IEA Wind Task 32 serves as an international platform for the research community and industry to identify and mitigate barriers to the use of lidars in wind energy applications. The workshop "Optimizing Lidar Design for Wind Energy Applications" was held in July 2016 to identify lidar system properties that are desirable for wind turbine control applications and help foster the widespread application of lidar-assisted control (LAC). One of the main barriers this workshop aimed to address is the multidisciplinary nature of LAC. Since lidar suppliers, wind turbine manufacturers, and researchers typically focus on their own areas of expertise, it is possible that current lidar systems are not optimal for control purposes. This paper summarizes the results of the workshop, addressing both practical and theoretical aspects, beginning with a review of the literature on lidar optimization for control applications. Next, barriers to the use of lidar for wind turbine control are identified, such as availability and reliability concerns, followed by practical suggestions for mitigating those barriers. From a theoretical perspective, the optimization of lidar scan patterns by minimizing the error between the measurements and the rotor effective wind speed of interest is discussed. Frequency domain methods for directly calculating measurement error using a stochastic wind field model are reviewed and applied to the optimization of several continuous wave and pulsed Doppler lidar scan patterns based on commercially-available systems. An overview of the design process for a lidar-assisted pitch controller for rotor speed regulation highlights design choices that can impact the usefulness of lidar measurements beyond scan pattern optimization. Finally, using measurements from an optimized scan pattern, it is shown that the rotor speed regulation achieved after optimizing the lidar-assisted control scenario via time domain simulations matches the performance predicted by the theoretical frequency domain model.
In order to investigate laser Doppler (LD) flux motion in healthy subjects and patients with peripheral arterial occlusive disease (PAOD), spectrum analysis of LD signals is needed. Autoregressive analysis (AR) is presented as an alternative method of power spectrum estimation. This procedure is compared to the commonly used fast Fourier transform algorithm (FFT) by describing the analytical power of both spectra in the analysis of flux motion waves. LD signals were recorded from the forefoot of 8 healthy volunteers and 11 patients with different degrees of PAOD. The flux, concentration of moving blood cells and velocity signal was digitized and stored for off-line analysis. Special software was designed to calculate AR and FFT spectra of the LD signals and to compare the suitability of both methods for the spectral analysis of LD recordings. Additionally, three-dimensional spectrum diagrams were calculated to demonstrate time-dependent flux changes during standardized provocation maneuvers. AR facilitates the determination of frequency and amplitude of flux motion waves as compared to the FFT. Low frequency-large amplitude waves (LF waves) were detected in both groups. High frequency-small amplitude waves (HF waves), which predominantly appear in severe ischemia, were observed in 7 of the 11 patients and in 2 of the 8 controls. The spectra revealed pulsatile waves in all healthy controls, but only in 1 of the 11 patients. AR modelling allows a reliable description of important flux motion components and has considerable advantages in spectral estimation of LD signals as compared to the FFT.
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