AU2021106383A4 - Method for Calculating Reliability of Distribution System Based on Equipment Reliability Cloud Model - Google Patents
Method for Calculating Reliability of Distribution System Based on Equipment Reliability Cloud Model Download PDFInfo
- Publication number
- AU2021106383A4 AU2021106383A4 AU2021106383A AU2021106383A AU2021106383A4 AU 2021106383 A4 AU2021106383 A4 AU 2021106383A4 AU 2021106383 A AU2021106383 A AU 2021106383A AU 2021106383 A AU2021106383 A AU 2021106383A AU 2021106383 A4 AU2021106383 A4 AU 2021106383A4
- Authority
- AU
- Australia
- Prior art keywords
- cloud
- failure rate
- reliability
- transformer
- distribution system
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Ceased
Links
- 238000000034 method Methods 0.000 title claims abstract description 21
- 238000010606 normalization Methods 0.000 claims abstract description 15
- 238000004422 calculation algorithm Methods 0.000 claims abstract description 7
- 238000005192 partition Methods 0.000 claims abstract description 5
- 238000004364 calculation method Methods 0.000 claims description 6
- 230000004888 barrier function Effects 0.000 claims description 3
- 238000011156 evaluation Methods 0.000 abstract description 10
- 238000004458 analytical method Methods 0.000 abstract description 3
- 238000012545 processing Methods 0.000 abstract description 2
- 238000012360 testing method Methods 0.000 description 9
- 238000010586 diagram Methods 0.000 description 2
- 238000004088 simulation Methods 0.000 description 2
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000006243 chemical reaction Methods 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 230000005611 electricity Effects 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 238000004519 manufacturing process Methods 0.000 description 1
- 238000011158 quantitative evaluation Methods 0.000 description 1
- 238000011160 research Methods 0.000 description 1
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
- G06Q50/06—Energy or water supply
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
- G01R31/50—Testing of electric apparatus, lines, cables or components for short-circuits, continuity, leakage current or incorrect line connections
- G01R31/62—Testing of transformers
Landscapes
- Engineering & Computer Science (AREA)
- Business, Economics & Management (AREA)
- Economics (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Health & Medical Sciences (AREA)
- Water Supply & Treatment (AREA)
- Public Health (AREA)
- Power Engineering (AREA)
- General Health & Medical Sciences (AREA)
- Human Resources & Organizations (AREA)
- Marketing (AREA)
- Primary Health Care (AREA)
- Strategic Management (AREA)
- Tourism & Hospitality (AREA)
- General Business, Economics & Management (AREA)
- Theoretical Computer Science (AREA)
- Supply And Distribution Of Alternating Current (AREA)
Abstract
The invention provides a method for calculating reliability of distribution system
based on equipment reliability cloud model, which comprises the following steps:
collecting historical statistical data of line failure rate and transformer failure rate of a
distribution system to be evaluated; normalized treatment; using backward cloud
generator, the digital characteristics of cloud model of line fault rate and transformer
fault rate are calculated; using forward cloud generator, the cloud drops of line failure
rate and transformer failure rate are calculated; inverse normalization processing; the
reliability of distribution system is calculated by feeder partition algorithm, and
SAIFI, SAIDI and ASAI values reflecting the reliability of distribution system are
obtained. Make the above values into graphs, and analyse the reliability of the
distribution system according to the graphs. The reliability evaluation method of
distribution system based on cloud model can not only obtain the qualitative law
among parameters, but also quantitatively evaluate the reliability of power system,
which has good universality and is also suitable for the reliability evaluation of
complex distribution systems.
FIGURES
Historical statistical Historical statistical data
data ofline failure rates of.'
transformer failure rate
Normalize the statistical Normalize the statistical
data' data.'
Backward cloud Backward cloud
generator generator
Exx Enx Hex Exv Eny Hev
Forward cloud generator Forward cloud generator.
