Before we jump into the course let us review a few basic concepts
related to machine learning and wind turbines.
What is machine learning? It is the application of artificial intelligence
for systems to automatically learn and improve without be programmed explicitly.
In general machine learning algorithms can be characterized as supervised, semi-supervised,
unsupervised or reinforced. In this course we will use a supervised machine learning
algorithm which applies past learning to new data using labeled examples.
They work by generating a function to make predictions from a training data set,
they then compare the output of this function with the expected training output and adjust
the function accordingly. The specific algorithm that will be used in this course
is the decision tree regressor. This algorithm builds regression models in tree structure form
breaking down a dataset into smaller and smaller subsets developing an associated
decision tree with a specific tree depth. Next, let us review the operation of wind turbines.
Wind has kinetic energy that can be converted into electrical energy through a wind turbine.
These turbines operate by capturing this wind energy to turn rotor blades that
run a generator. Wind turbines are comprised of several main parts which include the foundation,
tower, nacelle, rotor and transformer. The foundation is the base of the turbine that
keeps the structure secured to the ground. The tower is the largest and most visual aspect of
the structure. The tower size will vary based on the size of the turbine, required height to
achieve optimal wind input and the size of the rotor. The nacelle is an important structure
that connects the tower to the generator and to the rotor. The rotor is comprised of blades that
capture the wind and run the turbine. Lastly the transformers stores electricity that is produced.
A control inside the base of the tower ensures all aspects of the turbine are
running properly. Most of the inner workings of the turbine are located inside the nacelle.
The rotor is attached to the nacelle by way of the main shaft; as the wind turns the rotor it
turns the large main shaft which is attached to a gearbox. The gearbox is used to turn the low
rotations per minute or RPMs of the rotor into a much higher number of rotations to generate more
electricity. This ratio will vary depending on the size of the turbine. As an example the
gearbox in an industrial sized turbine can turn 22 rpms into around 1500 revolutions.
The rotors and turbines are governed by a rule known as Betz's law, which states that a turbine
may only capture fifty nine point three percent of the energy from the wind. Betz's limit refers
to the fact that if more than this amount of wind energy is captured a buildup in front of
the rotor creates a limiting factor for future wind capture. This fact is relevant as we will
see that after certain limit higher wind speeds do not translate to additional power generated.
A typical wind turbine power curve is shown here which plots active power in kilowatts versus wind
speed in meters per second. The current speed is the wind speed value which causes the active
power to begin rising, while the cutout speed is the wind speed which causes the active power
to begin dropping. The rated speed is the wind speed which causes the active power to plateau
while the rated power is the corresponding active power value at the rated speed. This graph is true
for a fixed air temperature, but changes in air temperature will cause this power curve to shift
as per this graph air temperature variations shift the power curve up at lower temperatures and shift
it down at higher temperatures. Rho is the air density which is affected by air temperature.
Thus at a fixed wind speed you would want a lower air temperature in order
to generate more active power. The wind turbine power curve will become
important later when we prepare our wind from data set for machine learning in
order to predict active power from a given wind speed and air temperature.