CN102593828B - Reliability assessment method for electric distribution network with WTG (wind turbine generator) - Google Patents

Reliability assessment method for electric distribution network with WTG (wind turbine generator) Download PDF

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CN102593828B
CN102593828B CN201210047425.9A CN201210047425A CN102593828B CN 102593828 B CN102593828 B CN 102593828B CN 201210047425 A CN201210047425 A CN 201210047425A CN 102593828 B CN102593828 B CN 102593828B
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CN102593828A (en
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谢开贵
孙若笛
胡博
李春燕
张晓�
齐雪雯
高英
廖庆龙
王光强
段盼
王岸
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Chongqing University
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Abstract

The invention provides a reliability assessment method for an electric distribution network with a wind turbine generator (WTG), which takes the relativity of wind speed and load into consideration. The reliability assessment method comprises the step of collecting original data of the electric distribution network, constructing a WTG power output model, determining the scope of power supply capability of wind power, constructing normal distribution functions of wind speed and load, analyzing the timing sequence wind speed and load curves to obtain the average value and the standard deviation of two normal distribution functions, constructing a wind speed-load bivariate distribution function, sampling the wind speed and load sequence with the Monte Carlo method, calculating the scope of the WTG power output and the power supply corresponding to the sampled sequences, calculating reliability indexes with a partition method, and averaging all the calculated reliability indexes to finally obtain a reliability index of the electric distribution network with WTG in consideration of the relativity of wind speed and load. Because the influences of the relativity of wind speed and load and the sequential load curve are taken into consideration, and a binary combination distribution function is adopted for analyzing the relativity of wind speed and load, the accuracy of the reliability assessment of the electric distribution network with the wind turbine generator (WTG) is enhanced.

Description

A kind of distribution network reliability evaluation method containing WTG
Technical field
The present invention relates to, to containing wind turbine group distribution network reliability evaluation method, specifically refer to consider that wind speed-load correlation, to containing wind turbine group distribution network reliability evaluation method, belongs to electric power project engineering field.
Background technology
In recent years, wind energy power technology has obtained fast development, by wind-powered electricity generation unit access power distribution network, is the intelligentized development trend of power distribution network, wind-powered electricity generation unit, and English full name is: wind turbine generator, referred to as " WTG ".In containing the power distribution network of WTG, wind speed and load are affected by the climatic factors such as temperature, season, weather all, in prior art, to containing in the distribution network reliability evaluation method of WTG, do not consider the impact of wind speed-load correlation and sequential load curve, the fail-safe analysis accuracy of making is not high.
Summary of the invention
The problems referred to above that exist for prior art, the present invention proposes a kind of distribution network reliability evaluation method containing WTG, can be to make assessment exactly containing the distribution network reliability of WTG.
The present invention is achieved in that a kind of containing WTG distribution network reliability evaluation method, it is characterized in that: the initial data that gathers power distribution network; Build WTG exert oneself model and definite wind-powered electricity generation power supply capacity scope; Build wind speed normal distyribution function and load normal distyribution function, by computer system, the analyzing and processing of sequential wind speed and load curve is obtained mean value and the standard deviation of the two normal distyribution function; Build wind speed-load bivariate distribution function; By computer system, adopt Monte Carlo method to the sequential sampling of wind speed-load; By the initial data input computer system of power distribution network, calculate the WTG corresponding with sampled sequence and exert oneself and power supply capacity scope, adopt the reliability index of block algorithm computing system; Each reliability index mean value to gained by computer, finally obtains the distribution network reliability index containing WTG consideration wind speed-load correlation again, and concrete steps comprise:
