Wind Energy Handbook. Michael Barton Graham

Чтение книги онлайн.

СКАЧАТЬ over and around hills and mountains and funnelling through passes or along valleys aligned with the flow. Equally, topography may produce areas of reduced wind speed, such as sheltered valleys or areas in the lee of a mountain ridge or where the flow patterns result in stagnation points.

      Thermal effects may also result in considerable local variations. Coastal regions are often windy because of differential heating between land and sea. While the sea is warmer than the land, a local circulation develops in which surface air flows from the land to the sea, with warm air rising over the sea and cool air sinking over the land. When the land is warmer, the pattern reverses. The land will heat up and cool down more rapidly than the sea surface, and so this pattern of land and sea breezes tends to reverse over a 24‐hour cycle. These effects were important in the early development of wind power in California, where an ocean current brings cold water to the coast, not far from desert areas that heat up strongly by day. An intervening mountain range funnels the resulting air flow through its passes, generating locally very strong and reliable winds (which are well correlated with peaks in the local electricity demand caused by air conditioning loads).

      Thermal effects may also be caused by differences in altitude. Thus, cold air from high mountains can sink down to the plains below, causing quite strong and highly stratified ‘downslope’ winds.

      The brief general descriptions of wind speed variations in Sections 2.1–2.5 are illustrative, and more detailed information can be found in standard meteorological texts. Section 10.1.3, in Chapter 10, describes how the wind regimes at candidate sites can be assessed, while wind forecasting is covered in Section 2.9 and Section 11.6.3.

      Section 2.6 presents a more detailed description of the high frequency wind fluctuations known as turbulence, which are crucial to the design and operation of wind turbines and have a major influence on wind turbine loads. Extreme winds are also important for the survival of wind turbines, and these are described in Section 2.8.

2.3 Long‐term wind speed variations

      There is evidence that the wind speed at any particular location may be subject to very slow long‐term variations. Although the availability of accurate historical records is a limitation, careful analysis by, for example, Palutikof et al. (1991) has demonstrated clear trends. Clearly these may be linked to long‐term temperature variations for which there is ample historical evidence. There is also much debate at present about the likely effects of global warming, caused by human activity, on climate, and this will undoubtedly affect wind climates in the coming decades.

      Apart from these long‐term trends, there may be considerable changes in windiness at a given location from one year to the next. These changes have many causes. They may be coupled to global climate phenomena such as el niño, changes in atmospheric particulates resulting from volcanic eruptions, and sunspot activity, to name a few.

      These changes add significantly to the uncertainty in predicting the energy output of a wind farm at a particular location during its projected lifetime.

2.4 Annual and seasonal variations

      While year‐to‐year variation in annual mean wind speeds remains hard to predict, wind speed variations during the year can be well characterised in terms of a probability distribution. The Weibull distribution has been found to give a good representation of the variation in hourly mean wind speed over a year at many typical sites. This distribution takes the form

      (2.1)

      where F(U) is the fraction of time for which the hourly mean wind speed exceeds U. It is characterised by two parameters, a ‘scale parameter’ c and a ‘shape parameter’ k, which describes the variability about the mean. The parameter c is related to the annual mean wind speed by the relationship

(2.2)

      where Γ is the complete gamma function. This can be derived by consideration of the probability density function

(2.3)

      because the mean wind speed is given by

      (2.4)

A special case of the Weibull distribution is the Rayleigh distribution, with k = 2, which is actually a fairly typical value for many locations. In this case, the factor Γ(1 + 1/k) has the value . A higher value of k, such as 2.5 or 3, indicates a site where the variation of hourly mean wind speed about the annual mean is small, as is sometimes the case in the trade wind belts, for instance. A lower value of k, such as 1.5 or 1.2, indicates greater variability about the mean. A few examples are shown in Figure 2.2. The value of Γ(1 + 1/k) varies little, between about 1.0 and 0.885: see Figure 2.3.

Figure 2.2 Example Weibull distributions

Figure 2.3 The factor Γ(1 + 1/k)

The Weibull distribution of hourly mean wind speeds over the year is clearly the result of a considerable degree of random variation. However, there may also be a strong underlying seasonal component to these variations, driven by the changes in insolation during the year as a result of the tilt of the earth's axis of rotation. Thus, in temperate latitudes the winter months tend to be significantly windier СКАЧАТЬ