Dynamic Equivalent Modeling of Motors Based on Improved Hierarchical Clustering Algorithm

As the most important part of the power system load, the dynamic characteristics of motors are very important for power system simulation. The calculation is very large if all motors are modeled respectively. In order to reduce the amount of calculations, a dynamic equivalent modeling method of motors based on the improved hierarchical clustering algorithm is proposed in this paper. The two state parameters, the load rate and the product of inertia time constant and rotor resistance, are taken as clustering indexes. The improved hierarchical clustering algorithm is used to cluster dynamically the motors. And with the pseudo t2 statistics, it can obtain the best clustering results. Then it establishes the dynamic multi-machine equivalent model after each motor group equivalent. With the example system, the simulation results show that the proposed dynamic equivalent method can keep the dynamic characteristics of models and improve the equivalent accuracy.

[1]  J. Duncan.  Corrigendum: Linear induction motor equivalent circuit model , 1983 .

[2]  L. Ferraris,et al.  Induction Motor Equivalent Circuit Including the Stray Load Losses in the Machine Power Balance , 2008, IEEE Transactions on Energy Conversion.

[3]  J. H. Chow,et al.  Large-scale system testing of a power system dynamic equivalencing program , 1998 .

[4]  Lei Huang,et al.  Research on load model of VF based motor drive , 2016, 2016 IEEE International Conference on Power System Technology (POWERCON).

[5]  Datong Qin,et al.  Dynamic characteristics of motor-gear system under load saltations and voltage transients , 2018 .

[6]  L.A. Dessaint,et al.  Generating units aggregation for dynamic equivalent of large power systems , 2004, IEEE Power Engineering Society General Meeting, 2004..

[7]  Vipin Kumar,et al.  Chameleon: Hierarchical Clustering Using Dynamic Modeling , 1999, Computer.

[8]  Sung-Kwan Joo,et al.  Enhancement of coherency identification techniques for power system dynamic equivalents , 2001, 2001 Power Engineering Society Summer Meeting. Conference Proceedings (Cat. No.01CH37262).

[9]  Abid Aicha,et al.  Induction Motor Modeling Based on a Fuzzy Clustering Multi-Model—A Real-Time Validation , 2015 .

[10]  S.E.M. de Oliveira,et al.  Modal dynamic equivalent for electric power systems. I. Theory , 1988 .

[11]  O. Ondel,et al.  Detection of induction motor faults by an improved artificial ant clustering , 2011, IECON 2011 - 37th Annual Conference of the IEEE Industrial Electronics Society.

[12]  Wenchun Zhu,et al.  An interim dynamic induction motor model for stability studies in the WSCC , 2003, 2003 IEEE Power Engineering Society General Meeting (IEEE Cat. No.03CH37491).

[13]  T. Omata,et al.  Aspects of voltage responses of induction motor loads , 1998 .

[14]  Thomas A. Lipo,et al.  Analysis of a concentrated winding induction machine for adjustable speed drive applications. I. Motor analysis , 1991 .

[15]  G.J. Berg,et al.  Dynamic single-unit representation of induction motor groups , 1976, IEEE Transactions on Power Apparatus and Systems.

[16]  S.E.M. de Oliveira,et al.  Modal dynamic equivalent for electric power systems. II. Stability simulation tests , 1988 .

[17]  Pavol Fedor,et al.  Analysis of the Effect of Clustering on an Asynchronous Motor Fuzzy Model , 2013, SOCO-CISIS-ICEUTE.

[18]  Leon M. Tolbert,et al.  Static and Dynamic Power System Load Emulation in a Converter-Based Reconfigurable Power Grid Emulator , 2016 .

[19]  G. Gross,et al.  A Comparative Analysis of Congestion Management Schemes under a Unified Framework , 2002, IEEE Power Engineering Review.