Volume 6, Issue 3, June 2018, Page: 28-37
Parameters Identification of Induction Motor Model Based on Manufacturer Data and Sequential Quadratic Programming
Weiping Liao, Jiangmen Power Supply Bureau, Guangdong Power Grid Co., Ltd., Jiangmen, China
Yan Zhang, Jiangmen Power Supply Bureau, Guangdong Power Grid Co., Ltd., Jiangmen, China
Rui Zhou, Jiangmen Power Supply Bureau, Guangdong Power Grid Co., Ltd., Jiangmen, China
Received: Jun. 24, 2018;       Accepted: Jul. 25, 2018;       Published: Dec. 18, 2018
DOI: 10.11648/j.acis.20180603.11      View  846      Downloads  114
In order to precaution and control the transient voltage stability of the receiving-end system, it is very necessary to quickly and accurately calculate the model parameters for induction motor synthesis load. There are several methods to obtain the model parameters of induction motor, in which, estimating the model parameters of induction motor using for power system stability analysis according to the nameplate data is a significant promising approach with potential application. In this paper, a new optimized mathematical model for identification of induction motor single-cage and double-cage parameters are proposed, it’s overcome the deficiency of artificially adding approximate constraints in parameter identification of induction motor models from induction motor manufacturer data. Minimization of the induction motor efficiency deviation is taken as the goal and important induction motor performance indicators, such as the stator current, the input reactive power, the maximum electromagnetic torque and the starting parameters, are equal to their manufacturer values are regarded as constraints, the sequential quadratic programming (SQP) is used to solve the nonlinear problem. The proposed new mathematical model and algorithm were verifed on a sample of 6 induction motors of different capacity, manufacturers, and rated voltage. The induction motor performance characteristics supplied by the manufacturer and used to identification parameters of induction motor are then calculated, using the equivalent circuit estimated parameters themselves. In all the studied cases, the calculated induction motors performance indexes are found to be in excellent agreement with the manufacturer data. Comparison with other methods shows that the induction motor model parameters obtained by this method can reflect the working characteristics of induction motor single-cage and double-cage model more accurately.
Induction Motor, Manufacturer Data, Parameter Identification, Sequential Quadratic Programming
To cite this article
Weiping Liao, Yan Zhang, Rui Zhou, Parameters Identification of Induction Motor Model Based on Manufacturer Data and Sequential Quadratic Programming, Automation, Control and Intelligent Systems. Vol. 6, No. 3, 2018, pp. 28-37. doi: 10.11648/j.acis.20180603.11
Copyright © 2018 Authors retain the copyright of this article.
This article is an open access article distributed under the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
GU Zhuoyuan, TANG Yong, YI Jun, et al. Study on Mechanism of Interrelationship Between Power System Angle Stability and Induction Motor Stability [J], Power System Technology, 2017, 41(12): 2499-2505.
XIE Haipeng, HE Jian, BIE Zhaohong, et al., Reliability Evaluation of Large-scale Hybrid AC/DC Grids Based on SOCP Load Shedding Model [J], Power System Technology, 2016, 40(12): 3761-3767.
J. Modarresi, E. Gholipour, A. Khodabakhshian, “A comprehensive review of the voltage stability indices,” Renewable and Sustainable Energy Reviews, vol. 63, pp. 1–12, Sep, 2016.
H. Y. Yuan, F. X. Li, Hybrid voltage stability assessment (VSA) for N−1 contingency, Electric Power Systems Research, vol. 122, pp. 65–75, May. 2015.
Zhou Qinyong, Zhang Yantao, He Hailei, et al. A practical site selection method for dynamic reactive power compensation in multi-infeed DC power grid [J]. Power System Technology, 2014, 38(7): 1753-1757.
Tang Yong, Zhang Hongbin, Hou Junxian, et al. Study on essential principle and methods for load modeling [J]. Power System Technology, 2007, 31(4): 1-5.
Li Peng, Yu Yixin, Jia Hongjie. A study on models and methods for identifying of voltage stability limit more precisely [J]. Proceedings of the CSEE, 2004, 24(10): 21-26.
Zhao Bing, Tang Yong, Zhang Wenchao, et al. Determination of induction motor simulation model parameters based on the motor manufacturer Data [J]. Power System Technology, 2010, 30(1): 52-58.
Rogers G. J, Shirmohammadi D., Induction machine modeling for electromagnetic transient program [J]. IEEE Trans. On Energy Conversion, 1987, 2(4): 622–628.
Abdelaziz M. M. A, El-Saadany E. F. Estimation of Induction Motor Single-Cage Model Parameters from Manufacturer Data [J]. 2013 IEEE Power and Energy Society General Meeting, 2013: 1-5.
Sakthivel V. P, Bhuvaneswari R, Subramanian S. Multi- objective parameter estimation of induction motor using particle swarm optimization [J]. Engineering Applications of Artificial Intelligence, 2010, 23: 302–312.
ABRO A. G, Saleh J. M. Multiple-global-best guided artificial bee colony algorithm for induction motor parameter estimation [J]. IEEE Trans. On Energy Conversion, 2014, 22:620-636.
Pedra J, Corcoles F. Estimation of induction motor double-cage model parameters from manufacturer data [J]. IEEE Trans. On Energy Conversion, 2004, 19(2): 310-316.
Pedra J, Corcoles F. On the Determination of Induction Motor Parameters From Manufacturer Data for electro- magnetic transient programs [J]. IEEE Trans. On Energy Conversion, 2008, 23(4): 1709-1718.
Tang Yunqiu, Luo yingli, Liang Yanping. Electrical Machinery [M]. Beijing: China Machine Press, 2008.
Philip E. GILL. User’s Guide for SNOPT Version 7: Software for Large-Scale Nonlinear Programming, 2008.
Jiang Aipeng. Large scale reduced space SQP algorithm and its application in process system optimization [D]. Hangzhou: Zhejianguniversity, 2008.
Ma Changfeng. Optimization method and its MATLAB program esign [M]. Beijing: Science Press, 2010: 211-251.
China Electric Power Science Research Institute, PSD-BPA Transient stability program user manual, Chinese Version 4.15.
Browse journals by subject