Step 1: the initial data that gathers power distribution network: wind energy turbine set sequential air speed data, IEEE-RTS system loading data, each element failure rate of power distribution network, repair time, each component reliability parameter and each load point amount, the incision wind speed of WTG, rated wind speed, cut-out wind speed, the connecting relation in power distribution network between circuit, transformer, switchgear, load point and WTG;
Step 2: build WTG exert oneself model and definite wind-powered electricity generation power supply capacity scope
2.1: build the WTG model of exerting oneself
Adopt quadratic function approximate representation WTG to exert oneself p t with wind speed v t relation:
(1)
In formula, v ci , v r with v co piecemeal is incision wind speed, rated wind speed and the cut-out wind speed of WTG, p r for rated output power;
2.2: determine wind-powered electricity generation power supply capacity scope
Power distribution network is divided into m1 ~ m7 seven piecemeals, establish piecemeal s(1≤ s≤ 7) have mindividual element, and the i(1≤ i≤ m) individual element failure rate and repair time piecemeal be λ i with γ i , piece sequivalent fault rate λ s with equivalent mean time to repair γas follows:
(2)
Figure 649938DEST_PATH_IMAGE003
(3)
If WTG exists tconstantly exert oneself into p t , it is to load piece m i supply path on pass through nindividual piece, the ipiece m i total load be l mi , it can be right m i the condition restoring electricity can be expressed as formula (4):
Figure 232098DEST_PATH_IMAGE004
(4)
By (4) formula, can obtain every kind of WTG order and the ability to load restoration power supply in situation of exerting oneself;
Step 3: build wind speed normal distyribution function and load normal distyribution function, by the analysis of sequential wind speed and load curve being obtained to mean value and the standard deviation of the two normal distyribution function;
3.1: build wind speed normal distyribution function
Formula (5) is wind farm wind velocity normal distyribution function:
Figure 46470DEST_PATH_IMAGE005
(5)
In formula, vrepresent wind speed; μ 1with σ 1the mean value and the standard deviation that represent respectively wind speed, to this wind energy turbine set, its wind speed normal distribution model parameter is: μ 1=5.9826, σ 1=3.17, v~ N ( μ 1, σ 1 2);
3.2: build load normal distyribution function
Formula (6) provides IEEE-RTS system loading normal distyribution function:
Figure 806616DEST_PATH_IMAGE006
(6)
In formula, lrepresent load power; μ 2with σ 2the mean value of Partitioning Expression of A load power and standard deviation, the parameter piecemeal of IEEE-RTS system loading normal distribution model is μ 2=0.6142, σ 2=0.1482, l~ N ( μ 2, σ 2 2);
Step 4: build wind speed-load bivariate distribution function
4.1: the binary normal function that builds bivector
For any stochastic variable x 1, x 2if, its piecemeal Normal Distribution, bivector x=( x 1, x 2) t the binary normal distyribution function of obedience formula (7):
Figure 260600DEST_PATH_IMAGE007
(7)
In formula, μ 1, μ 2piecemeal is x 1, x 2mean value, σ 1 2, σ 2 2piecemeal is x 1, x 2variance, ρbe x 1with x 2coefficient correlation, if ρ=0, x 1with x 2separate; If ρ>0, x 1with x 2positive correlation; If ρ<0, x 1with x 2negative correlation;
4.2: build wind speed-load binary normal distyribution function
Wind speed normal distyribution function and load normal distyribution function in step 3, have been built, v~ N ( μ 1, σ 1 2), l~ N ( μ 2, σ 2 2), bivector y=( v, l) t obey binary normal distyribution function, the correlation of the two is by coefficient correlation ρrepresent, wind speed and two seasonal effect in time series coefficient correlations of load are defined as follows:
Figure 459500DEST_PATH_IMAGE008
(8)
In formula, v t with l t piecemeal is wind speed and Load Time Series tvalue constantly; μ 1with μ 2, σ 1with σ 2piecemeal is two seasonal effect in time series mean values and standard deviation; nfor seasonal effect in time series length;
