188
Wind Energy Modeling
The fuzzy logic controller (FLC) robustness has been calculated considering its
nonlinear procedures and characteristics in order to model wind energy to convert it
into electricity energy with respect to the Mamdani fuzzy inference system [41, 42].
The basic configuration of this fuzzy logic controller includes fuzzifications which
determines the net wind energy conversion rate [18, 43]. Thus, the fuzzy inference
system (FIS) evaluates the FLC mechanisms in order to determine the wind energy
conversation process (modeling) by presenting the control rules of the fuzzy block
diagram (Fig. 10.7). The fuzzification block simply reveals the calculated risk output of wind energy which is controlled by the rules of the defuzzification of wind
turbine [44–46].
This FLC is utilized to determine the wind turbine and its DFIG subsystem to
deal with the wind energy transformation mechanism to produce electric energy
which is expressed by
e
n
n
e
n
n
n
n
Ω
Ω
Ω
Ω
∆
Ω
Ω
g
g
g
g
g
g
( )
∗
( )
∗
=
( )− ( )
=
( )−
−
( )
1
(10.28)
Fig. 10.6 The block diagram of the MPPT control considering the enslaved DFIG velocity of the
aerodynamic subsystem to determine the rate of wind intake and process
10 Zero-Emission Vehicles
Wind Energy Modeling
The fuzzy logic controller (FLC) robustness has been calculated considering its
nonlinear procedures and characteristics in order to model wind energy to convert it
into electricity energy with respect to the Mamdani fuzzy inference system [41, 42].
The basic configuration of this fuzzy logic controller includes fuzzifications which
determines the net wind energy conversion rate [18, 43]. Thus, the fuzzy inference
system (FIS) evaluates the FLC mechanisms in order to determine the wind energy
conversation process (modeling) by presenting the control rules of the fuzzy block
diagram (Fig. 10.7). The fuzzification block simply reveals the calculated risk output of wind energy which is controlled by the rules of the defuzzification of wind
turbine [44–46].
This FLC is utilized to determine the wind turbine and its DFIG subsystem to
deal with the wind energy transformation mechanism to produce electric energy
which is expressed by
e
n
n
e
n
n
n
n
Ω
Ω
Ω
Ω
∆
Ω
Ω
g
g
g
g
g
g
( )
∗
( )
∗
=
( )− ( )
=
( )−
−
( )
1
(10.28)
Fig. 10.6 The block diagram of the MPPT control considering the enslaved DFIG velocity of the
aerodynamic subsystem to determine the rate of wind intake and process
10 Zero-Emission Vehicles