Faulty-feeder detection in neutral point noneffectively grounded distribution networks consistently attracts research attention since it directly affects quality and safety of energy supply. Most modern research on faulty-feeder detection tends to apply more complex digital signal processing techniques and deeper neural networks in order to better extract and learn as many detailed characteristics as possible. However, these approaches may easily result in overfitting and high computational cost, which cannot meet requirements for detection accuracy and efficiency in practical applications. This paper proposes an innovative waveform encoding method and details a simple convolutional neural network (CNN) with one layer of convolution used for identification, which seeks to improve detection accuracy and efficiency simultaneously. First, sparse characteristics of waveforms are utilized to encode into compact vectors, and a waveform-vector matrix is generated. Second, to deduce waveform-vector matrix, a simple CNN with multi-scale filters and one layer of convolution is established. Finally, a methodology for faulty-feeder detection is proposed, and both detection accuracy and efficiency are considerably enhanced. Comparative studies have confirmed clear superiority of the developed method, which outperforms existing approaches in both detection accuracy and efficiency, thus highlighting its significant potential for application.

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.