Offshore Wind Project 2-2013 Laser Wind Sensor Performance Validation with an Existing

A new approach to laser wind sensor measurement validation is described and demonstrated. The new approach relies on the paired-t statistical method to generate a time series of differences between two sets of measurements. This series of differences is studied to help identify and explain time intervals of operationally significant differences, which is not possible with the traditional approach of relying on the squared coefficient of variation as the primary metric. The new approach includes estimating a confidence interval for the mean difference and establishing a level of meaningful difference for the mean difference, and partitioning the data set based on wind speed. To demonstrate the utility of the new approach, measurements made by a laser wind sensor mounted on a floating buoy are compared first with those made by a second laser wind sensor mounted on a nearby small island for which the co-efficient of variation is high (> 99%). It was found that time intervals when high differences in wind speed occurred corresponded to high differences in wind direction supporting a hypothesis that the two laser wind sensor units are not always observing the same wind resource. Furthermore, the average difference for the 100m range gate is positive, statistically signficant (=0.01) and slightly larger than the precision of the gages, 0.1m/s. One possible cause of this difference is that the surface roughness over land is slowing the wind at 100m slightly. A second comparison was made with previously existing cup anemometers mounted on a metrological mast located on-shore. The cup anemometers are about 8m lower than the center of the lowest range gate on the laser wind sensor. The data was partitioned into three sets: not windy (average wind speed at the cup anemometers ≤ 6.7m/s) windy but no enhanced turbulence (average wind speed at the cup anemometers > 6.7m/s), and windy with enhanced turbulence. Periods of enhanced turbulence are associated with the passage of a cold frontal boundary. The paired-t analysis for the not windy data set showed a difference in the average wind speeds of 0.096m/s, less in absolute value than the precision of the gages. The negative sign indicates slower wind speed over land as well as at a lower height, which is expected. Similar results were obtained for the windy with no enhanced turbulence data set. In addition, the average difference was not statistically significant (=0.01). The windy with enhanced turbulence data set showed significant differences between the buoy mounted laser wind sensor and the on-shore mast mounted cup anemometers. The sign of the average difference depended on the direction of the winds in the periods of enhanced turbulence. Mean turbulent kinetic energy was measured to be greater when air flow into Muskegon Lake was predominantly from over land versus when air flow was predominantly from Lake Michigan. The higher mean turbulent kinetic energy for flow originating over land would likely be due to greater surface roughness experienced by the overland flow. Overall, the value of the new approach in obtaining validation evidence has been demonstrated. In this case, validation evidence is obtained in periods of no enhanced turbulence. Differences in wind speed during periods of enhanced turbulence are isolated in time, studied and are correlated in time with differences in wind direction. 1.0 Introduction The focus of wind project developers has expanded from land-based wind farms to include off-shore sites, with increasing interest toward constructing taller turbines in deeper waters. One critical, prerequisite step in each project is an assessment of available wind resources. For decades, meteorological (“met”) masts with cup anemometers have been relied upon to record wind speed and wind vanes to record direction. However, the use of such met masts may not be feasible in deep water locations or to reach the hub height of taller turbines. While met masts are relatively easy to install on terrestrial sites, installation at offshore locations can be prohibitively difficult as well as publically and politically controversial. Offshore met towers range in price from $2.5 million for installation in relatively shallow water (e.g. Cape Wind, Massachusetts) to more than $10 million in deeper water up to 30m (e.g. FINO 1, Germany) (Wissemann, 2008). Met towers in water in excess of 30m may not be cost effective. Fixed met masts cannot be easily moved to support other projects. In many cases, a fixed platform requires permits and/or bottomland leases from regulatory authorities. Obtaining such permits can be a lengthy process. Once a met tower is installed, it is difficult to change the heights at which the cup anemometers operate. The wind resources at hub height are often approximated through the use of mathematical and statistical models (Bagiorgas et al. 2012; Veigas and Iglesias 2012). Following Lu et al. (2002), the