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Production cost C
P
of the system is treated as a fuzzy variable since it is directly
proportional to the hourly load.
ning the input of the load capacity generator LCG are:
LCG = {Low, Below Average, Average, Above Average, High}
The incremental cost IC is indicated by the following fuzzy sets:
IC = {Zero, Small, Large}
Fuzzy sets representing the incremental losses IL are as follows:
IL = {Low, Medium, High}
The production cost, taken as objective function is:
C
P
= {Low, Below Average, Average, Above Average, High}
Based on the cited fuzzy sets, membership functions are selected for each fuzzy
input/output variables as shown in the following
Fuzzy sets de
figure (Fig.
1
):
In this paper, we have chosen a triangular form to illustrate the membership
functions while choosing the If-Then rules to link the input/output fuzzy variables
as shown in the following table (Table
1
):
The fuzzy decision-making of the fuzzy logic approach is based on three data
processing runs of the variables to control. A
first data processing run consists of a
fuzzi
cation (Azar
2010b
), in one second phase the corrector deduces the fuzzy
inferences according to imposed conditions
'
and in a third phase of calculation;
each corrector applies a method of defuzzi
cation to deduce a non fuzzy vector of
command. The method used in order to evaluate this vector, consists in determining
the X-coordinate of the centre of gravity of the surface swept by the fuzzy
deductions (Eq.
13
).
BAV
AV
AAV
L
S
1
H
Z
LG
1
0.5
0.5
0
0
0
100
200
300
18
20
22
24
LCG
IC
BAV
AV
AAV
1
Z
L
1
L
H
S
0.5
0.5
0
0
0.5
1
1.5
2
2.5 3
x 10
4
0
5
10
15
20
C
P
IL
Fig. 1 Membership function of input/output variables
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