Inverse normalization treatment Inverse normalization treatment
on the cloud drops+' on th e cloud dr ops+'
Feederzoning algorithm+'
SAIFI, SAIDI and ASAIvalues of system reliability
Figure 1
Description
Historical statistical Historical statistical data data ofline failure rates of.' transformer failure rate
Normalize the statistical Normalize the statistical data' data.'
Backward cloud Backward cloud generator generator
Exx Enx Hex Exv Eny Hev
Forward cloud generator Forward cloud generator.
Inverse normalization treatment Inverse normalization treatment on the cloud drops+' on th e cloud drops+'
Feederzoning algorithm+'
SAIFI, SAIDI and ASAIvalues of system reliability
Figure 1
Method for Calculating Reliability of Distribution System Based on Equipment
Reliability Cloud Model
The invention relates to a reliability evaluation method of a distribution network
part of an electric power system, in particular to a method for calculating reliability of
distribution system based on equipment reliability cloud model, and belongs to the
technical field of electric power engineering.
With the continuous development of social economy, electricity plays an
increasingly important role in people's production and life. Large-scale power outages
can not only cause huge economic losses, but also endanger social stability.
Quantitative evaluation of power system reliability has attracted people's attention.
In the prior art, distribution system reliability evaluation methods are divided
into two categories: simulation method and analytical method. The analytical method
is mainly fault enumeration method, and when the scale is small, the fault
enumeration method has better effect; when the system scale is large and complex
factors in actual operation need to be considered, the simulation method is more
effective.
However, the basis of evaluating the reliability of distribution system is the
original reliability parameters of components, which may be uncertain due to weather,
statistical time or statistical error. The uncertainty of the original data of power system
reliability includes randomness and fuzziness. At this time, if the reliability of power system is quantitatively evaluated by using fixed parameters, the results are not in line with the actual situation, and it is unreasonable to evaluate with such results, which will lead to a large deviation between the evaluation results and the actual situation.
How to get the qualitative rule between parameters and evaluate the reliability of
power system quantitatively is one of the technical problems in the reliability research
domain. Based on this, it is necessary to provide a method for calculating reliability of
distribution system based on equipment reliability cloud model which can better
reflect the real reliability level of the system.
Aiming at the defects of the prior art, the invention provides a method for
calculating reliability of distribution system based on equipment reliability cloud
model.
In order to achieve the above purpose, the invention adopts the following
technical scheme:
A method for calculating reliability of distribution system based on equipment
reliability cloud model comprises the following steps:
(1) Collecting historical statistical data of line failure rate and transformer failure
rate of the distribution system to be evaluated;
(2) Normalize the statistical data over the years;
(3) Using the backward cloud generator, the digital characteristics of the cloud
model of the line failure rate and the transformer failure rate are calculated from the
statistical data of the past years after normalization.
(4) Using the forward cloud generator, the cloud drops of the line fault rate and
the transformer fault rate are calculated respectively according to the cloud model
digital characteristics of the line fault rate and the transformer fault rate;
(5) Carrying out inverse normalization treatment on the cloud drops of the line
failure rate and the transformer failure rate;
(6) Taking the cloud drops of one line failure rate and one transformer failure
rate after inverse normalization as a group, taking each group of cloud drops as the
reliability parameter of distribution network, calculating the reliability of distribution
system by using feeder partition algorithm, and obtaining the values of system
average power outage frequency SAIFI, system average power outage duration SAIDI
and average power supply availability ASAI which reflect the reliability of
distribution system;
(7) Drawing the SAIFI, SAIDI and ASAI values into graphs, and analyzing the
reliability of the distribution system to be evaluated according to the graphs.
