Digital Twin of a Frequency Converter of an Asynchronous Machine for a Real Time Prediction in Stationary Operating Points Utilizing Machine Learning Methods

This paper presents the development and implementation of digital twin models for a frequency converter in stationary operating points, using machine learning techniques to predict key electrical parameters, including inverter voltage, current, frequency, and IGBT temperature. Four different modeling approaches, each using different machine learning methods, were investigated: a simple neural network structure, a parallel model structure, a serial model structure, and a serial physical model structure. The primary objective of this study was to compare the predictive accuracy of these models and identify the most effective approach. Each model was trained, optimized, and evaluated using real-world data collected from a back-to-back test bench setup. After extensive data pre-processing and normalization, the model performance was evaluated based on error metrics such as mean absolute error, maximum absolute error and mean relative error. The serial physical model structure demonstrated the highest prediction accuracy, achieving a mean relative error of 0.2 % for inverter IGBT temperature, 0.4 % for inverter current, 0.7 % for inverter voltage, and 0.2 % for inverter frequency. This study highlights the importance of incorporating physically meaningful input parameters to improve model performance. Furthermore, unlike many conventional parameter prediction methods, the proposed approach is capable of real-time computation due to its low computational effort. This real-time capability significantly expands its potential applications in various domains. The results highlight the potential of digital twin technologies combined with machine learning to improve the efficiency and reliability of power electronic systems. The proposed methodology supports advanced condition monitoring and system optimization, with potential applications in electromobility and other industrial sectors.