Pseudo-Measurement Generation Method Based on GCN-Transformer for Lightweight Measurement Distribution Networks

With the large-scale integration of distributed generation, the scale and complexity of distribution networks are constantly increasing, making it essential to enhance the real-time situational awareness of distribution networks. In response to the current issues of insufficient real-time measurement data in distribution networks, this paper proposes a pseudo-measurement generation method that combines Graph Convolutional Network (GCN) and Transformer. The GCN-Transformer takes network topology information and real-time measurements from buses as the input to generate high-precision pseudo-measurement data, further achieving system observability. The feasibility of the proposed method is verified through simulation based on the IEEE 33-bus system.