The IEC standards prescribe an inflow wind field based on models with empirical assumptions to perform load calculations. The use of preview wind speed measurements using nacelle-mounted Lidars allows mitigation of structural loads by suggesting appropriate control action. This relationship is affected by uncertainty in site conditions and the dynamic control of wind turbine in different operation regimes. Although efforts have been put to derive the relation between the aerodynamic loading and the wind speed, there is scope to explore this relation using measurements. Deriving the load statistics based on the inflow wind field is therefore necessary to establish the correct control strategies. This study focuses on two aspects: firstly, the effect of variability in the inflow conditions due to wind speed fluctuations, turbulence and wind evolution on loading statistics and secondly, the effect of different wind turbine operation modes and Lidar measurement ranges on loading statistics. By understanding the effect of these two aspects on aerodynamic loading, a suitable control strategy can be designed by establishing correlation and coherence based look-up tables with inflow-loading relationship for each operation regime. The study defines a correlation between the Lidar measured wind speed and aerodynamic loading for three Lidar measurement ranges at below and above rated operation modes. Strong correlations are observed at above-rated operation, while uneven correlations at below-rated operation are observed. Strong correlations are observed for turbulence intensities higher than 12%. The preview distances of 50 m and 110 m provide the high correlation estimates The study of coherence between these two signals provides useful insights on the evolution of wind, the length scales present and the frequencies. The coherence remains high for wavelengths upto 200m for above-rated and below-rated operations, the time scale was found to be around 2-2.5 s and the decay parameter ranges from 2 to 7.

Increasing demands in decentralized power plants have focused attention on Vertical Axis Wind Turbines (VAWTs). However, accessing high range of power from VAWTs is an impediment due to increased loads on the turbine blades. Here, we derive an optimal pitching action that reduces the periodic disturbance on turbine blades of VAWTs without affecting their power production. A control technique called Subspace Predictive Repetitive Control (SPRC) alongwith a LQ Tracker is used for recursive identification to estimate the parameters of VAWT model and further provide an optimal control law accordingly. Basis functions have been used to reduce the dimensionality of the control problem. Simulation results show a great potential of the data-driven SPRC approach coupled with LQ Tracker in reducing the turbine loads on VAWTs.