Wind gust detection and load mitigation using artificial neural networks assisted control

Abstract

ABSTRACTThe design of a wind turbine implies the simulation of definite conditions as specified in the standards. Among those operational conditions, rare events such as extreme gusts or external faults are included, which may cause high structural loads. Such extreme design load cases usually drive the design of some of the main components of the wind turbine: tower, blades and mainframe. Two different strategies are hence presented to mitigate the loads, deriving from extreme load cases, on the basis of the detection of wind gusts by means of ad hoc synthesized artificial neural networks. This tool is embedded into the main control algorithm and allows it to detect the gust in advance, to anticipate the control reaction, and by doing so reducing extreme loads. One of the strategies performs a controlled stop when wind gust is detected. The other rides through wind gusts without stopping, i.e., without affecting the wind turbine normal operation. Aeroelastic simulations of the Alstom Wind's wind turbines using these techniques have shown significant reductions in the extreme loads for all standard IEC 61400‐1, edition 2 DLC 1.6 cases. In particular, the overall ultimate loads are largely reduced for blade root and tower base bending moments, with a direct impact on the structural design of those components. Copyright © 2013 John Wiley & Sons, Ltd.