Anomalous data evaluation for reliable updates of trustworthy power system digital twins

A digital twin (DT) should continuously and accurately reflect the dynamic state of its physical twin (PT). In safety-critical and time-sensitive domains such as power systems, anomalies in PT data stemming from measurement or communication malfunctions, can compromise DT updates and the integrity of DT-driven applications. To overcome this risk, it is essential to identify and correct anomalous PT data prior to DT updates. However, conventional DT implementations lack mechanisms for evaluating PT data before executing updates, posing challenges to operational reliability and system safety. This paper proposes a trustworthy digital twin (TwDT) concept with the PT data evaluation function, supported by a denoising physics-informed autoencoder (De-PI-AE) algorithm. The PT data evaluation function of a TwDT comprises three key steps: i ) detection for direct updates using trustworthy data while flagging anomalous PT data, ii ) identification for localisation of the specific anomalous values, and iii ) estimation for correcting previously identified anomalous values. The inclusion of each component effectively forms an entire PT data evaluation function to ensure reliable and continuous updates. A power system digital twin (PSDT) is used to demonstrate the development and effectiveness of the proposed De-PI-AE algorithm to realise the TwDTs, highlighting its strong potential to be embedded into DT update process. The De-PI-AI solution outperforms other artificial intelligence-based (AI) algorithms, achieving detection (88.5%), identification (98.9%), and estimation (mean square error reduced by 98.8%) of the specific PT data anomaly. The proposed solution enables the detection, identification, and estimation under one framework, while achieving positive performance gains. The proposed TwDT with the PT data evaluation function can be adopted and expanded to more DTs within power systems and beyond.