With above-mentioned wind farm wind velocity and IEEE-RTS load data, calculate ρ=0.1755, represent that the two has positive correlation, obtain thus wind speed-load binary normal distyribution function:
Figure 66062DEST_PATH_IMAGE009
(9)
Step 5:, calculate the WTG corresponding with sampled sequence and exert oneself and power supply capacity scope the sequential sampling of wind speed-load by Monte Carlo method, adopt the reliability index of block algorithm computing system;
The 5.1:Monte Carlo methods of sampling is:
1) produce at random two standardized normal distribution random numbers, ω=( v 1, l 1);
2) covariance matrix to wind speed and load Ωdecompose, make Ω= lL t, obtain matrix l;
3) utilize matrix lright ωconvert, make ω '= l ω t + μ t ω ; Wherein, ω t with μ t ω piecemeal is ωwith μ ω transposition, μ ω =( μ 1, μ 2), ω 'be required wind speed-load sequence ( v, l);
4) all wind speed-load sequences that obtain with sampling, obtain corresponding with it binary normal distyribution function according to formula (5)-(9) f 1( v, l), by interval integral, calculate f 1( v, l) with the error of formula (9), if e<10 -3, stop sampling, otherwise repeat above step, until meet error condition;
If frequency in sampling is m, power distribution network is divided into npiece, can obtain by sampling mwind speed-load sequence of the two distribution situation of individual consideration and correlation;
5.2: piecemeal computed reliability index
According to formula (1) and (4), piecemeal calculate each wind speed-load sequence ( v i , l i ) (1≤ i≤ m) corresponding WPG exerts oneself p i with supply district, and by formula (2) and (3) computing block s(1≤ s≤ n) λ s with γ s , piece sannual interruption duration u s shown in (10):
Figure 59425DEST_PATH_IMAGE010
(10)
If piece snumber of users be n s , total load is l s , Reliability Index SAIFI, SAIDI, ASAI and ENS can be obtained by formula (11)-(14):
Figure 706DEST_PATH_IMAGE011
(11)
(12)
(13)
Figure 628630DEST_PATH_IMAGE014
(14)
Step 6: computed reliability index mean value
All various reliability indexs that calculate by step 5 are averaged, finally obtain considering containing WTG the Reliability Index of wind speed-load correlation.
Advantage with respect to prior art:
In the present invention, to containing in the distribution network reliability evaluation method of WTG, considered the impact of wind speed-load correlation and sequential load curve, therefore improved the accuracy to the evaluating reliability of distribution network containing WTG; Adopt binary joint distribution function to resolve simultaneously and describe wind speed-load correlation, make the accuracy of the evaluating reliability of distribution network containing WTG further improve.
Accompanying drawing explanation
Fig. 1-Dutch wind energy turbine set sequential wind speed curve in 1981.
Fig. 2-be IEEE-RTS system year sequential load curve.
Fig. 3-amended RBTS-BUS6 feeder line 4 is winding diagram.
Embodiment
Below to by reference to the accompanying drawings the present invention being described in further detail.
With reference to the accompanying drawings 1 and 2, a kind of containing WTG distribution network reliability evaluation method, gather the initial data of power distribution network; Build WTG exert oneself model and definite wind-powered electricity generation power supply capacity scope; Build wind speed normal distyribution function and load normal distyribution function, by computer system, the analyzing and processing of sequential wind speed and load curve is obtained mean value and the standard deviation of the two normal distyribution function; Build wind speed-load bivariate distribution function; By computer system, adopt Monte Carlo method to the sequential sampling of wind speed-load; By the initial data input computer system of power distribution network, calculate the WTG corresponding with sampled sequence and exert oneself and power supply capacity scope, adopt the reliability index of block algorithm computing system; Each reliability index mean value to gained by computer, finally obtains the distribution network reliability index containing WTG consideration wind speed-load correlation again, and concrete steps comprise:
Step 1: the initial data that gathers power distribution network: Dutch De Kooy wind energy turbine set 1981-2010 sequential air speed data, IEEE-RTS system loading data, each element failure rate of power distribution network, repair time, each component reliability parameter and each load point amount, the incision wind speed of WTG, rated wind speed, cut-out wind speed, the connecting relation in power distribution network between circuit, transformer, switchgear, load point and WTG;
