-adaptive biased characteristic curve and considering cross-country faults

The differential protection of transformers sometimes does not have reliable performance facing some faults and events such as saturation of current transformers (CTs) during internal and external faults, inrush currents, and cross-country faults (CCFs). In this paper, a new transformer differential protection scheme based on the self-adaptive biased characteristic curve and considering CCFs and various disturbances are used. First, the maximum overlap discrete wavelet transform (MODWT) was used to extract the superior characteristics of sampled differential current of the transformer at different levels. Then, by calculating the duty cycle (D) values of energy coefficients extracted in each periodicity by MATLAB software, a new self-adaptive biased characteristic curve will be obtained in different operating conditions. Finally, the percentage differential protection operation is adjusted based on the new adaptive characteristic curve zones. Differential current sampling under different operating conditions was performed on a real three-phase transformer in EMTP software. The simulation results show that the proposed adaptive differential protection scheme can accurately detect various faults and events occurring in the transformer and increase system reliability.