IoT Empowered Early Warning of Transmission Line Galloping Based on Integrated Optical Fiber Sensing and Weather Forecast Time Series Data

Iced transmission line galloping poses a significant threat to the safety and reliability of power systems, leading directly to line tripping, disconnections, and power outages. Existing early warning methods of iced transmission line galloping suffer from issues such as reliance on a single data source, neglect of irregular time series, and lack of attention-based closed-loop feedback, resulting in high rates of missed and false alarms. To address these challenges, we propose an Internet of Things (IoT) empowered early warning method of transmission line galloping that integrates time series data from optical fiber sensing and weather forecast. Initially, the method applies a primary adaptive weighted fusion to the IoT empowered optical fiber real-time sensing data and weather forecast data, followed by a secondary fusion based on a Back Propagation (BP) neural network, and uses the K-medoids algorithm for clustering the fused data. Furthermore, an adaptive irregular time series perception adjustment module is introduced into the traditional Gated Recurrent Unit (GRU) network, and closed-loop feedback based on attention mechanism is employed to update network parameters through gradient feedback of the loss function, enabling closed-loop training and time series data prediction of the GRU network model. Subsequently, considering various types of prediction data and the duration of icing, an iced transmission line galloping risk coefficient is established, and warnings are categorized based on this coefficient. Finally, using an IoT-driven realistic dataset of iced transmission line galloping, the effectiveness of the proposed method is validated through multi-dimensional simulation scenarios.