Design and implementation of a variable-structure adaptive fuzzy-logic yaw controller for large wind turbines

Abstract

The current trend of wind energy generation industry is to use large and ultra-large wind turbines that can reach more than 10 MW in ratings; especially in off-shore wind farms. Therefore, more emphasis is being given by researchers to increase the harvested energy by each individual wind turbine. Previously, more focus has been given to pitch control techniques of turbine blades for improving the harvested energy and lowering the turbine maintenance cost. However, still not enough work is done to investigate the effectiveness of nacelle yaw control in improving the harvested energy specifically for large wind turbines. In this paper, we introduce a new yaw controller based on adaptive fuzzy systems. The control objective of the proposed controller is to effectively track of the wind direction by yaw motion of the turbine nacelle. For that reason, it is a fuzzy-logic-based controller that has the capability to adaptively tune its rule base online. The change in the fuzzy rule base is done using a variable structure direct adaptive control algorithm to achieve the pre-defined control objectives. This algorithm has two advantages. First, it has a good performance in the training phase as it makes use of the initial rule base defined for the fuzzy logic yaw controller. Second, it has a robust estimator since it depends on a variable structure technique. The adaptive nature of the proposed controller significantly reduces the rule base size and improves its performance. The previous statement is verified through three levels of testing. The first level is Model-In-the-Loop (MIL) MATLAB/SIMULINK extensive simulations, with the performance results get compared to that of a carefully tuned Proportional-Integral-Differential (PID) controller. The second level of testing is through Software-In-the-Loop (SIL) testing using the same use cases. The last level is the Processor-In-the-Loop (PIL) experimental tests using a Texas Instruments TMS320F28335 digital signal processing board.