Step 2: build WTG exert oneself model and definite wind-powered electricity generation power supply capacity scope
2.1: build the WTG model of exerting oneself
Adopt quadratic function approximate representation WTG to exert oneself p t with wind speed v t relation:
Figure 60136DEST_PATH_IMAGE001
(1)
In formula, v ci , v r with v co piecemeal is incision wind speed, rated wind speed and the cut-out wind speed of WTG, p r for rated output power;
2.2: determine wind-powered electricity generation power supply capacity scope
Power distribution network is divided into m1 ~ m7 seven piecemeals, establish piecemeal s(1≤ s≤ 7) have mindividual element, and the i(1≤ i≤ m) individual element failure rate and repair time piecemeal be λ i with γ i , piece sequivalent fault rate λ s with equivalent mean time to repair γas follows:
Figure 272943DEST_PATH_IMAGE002
(2)
Figure 916413DEST_PATH_IMAGE003
(3)
If WTG exists tconstantly exert oneself into p t , it is to load piece m i supply path on pass through nindividual piece, the ipiece m i total load be l mi , it can be right m i the condition restoring electricity can be expressed as formula (4):
Figure 438531DEST_PATH_IMAGE004
(4)
By (4) formula, can obtain every kind of WTG order and the ability to load restoration power supply in situation of exerting oneself;
Step 3: build wind speed normal distyribution function and load normal distyribution function, by the analysis of sequential wind speed and load curve being obtained to mean value and the standard deviation of the two normal distyribution function;
3.1: build wind speed normal distyribution function
Formula (5) is Dutch De Kooy wind farm wind velocity normal distyribution function:
Figure 167452DEST_PATH_IMAGE005
(5)
In formula, vrepresent wind speed; μ 1with σ 1the mean value and the standard deviation that represent respectively wind speed, to this wind energy turbine set, its wind speed normal distribution model parameter is: μ 1=5.9826, σ 1=3.17, v~ N ( μ 1, σ 1 2);
3.2: build load normal distyribution function
Formula (6) provides IEEE-RTS system loading normal distyribution function:
Figure 183950DEST_PATH_IMAGE006
(6)
In formula, lrepresent load power; μ 2with σ 2the mean value of Partitioning Expression of A load power and standard deviation, the parameter piecemeal of IEEE-RTS system loading normal distribution model is μ 2=0.6142, σ 2=0.1482, l~ N ( μ 2, σ 2 2);
Step 4: build wind speed-load bivariate distribution function
4.1: the binary normal function that builds bivector
For any stochastic variable x 1, x 2if, its piecemeal Normal Distribution, bivector x=( x 1, x 2) t the binary normal distyribution function of obedience formula (7):
Figure 681927DEST_PATH_IMAGE007
(7)
In formula, μ 1, μ 2piecemeal is x 1, x 2mean value, σ 1 2, σ 2 2piecemeal is x 1, x 2variance, ρbe x 1with x 2coefficient correlation, if ρ=0, x 1with x 2separate; If ρ>0, x 1with x 2positive correlation; If ρ<0, x 1with x 2negative correlation;
4.2: build wind speed-load binary normal distyribution function
Wind speed normal distyribution function and load normal distyribution function in step 3, have been built, v~ N ( μ 1, σ 1 2), l~ N ( μ 2, σ 2 2), bivector y=( v, l) t obey binary normal distyribution function, the correlation of the two is by coefficient correlation ρrepresent, wind speed and two seasonal effect in time series coefficient correlations of load are defined as follows:
Figure 640525DEST_PATH_IMAGE008
(8)
In formula, v t with l t piecemeal is wind speed and Load Time Series tvalue constantly; μ 1with μ 2, σ 1with σ 2piecemeal is two seasonal effect in time series mean values and standard deviation; nfor seasonal effect in time series length;
With above-mentioned De Kooy wind farm wind velocity and IEEE-RTS load data, calculate ρ=0.1755, represent that the two has positive correlation, obtain thus wind speed-load binary normal distyribution function:
Figure 528846DEST_PATH_IMAGE009
(9)
Step 5:, calculate the WTG corresponding with sampled sequence and exert oneself and power supply capacity scope the sequential sampling of wind speed-load by Monte Carlo method, adopt the reliability index of block algorithm computing system;
The 5.1:Monte Carlo methods of sampling is:
1) produce at random two standardized normal distribution random numbers, ω=( v 1, l 1);
2) covariance matrix to wind speed and load Ωdecompose, make Ω= lL t, obtain matrix l;
