Sensor fault-tolerant control for wind turbines: an iterative learning method

Abstract

The combined wind speed estimator and tip speed ratio (WSE-TSR) tracking control scheme is widely used to regulate power production for large-scale modern wind turbines. Although very effective, such an advanced control scheme, based on the prior model information, is highly dependent on external measurements. For partial-load region control, the only external information involved is commonly the measured rotor or generator speed. Inaccuracy in such sole measurement results in an unintended turbine operation and might lead to sub-optimal power production and instability. This paper presents a fault-tolerant control (FTC) method, which aims to eliminate the sensor fault effects for modern wind turbine systems. To fulfil this goal, an iterative learning scheme is proposed to detect and estimate the multiplicative sensor fault, on which an adaptive FTC law is formulated such that the effects of the sensor fault are eliminated. Case studies show that the proposed iterative learning FTC method performs well in detecting, estimating, and accommodating the sensor fault under realistic turbulent wind conditions. The advanced wind turbine controller can maintain its control performance even under faulty conditions, preventing further damage to other turbine components and allowing for continuous power production.