DTformer: An Efficient Digital Twin Model for Loss Measurement in UHVDC Transmission Systems
Yufeng Sun, Ying Shi, Qin Hu, Changjun Xie, Tao Su
Abstract
Ultra-high Voltage Direct Current system (UHVDC) can realize long distance transmission, and its loss measurement has great significance for loss reduction and fault diagnosis. Currently, loss measurements can be performed by model-driven and data-driven approaches. However, poor operating conditions can lead to system sensing errors, which can significantly reduce the accuracy and speed of measurement. To deal with this problem, we propose a digital twin architecture and constructs a data-driven learning model. The model, DTformer, is embedded in the digital twin architecture. And it has three remarkable processes: encoder-decoder based random feature extraction, loss measurement transfer learning and knowledge distillation. These processes extract parameter information and topology features from the data of UHVDC. The model has good accuracy, response time and resistance to the missing data. The comparison experiment shows that DTformer outperforms popular feature extraction models with an average decrease of 38.80% in RMSE and 42.82% in MAE, and the best R2 value is closest to 1. The analyses of lightweight and missingness show that the DTformer also has significant response time improvement and missing resistance. Finally, we also implement the visualization of digital twin to enable operators to perform monitoring, computation, analysis and management of UHVDC.