In the historical statistical data mentioned in step (1), the line failure rate array is
represented by X, and the ith element in X is represented by xi, where i = 1, 2, ... n;
transformer failure rate array is represented by Y, and the ith element in Y is
represented by yi, where i = 1, 2, ... n;
The method for normalizing statistical data over the years mentioned in step (2)
is as follows: x, -( x)/n]
According to , normalize the statistical data of line
failure rate over the years to obtain a normalized line failure rate array Xb,xbi
represents the ith element in Xb, where i = 1, 2, ... n;
According to , the statistical data of transformer failure rate over
the years are normalized, and the normalized line failure rate array yb is obtained, yb,
represents the ith element in Yb, where i = 1, 2, ... n.
In step (3), the specific steps of calculating the digital features of the cloud
model by using the reverse cloud generator are as follows:
1) According to the line failure rate array X, calculate the quantitative sample
- 1ix* c I fx-) mean , the second-order center distance of samples is n-I-,
the fourth-order center of the sample is
In the same way, according to the transformer failure rate array yb, the
-= I ty quantitative sample mean is " , the second-order center distance of samples is
h C-i y Y ~~ , and the fourth-order center distance of samples is
2) Let the expectations of line failure rate and transformer failure rate be their
Exx=X , Exy=Yt respective mean values, that is
{En 2+ He =C2 9He+18He 2 En2 +3En 4 =C 3) Using formula , the entropy and super
entropy of line fault rate and transformer fault rate are
Enx Hex= C;- Eny= 9C - , Hey C - C
4) output cloud model digital features, including Expectation ex, Entropy en and
super entropy He, in which the cloud model digital features of line fault rate are
expectation Exx, entropy Enx and super entropy Hex, and the cloud model digital
features of transformer fault rate are expectation Exy, entropy Eny and super entropy
Hey.
In step (4), the specific steps of calculating cloud drops by using the forward
cloud generator are as follows:
For line failure rate:
1)Input the digital characteristics of the cloud model of the line failure rate and
the number N of cloud drops, and determine the number N of cloud drops, subject to
the cloud drop distribution that can clearly see the calculation results;
2)Generate a normal random number Enx' N (Enx, Hex2) with Enx as
expectation and Hex2 as variance.
3) Generate a normal random number with Exx as
expectationand asvariance.
4) According to equation , calculate the certainty degree mxi ofX'i,
and x'i with certainty degree mxi is a cloud dropin the number domain;
5) Repeat steps 1 to 4 until the required N cloud drops are generated;
Similarly, for transformer failure rate:
1) Input the digital characteristics of the transformer failure rate cloud model and
the number N of cloud drops, and determine the number N of cloud drops, so as to see
clearly the cloud drop distribution of the calculation results;
2) Generate a normal random number Eny' =N (Eny, Hey) with Eny as
expectation and Hey2 as variance.
3) Generate a normal random number y = N(Exy, Eny' ) with Exy as
expectation and Eny'i as variance.
4) According to #= , calculate the certainty myi of y'i, and y'i with
certainty myi is a cloud drop in the number domain;
5) Repeat steps 1 to 4 until the required N cloud drops are generated;
The method for inversely normalizing randomly generated cloud drops in step
(5) is as follows:
x"=x [x-( x)/n]f+( x,)n 1) According to formula , where i =
1, 2, ... n, the drops of line fault and barrier rate cloud are inversely normalized;
y , =y 4 [y, -(" y,) / n]2 +( y,) / n 2) According to formula x , where i =
1, 2, ... n, the cloud drops of transformer and failure rate are inversely normalized.
Compared with the previous technology, the invention has the following
beneficial effects:
According to the invention, the cloud model is introduced to solve the
randomness and fuzziness of the original parameters of the power distribution system,
and the uncertain numerical values with randomness and fuzziness which change
within a certain range in engineering are effectively processed; cloud model can well
reflect the relationship between qualitative and quantitative, and the degree of
certainty of reliability index can be obtained by evaluating with cloud model, which
can better explain the influence of uncertainty on results and reduce the influence of
uncertainty on reliability evaluation results. The reliability index results obtained are
more in line with the actual situation and have certain practical value.
