Abstract：Aiming at the problem that PMSM parameter identification is prone to fall into local optimum, which leads to low accuracy and slow identification speed, a hybrid Variation RAO algorithm (GCRAO) was proposed to be applied to PMSM parameter identification. According to the distribution characteristics of Gauss and Cauchy functions, the raO-1 algorithm was improved by introducing the strategy of self-selecting the optimal individual variation in three running stages in real time. A permanent magnet synchronous motor model based on vector control (SVPMSM) was built by matlab/Simulink under the dq axis coordinates, and the standard test function test and simulation experiment were carried out. The test results show that the local mining ability and global exploration ability of GCRAO algorithm are improved, which verifies the effectiveness of the improved RAO-1 algorithm. Simulation results show that compared with RAO-1 and PSO algorithms, GCRAO algorithm can identify electromagnetic parameters faster and has better convergence accuracy.
Wu H T, He Z Q, Zhu L, et al. Identification of permanent magnet Synchronous Motor Parameters based on Least Square Method ［J］. Electronic Technique, 201, 50(2): 48-49.
Wang X Y, Luo X Y, Gao P. Rotor-structure Design based on Vibration and Noise Optimization of electric vehicle Drive motor ［J］. Journal of Power Systems and Automation, 2023, 35(1): 44-50,77.
Wu Z Q, Du C Q, Zhang W, et al. Parameter identification of permanent magnet synchronous motor based on improved cuckoo search algorithm ［J］. Acta metrologica sinica, 2017, 38(5): 631-636.
Zhuang Y N, Sun H M. The application of MRAS algorithm in PMSM parameter identification ［J］. Electronic Technique, 2021, 50(6): 38-39.
Yang M C, Liu Y T, Sun J L, et al. Online multi-parameter identification of PMSM based on MRAS ［J］. Electrician technology, 2021, (9): 49-52.
［8］
Pei G, Liu J, Li L, et al. MRAS Based Online Parameter Identification for PMSM Considering VSI Nonlinearity［C］//IEEE. 2018 IEEE International Power Electronics and Application Conference and Exposition (PEAC). 2018.
Xiao X, Xu Q S, Wang Y T, et al. A Genetic Algorithm based parameter Identification method for embedded permanent Magnet Synchronous Motor ［J］. Journal of electrotechnics, 2014, 29 (3): 21-26.
［10］
Liu K, Zhang J. Adaline Neural Network Based Online Parameter Estimation for Surface-mounted Permanent Magnet Synchronous Machines［J］. Proceedings of the CSEE, 2010, 30(30): 68-73.
［12］
Zhang L, Zhang P, Liu Y, et al. Parameter Identification of Permanent Magnet Synchronous Motor Based on Variable Step-Size Adaline Neural Network［J］. Diangong Jishu Xuebao/Transactions of China Electrotechnical Society, 2018, 33: 377-384.
Wu D H, Huang X, Quan Y W, et al. Parameter identification of permanent magnet synchronous motor based on variation of coral reefs algorithm ［J］. Journal of system simulation, 2018, 30 (8): 3024-3032.
［3］
Li N, Ming Y, Xu D. An Adaptive Robust Predictive Current Control for PMSM with Online Inductance Identification［J］. International Review of Electrical Engineering, 2012, 7(2): 3845-3856.
Rao R V. Rao algorithms: Three metaphor-less simple algorithms for solving optimization problems［J］. International Journal of Industrial Engineering Computations, 2020(1): 107-130.
Xu G R, Yin Z D, Liu P, et al. Grid Connection Technology of Synchronous motor in New Energy Power field for Improving power System Stability ［J］. China electric power, 2018, 51(1): 44-50.
Fu X L, Gu H B, Cheng G C, et al. Identification of permanent magnet Synchronous Motor parameters based on Cauchy variable Particle Swarm Optimization algorithm ［J］. Journal of electrotechnics, 2014, 29 (5): 127-131.
Li L, Liu W, Cartes D A. Particle swarm optimization-based parameter identification applied to permanent magnet synchronous motors［J］. Engineering Applications of Artificial Intelligence, 2008, 21(7): 1092-1100.
［11］
Gu X, Hu S, Shi T, et al. Muti-Parameter Decoupling Online Identification of Permanent Magnet Synchronous Motor Based on Neural Network［J］. Diangong Jishu Xuebao/Transactions of China Electrotechnical Society, 2015.
2023, Vol. 44 
