Dynamic Differential Current-based Transformer Protection Using a Convolutional Neural Network

A reliable transformer protection method is crucial for power systems. Aiming at improving the generalization performance and response speed of multi-feature fusion based transformer protection, this paper presents a dynamic differential current by fusing pre-disturbance and post-disturbance differential currents in real time then developing a dynamic differential current based transformer protection focusing on the feature changes of differential current. Generally, the image of differential current can comprehensively embody the feature changes resulting from any disturbance. In addition, a short window is sometimes sufficient to clearly reflect the internal fault because the differential current will instantly change when an internal fault occurs. Therefore, in order to identify the running states reliably in the shortest possible time, multiple images, including the differential current from a pre-disturbance one cycle to a post-disturbance different time, are combined by time order to define a dynamic differential current. After the protection method is started, this dynamic differential current serves as input for the deep learning algorithm to identify the running states in real time. Once the transformer is identified as a faulty one, a tripping signal is issued and the protection method stops. The dynamic model experiments show that the proposed protection method has a strong generalization ability and rapid response speed.