Parameter Estimation for VSI-Fed PMSM Based on a Dynamic PSO With Learning Strategies

Liu, Zhaohua; Wei, Hua‐Liang; Zhong, Qing-Chang; Liu, Kan; Xiao, Xiao-Shi; Wu, Lianghong

doi:10.1109/tpel.2016.2572186

Cited by 126 publications

(47 citation statements)

References 44 publications

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“…5. The details of the steps of PSO are presented in [11,12]. The steps of the parameter estimation for the voltage-related transfer function H pu (s) in the active power model are taken as the example to be introduced as follows.…”

Section: Coherence Of Wts In Wfmentioning

confidence: 99%

Transfer function based equivalent modeling method for wind farm

Qian

et al. 2018

J. Mod. Power Syst. Clean Energy

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To effectively study the dynamics of power systems with large-scale wind farms (WFs), an equivalent model needs to be developed. It is well known that back-toback converters and their controllers are important for the dynamic responses of the wind turbine (WT) under disturbances. However, the detailed structure and parameters of the back-to-back converters and their controllers are usually unknown to power grid operators. Hence, it is difficult to build an accurate equivalent model for the WF using the component model-based equivalent modeling method. In this paper, a transfer function based equivalent modeling method for the WF is proposed. During modeling, the detailed structure and parameters of the WF are not required. The objective of the method is reproducing the output dynamics of the WF under the variation of the wind speed and power grid faults. A decoupled parameter-estimation strategy is also developed to estimate the parameters of the equivalent model. A WF that consists of 16 WTs is used to test the proposed equivalent model. Additionally, the proposed equivalent modeling method is applied to build the equivalent model for a real WF in Northwest China. The effectiveness of the proposed method is validated by the real measurement data.

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Section: Coherence Of Wts In Wfmentioning

confidence: 99%

Transfer function based equivalent modeling method for wind farm

Qian

et al. 2018

J. Mod. Power Syst. Clean Energy

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“…space is deemed as a particle in PSO (short for particle swarm optimization) algorithm. m particles are first generated in the solution space in the optimization calculation [21][22][23]. The position vector of the ith particle in the particle swarm is x i = (x i1 , x i2 , .…”

Section: Pso Algorithm Each Latent Solution In D-dimensionalmentioning

confidence: 99%

Application of Simulated Annealing Particle Swarm Optimization Based on Correlation in Parameter Identification of Induction Motor

Wang

Liu

2018

Mathematical Problems in Engineering

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The strengths and weaknesses of correlation algorithm, simulated annealing algorithm, and particle swarm optimization algorithm are studied in this paper. A hybrid optimization algorithm is proposed by drawing upon the three algorithms, and the specific application processes are given. To extract the current fundamental signal, the correlation algorithm is used. To identify the motor dynamic parameter, the filtered stator current signal is simulated using simulated annealing particle swarm algorithm. The simulated annealing particle swarm optimization algorithm effectively incorporates the global optimization ability of simulated annealing algorithm with the fast convergence of particle swarm optimization by comparing the identification results of asynchronous motor with constant torque load and step load.

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“…Advanced robust techniques are often introduced to design control law, such as the control-Lyapunov function [13], disturbance estimation [14], and differential flatness [15]. Another strategy lies in the parameter identification, such as a recursive inductance estimator [9], multi-parameter identification with a decoupling method [16], model reference adaptive technique [17], extended Kalman filter [18], modified particle swarm optimization [19], Adaline neural network [20], etc. All those methods aim to acquire accurate parameters, which is not necessary in the PCC schemes.…”

Section: Introductionmentioning

confidence: 99%

Unified Predictive Current Control of PMSMs with Parameter Uncertainty

Tang

Dai

2019

Electronics

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Predictive current control (PCC) applied on permanent magnet synchronous motors (PMSMs) has been developed into mainly three methods: the conventional finite-control-set PCC, the double voltage vectors PCC, and deadbeat PCC. However, each approach has its particular calculation way for voltage vectors selection and respective execution duration. This paper, based on the deadbeat idea, presents a unified predictive current control scheme of PMSMs. Under this scheme, the prior three classes are able to be clearly unified into one frame with lower calculation effort. Furthermore, to cope with problem of parameter mismatch in dq-axis current predictive model, a integrated identification method is proposed. Firstly, data selectors are designed to reject abnormal data of sampling signals, and then the interval-varying multi-innovation least squares algorithm is combined with forgetting factor (V-FF-MILS) to approximate the error terms caused by electromagnetic parameters error. The estimated results are online fed to the model of PMSM to enhance its accuracy. Finally, the processor in loop (PIL) simulation results verify that the proposed integrated scheme has advantages in current control of PMSMs with large-scale parameter uncertainty.

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Parameter Estimation for VSI-Fed PMSM Based on a Dynamic PSO With Learning Strategies

Cited by 126 publications

References 44 publications

Transfer function based equivalent modeling method for wind farm

Transfer function based equivalent modeling method for wind farm

Application of Simulated Annealing Particle Swarm Optimization Based on Correlation in Parameter Identification of Induction Motor

Unified Predictive Current Control of PMSMs with Parameter Uncertainty

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