Multi-Sensor Data Fusion Method for Smart Substations Based on Hierarchical Attention Network and Transfer Learning
Bing Liu, Mengyang Jia, Zhou Jinyu, Xiaotong Xu, Yanmin Yang · 2024
The increasing complexity of modern power substations necessitates advanced data fusion techniques for intelligent monitoring, diagnosis, and decision-making. This paper proposes a novel multi-sensor data fusion framework for smart substations, leveraging hierarchical attention networks (HANs) and transfer learning techniques. The HAN effectively captures the multi-level dependencies and importances of different sensors, while transfer learning adapts the pre-trained model to specific substation environments. Extensive experiments on two real-world datasets demonstrate the superiority of the proposed framework over state-of-the-art methods in sensor fault detection and substation state estimation tasks, achieving an average$\mathbf{F 1}$-score of 0.95 and a mean squared error of 0.018, respectively. The framework's practical applicability is showcased through case studies, highlighting its potential for intelligent monitoring, diagnosis, and decision-making in smart substations. Future research directions include incorporating additional contextual information, developing online learning mechanisms, enhancing interpretability, and exploring scalability aspects. The proposed framework represents a significant step towards revolutionizing the operation and maintenance of smart substations, contributing to a more reliable, efficient, and sustainable power grid.