Dynamic Differential Current-Based Transformer Protection Using Convolutional Neural Network

A reliable transformer protection method is crucial for power system. 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 develops 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 resulted from any disturbance. Besides, a short window is sometimes sufficient to reflect the internal fault clearly 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, which includes the differential current from pre-disturbance one cycle to post-disturbance different time, are combined in order of time to define a dynamic differential current. After the protection method is started, this dynamic differential current serves as input of 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. © 2022 China Electric Power Research Institute. All rights reserved.