The Online Parameter Identification Method of Permanent Magnet Synchronous Machine under Low-Speed Region Considering the Inverter Nonlinearity

Zhang, Qiushi; Fan, Ying

doi:10.3390/en15124314

Cited by 10 publications

(4 citation statements)

References 30 publications

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“…According to the identification time in system operation, parameter identification methods are divided into offline identification and online identification [7,8]. Offline identification method can be applied to different types of motors, but it is necessary to choose the appropriate method according to the actual situation of motors [9]. In [10], modal analysis is adopted to analyze the torsional vibration frequency response of the motor, find out the sound source of the motor, and obtain the vibration mode of the motor.…”

Section: Introductionmentioning

confidence: 99%

PMSWG Parameter Identification Method Based on Improved Operator Genetic Algorithm

Cheng,

Zhang,

Zhang

2024

PIER C

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Permanent Magnet Synchronous Wind Generator (PMSWG) parameter identification method with improved operator genetic algorithm is proposed for the influence of perturbations caused by mechanical parameter changes on the dynamic performance of motor speed control system. Firstly, current with i d = 0 and i d ̸ = 0 are injected into axis d respectively to design the fitness function. Through quantum coding, the genetic algorithm can obtain better population and fitness in the early stage, and find better solutions in the search space. At the same time, the cross method of two random numbers is used to make the cross variable not restricted in a range, which enhances the global search ability. Finally, the update strategy of hybrid mutation composed of Gaussian mutation and Cauchy mutation is introduced to ensure the global search ability of the algorithm, and the accuracy of the optimization results is improved. Experiments show that the proposed method avoids local optimization and achieves global optimization, which can further improve the convergence speed and identification accuracy of the algorithm.

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Section: Introductionmentioning

confidence: 99%

PMSWG Parameter Identification Method Based on Improved Operator Genetic Algorithm

Cheng,

Zhang,

Zhang

2024

PIER C

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“…Compared to offline identification methods, ideal online methods can accurately estimate motor parameters in real time, enhancing system control performance [2][3][4]. Current online parameter identification methods for permanent magnet synchronous motors primarily utilize least squares [5][6][7], model reference adaptive [8][9][10], and Kalman filter algorithms [11][12][13].…”

Section: Introductionmentioning

confidence: 99%

Parameter Identification of Permanent Magnet Synchronous Motor with Dynamic Forgetting Factor Based on H∞ Filtering Algorithm

Yuan,

Chang,

Zhang

2023

Actuators

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To address system parameter changes during permanent magnet synchronous motor (PMSM) operation, an H∞ filtering algorithm with a dynamic forgetting factor is proposed for online identification of motor resistance and inductance. First, a standard linear discrete PMSM parameter identification model is established; then, the discrete H∞ filtering algorithm is derived using game theory reducing state and measurement noise influence. A cost function is defined, solving extremes values of different terms. A dynamic forgetting factor is introduced to the weighted combination of initial and current measurement noise covariance matrices, eliminating identification issues from different initial values. On this basis, a dynamic forgetting factor is added to weigh the combination of the initial measurement noise covariance matrix and the current measurement noise covariance matrix, which eliminates the influence of the discrimination error caused by the different initial values. Finally, the identification model is built in MATLAB/Simulink for simulation analysis to verify the feasibility of the proposed algorithm. The simulation results show the proposed H∞ filtering algorithm rapidly and accurately identifies resistance and inductance values with significantly improved robustness. The forgetting factor enables quick stable recognition even with poor initial values, enhancing PMSM control performance.

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“…To solve the problem of simultaneous state estimation and parameter identification for a class of nonlinear systems, Alvaro-Mendoza [22] designed an adaptive observer based on the sliding mode method, whose main advantages are that it combines the robustness and finite-time convergence of the sliding mode observer, as well as the simple tuning of the high-gain observer, reducing the tuning effort. In addition, some researchers have adopted intelligent algorithms, such as particle swarm optimization (PSO), genetic algorithm (GA), neural network algorithm (NNA), and other intelligent algorithms to realize online parameter identification and have made some breakthroughs [23][24][25].…”

Section: Introductionmentioning

confidence: 99%

Adaptive Multi-Parameter Estimation of Inertial Stabilization Platform with Unknown Load

Zheng

Xie

et al. 2023

Actuators

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In order to improve the state monitoring and adaptive control capability of inertial stabilization platforms (ISPs) with unknown loads, it is necessary to estimate the dynamic parameters comprehensively online. However, most current online estimation methods regard the system as a linear dual-inertia model which neglects the backlash and nonlinear friction torque. It reduces the accuracy of the model and leads to incomplete and low accuracy of the estimated parameters. The purpose of this research is to achieve a comprehensive and accurate online estimation of multiple parameters of ISPs and lay a foundation for state monitoring and adaptive control of ISPs. First, a dual-inertia model containing backlash and nonlinear friction torque of the motor and load is established. Then, the auto-regressive moving average (ARMA) model of the motor and load is established by the forward Euler method, which clearly expresses the online identification formula of the parameters. On this basis, the adaptive identification method based on the recursive extended least squares (RELS) algorithm is used to realize the online estimation of multiple parameters. The simulation and experimental results show that the proposed adaptive multi-parameter estimation method can realize the simultaneous online identification of the moment of inertia of the load, the damping coefficient of motor and load, the transmission stiffness, the Coulomb friction torque of motor and load, and the backlash, and the steady-state error is less than 10%. Compared with the traditional linear dual-inertia model, the similarity between the model based on the proposed adaptive parameter estimation algorithm and the actual system is increased by 65.3%.

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The Online Parameter Identification Method of Permanent Magnet Synchronous Machine under Low-Speed Region Considering the Inverter Nonlinearity

Cited by 10 publications

References 30 publications

PMSWG Parameter Identification Method Based on Improved Operator Genetic Algorithm

PMSWG Parameter Identification Method Based on Improved Operator Genetic Algorithm

Parameter Identification of Permanent Magnet Synchronous Motor with Dynamic Forgetting Factor Based on H∞ Filtering Algorithm

Adaptive Multi-Parameter Estimation of Inertial Stabilization Platform with Unknown Load

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