Note: This page has been translated by MathWorks. Please click here

To view all translated materials including this page, select Japan from the country navigator on the bottom of this page.

To view all translated materials including this page, select Japan from the country navigator on the bottom of this page.

This example uses Symbolic Math Toolbox and the Statistics and Machine Learning Toolbox to explore and derive a parametric analytical expression for the average power generated by a wind turbine.

The parametric equation can be used for evaluating various wind turbine configurations and wind farm sites. More information see Wind Resource Assessment.

**Background**

The total power delivered to a wind turbine can be estimated by taking the derivative of the wind's kinetic energy. This results in the following expression:

(1)

A is the swept area of turbine blades, in

ρ = air density, in

u = wind speed, in

The process of converting wind power to electrical power results in efficiency losses, as described in the diagram below.

The electrical power output of a practical wind turbine can be described using the following equation:

(2) where

The overall efficiency is between 0.3 and 0.5, and varies with both wind speed and rotational speed of the turbine. For a fixed rotational speed, there is a rated wind speed at which electrical power generated by the wind turbine is near its maximum (), and overall efficiency at this point is denoted .

(3)

Assuming a fixed rotational speed, the electrical power output of the wind turbine can be estimated using the following profile:

Where

= rated wind speed

= cut-in speed, the speed at which the electrical power output rises above zero and power production starts

= furling wind speed, the speed at which the turbine is shut down to prevent structural damage

As seen in the figure, we assume that output power increases between uc and ur, then is at a constant maximum value between and . Power output is zero for all other conditions.

We define a piecewise function that described turbine power.

syms Per C_1 C_2 k u u_c u_f u_r Pe = piecewise(u < u_c, 0, u_c <= u <= u_r, C_1 + C_2 * u^k, (u_r <= u) <= u_f, Per, u_f < u, 0)

Pe =

Where variables and are defined as follows:

C_1 = (Per * u_c^k)/(u_c^k - u_r^k)

C_1 =

C_2 = Per/(u_r^k - u_c^k)

C_2 =

The rated power output offers a good indication of a how much power a wind turbine is capable of producing, however we'd like to estimate how much power (on average) the wind turbine will actually deliver. To calculate average power, we need to account for external wind conditions. A Weibull distribution does a good job at modeling the variance in wind, therefore the wind profile can be estimated using the following probability density function:

(4)

In general, larger 'a' values indicate a higher median wind speed, and larger 'b' values indicate reduced variability.

We use the Statistics and Machine Learning Toolbox to generate a Weibull distrobution and illustrate the variability in wind at our wind farm site (a=12.5, b=2.2):

a = 12.5; b = 2.2; N = 1000; pd = makedist('Weibull','a',a,'b',b)

pd = WeibullDistribution Weibull distribution A = 12.5 B = 2.2

r = wblrnd(a,b,[1 N])

```
r =
Columns 1 through 7
6.0811 4.3679 17.3751 4.1966 8.7677 18.3517 13.9761
Columns 8 through 14
9.9363 3.0039 2.7496 16.5233 2.5333 3.0151 10.7854
Columns 15 through 21
6.3169 16.9442 11.6922 4.1418 6.4460 2.9379 8.4449
Columns 22 through 28
21.6033 5.4887 3.6903 8.1241 6.9789 7.1974 12.1293
Columns 29 through 35
8.4485 16.1833 7.7371 21.9390 14.0043 20.8297 18.3668
Columns 36 through 42
5.9351 7.8970 13.3122 3.2335 21.7093 11.4461 12.2905
Columns 43 through 49
6.8609 6.3983 15.8128 10.7241 11.3478 8.5754 7.6896
Column 50
7.0249
```

x = linspace(0,34,N); y = pdf(pd,x); plot(x,y,'LineWidth',2) hold on histogram(r,15,'Normalization','pdf') hold off title('Weibull Distribution of Wind Speeds') xlabel('Wind Speed (m/s)')

The average power output from a wind turbine can be obtained using the following integral:

(5)

Power is zero when wind speed is less than the cut in wind speed and greater than furling wind speed . Therefore, the integral can be expressed as follows:

(6)

There are two distinct integrals in equation (7). We plug equation (4) into these integrals and simplify them using the substitutions: and . This simplifies our original integrals to the following:

(7)

(8)

Solving these integrals and then replacing x with yields:

syms a b x int1 = int(exp(-x), x); int1 = subs(int1, x, (u/a)^b)

int1 =

int2 = int(x * exp(-x) * a^b, x); int2 = subs(int2, x, (u/a)^b)

int2 =

Subsituting the results into equation (6) yields an equation for average power output of the wind turbine.

Peavg = subs(C_1 * int1, u, u_r) - subs(C_1 * int1, u, u_c) + subs(C_2 * int2, u, u_r) - subs(C_2 * int2, u, u_c) + subs(Per * int1, u, u_f) - subs(Per * int1, u, u_r)

Peavg =

We have used the Symbolic Math Toolbox to develop a parametric equation which can be used to perform simulation studies to determine the average power generated for various wind turbine configurations and wind farm sites.

http://www.mathworks.com/company/aboutus/policies_statements/piracy.html?s_tid=gf_pir

© 2016 The MathWorks, Inc.

Was this topic helpful?