estimation of the variation of wind speed with height is obtained using a power law relationship with which the wind speed (V) at hub height (Z) is estimated from the wind speed (V0) measured at some reference height (Z0), usually between 3m and 10m. ( ) (1) Lu et al. (2002) note that the exponent, α, varies with height, time of day, season, nature of the terrain, wind speeds, and temperature. While a value of one-seventh is typically used, the value can be estimated for a given flow condition if the wind speed is known at two heights. The value obtained from these two measurements can then be applied to estimate the wind speed at a third level, in this case the hub height. Alternatively, in its report Large Scale Offshore Wind Power in the United States, the National Renewable Energy Laboratory noted a need for tools that can measure wind speeds at multiple locations and determine wind shear profiles up to hub height. The report authors also identified a need for stable buoy platforms to support the aforementioned assessment tools (Musial and Ram 2010). To address this issue, a number of remote sensing technologies have emerged as potential alternatives to met tower mounted cup anemometers such as light detection and ranging (LiDAR), sound detection and ranging (SoDAR) and airborne synthetic aperture radar (SAR) sensors (Hasanger et al. 2008). LiDAR and SoDAR operate similarly in that a signal (light or sound of a particular frequency) is emitted by the unit, the signal reflects off dust particles in the atmosphere, and the sensor captures and records the return signal. As the signal reflects off the moving dust particles, its frequency decreases (the Doppler effect). As wind speeds increase, so do the speeds of atmospheric particles. A large decrease in signal frequency is associated with faster wind speed (Hasanger et al. 2008). The data collected by cup anemometers has long been trusted. However, there is comparatively little experience with the use of remote sensing technologies particularly in an offshore location. Thus, validation is a particularly critical step in the wind resource data collection process when such a device is used offshore. Validation has to do with gathering evidence that the collected data, such as wind speed and direction at various heights above the water surface, can be relied upon in computing power and energy potential as well as for decision making regard project economic viability (Sargent, 2012). One common form of validation evidence is comparison to a trusted gage such as a previously calibrated and tested cup anemometer posted on a met tower nearby or a second remote sensing unit operated in parallel. There are several reports of such validation activities regarding the comparison of laser wind sensor units (LWS) with cup anemometers mounded on met masts in onshore and offshore settings. Danish researchers reported R values of 0.99 for heights ranging from 60m to 116.5m and all wind speeds (Kindler et al. 2009). Hasanger et al. (2011) reported results of a validation experiment at the Horns Rev, Denmark. LWS measurements were compared to three met masts at 63m and found a high level of agreement (R = 0.97-0.98). The measurement bias ranged from 0.12-0.15m/s. LWS. Cup anemometer measurements from the FINO platform (Westerhellweg et al. 2010) also showed a high level of agreement (R = 0.99) and a bias of -0.15 m/s to 0.08 m/s at heights from 70m to more than 100m. These and other studies lead to the conclusion that remote sensing of wind speeds using LWS produces results indistinguishable from those of a traditional met tower. Mounting an LWS unit on a floating platform introduces wave motion that could affect wind measurement and thus requires compensation. A National Renewable Energy Laboratory report made the following suggestion. To gain enough confidence for these systems to replace the conventional met mast, a large amount of experience with commercial projects at sea will be needed. This will require, in turn, close cooperation among private technology companies, offshore developers and operators, and government R&D programs at the US Department of Energy (DOE) and BOEM [Bureau of Ocean Energy Management], both in terms of taking the data and verifying the results. Once a reliable and proven track record has been established, the improved accuracy for wind and energy production measurements will remove a significant amount of risk from developers (Musial and Ram, 2010). Pichugina et al. (2012) were among the first to document the use of shipboard LWS sensors with motion compensation. Their preliminary error propagation model suggested a wind speed precision of less than 0.10m/s for 15-minute averaged data. The authors noted that “work is needed, perhaps involving comparisons with lidars or tall towers mounted on a fixed offshore platform, to establish how closely the shipboard HRDL [LiDAR] system approximates the high precision that is obtainable during land based observations” (Pichugina et al. 2012,