3) utilize matrix lright ωconvert, make ω '= l ω t + μ t ω ; Wherein, ω t with μ t ω piecemeal is ωwith μ ω transposition, μ ω =( μ 1, μ 2), ω 'be required wind speed-load sequence ( v, l);
4) all wind speed-load sequences that obtain with sampling, obtain corresponding with it binary normal distyribution function according to formula (5)-(9) f 1( v, l), by interval integral, calculate f 1( v, l) with the error of formula (9), if e<10 -3, stop sampling, otherwise repeat above step, until meet error condition;
If frequency in sampling is m, power distribution network is divided into npiece, can obtain by sampling mwind speed-load sequence of the two distribution situation of individual consideration and correlation;
5.2: piecemeal computed reliability index
According to formula (1) and (4), piecemeal calculate each wind speed-load sequence ( v i , l i ) (1≤ i≤ m) corresponding WPG exerts oneself p i with supply district, and by formula (2) and (3) computing block s(1≤ s≤ n) λ s with γ s , piece sannual interruption duration u s shown in (10):
Figure 411352DEST_PATH_IMAGE010
(10)
If piece snumber of users be n s , total load is l s , Reliability Index: 1. SAIFI, i.e. system System average interruption frequency, Suo Xie SAIF, English full name is: system average interruption frequency; 2. SAIDI, system is on average stopped transport the duration, and English full name is: system average interruption duration index; 3. ASAI, the availability factor of on average powering, English full name is: average service availability index; 4. ENS, i.e. system short of electricity amount, English full name is: energy not supplied, SAIFI, SAIDI, ASAI and ENS can be obtained by formula (11)-(14):
(11)
Figure 893335DEST_PATH_IMAGE012
(12)
(13)
Figure 689569DEST_PATH_IMAGE014
(14)
Step 6: computed reliability index mean value
All various reliability indexs that calculate by step 5 are averaged, finally obtain considering containing WTG the Reliability Index of wind speed-load correlation.
Embodiment: add 3 computational analysis after WTG in RBTS-BUS6 feeder line 4 systems, air speed data is taken from De Kooy wind energy turbine set 1981-2010 sequential wind speed.
RBTS-BUS6 feeder line 4 systems have 23 load point, 1183 users, and system peak load is 10.9284MW, and average load is 4.8155MW, and annual gas load curve is taken from IEEE-RTS system year sequential load curve.
Fig. 3 provides and adds 3 RBTS-BUS6 feeder line 4 systems after WTG.Wherein, node 13,22 and 25 is WTG access point, and rated capacity is 1.0MW.WTG cuts wind speed v ci , rated wind speed v r and cut-out wind speed v co piecemeal is 3,13,25m/s, and WTG and distribution access point are equipped with isolating switch.In system, each component reliability parameter and the data based common practise of each load point are determined, the result that wind speed-load joint probability distribution data the present invention the 3rd step is calculated.
(1) impact of WTG access on system reliability while not considering wind speed-load correlation
Reliability Index before and after table 1 access wind-powered electricity generation
Figure 352019DEST_PATH_IMAGE015
Before and after wind-powered electricity generation access, the system ENS index piecemeal of two kinds of load model calculating differs 25.9622 and 25.3635 (MWh/) as shown in Table 1.This shows, load variations is very large to reliability effect, for guaranteeing the accuracy of result of calculation, can not replace load curve by average load.When WTG rated power is 1.0MW while taking into account system annual gas load curve, the access of wind-powered electricity generation makes system ENS index reduce by 3.2254 (MWh/), and SAIDI index reduces by 0.0876 (h/ family), and ASAI index improves 0.00001.This shows: when not considering wind speed-load correlation, the access of WTG improves to some extent system reliability, but effect not obvious.
(2) impact of WTG access on system reliability while considering wind speed-load correlation
Table 2 is considered the Reliability Index contrast of wind speed-load correlation front and back
Figure 403152DEST_PATH_IMAGE016
Table 2 has reflected the impact of wind speed-load correlation on system reliability.Consider that correlation front and back WTG is 3.2254 and 9.0311 (MWh/) to the contribution of system ENS (Δ ENS) piecemeal, while considering correlation, Δ ENS is about 2.8 times while not considering correlation.This shows: wind speed-load correlation is larger on reliability results impact.
