Abstract
In order to precisely control the wind power generation systems under nonlinear variable wind velocity, this paper proposes a novel maximum power tracking (MPPT) strategy for wind turbine systems based on a hybrid wind velocity forecasting algorithm. The proposed algorithm adapts the bat algorithm and improved extreme learning machine (BA-ELM) for forecasting wind speed to alleviate the slow response of anemometers and sensors, considering that the change of wind speed requires a very short response time. In the controlling strategy, to optimize the output power, a state feedback control technique is proposed to achieve the rotor flux and rotor speed tracking purpose based on MPPT algorithm. This method could decouple the current and voltage of induction generator to track the reference of stator current and flux linkage. By adjusting the wind turbine mechanical speed, the wind energy system could operate at the optimal rotational speed and achieve the maximal power. Simulation results verified the effectiveness of the proposed technique.
Highlights
Wind, used as widely distributed huge reserves of green energy [1], has dramatic increased in grid-connected power these years
These technique adopted by maximum power point tracking (MPPT) controller mainly can be categorized into four types: by controlling of Tip Speed Ratio (TSR), adopting Power Signal Feedback (PSF) control, Perturb and Observe (P&O) method, and Optimal Torque Control (OTC) method
Extreme Learning Machine (ELM) is a supervised learning algorithm originated from single-hidden layer feed-forward neural networks (SLFNN) proposed by Guangbin Huang [15], and it achieves high precision in the performance of classification and forecasting
Summary
Wind, used as widely distributed huge reserves of green energy [1], has dramatic increased in grid-connected power these years. P&O bat algorithm maximum power point tracking variable speed wind turbine adopting power signal feedback tip speed ratio optimal torque control fuzzy inference system extreme learning machine single-hidden layer feed-forward neural networks mean absolute error mean square error voltage oriented control perturb and observe These conventional techniques fail to consider that the wind speed is a discrete nonlinear parameter set which is not compliant to a certain law of variation. Some scholars even proposed hybrid models with the combination of artificial intelligence algorithm and conventional control methods to achieve a higher efficiency [14] Though it can effectively deal with high non-linearity of wind turbines, the training process is time-costing and introduces a huge amount of iteration parameters like weights and bias into systems [15].
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