Szczegóły

Tytuł artykułu

Online parameter identification of SPMSM based on improved artificial bee colony algorithm

Tytuł czasopisma

Archives of Electrical Engineering

Rocznik

2021

Wolumin

vol. 70

Numer

No 4

Afiliacje

Wu, Chunli : College of Information Science and Engineering, Northeastern University, China ; Jiang, Shuai : College of Information Science and Engineering, Northeastern University, China ; Bian, Chunyuan : College of Information Science and Engineering, Northeastern University, China

Autorzy

Słowa kluczowe

artificial bee colony algorithm ; Euclidean distance ; online identification ; parameter identification ; surface-mounted permanent magnet synchronous motor

Wydział PAN

Nauki Techniczne

Zakres

777-790

Wydawca

Polish Academy of Sciences

Bibliografia

[1] Boileau T., Leboeuf N., Nahid-Mobarakeh B., Online identification of PMSM parameters: parameter identifiability and estimator comparative study, IEEE Transactions on Industry Applications, vol. 47, no. 4, pp. 1944–1957 (2011), DOI: 10.1109/TIA.2011.2155010.
[2] Ichikawa S., Tomita M., Doki S., Sensorless control of permanent-magnet synchronous motors using online parameter identification based on system identification theory, IEEE Transactions on Industrial Electronics, vol. 53, no. 2, pp. 363–372 (2006), DOI: 10.1109/TIE.2006.870875.
[3] Jian-fei S., Bao-jun G., Yan-ling L., Research of parameter identification of permanent magnet synchronous motor online, Electric Machines and Control, vol. 22, no. 3, pp. 17–24 (2018), DOI: 10.15938/j.emc.2018.03.003.
[4] Fan S., LuoW., Zou J., A hybrid speed sensorless control strategy for PMSM based on MRAS and fuzzy control, Proceedings of 7th International Power Electronics and Motion Control Conference, Harbin, China, pp. 2976–2980 (2012), DOI: 10.1109/IPEMC.2012.6259344.
[5] Shi Y., Sun K., Huang L., Online identification of permanent magnet flux based on extended Kalman filter for IPMSM drive with position sensorless control, IEEE Transactions on Industrial Electronics, vol. 59, no. 11, pp. 4169–4178 (2012), DOI: 10.1109/TIE.2011.2168792.
[6] Liu K., Zhang J., Adaline neural network based online parameter estimation for surface-mounted permanent magnet synchronous machines, Proceedings of the CSEE, vol. 30, no. 30, pp. 68–73 (2010).
[7] Gu X., Hu S., Shi T., Muti-parameter decoupling online identification of permanent magnet synchronous motor based on neural network, Transactions of China Electrotechnical Society, vol. 30, no. 6, pp. 114–121 (2015).
[8] Liwei Z., Peng Z., Yuefeng L., Parameter identification of permanent magnet synchronous motor based on variable step-size Adaline neural network, Transactions of China Electrotechnical Society, vol. 33, no. z 2, pp. 377–384 (2018).
[9] Peerez J.N.H., Hernandez O.S., Caporal R.M., Parameter identification of a permanent magnet synchronous machine based on current decay test and particle swarm optimization, IEEE Latin America Transactions, vol. 11, no. 5, pp. 1176–1181 (2013), DOI: 10.1109/TLA.2013.6684392.
[10] Liu Z., Wei H., Zhong Q., Parameter estimation for VSI-Fed PMSM based on a dynamic PSO with learning strategies, IEEE Transactions on Power Electronics, vol. 32, no. 4, pp. 3154–3165 (2017), DOI: 10.1109/TPEL.2016.2572186.
[11] Liu Z., Wei H., Li X., Global identification of electrical and mechanical parameters in PMSM drive based on dynamic self-learning PSO, IEEE Transactions on Power Electronics, vol. 33, no. 12, pp. 10858–10871 (2018), DOI: 10.1109/TPEL.2018.2801331.
[12] Sandre-Hernandez O., Morales-Caporal R., Rangel-Magdaleno J., Parameter identification of PMSMs using experimental measurements and a PSO algorithm, IEEE Transactions on Instrumentation and Measurement, vol. 64, no. 8, pp. 2146–2154 (2015), DOI: 10.1109/TIM.2015.2390958.
[13] Liu X., Hu W., Ding W., Research on multi-parameter identification method of permanent magnet synchronous motor, Transactions of China Electrotechnical Society, vol. 35, no. 6, pp. 1198–1207 (2020).
[14] Liu C., Zhou S., Liu K., Permanent magnet synchronous motor multiple parameter identification and temperature monitoring based on binary-modal adaptive wavelet particle swarm optimization, Acta Automatica Sinica, vol. 39, no. 12, pp. 2121–2130 (2013), DOI: 10.3724/SP.J.1004.2013.02121.
[15] Fu X., Gu H., Chen G., Permanent magnet synchronous motors parameters identification based on Cauchy mutation particle swarm optimization, Transactions of China Electrotechnical Society, vol. 29, no. 5, pp. 127–131 (2014).
[16] Guo-han L., Jing Z., Zhao-hua L., Kui-yin Z., Parameter identification of PMSM using improved comprehensive learning particle swarm optimization, Electric Machines and Control, vol. 19, no. 1, pp. 51–57 (2015).
[17] San-yang L., Ping Z., Ming-min Z., Artificial bee colony algorithm based on local search, Control and Decision, vol. 29, no. 1, pp. 123–128 (2014).
[18] Ding X., Liu G., Du M., Efficiency improvement of overall PMSM-Inverter system based on artificial bee colony algorithm under full power range, IEEE Transactions on Magnetics, vol. 52, no. 7, pp. 1–4 (2016), DOI: 10.1109/TMAG.2016.2526614.
[19] Zawilak T., Influence of rotor’s cage resistance on demagnetization process in the line start permanent magnet synchronous motor, Archives of Electrical Engineering, vol. 69, no. 2, pp. 249–258 (2020), DOI: 10.24425/aee.2020.133023.

Data

2021.11.30

Typ

Article

Identyfikator

DOI: 10.24425/aee.2021.138260 ; e-ISSN 2300-2506
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