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

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Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
The artificial bee colony (ABC) intelligence algorithm is widely applied to solve multi-variable function optimization problems. In order to accurately identify the parameters of the surface-mounted permanent magnet synchronous motor (SPMSM), this paper proposes an improved ABC optimization method based on vector control to solve the multi-parameter identification problem of the PMSM. Because of the shortcomings of the existing parameter identification algorithms, such as high computational complexity and data saturation, the ABC algorithm is applied for the multi-parameter identification of the PMSM for the first time. In order to further improve the search speed of the ABC algorithm and avoid falling into the local optimum, Euclidean distance is introduced into the ABC algorithm to search more efficiently in the feasible region. Applying the improved algorithm to multi-parameter identification of the PMSM, this method only needs to sample the stator current and voltage signals of the motor. Combined with the fitness function, the online identification of the PMSM can be achieved. The simulation and experimental results show that the ABC algorithm can quickly identify the motor stator resistance, inductance and flux linkage. In addition, the ABC algorithm improved by Euclidean distance has faster convergence speed and smaller steady-state error for the identification results of stator resistance, inductance and flux linkage.
Rocznik
Strony
777--790
Opis fizyczny
Bibliogr. 19 poz., rys., tab., wz.
Twórcy
autor
  • College of Information Science and Engineering, Northeastern University China
autor
  • College of Information Science and Engineering, Northeastern University China
  • College of Information Science and Engineering, Northeastern University China
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., Luo W., 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. z2, pp. 377–384 (2018).
  • [9] Peěrez 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.
Uwagi
Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2021).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-ffb9c30d-f8ee-481a-91e7-ce06926cc733
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