A novel strategy based on group method of data handling neural network for detection of inrush current and preventing the mal-operation of the differential relay

Low impedance differential relays are widely used in the protection systems of power transformers. While being highly reliable, differential relays can misidentify the inrush currents generated during the switching of power transformers as faults and issue a tripping command when one is not needed. Therefore, these protection systems need a mechanism to differentiate between inrush currents and faults in order to prevent unnecessary activation. Accordingly, this paper presents a new method based on a group method of data handling (GMDH) neural network for differentiating faults from inrush currents. The proposed method can quickly detect a wide variety of faults that may occur simultaneously with inrush currents and is perfectly noise-resistant. The proposed method is compared with the conventional methods used in the industry, namely second harmonic and zero-crossing methods. The results demonstrate the ability of the proposed method to outperform conventional methods under a wide variety of operating conditions.