A stator current locus approach to induction machine parameter estimation

Author(s):  
David M. Reed ◽  
Kan Zhou ◽  
Heath F. Hofmann ◽  
Jing Sun
2020 ◽  
Vol 10 (9) ◽  
pp. 3222
Author(s):  
Tin Benšić ◽  
Toni Varga ◽  
Marinko Barukčić ◽  
Vedrana Jerković Štil

This paper presents a method for selecting the sampling time for induction machine parameter estimation from the machine line start measurements. the metaheuristic optimization method is used to find the optimal Prony exponential series approxiamtion of the line start transient current. From the optimal approximation, poles of the linearized induction machine model are computed and used to determine the optimal sampling time. the results show that sampling frequencies needed for parameter estimation are much lower than 1–15 kHz commonly used today. This reduces the necessary amount of collected data and the computing power needed for the estimation. the optimal sampling time is computed for the simulated and for the measured data. Referenced parameter estimation technique is tested for the measured transient showing benefits of using the optimal sampling time.


Mathematics ◽  
2020 ◽  
Vol 8 (6) ◽  
pp. 1024
Author(s):  
Martin Ćalasan ◽  
Mihailo Micev ◽  
Ziad M. Ali ◽  
Ahmed F. Zobaa ◽  
Shady H. E. Abdel Aleem

This paper presents the usage of the hybrid simulated annealing—evaporation rate water cycle algorithm (SA-ERWCA) for induction machine equivalent circuit parameter estimation. The proposed algorithm is applied to nameplate data, measured data found in the literature, and data measured experimentally on a laboratory three-phase induction machine operating as an induction motor and as an induction generator. Furthermore, the proposed method is applied to both single-cage and double-cage equivalent circuit models. The accuracy and applicability of the proposed SA-ERWCA are intensively investigated, comparing the machine output characteristics determined by using SA-ERWCA parameters with corresponding characteristics obtained by using parameters determined using known methods from the literature. Also, the comparison of the SA-ERWCA with classic ERWCA and other algorithms used in the literature for induction machine parameter estimation is presented. The obtained results show that the proposed algorithm is a very effective and accurate method for induction machine parameter estimation. Furthermore, it is shown that the SA-ERWCA has the best convergence characteristics compared to other algorithms for induction machine parameter estimation in the literature.


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