This paper presents the results of an advanced control strategy that employs active trailing edge flaps to reduce the fatigue loads of an experimental wind turbine. The strategy, called repetitive model predictive control, is a multiple-input multipleoutput controller that aims at the alleviation of out-of-plane blade root bending moments. The strategy incorporates the control commands, output errors, and state deviation from the previous rotation. This way, the time lag in the strain sensor input due to the blade inertia is compensated. Additionally, a strategy to limit the computational costs is presented. The load alleviation performance is evaluated at different yaw cases and compared with different individual flap control strategies. The repetitive model predictive control is able to reduce the fatigue loads by up to 23% compared with the better performing individual flap control strategy. This improvement in load reduction is accompanied by an increase in flap travel of up to 7% compared with the individual flap control strategies. K E Y W O R D S fatigue load control, individual flap control, repetitive control, trailing edge flaps
| INTRODUCTIONWind energy has become one of the most important sources of renewable energy. In order for this trend to continue, the cost of energy of this technology needs to be as low as possible. A crucial factor for the cost of energy is the employed material for building a turbine. Lowering material input can be achieved by reducing the loads acting on the turbine components. These in turn can be decreased by a load control strategy specifically designed for this purpose. As an example, cyclic and individual pitch control has been studied by various institutes. [1][2][3][4] Substantial research was also carried out into smart rotor control which could supplement pitching of the rotor blades. In particular, employing locally distributed active flow control devices on the blades has drawn a lot of attention in the community. Due to their high bandwidth and control authority, trailing edge flaps are one of the most promising active flow control options. 5 In particular, it was shown that trailing edge flaps achieve the same load reduction for fatigue loads as individual pitch control. 3 A critical aspect of active load control is the choice of sensors for the control strategy. 6 Mostly, active flow control aims at the alleviation of the out-of-plane turbine loads. Therefore, employing a feedback control on the out-of-plane blade root bending moment is a common choice.
This paper contributes to modeling and supervision of multi-stage centrifugal compressors coping with real-gas processes and steady to highly transient operating conditions. A novel dynamic model is derived, and the incorporation of the generic Lee-Kesler-Plöcker real-gas equation of state and its derivatives is presented. The model allows for embedding arbitrarily shaped performance maps, based on state-of-the-art polytropic change-of-state compressor characteristics. As the validity of these maps is a key issue for simulation and model-based monitoring, performance maps are treated as time-variant, and their shape is to be identified and monitored during operation. The proposed real-time map estimation scheme comprises an Unscented Kalman Filter and a newly proposed algorithm, referred to as Recursive Map Estimation. The combination yields a novel parameter and state estimator, which is expected to be superior if some parameters are characterized by a distinct operating point dependency. Two additional time-variant parameters are provided for monitoring: The first indicates the level of confidence in the local estimate, and the second points to drastic performance map alterations, which may be further exploited in fault detection. A modified reference simulation of a two-stage supercritical carbon dioxide compressor with known state trajectories, performance maps, and alterations demonstrates the successful application of the entire monitoring scheme, and serves for a discussion of the results.
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