Distributed Fault Location Monitoring of Power Transmission Lines based on IoT Configuration Tool Development

Background: At present, positioning detection in complex high-voltage transmission lines is affected by the grid topology, line length, etc., making it difficult to accurately locate the fault point, and the multi-sensor data collection time is long. This paper takes advantage of the real-time collection of IoT configuration tools and the topological space and temporal dependency capture of the GAT-BiLSTM model to perform distributed fault location and detection on transmission lines and improve the accuracy of fault location and detection. Methods: The study first uses the IoT configuration tool to develop a collection system to achieve real-time data collection and processing. Then, the GAT-BiLSTM model is built using the cascade paradigm, in which GAT is used to capture the topological spatial characteristics of the transmission line and generate an embedded representation of each node. BiLSTM is based on the features extracted by GAT to capture the timing pattern of fault occurrence in both directions, and the final prediction of fault location is generated through the fully connected layer. Finally, the fault location monitoring method combining the IoT configuration tool and GAT-BiLSTM is applied to high-voltage transmission lines in a certain area. Results: The results show that the RMSE of the GAT-BiLSTM model is only 10.7m, which is 2.4m less than that of CNN-LSTM, and the collection efficiency of the IoT configuration tool is as high as 99.5%. Concluding Remarks: The results show that combining the IoT configuration tool and the GAT-BiLSTM model can effectively improve the accuracy of distributed fault location monitoring of transmission lines, improve collection efficiency, and ensure that transmission lines can be quickly restored.