Digital twin-driven identification of fault situation in distribution networks connected to distributed wind power

Abstract

The gradual integration of distributed wind power into distribution networks presents significant challenges for identifying faults, as it requires accurate and timely fault identification based on large-scale data. This paper proposes a novel fault situation identification framework driven by digital twins to overcome the existing bottleneck of asynchronous online fault identification and offline post-event analysis. The framework employs a high-precision digital twin avatar with parallel strategies of fully electromagnetic transient calculation to obtain the real-time operation status of distribution networks connected with distributed wind power. Furthermore, the framework employs skip-connected dilated causal convolution to mine meaningful multi-time-scale features from massive data, while using an automatic hyper-parameter tuning strategy. A case study based on IEEE 33-node standard distribution network and a real-world distribution network demonstrates the effectiveness of this framework, achieving ultra-real-time and high-precision simulation and effective fault identification even under noise or missing data, accelerating traditional post-fault analysis to microsecond-level situation identification.