Figure 1 is a flow chart of the reliability evaluation method of distribution
system based on cloud model according to the present invention.
Figure 2 is the wiring diagram of RBTS-BUS6 standard test system.
Figure 3 shows the SAIFI index of BTS-BUS6 standard test system.
Figure 4 shows the SAIDI index of BTS-BUS6 standard test system.
Figure 5 shows ASAI index of BTS-BUS6 standard test system.
The following will further illustrate the technical scheme of the present invention
through examples.
Example 1
In this embodiment, the reliability of the RBTS-BUS6 standard test system is
evaluated by using the evaluation method of the present invention. There are 23 load
points in the RBTS-BUS6 standard test system, and each load point serves as a user.
It is set that the on-off equipment (circuit breaker, isolating switch and fuse) in the
system is faultless, that is, the switching equipment operates reliably; the main power
supply is fully reliable; at the same time, the failure of load equipment is not
considered, that is, the failure rate of load nodes is zero. The wiring diagram of
RBTS-BUS6 standard test system is shown in fig. 2 of the specification, and the line
length parameters of RBTS-BUS6 standard test system are shown in table 1.
Table 1 line length parameters of rbts-bus6 standard test system
Line number line length/km Line number lne length/kmw 2 2.8 51 3.2 6 2.5 55 2.5 10 1.6 59 3.2 14 0.9 63 1.6 18 .6 65 0.8 22 2.5 68 2.8 24 0.6 72 2.5 27 1.6 76 3.2 29 0.8 so 2.8 32 0.9 84 2.5 34 3.2 86 0.75 38 2.8 89 1.6
40 0.6 93 3.2 43 5.1 97 2.8 47 2.8
The invention relates to a reliability evaluation method of a distribution system
based on a cloud model, which comprises the following specific steps:
1. Collect historical statistical data of the line failure rate and transformer failure
rate of the distribution system to be evaluated. In the historical statistical data, the line failure rate array is represented by X, and xi represents the ith element in X, where i =
1, 2, ... n; transformer failure rate array is represented by Y, and the ith element in y is
represented by yi, where i = 1, 2, ... n. The historical statistical data of line failure rate
x and transformer failure rate y in this embodiment come from the data of distribution
network in a province of China Southern Power Grid from 1996 to 2013, as shown in
Table 2.
Table 2 Statistical data of line failure rate and transformer failure rate over the
years
NO. Year Linefailurerate Transformerfailurerate.> (time/km • year) (time/individual year)
1 2013 0.1523 0.0066
2 2012 0.0685 0.0038 3 2011 0.2559 0.0174 4 2010 0.0699 0.0121 5 2009 0.2057 0.0336 6 2008 0.3670 0.0210 7 2007 0.1456 0.0091 8 2006 0.0787 0.0050 9 2005 0.2095 0.0383 10 2004 0.1036 0.0085 II 2003 0.2443 0.0080 12 2002 0.5111 0.0181 13 2001 0.3030 0.0020 14 2000 0.2077 0.0080 15 1999 0.1658 0.0126 16 1998 0.1100 0.1000 17 1997 0,1456 0.0086 18 1996 0.0370 0.0060
2. Normalize the statistical data over the years.
x, ,/18
According to , the historical statistical data x of line
failure rate collected in step (1) is normalized to obtain the normalized line failure rate
Xbxbirepresents the ith element in Xb, where i= 1, 2, ... 18;
xy /18
According to formula , the statistical data y of
transformer failure rate collected in step (1) is normalized to obtain the normalized
transformer failure rate yb bi represents the ith element in yb, where i = 1, 2, ... 18.
