Resource Documents — latest additions
Documents presented here are not the product of nor are they necessarily endorsed by National Wind Watch. These resource documents are provided to assist anyone wishing to research the issue of industrial wind power and the impacts of its development. The information should be evaluated by each reader to come to their own conclusions about the many areas of debate.
Estimating maximum global land surface wind power extractability and associated climatic consequences
Author: Miller, Lee; Gans, Fabian; and Kleidon, Axel
Abstract. The availability of wind power for renewable energy extraction is ultimately limited by how much kinetic energy is generated by natural processes within the Earth system and by fundamental limits of how much of the wind power can be extracted. Here we use these considerations to provide a maximum estimate of wind power availability over land. We use several different methods. First, we outline the processes associated with wind power generation and extraction with a simple power transfer hierarchy based on the assumption that available wind power will not geographically vary with increased extraction for an estimate of 68 TW. Second, we set up a simple momentum balance model to estimate maximum extractability which we then apply to reanalysis climate data, yielding an estimate of 21 TW. Third, we perform general circulation model simulations in which we extract different amounts of momentum from the atmospheric boundary layer to obtain a maximum estimate of how much power can be extracted, yielding 18–34TW. These three methods consistently yield maximum estimates in the range of 18–68 TW and are notably less than recent estimates that claim abundant wind power availability. Furthermore, we show with the general circulation model simulations that some climatic effects at maximum wind power extraction are similar in magnitude to those associated with a doubling of atmospheric CO₂. We conclude that in order to understand fundamental limits to renewable energy resources, as well as the impacts of their utilization, it is imperative to use a “top- down” thermodynamic Earth system perspective, rather than the more common “bottom-up” engineering approach.
L. M. Miller, F. Gans, and A. Kleidon
Max Planck Institute for Biogeochemistry, Jena, Germany; and International Max-Planck Research School for Earth System Modeling, Hamburg, Germany
Earth System Dynamics, 2, 1–12, 2011
See also: “How does the Earth system generate and maintain thermodynamic disequilibrium and what does it imply for the future of the planet?” by Axel Kleidon, Philosophical Transactions of the Royal Society A (2012) 370, 1012–1040
Author: Voigt, Christian; Lehnert, Linn; et al.
[Abstract] The catastrophic nuclear meltdowns at Fukushima triggered a worldwide demand for renewable energy. As one of the few countries, Germany decided on an accelerated shift towards green energy, resulting in substantial conflicts with international conservation goals. Currently, large numbers of wind power facilities are erected in Germany, yet with unforeseen consequences for wildlife, particularly for endangered and protected bats. Presumably, more than 250,000 bats are killed annually due to interactions with German wind turbines, and total losses may account for more than two million killed bats over the past 10 years, if mitigation measures were not practiced. More than 70 % of killed bats are migrants, because major migratory routes cross Germany. Consequently, Germany’s environmental policy is key to the conservation of migratory bats in Europe. Prospective increases in wind power will lead to the installation of larger wind turbines with potentially devastating consequences for bats. The higher net energy production of modern wind turbines at low wind speeds may exacerbate the conflict between green energy and conservation goals since revenue losses for companies increase. We conclude that evidence-based action plans are urgently needed to mitigate the negative effects of the operation of wind energy facilities on wildlife populations in order to reconcile environmental and conservation goals.
Christian C. Voigt
Linn S. Lehnert
Leibniz Institute for Zoo and Wildlife Research, Berlin, Germany
Faculty of Veterinary Medicine, Latvia University of Agriculture, Jelgava, Latvia
Büro für Faunistik und Landschaftsökologie, Bingen am Rhein, Germany
Freilandforschung, Bremen, Germany
European Journal of Wildlife Research
Author: Schomer, Paul; and Hessler, George
Recently [Steve Cooper, The Acoustic Group,] has completed a first of its kind test regarding the acoustical emissions of wind turbines. His is the first study of effects on people that includes a cooperating windfarm operator in conjunction with a researcher that does not work exclusively for windfarms. This study makes three very simple points:
- There is at least one non-visual, non-audible pathway for wind turbine emissions to reach, enter, and affect some people
- This is a longitudinal study wherein the subjects record in a diary regularly as a function of time the level of the effects they are experiencing at that time
- This periodic recording allows for responses as the wind-turbine power changes up and down, changes not known by the subject
The results are presented in a 218 page report augmented by 22 appendices spread over 6 volumes so that every single detail in the study has been documented for all to see and examine. The methods and results are totally transparent. The 22 appendices and the main text exhaustively document everything involved with this study.
