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.
Highlights
The uncertainty of wind speed makes the output power of the wind power generation system fluctuate greatly [1,2,3]
In [4], a model predictive control (MPC)-based optimal active power control scheme for a doubly-fed induction generator (DFIG) was proposed, which was applied to wind farms with a distributed energy storage system (ESS)
V v where η is the conversion efficiency of wind energy, P is the mechanical power of wind turbines (WTs), ρ represents air density, S stands for the swept area of the blade, λ is the tip speed ratio, β is the pitch angle, v is the wind speed, ω is the speed of the main shaft, and R is the diameter of the wind turbine
Summary
The uncertainty of wind speed makes the output power of the wind power generation system fluctuate greatly [1,2,3]. In [4], a model predictive control (MPC)-based optimal active power control scheme for a doubly-fed induction generator (DFIG) was proposed, which was applied to wind farms with a distributed energy storage system (ESS). The results showed that such a control scheme can greatly reduce the control error of active power for WTs. an online model-based predictive control method was proposed in [5], which was used for the real-time optimal operation of a wind power integrated system including demand response (DR) and ESS. An online model-based predictive control method was proposed in [5], which was used for the real-time optimal operation of a wind power integrated system including demand response (DR) and ESS This method took into account all the interaction effects of the control facilities according to the estimated output of the future wind farms and realized the maximum utilization of wind power.
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