Enhanced Neural Real-Time Digital Twin for Electrical Drives

Featured Application: The proposed neural real-time digital twin can be applied to offshore wind energy conversion systems for online condition monitoring and predictive maintenance of PMSG-based generators and power converters. By enabling sensorless estimation of degradation-sensitive parameters such as stator resistance, synchronous inductance, and DC-link capacitance, the method supports early fault detection, reduced downtime, and improved system reliability. The embedded implementation also makes it suitable for integration into industrial drive controllers and renewable energy platforms. This paper presents a real-time digital twin (DT) of the power conversion system used in offshore wind applications. The proposed DT is exploited to identify key electrical parameters of both the permanent magnet synchronous generator (PMSG) and the three-phase boost rectifier and has been developed with a Condition Monitoring (CM)-oriented approach. A Gated Recurrent Unit (GRU) neural network is adopted as a real-time digital model (RTDM) to estimate online the PMSG phase resistance and synchronous inductance, as well as the DC-link capacitance at the rectifier output. The network is trained in MATLAB using data generated by a Typhoon HIL 606 emulator, covering both balanced and unbalanced operating conditions and a wide range of parameter variations. The trained GRU is then deployed on the control board and implemented in LabVIEW Real-Time for embedded execution. Experimental tests on a PMSG-based generating unit confirm the effectiveness of the proposed RTDM, achieving low root-mean-square and mean percentage errors in parameter estimation. The results demonstrate that the enhanced neural real-time DT is a promising tool for condition monitoring and predictive maintenance of power conversion systems in offshore wind applications.