Six subjects, 3 couples from different homes are the participants in this study. They do not represent the average resident in the vicinity of a wind farm. Rather, they are self-selected as being particularly sensitive and susceptible to wind farm acoustic emissions, so much so that one couple has abandoned their house. Cooper finds that these six subjects are able to sense attributes of the wind turbine emissions without there being an audible or visual stimulus present. More specifically, he finds that the subject responses correlate with the wind turbine power being generated but not with either the sound or vibration.
Although the very nature of a longitudinal study provides for a finding of cause and effect, some will undoubtedly argue that a correlation does not show cause and effect. In this case they must postulate some other thing like an unknown “force” that simultaneously causes the wind turbine power being generated and symptoms such as nausea, vertigo, and headaches to change up and down together. But that is the kind of “creative” logic it takes to say that this correlation does not represent cause-and-effect. So, rather than making such groundless arguments, perhaps something like an “expert statistical analysis” can be expected “proving” this is not a “valid sample” of the public at large, or proving the study does not do something else it was never intended to do.
So it is important to sort out what, by design, this study was intended to do and does do, and what, by design, it was not intended to do and does not do. This study is not in any way a sample of the general population nor is it in any way a sample of the general population in the vicinity of windfarms. According to Cooper’s report, this study was intended to address the issue of complaints from residents in the vicinity of Pacific Hydro’s Cape Bridgewater Wind Farm. Pacific Hydro requested the conduct of an acoustic study at 3 residential properties to ascertain any identifiable noise impacts of the wind farm operations or certain wind conditions that could relate to the complaints that had been received. The study was to incorporate three houses that are located between 650 m and 1600 m from the nearest turbine. This research represents a case study at 3 houses, each with one couple, 6 people. This is one sample, and only one sample, of a small group of people who are all self-selected as being very or extremely sensitive to wind turbine acoustic emissions. A similar group could be assembled elsewhere such as in Shirley Wisconsin, USA or Ontario Canada.
This study finds that these 6 people sense the operation of the turbine(s) via other pathways than hearing or seeing, and that the adverse reactions to the operations of the wind turbine(s) correlates directly with the power output of the wind turbine(s) and fairly large changes in power output.
Attempts may be made to obfuscate these simple points with such arguments as it cannot be proved that infrasound is the cause of the discomfort. But that again is a specious argument. The important point here is that something is coming from the wind turbines to affect these people and that something increases or decreases as the power output of the turbine increases or decreases. Denying infra-sound as the agent accomplishes nothing. It really does not matter what the pathway is, whether it is infra-sound or some new form of rays or electromagnetic field coming off the turbine blades. If the turbines are the cause, then the windfarm is responsible and needs to fix it. Anyone who truly doubts the results should want to replicate this study using independent acoustical consultants at some other wind farm, such as Shirley Wisconsin, USA, where there are residents who are self-selected as being very or extremely sensitive to wind turbine acoustic emissions.
Some may ask, this is only 6 people, why is it so important? The answer is that up until now windfarm operators have said there are no known cause and effect relations between windfarm emissions and the response of people living in the vicinity of the windfarm other than those related to visual and/or audible stimuli, and these lead to some flicker which is treated, and “some annoyance with noise.” This study proves that there are other pathways that affect some people, at least 6. The windfarm operator simply cannot say there are no known effects and no known people affected. One person affected is a lot more than none; the existence of just one cause-and-effect pathway is a lot more than none. It only takes one example to prove that a broad assertion is not true, and that is the case here. Windfarms will be in the position where they must say: “We may affect some people.” And regulators charged with protecting the health and welfare of the citizenry will not be able to say they know of no adverse effects. Rather, if they choose to support the windfarm, they will do so knowing that they may not be protecting the health and welfare of all the citizenry.
 Independent Consultants are those who have worked for both industry and communities, and or have espoused the need for research to sort out the issues of people reacting to non-audible non-visual stimuli.
 Cooper’s test shows cause and effect for at least one non-visual, no-audible pathway to affect people. If one only wanted to test for the ability to sense the turning on of wind turbines, and not replicate the cause and effect portion of Cooper’s study, this reduced test could be accomplished in one to two months with a cooperative windfarm where there are residents who are self-selected as being very or extremely sensitive to wind turbine acoustic emissions and who also assert that they have this sensing ability. This study, a subset of the full Cooper tests, would only prove, again, that non-visual, non-auditory pathways exist by which wind turbine emissions may affect the body and “signal” the brain.
Paul D. Schomer, Ph.D., P.E.
Schomer and Associates, Inc.
Standards Director, Acoustical Society of America
Hessler Associates, Inc.
