查看文章

High impedance faults (HIFs) on overhead power lines are known to cause fires. They are difficult to detect using conventional protection relays because the fault current is insufficient to cause tripping. The delay in detecting HIFs can result in severe bushfires and energy losses; hence a high throughput, low latency detection scheme needs to be developed for HIF detection. Moreover, the complexities associated with HIF detection demands signal processing techniques combined with artificial intelligence to achieve higher detection accuracy. This paper proposes a sustainable deep learning-based approach in an edge device, that can be mounted on top of a power pole to detect HIFs in real-time. Data acquisition, feature extraction, and deep learning based fault identification are performed in an embedded edge node to achieve higher throughput, reduced latency as well as offload the network traffic. Furthermore …