{'fraction_non_alphanumeric': 0.022888713496448304, 'fraction_numerical': 0.0, 'mean_word_length': 5.096153846153846, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Twin-Model Based on Model Order Reduction for Rotating Motors

With the advent of the digital age, real-time monitoring, diagnosis, and control become essential demands in the life-cycle management of electrical equipment. The methods for behavior modeling that allows accurate and real-time physical simulation gain increasing attention in recent years. Construction of the behavior model in a parametric space needs a large amount of simulation data based on the physics model which is very time-consuming. This work proposes a methodology by using the model order reduction (MOR) technique based on the proper orthogonal decomposition (POD) to build compact behavior model of rotating electric motors. With the help of the reduced-order model, the computation time to generate simulation data in a parametric space is largely reduced. The association of the behavior models with the mechanical rotation equation leads to twin models of the motors that can reflect accurately their physical state in real time. Results show that the twin models can provide an accurate time-domain response of the motor (including the speed, the torque, as well as the power angle) when compared to the finite-element method (FEM) simulation while the simulation time is negligible.