International Core Journal of Engineering 2020-26 | Page 38
weight.
TABLE II. T ABLE FOR FUZZY QUANTUM VALUE
V. S IMULATION A NALYSIS
Corresponding value a , b
Fuzzy language description
The paper will take the example of the settlement risk of
China's X province power market as an example. Assume
that there are four risks for the settlement of the power
market in China's X province, recorded as c i i 1, 2,3, 4 .
0.3,0.8
0,0.5
0.5,1
most
At least half
as much as possible
Among them c 1 are data risks, c 2 are credit risks, c 3 are tax
risks and c 4 are policy risks.
ĺ Then, the deviation degree d i of risk factor c i over
other risk factors, and the expectation d i O of the deviation
degree of risk factors are calculated. The calculation
formulas of d i and d i O are shown in Equation (8) and (9).
d i
Z 1 b i 1 Z 2 b i 2
Z n b i in
According to the method described above, it is assumed
that two experts participate in the assessment of the risk of
settlement in the power market, and assume that the two
experts have the same level, that is, the weight
is E 1 E 2 0.5 .The complementary judgment matrix of risk
factors established by various experts is shown in Table III
and Table IV
(8)
Where, d i is the triangular fuzzy number and recorded
as d i
d
L
i
, d i M , d i U .
1
ª 1 O d i L d i M O d i U º ¼
2 ¬
d i O
TABLE III. E XPERT 1:R ISK FACTORS FOR COMPLEMENTARY JUDGE
(9)
MATRIX
Ļ Finally, vector V is proposed according to the Eq.(10).
O
d i normalized V i and sorted according to the result.
Among them V
V 1 , V 2 ,
V i
d i
, V n .
O
n
¦ d O
A
C1 C1 C2 C3 C4
(0.5,0.5,0.5) (0.4,0.5,0.7) (0.3,0.6,0.7) (0.5,0.7,0.9)
C2 (0.3,0.5,0.6) (0.5,0.5,0.5) (0.4,0.5,0.6) (0.3,0.6,0.8)
C3 (0.3,0.4,0.7) (0.4,0.5,0.6) (0.5,0.5,0.5) (0.4,0.6,0.7)
C4 (0.1,0.3,0.5) (0.2,0.4,0.7) (0.3,0.5,0.6) (0.5,0.5,0.5)
(10)
j
j 1
TABLE IV. E XPERT 2:R ISK FACTORS FOR COMPLEMENTARY JUDGE
MATRIX
D. Multi-expert opinion aggregation synthesis sorting
method
According to the risk factor ranking method described
above, it can be concluded that the single expert ranks the
different risk factors. In order to collect the ranking of risk
factors of different experts, this article introduces the weight
indicator to assemble the multi-expert opinions. The
collective method is as shown in Equation (11).
V i
E 1 V 1 i E 2 V 2 i
E k V ki
A
C1 C1 C2 C3 C4
(0.5,0.5,0.5) (0.4,0.5,0.6) (0.4,0.6,0.7) (0.4,0.6,0.7)
C2 (0.4,0.5,0.6) (0.5,0.5,0.5) (0.4,0.5,0.6) (0.3,0.5,0.7)
C3 (0.3,0.4,0.6) (0.4,0.5,0.6) (0.5,0.5,0.5) (0.4,0.6,0.7)
C4 (0.3,0.4,0.6) (0.3,0.4,0.6) (0.3,0.5,0.6) (0.5,0.5,0.5)
Take O 0.5 , calculate the expectation of each
triangular fuzzy number, and sort the above expectations to
obtain b i ik , the expected order under different expert
evaluation is shown in Tab. V.
(11)
Where, V ki is the sorting results of kth expert, E k is the
TABLE V. R ANK THE EXPECTATIONS OF DIFFERENT EXPERT EVALUATIONS
C1
C2
C3
C4
(0.5,0.7,0.9)
(0.3,0.6,0.8)
(0.4,0.6,0.7)
(0.5,0.5,0.5)
Expert 1
(0.3,0.6,0.7)
(0.4,0.5,0.7)
(0.45,0.5,0.55)
(0.45,0.5,0.55)
(0.45,0.5,0.55)
(0.45,0.5,0.55)
(0.3,0.5,0.6)
(0.2,0.4,0.7)
(0.5,0.5,0.5)
(0.3,0.5,0.6)
(0.3,0.4,0.7)
(0.1,0.3,0.5)
(0.4,0.6,0.7)
(0.4,0.5,0.6)
(0.4,0.6,0.7)
(0.5,0.5,0.5)
Expert 2
(0.4,0.6,0.7)
(0.5,0.55,0.6)
(0.4,0.5,0.6)
(0.4,0.5,0.6)
(0.45,0.5,0.55)
(0.45,0.5,0.55)
(0.3,0.5,0.6)
(0.3,0.4,0.6)
possible, its weighting vector is Z
After determining the expected value rankings of
different expert evaluations, the paper introduces a fuzzy
quantization operator to calculate the average value of each
risk factor. In this study, the average of the risk factors for
different situations is calculated for the three languages.
(0.5,0.55,0.6)
(0.4,0.5,0.6)
(0.3,0.4,0.6)
(0.3,0.4,0.6)
0,0,0.5,0.5
.
In this example, the language fuzzification is described
as the majority. According to the previous calculation
method, the average value of each risk factor is calculated.
The calculation results are shown in Table VI.
ķ When language fuzzification is described as the
majority, its weighting vector is Z 0,0.4,0.5,0.1 ;
TABLE VI. T HE AVERAGE VALUE OF EACH RISK FACTOR UNDER
DIFFERENT LANGUAGE FUZZY DESCRIPTION
ĸ When the language ambiguity is described as at least
half, the weighting vector is Z 0.5,0.5,0,0 ;
C1
C2
C3
C4
Ĺ When language fuzzification is described as much as
16
Expert 1
(0.37,0.54,0.68)
(0.435,0.5,0.555)
(0.435,0.49,0.565)
(0.23,0.43,0.64)
Average
0.5325
0.4975
0.495
0.4325
Expert 2
(0.46,0.57,0.64)
(0.4,0.5,0.6)
(0.435,0.49,0.555)
(0.3,0.44,0.6)
Average
0.56
0.5
0.4925
0.445