10 February 2015
Author: MG Acoustics
The measurement of sound at very low frequencies, below 100 Hz, is difficult and requires the use of special instruments. In the Wind Turbine Noise & Health Study conducted by Health Canada, microbarometers were used because they are capable of measuring frequencies between 0.1 Hz and 100 Hz. Microbarometers were installed at distances of 125 m, 2.5 km, 5 km and 10 km from the nearest wind turbine in […] wind turbines.
The noise levels from a wind turbine can and has been predicted using mathematical models. However, commercially available software that is in general use today cannot be used for very low frequencies and long distances. For example, the calculation procedure published by the International Standards Organization (ISO) is not intended to be used for frequencies below 63 Hz. Further, the ISO procedure was not originally intended to be used for distances greater than 1 km or for sources as high as modern day wind turbines […]. …
One key source of noise from wind turbines is the periodic passage of the wind turbine blades in front of the main supporting mast. There must be sufficient wind for the operation of a wind turbine. However, for wind speeds greater than approximately 8 metres per second (m/s), the wind turbine rotors in the current study were restricted to rotational speeds no greater than 16 revolutions per minute (RPM). With three blades spinning at 16 RPM, a blade passes the mast 48 times per minute, or once every 0.8 second (i.e. 0.8 Hz). Measurements of wind turbine noise showed a peak in the spectrum at 0.8 Hz with additional peaks at 1.6 Hz, 2.4 Hz, 3.2 Hz, 4.0 Hz, 4.8 Hz, 5.6 Hz, 6.4 Hz, 7.2 Hz, and 8.0 Hz. The presence of a peak at 0.8Hz and the associated harmonics of this frequency in field measurements confirms that the measured sound is (at least partially) resulting from wind turbine operations. These frequencies below 20 Hz are generally called infrasound. At frequencies measured between 20 and 100 Hz, there are other sources of noise, such as the gearbox or generator.
Like all sound, when wind turbine noise propagates over flat ground, levels generally decrease as one moves away from the source. Estimating what the levels will be at any given receptor requires an understanding of how fast the levels decrease over distance. The main factor affecting the decrease in levels for frequencies below 100 Hz is the prevailing weather conditions. [The prevailing weather conditions refer to the wind speed, wind direction, the amount of cloud cover, and daytime or nighttime.]
During the day, the sun heats the ground resulting in an air temperature that is highest close to the ground. Sound travels faster in warm air than cold air. As a consequence, when traveling along the ground, sound will bend from the warmer air towards the colder air above causing the sound rays to curve upwards away from the ground as illustrated in Figure 1. Sound rays also curve upwards when the sound is propagating upwind. The upward-curving rays cause the sound at ground level to decrease in level very rapidly as one moves away from the turbines.
During the night, the ground cools faster than the air causing a temperature inversion. During an inversion, sound rays curve downward towards the ground as illustrated in Figure 2. Sound rays also curve downward when the sound is propagating downwind. The downward-curving rays cause the sound to decrease in level relatively slowly as one moves away from the turbines.
The difference between daytime and nighttime conditions influenced the ability to measure wind turbine noise in this study, Thus, noise from wind turbines was more often measured during the night or downwind from the turbines at distances beyond 2.5 km.
The topology [hills and valleys of non-flat ground] of the surrounding area is another import ant factor that affects how sound levels will decrease over distance. If the wind turbine is hidden by a hill, the sound levels will be reduced. On the other hand if the wind turbine is situated on the top of a hill, the sound levels are often apparent at larger distances .
Other factors that normally affect how sound levels decrease as the distance to a source of noise increases include the type of ground cover (snow, grass, pine needles, asphalt, etc.) and air absorption. [Air absorption is caused by the collision of the air molecules. The collisions produce a rapid decrease in sound level at high frequencies as sound propagates, but has only a negligible impact on frequencies below 100 Hz.] Sound frequencies above 100 Hz do not propagate far when there is a fresh layer of snow, for example. Instabilities in the air called turbulence can also affect how sound levels drop under certain weather conditions. However, for wind turbine frequencies below 100 Hz, these other factors are not significant.
In addition to the prevailing weather conditions, topography and ground coverage, the number of wind turbines in operation will determine the levels measured by the microbarometers. In the current study, measurements were based on the collective contribution from only four wind turbines. As the number of turbines increases, the noise levels are expected to increase. Similarly, if the number of turbines decreases, the noise levels are expected to decrease.