Table 3 Line failure rate and transformer failure rate after normalized treatment
NO. Year Line failure rate Transformer failure rate (time/km • year) (time/individual • year).,
1 2013 -0.205 -0.4653 2 2012 -0.878 -0.587 3 2011 0.627 0.0041 4 2010 -0.867 -0.2263 5 2009 0.224 0.7082 6 2008 1.519 0.1606 7 2007 -0.259 -0.3567 8 2006 -0.796 -0.5349 9 2005 0.254 0.9125 10 2004 -0.596 -0.3827 it 2003 0.534 -0.4045 12 2002 2.676 0.0345 13 2001 1.005 -0.6653 14 2000 -1.206 -0.7174 15 1999 -0.097 -0.2045 16 1998 -0.545 3.5943
17 1997 -0.259 -0.3784 18 1996 -1.131 -1.1311
3. Using the backward cloud generator, the numerical characteristics of the cloud
model of the line fault rate and the transformer fault rate are calculated from the
statistical data of the past years after normalized processing. The backward cloud
generator is a conversion model from quantitative values to qualitative concepts,
which can convert a certain amount of accurate data into qualitative concepts
represented by digital features. The specific steps are as follows:
(1) According to the normalized line failure rate array X, the sample mean value
I IN I IN -7
the second-order center distance and the
fourth-order center distance 1-1 are calculated.
In the same way, according to the normalized transformer failure rate array yb, the
- j1 '
average sample 18 the second-order center distance
18-1l and the fourth-order center distance 18C=4
are calculated.
(2) Let the expected Exx and Exy of line failure rate and transformer failure rate
be their respective mean values of Xand Y, that is, and Y=
. (3) The entropy and super-entropy of line fault rate and transformer fault rate are
En2 + He' =C,
calculated by formula 2 +3En= 9He+8He'En , that is
Enx= 9 - 9C~C, , Hex= C- 9C -C" Eny= -c -C4 9C ' C41-c,___ 6 C" , Hy= C, 6 666
(4)Output digital features of cloud model, including Expectation Ex, Entropy En
and super entropy He. According to the calculation, the numerical characteristics of
cloud model for line fault rate are expected Exx = 4.5643x10-16, entropy Enx =0.9681
and super entropy Hex=0.2508, and the numerical characteristics of cloud model for
transformer fault rate are expected exy =-1.7270x10-16, entropy Eny=0.6264 and
super entropy hey = 1.0365.
4. Using the forward cloud generator, the cloud drops of line fault rate and
transformer fault rate are calculated from the cloud model digital characteristics of
line fault rate and transformer fault rate respectively. The specific steps are as follows:
For the line failure rate,
(1 Input the digital characteristics of cloud model of line failure rate and the
number of cloud drops, which is set as 2000 in this implementation;
(2 Generate a normal random number Enx'i = N (0. 9681, 0. 25082) with Enx=
0.9681 as expectation and Hex2 = 0. 25082 as variance.
x = N(4.5643x10 1,EnxI) (3) Generate a normal random number with
Exx=4.5643x10- 16 as expectation and as variance.
(4) According to formula A =, the certainty degree mxi of x'i is
calculated, and x'i with certainty degree mxi has a cloud drop of line failure rate in the
number domain;
(5) Repeat steps 1 to 4 until the required 2000 cloud drops are produced.
Similarly, for transformer failure rate:
(1) Input the digital characteristics of the transformer failure rate cloud model and
the number of cloud drops, which is set as 2000 in this implementation;
(2) Generate a normal random number Eny 'i = N (0. 6264, 1. 03652)with Eny=
0.6264 as expectation and Hey 2 = 1.03652 as variance.
(3) Generate a normal random number =N(-1.7270x10",Eny)with
Exy= 1. 7270 X 10 " as expectation and Eny'i as variance.
(4) Calculate the certainty myi of y'i according to formula = , and y'i
with certainty myi is a cloud drop of transformer barrier rate in number domain;
(5) Repeat steps 1 to 4 until the required 2000 cloud drops are generated;
5. The cloud drops of line failure rate and transformer failure rate are inversely
normalized. As the generated cloud drops are random, the following takes x'i of one
line failure rate and y'i of one transformer failure rate as examples, and the calculation
of any other cloud drops adopts the same method.
