Then the estimates of all 144 trials (i.e., 144 for wind direction and 144 for wind speed) were averaged over the three randomized repetitions for each combined wind direction (16 in total) and wind speed condition (three in total), which resulted in 48 mean estimates for wind direction and 48 mean estimates for wind speed per participant.
The values of hourly wind speed were averaged to obtain the mean monthly and mean yearly wind speeds.
The fundamental equation governing the mechanical power of the wind turbine is {P}_w=frac{1}{2}{C}_pleft(lambda, beta right)rho {AV}^3 (13)where ρ is the air density (kg/m3), C p is the power coefficient, A is the intercepting area of the rotor blades (m2), V is the average wind speed (m/s), and λ is the tip speed ratio [16].
The MF radar 2-min wind estimates were averaged to produce hourly averages to match the meteor radar hourly averaged estimates.
where R is the radius of the turbine (m), ω is the angular speed (rad/s), and V is the average wind speed (m/s).
In these equations, α and β denote the shape parameters of β distribution, (P_{text{PV}})((P_{text{PV,max}})) is the (maximum) output of the PV generator, v represents the wind speed, k and c are the shape parameter and scale parameter for two-parameter Weibull distribution, respectively, (c = bar{v}/left( {1 + 1/k} right)), and (bar{v}) is the average wind speed for the time period being modeled.
The airflow rate is calculated as where Qinf is the airflow rate, Ainf is the effective air leakage area, ks is the stack coefficient, kw is the wind coefficient, Tin and Tout are the average indoor and outdoor temperatures over time interval of calculation (24 h), respectively, and U is the average wind speed over time interval of calculation (24 h).
The fit of the model where the single climate variable was the average wind speed during November through December (UV NDt) or the average dust concentration during October through December (Dust ODt) was statistically indistinguishable from the model that included U NDt, consistent with the high correlations among these variables (Pearson correlation coefficients 0.84 0.86).
References [4, 5] proposed two methods: the first one is the average wind method, which sets a criterion, differences in wind speeds <2 m/s, for the classification of incoming winds, and the average winds of the ones incident on aggregated wind turbines (AWTs) are used as equivalent winds.
The index can be average temperature thresholds, rainfall levels, wind speeds or any combination of variables that represent the risk to which the business is exposed.