One of the main challenges found in this study related to measurement of wind turbine noise below 100 Hz was separating the wind turbine noise from the ambient background noise. The ambient background noise is comprised of noise generated by man-made sources, such as highway traffic, trains, aircraft, and industry, and by naturally-occurring sources including surf-generated infrasound noise. The separation was only possible when the measured spectra showed the characteristic peaks related to the blade passage frequency as discussed above.
Figure 3 shows noise generated by a wind turbine clearly evident above the ambient background. The red peaks are the infrasonic frequencies between 0.8 Hz and 8.0 Hz produced by the […] windturbines. They are an example measured by the microbarometer at a distance of 2.5 km during one night. The blue line is the ambient background noise. Observe that the blade passage frequency of 0.8 Hz is just evident above the ambient background noise around this frequency.
By comparison, Figure 4 shows what the microbarometer typically measures at the same distance during a sunny day (note that Figure 4 and Figure 3 are plotted on the same scale). The peaks characteristic of the wind turbines have dropped in level so much that they cannot be seen above the ambient background noise even though the turbines were in operation. Finally, we note that as the wind speed increases, both the ambient background noise levels and the wind turbine frequencies (when present) will change.
Levels measured by the microbarometers can be compared to theoretical predictions at different distances from the wind turbines by focusing on the characteristic wind turbine peaks. A comparison for the infrasonic frequency of 2.4 Hz is shown in Figure 5. The red circles are the average levels measured during several nights when there was an inversion and the microbarometers were downwind from the wind turbines. Using the temperature measured at heights of 2 m and 10 m, wind speed and wind direction measured at a height of I0 m at a distance of 2.5 km from the wind turbines, the computational models predict levels shown by the red line. The measured levels (circles) and the predicted levels (line) are in agreement at 2.5 km, 5 km, and 10 km.
The magenta circles in Figure 5 are the levels at 2.4 Hz measured during an afternoon in July. For this daytime condition, the levels decrease much more rapidly as one moves away from the wind turbines, but can still be measured at 2.5 km and 5 km during this July afternoon. However, at 10 km, the microbarometer cannot measure the contribution from the wind turbines at this infrasonic frequency. The magenta line represents the predicted levels obtained using the weather station measurements. The measured levels and the predicted levels are in agreement at 2.5 km and 5 km.
The drop in levels seen in Figure 5 at 2.4 Hz was also seen at the other frequencies during the same weather conditions. For example, the red circles in Figure 6 are the average levels at a frequency of 30 Hz also measured during several nights when there was an inversion and the microbarometers were downwind from the turbines. Note that at 10 km, the microbarometer was not able to measure this frequency. The red line shows the predicted levels which agree with the measured levels at 2.5 km and 5 km. It was further found that it was not possible to separate wind turbine noise from the ambient background noise at frequencies above 30 Hz and at distances of 2.5 km and beyond.
Observe in Figures 5 and 6 that the levels are dropping relatively slowly as one moves from 2.5 km to 10 km during the nighttime inversions (red lines). In fact the levels are dropping by 3 dB for each doubling of distance. For example, the level at 2.4 Hz is 49 dB at 5 km and drops to 46 dB at 10 km.
At distances over 1 km the 3 dB decrease in level for each doubling of distance is typical o flow frequency sound below 100 Hz during an inversion and/or downwind propagation and has been observed in other wind turbine noise studies. [At distances less than 1 km, the decrease in level when only one turbine is in operation will not be the same as when all four are in operation, for example.] For comparison, at frequencies above 100 Hz, the type of ground cover and air absorption become much more significant at affecting how sound levels decrease with distance. For example, traffic noise contains frequencies above 100 Hz. According to the calculation procedure of the ISO, traffic noise is expected to decrease by 7 to 8 dB for eve1y doubling of distance between 0.5 km and 2 km during a temperature inversion or downwind from the traffic.
One outcome of this study is a general idea of how often wind turbine noise below 100 Hz is likely to be measured up to a distance of 10 km. Weather observations published by Environment Canada were monitored for an entire year. The weather observations were classified in terms of wind speed and daytime and nighttime cloud cover. For example, in 2013 at one weather station, nighttime inversion conditions occurred about 45% of the time (or 3942 hours out of 8760 hours. Thus, taking into account wind speed and direction, infrasonic frequencies from wind turbines are likely be measured fairly often at distances up to10 km, and even beyond.
In conclusion, measuring wind turbine noise below 100 Hz requires special equipment. The ability to measure wind turbine noise depends strongly on the prevailing weather conditions and the ambient background noise, especially at large distances. Further, it was found that wind turbine noise below 100 Hz can be predicted with accuracy down to a frequency of 1.6 Hz and up to distances of 10 km using on-site weather station measurements.