Within the VAWT group, there are two basic designs -- one is a drag design, and the other is a lift design. It's easy to distinguish which type is which. If the turbine rotor turns with the wind, because it blocks the wind, it is a drag design. If it goes against or across the wind, then it is a lift design.
Since a turbine that blocks the wind cannot go faster than the wind, these designs, though relatively simple to make, are not very efficient. Typical drag or Savonius turbines run in the 7-14 percent range, with many around the 10 percent mark. Since there have been a large number of these devices, their low performance figures are often used to discount all versions of VAWTs. However, the lift design or Darrius type VAWT is capable of efficiency comparable to small HAWTs.
Below is some data taken from a VAWT designed by Wind-Sail rated at 3Kw at a wind speed of 11m/sec. Note that a HAWT of the same power rating was installed in the same field to allow comparison data to be collected.
A set of data collected over several days and exceeding 500,000 data points was taken, and a section of data was extracted to provide the following graphics. The selection basis was wind speeds exceeding 3.4m/sec. Since there is little useful power for wind speeds of less than 4m/sec, removal of these data points simply removes clutter from the display.
To ensure that power measurements were not being limited by the electronic controls, we had to use data points where the batteries connected to the system were not fully charged. To have a manageable but significant sample size, we limited the sample to 10,000 data points.
This graph is a representation of wind speed versus power output.
Checking a few data points, we find the expected cubic function of output power with wind speed, verifying that the turbine percentage of power extracted is relatively constant over the range of wind speeds in the test sample.
Next is the battery status over the test period. In order to fit the wind speed and the power on to the same scale, factors of 10 apply.
The electronic controller was set to load the turbine in a manner that optimized the power output. Through experimentation, it was found that a TSR of 2.4:1 provides the best average results. The following graph shows the controller holding to a narrow TSR range for wind speeds higher than 3.4m/sec.
Finally, here's a graph that shows turbine efficiency versus TSR.
As the graphic illustrates, for the TSR range of 2.25-2.5, the CP is centered around 40 percent. These figures have allowed a 10 percent loss in the alternator and another 10 percent loss in the control/conversion electronics (electrical power output/.8 = turbine mechanical power). These estimates are conservative, given that the power output of the test sample was in the range of 20 percent of unit capacity.
The data points above 60 percent are the result of turbine inertia. When the wind dies, the turbine keeps spinning. Since data points are snapshots, the energy in the flywheel produces momentary data points. This, combined with the cubic increase in power with wind velocity, requires statistical analysis to determine actual performance. The graphic display allows the reader to obtain an intuitive understanding of this turbine performance.
No doubt, some critics will complain that the sample of wind speed range is well below that at a wind farm. This is true, but it is vital to consider that the market for small wind turbines is not wind farms, but locations that resemble the test site. In short, we don't have to have strong winds in order to get decent efficiency. We continue to collect data, and the relatively small number of data points with higher wind speeds do indicate good turbine efficiency.
Richard Halstead is CTO of Wind-Sail. Evgeny Solomin, PhD, is associate professor in the South Ural State University
Department of Electric Engineering and Renewable Energy Sources.