Experimental Evaluation of Induction Machine Parameter Identification Considering Iron Loss
Li Jie1, Du Xi2, Song Haijun3, Zhong Yanru1
1.Xi'an University of Technology, Xi'an 710048 China; 2.771 Institute China Aerospace Science and Technology Corporation, Xi'an 710054 China; 3.TBEA Xi'an Electrical Technology Co. Ltd., Xi'an 710119 China
Abstract：For the loss model control based efficiency optimization strategies of induction machines, whether the motor parameters required in the strategy, especially those related to the iron loss and the copper losses, are accurately enough obtained, will directly affect the energy-saving effect. The induction machine model which does not neglect iron loss was established, the improved genetic algorithm is employed to identify all the electrical parameters of the induction machine including the iron loss equivalent resistance. At the same time, the constraint that the stator leakage inductance and the rotor leakage inductance equal each other is removed in order to approach practice. The data acquisition system is adopted to do the experiments to ensure that the induction machines which are installed into the production line can run without interruption. The experimental results show that the proposed scheme can provide accurate enough induction machine parameters which can reproduce the iron loss and the copper loss. This makes it possible to realize the maximum energy saving for efficiency optimization of induction machines.
李洁, 杜茜, 宋海军, 钟彦儒. 考虑铁损的异步电机参数辨识实验研究[J]. 电工技术学报, 2014, 29(3): 89-95.
Li Jie, Du Xi, Song Haijun, Zhong Yanru. Experimental Evaluation of Induction Machine Parameter Identification Considering Iron Loss. Transactions of China Electrotechnical Society, 2014, 29(3): 89-95.
[1] 余龙海. 电动机能效与节电技术[M]. 北京: 机械工业出版社, 2008. [2] Masood Hajian, Jafar Soltani, Gholamreza Arab Markadeh, et al. Adaptive nonlinear direct torque control of sensorless IM drives with efficiency optimization[J]. IEEE Trans. on Ind. Electron., 57(3): 975-985. [3] Ali Bechouche, Hamid Sediki, Djaffar Ould Abdeslam, et al. Identification of induction motor at standstill using artificial neural network[C]. IECON, 2010: 2908-2913. [4] Belloc C, Vagapov Y, Moreno P. A step voltage response method for identification of induction motor parameters at stand still[C]. ICET, 2006: 109-112. [5] He Yanhui, Feng Yupeng, Wang Yue, et al. Parameter identification of an induction motor at standstill using vector constructing method[C]. ECCE, 2010: 4204- 4209. [6] Zheng Jun, Wang Yunkuan, Qin Xiaofei, et al. An offline parameter identification method of induction motor[C]. WCICA , 2008: 8898-8901. [7] Young Su Kwon, Jeong Hum Lee, Sang Ho Moon, et al. Standstill parameter identification of vector- controlled induction motors using the frequency characteristics of rotor bars[J]. IEEE Trans. on Ind. Appl., 2009, 45(5): 1610-1618. [8] Jin Hai, Ma Shouguang. Application of genetic algorithms in parameters identification of asynchronous motor[C]. ICSMC, 2009: 4976-4981. [9] Chen Zhenfeng, Zhong Yanru, Li Jie. Parameter identification of induction motors using ant colony optimization[C]. CEC, 2008: 1611-1616. [10] Andrew Trentin, Pericle Zanchetta, Chris Gerada, et al. Optimized commissioning method for enhanced vector control of high-power induction motor drives[J]. IEEE Trans. on Ind. Electron., 2009, 56(5): 1708-1717. [11] Hassan M Emara, Wesam Elshamy, Bahgat A. Parameter identification of induction motor using modified particle swarm optimization algorithm[C]. ISIE, 2008: 841-847. [12] Campos Delgado D U, Espinoza Trejo D R, Santana Arce E. Parameters identification in induction motors following hyperplanes optimization[C]. ICEEE, 2009: 1-6. [13] Sundareswaran K, Shyam H N, Palaniand Joby James S. Induction motor parameter estimation using hybrid genetic algorithm[C]. ICIIS, 2008: 1-6. [14] Li Jie, Zhong Yanru. Modeling and simulation of induction machine taking iron loss and parameter variation into account[C]. IPEC, 2005: 651-655.
2014, Vol. 29 