(1) The line failure rate cloud drop inverse normalization formula:
x " = x $[x, - (I x,) /18]1+(' ,)1 X,= I x,)/1
=1.6529 x 0.1245+0.1778= 0.3836
(2) Inverse normalization formula of cloud drop for transformer failure rate:
, 18 1818 y", =y, 41#[y, -( y,) /18]2 + (Y-y,)/18 =8.2497x0.0230+0.0173 0.2070
6. The cloud drops of one line failure rate and one transformer failure rate after
inverse normalization are taken as a group, and each group of cloud drops is taken as
the reliability parameter of distribution network. The reliability of distribution system
is calculated by feeder partition algorithm, and the values of system average outage
frequency index SAIFI, system average outage duration index SAIDI and average
power supply availability index ASAI which reflect the reliability of distribution
system are obtained.
In this embodiment, the line failure rate and transformer failure rate after inverse
normalization have 2000 cloud drops, which are grouped into 2000 groups. Each group of cloud drops is taken as the reliability parameter of distribution network, and the reliability of distribution system is calculated by using feeder partition algorithm, and 2000 groups of SAIFI, SAIDI and ASAI values reflecting the reliability of distribution system are obtained.
7. With the instruction of "scatter3" of MATLAB software, the above 2000 sets of
reliability results are plotted into graphs, and the reliability of distribution system is
analyzed according to the graphs.
It can be seen from fig. 3 of the specification that the average outage frequency
index SAIFI of the system has the largest cloud drop density when the certainty
degree of the fault rate of the line and transformer is 1. Therefore, the SAIFI index
value corresponding to the cloud drop here is the most likely value of the system
reliability index.
It can be seen from the fig. 4 of the specification that the average outage duration
index SAIDI of the system has the largest cloud drop density when the certainty
degree of line and transformer failure rate is 1. Therefore, the SAIDI index value
corresponding to the cloud drop here is the most likely value of the system reliability
index.
It can be seen from fig. 5 of the specification that the cloud drop density of system
power supply availability index ASAI is the largest when the certainty degree of line
and transformer failure rate is 1. Therefore, the ASAI index value corresponding to
the cloud drop here is the most likely value of the system reliability index.
It can be seen from figs. 3-5 that the reliability evaluation method of distribution
system based on cloud model of the present invention can obtain the reliability index
values of the system under different certainty conditions. The greater the density of
cloud drops, the greater the possibility of this result, and the degree to which the
reliability index belongs to a certain value, which further illustrates the influence of
parameter uncertainty on the results.
Claims (5)
1. A method for calculating reliability of distribution system based on equipment
reliability cloud model comprises the following steps:
(1) Collecting historical statistical data of line failure rate and transformer failure
rate of the distribution system to be evaluated;
(2) Normalize the statistical data over the years;
(3) Using the backward cloud generator, the digital characteristics of the cloud
model of the line failure rate and the transformer failure rate are calculated from the
statistical data of the past years after normalization;
(4) Using the forward cloud generator, the cloud drops of the line fault rate and
the transformer fault rate are calculated respectively according to the cloud model
digital characteristics of the line fault rate and the transformer fault rate;
(5) Carrying out inverse normalization treatment on the cloud drops of the line
failure rate and the transformer failure rate;
(6) Taking the cloud drops of one line failure rate and one transformer failure
rate after inverse normalization as a group, taking each group of cloud drops as the
reliability parameter of distribution network, calculating the reliability of distribution
system by using feeder partition algorithm, and obtaining the values of system
average power outage frequency SAIFI, system average power outage duration SAIDI
and average power supply availability ASAI which reflect the reliability of
distribution system;
(7) Drawing the SAIFI, SAIDI and ASAI values into graphs, and analyzing the
reliability of the distribution system to be evaluated according to the graphs.
