Model Predictive Control with Fatigue-Damage Minimization through the Dissipativity Property of Hysteresis Operators

In this paper, we propose an approximation method for the well-known fatigue-damage estimation of rainflow counting (RFC) using the dissipativity property of hysteresis operators that can be embedded in model predictive control (MPC) frameworks. Firstly, we revisit results that establish the equivalence between RFC to an energy dissipation property of an infinite-dimensional operator of the Preisach hysteresis model. Subsequently, we approximate the Preisach model using a finite- dimensional differential Duhem hysteresis model and propose an extended MPC scheme that takes into account the dissipated energy of Duhem hysteresis model as a damage proxy in the optimization problem formulation. Lastly, we present an example of control design for damage minimization in the shaft of a wind turbine where we illustrate the proposed strategy

Wind Turbine Control with Active Damage Reduction through Energy Dissipation

In this paper we propose an active damage reduction control strategy for wind turbines based on dissipated energy. To this end we rely on the equivalences relating both damage in the rainflow counting sense and dissipated energy to the variations of Preisach hysteresis operators. Since dissipation theory is well suited for control systems, we adopt the dissipated energy of a Duhem hysteresis model that is described by a differential equation. Accordingly, we incorporate the dissipated energy into the optimal control problem formulation as a proxy to the damage. Lastly, the proposed strategy is evaluated with NREL’s FAST high-fidelity non-linear wind turbine

Structural health monitoring of offshore wind turbines: A review through the Statistical Pattern Recognition Paradigm

Offshore Wind has become the most profitable renewable energy source due to the remarkable development it has experienced in Europe over the last decade. In this paper, a review of Structural Health Monitoring Systems (SHMS) for offshore wind turbines (OWT) has been carried out considering the topic as a Statistical Pattern Recognition problem. Therefore, each one of the stages of this paradigm has been reviewed focusing on OWT application. These stages are: Operational Evaluation; Data Acquisition, Normalization and Cleansing; Feature Extraction and Information Condensation; and Statistical Model Development. It is expected that optimizing each stage, SHMS can contribute to the development of efficient Condition-Based Maintenance Strategies. Optimizing this strategy will help reduce labor costs of OWTs׳ inspection, avoid unnecessary maintenance, identify design weaknesses before failure, improve the availability of power production while preventing wind turbines׳ overloading, therefore, maximizing the investments׳ return. In the forthcoming years, a growing interest in SHM technologies for OWT is expected, enhancing the potential of offshore wind farm deployments further offshore. Increasing efficiency in operational management will contribute towards achieving UK׳s 2020 and 2050 targets, through ultimately reducing the Levelised Cost of Energy (LCOE)

Development of a self-tuned drive-train damper for utility-scale variable-speed wind turbines

This thesis describes the development of a procedure that tunes a wind turbine drivetrain damper (DTD) automatically. This procedure, when integrated into the controller of any utility-scale variable-speed wind turbine, will allow the turbine to autonomously and automatically tune its DTD on site. In practice this means that the effectiveness of the damper becomes independent on the accuracy of the model or the simulations used by the control engineers in order to tune the damper. This research is motivated by the fact that drive-train failures are still one of the biggest problems that stigmatises the wind turbine industry. The development of an automatically tuned DTD that alleviates the drive-train fatigue loads and thus increases the reliability and lifetime of the drive-train is thus considered very beneficial for the wind turbine industry. The procedure developed begins by running an experimental procedure to collect data that is then used to automatically system identify a linear model describing the drivetrain. Based on this model a single band-pass filter acting as a DTD is automatically tuned. This procedure is run for a number of times, and the resulting DTDs are compared in order to select the optimal one. The thesis demonstrates the effectiveness of the developed procedure and presents alternative procedures devised during research. Finally, insight into future work that could be performed is indicated in the last chapter of the thesis