Nonlinear model reduction-based induction motor aggregation

作者：Zhang, Gang^*; Du, Zhengchun; Ni, Yu; Li, Chongtao

来源：International Transactions on Electrical Energy Systems, 2016, 26(2): 398-411.

DOI：10.1002/etep.2089

• load modeling
• motor aggregation
• nonlinear model reduction
• empirical Gramian
• Galerkin projection
• adaptive genetic algorithms

This paper focuses on the problem of aggregating induction motors into a low-order equivalent model, which is very essential in large power system simulations. A new aggregating method is proposed based on nonlinear model reduction technique. The proposed method is developed directly from the detailed dynamic model of induction motors. By using empirical Gramians-based nonlinear model reduction techniques, a low-order load model, which can closely represent the dynamic characteristics of the detailed system, is obtained. The absence of drastic simplifications and the proper preservation of the input-output behavior of the original motors group allow the aggregated model to attain good accuracy. Two case studies are conducted to verify the validity of the proposed aggregating method. The simulation results show that this new method could attain high accuracy of simulation of all the individual induction motors under various operating conditions, especially when system is facing large disturbances.

• 出版日期2016-2
• 单位西安交通大学