Adaptive Multi-Model Switching Predictive Active Power Control Scheme for Wind Generator System

Hongwei Li, Kaide Ren, Shuaibing Li and Haiying Dong
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Hongwei Li: School of Automation and Electrical Engineering, Lanzhou Jiaotong University, Lanzhou 730070, China
Kaide Ren: School of Automation and Electrical Engineering, Lanzhou Jiaotong University, Lanzhou 730070, China
Shuaibing Li: School of New Energy and Power Engineering, Lanzhou Jiaotong University, Lanzhou 730070, China
Haiying Dong: School of New Energy and Power Engineering, Lanzhou Jiaotong University, Lanzhou 730070, China

Energies, 2020, vol. 13, issue 6, 1-12

Abstract: To deal with the randomness and uncertainty of the wind power generation process, this paper proposes the use of the clustering method to complement the multi-model predictive control algorithm for active power control. Firstly, the fuzzy clustering algorithm is adopted to classify actual measured data; then, the forgetting factor recursive least square method is used to establish the multi-model of the system as the prediction model. Secondly, the model predictive controller is designed to use the measured wind speed as disturbance, the pitch angle as the control variable, and the active power as the output. Finally, the parameters and measured data of wind generators in operation in Western China are adopted for simulation and verification. Compared to the single model prediction control method, the adaptive multi-model predictive control method can yield a much higher prediction accuracy, which can significantly eliminate the instability in the process of wind power generation.

Keywords: wind power generation; multi-model predictive control; fuzzy clustering (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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