Health-aware model predictive control of wind turbines using fatigue prognosis

Abstract

Wind turbine components are subject to considerable fatigue because of extreme environmental conditions to which they are exposed, especially those located offshore. Wind turbine blades are under significant gravitational, inertial, and aerodynamic loads, which cause their fatigue and degradation during the wind turbine operational life. A fatigue problem is often present at the blade root because of the considerable bending moments applied to this zone. Interest in the integration of control with fatigue load minimization has increased in recent years. This paper investigates the fatigue assessment using a rainflow counting algorithm and the blade root moment information coming from the sensor available in a high-fidelity simulator of a utility-scale wind turbine. Then, the integration of the fatigue-based system health management module with control is proposed. This provides a mechanism for the wind turbine to operate safely and optimize the trade-off between components' life and energy production. In particular, this paper explores the integration of model predictive control with the fatigue-based prognosis approach to minimize the damage of wind turbine components (the blades). A control-oriented model of the fatigue based on the rainflow counting algorithm is proposed to obtain online information of the blades' accumulated damage that can be integrated with model predictive control. Then, the controller objective function is modified by adding an extra criterion that takes into account the accumulated damage. The scheme is implemented and tested in a well-known wind turbine benchmark.