(3) impact of coefficient correlation on reliability index
The reliability index of system under the different coefficient correlations of table 3
Figure 328383DEST_PATH_IMAGE017
Table 3 is given under above-mentioned design conditions, and ρ piecemeal is 0,0.2,0.4,0.6,0.8 and the reliability index of 1.0 o'clock systems, and Δ ENS significantly increases with the increase of correlation coefficient ρ, and when ρ=1, Δ ENS is maximum.This shows: wind speed-load correlation is larger on Reliability Index impact.
(4) impact of WTG rated output on Reliability Index
Reliability Index during the different rated output of table 4 WTG
Figure 2012100474259100002DEST_PATH_IMAGE019
Table 4 provides WTG rated output and is respectively 1.0,1.5 and 2.0 MW, the reliability index of system while considering wind speed-load correlation.As shown in Table 4, with the increase of WTG rated output, the every reliability index of system all improves.

Claims (1)

1. containing a WTG distribution network reliability evaluation method, it is characterized in that: the initial data that gathers power distribution network; Build WTG exert oneself model and definite wind-powered electricity generation power supply capacity scope; Build wind speed normal distyribution function and load normal distyribution function, by computer system, the analyzing and processing of sequential wind speed and load curve is obtained mean value and the standard deviation of the two normal distyribution function; Build wind speed-load bivariate distribution function; By computer system, adopt Monte Carlo method to the sequential sampling of wind speed-load; By the initial data input computer system of power distribution network, calculate the WTG corresponding with sampled sequence and exert oneself and power supply capacity scope, adopt the reliability index of block algorithm computing system; Each reliability index mean value to gained by computer, finally obtains the distribution network reliability index containing WTG consideration wind speed-load correlation again, and concrete steps comprise:
Step 1: the initial data that gathers power distribution network: wind energy turbine set sequential air speed data, IEEE-RTS system loading data, each element failure rate of power distribution network, repair time, each component reliability parameter and each load point amount, the incision wind speed of WTG, rated wind speed, cut-out wind speed, the connecting relation in power distribution network between circuit, transformer, switchgear, load point and WTG;
Step 2: build WTG exert oneself model and definite wind-powered electricity generation power supply capacity scope
(1) build the WTG model of exerting oneself
Adopt quadratic function approximate representation WTG to exert oneself p t with wind speed v t relation:
Figure 2012100474259100001DEST_PATH_IMAGE001
(1)
In formula, v ci , v r with v co be respectively incision wind speed, rated wind speed and the cut-out wind speed of WTG, p r for rated output power;
(2) determine wind-powered electricity generation power supply capacity scope
Power distribution network is divided into m1 ~ m7 seven piecemeals, establish piecemeal s(1≤ s≤ 7) have mindividual element, and the i(1≤ i≤ m) be respectively failure rate and repair time of individual element λ i with γ i , piece sequivalent fault rate λ s with equivalent mean time to repair γ s " as follows:
(2)
(3)
If WTG exists tconstantly exert oneself into p t , it is to load piece m i supply path on pass through nindividual piece, the ipiece m i total load be l mi , it can be right m i the condition restoring electricity can be expressed as formula (4):
Figure 816735DEST_PATH_IMAGE004
(4)
By (4) formula, can obtain every kind of WTG order and the ability to load restoration power supply in situation of exerting oneself;
Step 3: build wind speed normal distyribution function and load normal distyribution function, by the analysis of sequential wind speed and load curve being obtained to mean value and the standard deviation of the two normal distyribution function;
(1) build wind speed normal distyribution function
Formula (5) is wind farm wind velocity normal distyribution function:
Figure 2012100474259100001DEST_PATH_IMAGE005
(5)
In formula, vrepresent wind speed; μ 1with σ 1the mean value and the standard deviation that represent respectively wind speed, to this wind energy turbine set, its wind speed normal distribution model parameter is: μ 1=5.9826, σ 1=3.17, v~ N ( μ 1, σ 1 2);