2. The method for evaluating reliability of distribution system according to claim
1 is characterized in that in the historical statistical data mentioned in step (1), the line
failure rate array is represented by X, and the ith element in X is represented by xi,
where i = 1, 2, ... n; transformer failure rate array is represented by Y, and the ith
element in Y is represented by yi, where i= 1, 2, ... n;
The method for normalizing statistical data over the years mentioned in step (2)
is as follows:
x, -(xj./n]
According to , normalize the statistical data of line
failure rate over the years to obtain a normalized line failure rate array Xb,xbi
represents the ith element in Xb, where i = 1, 2, ... n;
According to & - wn' , the statistical data of transformer failure rate over
the years are normalized, and the normalized line failure rate array yb is obtained, ybi
represents the ith element inYb, where i = 1, 2, ... n.
3. The method for evaluating reliability of distribution system according to claim
1 is characterized in that in step (3), the specific steps of calculating the digital
features of the cloud model by using the reverse cloud generator are as follows:
1) According to the line failure rate array Xb, calculate the quantitative sample
X =-Ix*b x-) mean , the second-order center distance of samples is
the fourth-order center of the sample is
In the same way, according to the transformer failure rate array yb, the
quantitative sample mean is , the second-order center distance of samples is
n , and the fourth-order center distance of samples is
2) Let the expectations of line failure rate and transformer failure rate be their
Exc = X, Exy = Y. respective mean values, that is
En 2 + He =C 9He4 +18He'En' +3En 4 = C 3) Using formula , the entropy and super
entropy of line fault rate and transformer fault rate are
Enx= 9c" C , Hex= C - 9C E,n 9C- -C ,Hey= CC
4) output cloud model digital features, including Expectation ex, Entropy en and
super entropy He, in which the cloud model digital features of line fault rate are
expectation Exx, entropy Enx and super entropy Hex, and the cloud model digital features of transformer fault rate are expectation Exy, entropy Eny and super entropy
Hey.
4. The method for evaluating reliability of distribution system according to claim
1 is characterized in that in step (4), the specific steps of calculating cloud drops by
using the forward cloud generator are as follows:
For line failure rate:
1) Input the digital characteristics of the cloud model of the line failure rate and
the number N of cloud drops, and determine the number N of cloud drops, subject to
the cloud drop distribution that can clearly see the calculation results;
2) Generate a normal random number Enx N (Enx, Hex' with Enx as
expectation and Hex2 as variance.
x = N{( Exx, Enx|) 3) Generate a normal random number 'Ewith Exx as
expectationand asvariance.
p,, eu
4) According to formula , calculate the certainty degree mxi of x'i,
and x'i with certainty degree mxi is a cloud dropin the number domain;
5) Repeat steps 1 to 4 until the required N cloud drops are generated;
Similarly, for transformer failure rate:
1) Input the digital characteristics of the transformer failure rate cloud model and
the number N of cloud drops, and determine the number N of cloud drops, so as to see
clearly the cloud drop distribution of the calculation results;
2) Generate a normal random number =N(Eny, with Eny as
expectation and Hey2 as variance.
3) Generate a normal random number = N(Exy, Eny' ) with Exy as
expectation and Eny'i as variance.
4) According to #= , calculate the certainty myi of y'i, and y'i with
certainty myi is a cloud drop in the number domain;
5) Repeat steps 1 to 4 until the required N cloud drops are generated.
5. The method for evaluating reliability of distribution system according to claim
1 is characterized in that the method for inversely normalizing randomly generated
cloud drops in step (5) are as follows:
X, = X I[x- ( x) I n]z +( x,)/n 1) According to formula , where i =
1, 2, ... n, the drops of line fault and barrier rate cloud are inversely normalized;
Y1 yV [y,-(Iy,)/n] +(Ziy,)/n 2) According to formula '~' '~' , where i =
1, 2, ... n, the cloud drops of transformer and failure rate are inversely normalized.