(2) build load normal distyribution function
Formula (6) provides IEEE-RTS system loading normal distyribution function:
Figure 460206DEST_PATH_IMAGE006
(6)
In formula, lrepresent load power; μ 2with σ 2the mean value and the standard deviation that represent respectively load power, the parameter of IEEE-RTS system loading normal distribution model is respectively μ 2=0.6142, σ 2=0.1482, l~ N ( μ 2, σ 2 2);
Step 4: build wind speed-load bivariate distribution function
(1) build the binary normal function of bivector
For any stochastic variable x 1, x 2if it is Normal Distribution, bivector respectively x=( x 1, x 2) t the binary normal distyribution function of obedience formula (7):
Figure 2012100474259100001DEST_PATH_IMAGE007
(7)
In formula, μ 1, μ 2be respectively x 1, x 2mean value, σ 1 2, σ 2 2be respectively x 1, x 2variance, ρbe x 1with x 2coefficient correlation, if ρ=0, x 1with x 2separate; If ρ>0, x 1with x 2positive correlation; If ρ<0, x 1with x 2negative correlation;
(2) build wind speed-load binary normal distyribution function
Wind speed normal distyribution function and load normal distyribution function in step 3, have been built, v~ N ( μ 1, σ 1 2), l~ N ( μ 2, σ 2 2), bivector y=( v, l) t obey binary normal distyribution function, the correlation of the two is by coefficient correlation ρrepresent, wind speed and two seasonal effect in time series coefficient correlations of load are defined as follows:
Figure 608422DEST_PATH_IMAGE008
(8)
In formula, v t with l t be respectively wind speed and Load Time Series tvalue constantly; μ 1with μ 2, σ 1with σ 2be respectively two seasonal effect in time series mean values and standard deviation; nfor seasonal effect in time series length;
With wind farm wind velocity and IEEE-RTS load data, calculate ρ=0.1755, represent that the two has positive correlation, obtain thus wind speed-load binary normal distyribution function:
Figure 2012100474259100001DEST_PATH_IMAGE009
(9)
Step 5:, calculate the WTG corresponding with sampled sequence and exert oneself and power supply capacity scope the sequential sampling of wind speed-load by Monte Carlo method, adopt the reliability index of block algorithm computing system;
(1) the Monte Carlo methods of sampling is:
1) produce at random two standardized normal distribution random numbers, ω=( v 1, l 1);
2) covariance matrix to wind speed and load Ωdecompose, make Ω= lL t, obtain matrix l;
3) utilize matrix lright ωconvert, make ω '= l ω t + μ t ω ; Wherein, ω t with μ t ω be respectively ωwith μ ω transposition, μ ω =( μ 1, μ 2), ω 'be required wind speed-load sequence ( v, l);
4) all wind speed-load sequences that obtain with sampling, obtain corresponding with it binary normal distyribution function according to formula (5)-(9) f 1( v, l), by interval integral, calculate f 1( v, l) with the error of formula (9), if ε <10 -3, stop sampling, otherwise repeat above step, until meet error condition;
If frequency in sampling is m, power distribution network is divided into npiece, can obtain by sampling mwind speed-load sequence of the two distribution situation of individual consideration and correlation;
(2) piecemeal computed reliability index
According to formula (1) and (4), calculate respectively each wind speed-load sequence ( v i , l i ) (1≤ i≤ m) corresponding WTG exerts oneself p i with supply district, and by formula (2) and (3) computing block s(1≤ s≤ n) λ s with γ s , piece sannual interruption duration u s shown in (10):
(10)
If piece snumber of users be n s , total load is l s , Reliability Index SAIFI, SAIDI, ASAI and ENS can be obtained by formula (11)-(14):
Figure 2012100474259100001DEST_PATH_IMAGE011
(11)
Figure 790059DEST_PATH_IMAGE012
(12)
Figure 2012100474259100001DEST_PATH_IMAGE013
(13)
Figure 350354DEST_PATH_IMAGE014
(14)；
Wherein, SAIFI refers to system System average interruption frequency, Suo Xie SAIF, and SAIDI refers to that system on average stops transport the duration, and ASAI refers on average power availability factor, and ENS refers to system short of electricity amount;
Step 6: computed reliability index mean value
All various reliability indexs that calculate by step 5 are averaged, finally obtain considering containing WTG the Reliability Index of wind speed-load correlation.
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