FIGURES 1/3
Figure 1
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| AU2021106383A AU2021106383A4 (en) | 2021-08-21 | 2021-08-21 | Method for Calculating Reliability of Distribution System Based on Equipment Reliability Cloud Model |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| AU2021106383A AU2021106383A4 (en) | 2021-08-21 | 2021-08-21 | Method for Calculating Reliability of Distribution System Based on Equipment Reliability Cloud Model |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| AU2021106383A4 true AU2021106383A4 (en) | 2021-11-04 |
Family
ID=78476649
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| AU2021106383A Ceased AU2021106383A4 (en) | 2021-08-21 | 2021-08-21 | Method for Calculating Reliability of Distribution System Based on Equipment Reliability Cloud Model |
Country Status (1)
| Country | Link |
|---|---|
| AU (1) | AU2021106383A4 (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115330559A (en) * | 2022-10-17 | 2022-11-11 | 国网浙江余姚市供电有限公司 | Power distribution network elasticity evaluation method and device based on information data time-space coordination |
-
2021
- 2021-08-21 AU AU2021106383A patent/AU2021106383A4/en not_active Ceased
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115330559A (en) * | 2022-10-17 | 2022-11-11 | 国网浙江余姚市供电有限公司 | Power distribution network elasticity evaluation method and device based on information data time-space coordination |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN107895176B (en) | Fog calculation system and method for wide-area monitoring and diagnosis of hydroelectric machine group | |
| CN111428355B (en) | Modeling method for intelligent comprehensive statistics of power load numbers | |
| CN109447441B (en) | Transient stability risk assessment method considering uncertainty of new energy unit | |
| CN108667005B (en) | Power grid static and dynamic combination vulnerability assessment method considering new energy influence | |
| CN103869192A (en) | Smart power grid line loss detection method and system | |
| CN105354675A (en) | AC/DC power grid cascading failure analysis method based on key power transmission section recognition | |
| CN108074035A (en) | More scene distribution formula photovoltaics access power distribution network operation risk assessment method system | |
| CN104361529A (en) | Reliability detecting and evaluating method of power distribution system on basis of cloud model | |
| CN114696466B (en) | State monitoring system and method suitable for low-voltage transformer area power distribution | |
| AU2021106383A4 (en) | Method for Calculating Reliability of Distribution System Based on Equipment Reliability Cloud Model | |
| CN110389268B (en) | Online monitoring system of electric power system | |
| CN108074048A (en) | It is included in the wind-electricity integration power system security methods of risk assessment of wind speed correlation properties | |
| CN104951654A (en) | Method for evaluating reliability of large-scale wind power plant based on control variable sampling | |
| Qu et al. | Research on short‐term output power forecast model of wind farm based on neural network combination algorithm | |
| CN108832630B (en) | Power grid CPS prevention control method based on expected accident scene | |
| CN112036718B (en) | Electric power system safety risk assessment method considering new energy uncertainty | |
| CN106056305A (en) | Power generation system reliability rapid assessment method based on state clustering | |
| CN109613372A (en) | A kind of electric network failure diagnosis method based on polynary electric network database | |
| CN104283208B (en) | The composition decomposition computational methods of the probability available transmission capacity of large-scale power grid | |
| CN113092933A (en) | LSTM-based single-phase earth fault line selection method and system | |
| CN108122054A (en) | A kind of electric system topology real-time computing technique calculated based on figure | |
| CN108090616A (en) | A kind of electric system Active Splitting optimal section searching method | |
| CN115034472A (en) | Distributed photovoltaic operation intelligent prediction management system | |
| Xu et al. | Transient stability assessment based on data-structure analysis of operating point space | |
| Li et al. | Decision tree-based real-time emergency control strategy for power system |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| FGI | Letters patent sealed or granted (innovation patent) | ||
| MK22 | Patent ceased section 143a(d), or expired - non payment of renewal fee or expiry |