CN103746370A - Wind-power-plant reliability modeling method - Google Patents

Abstract

The invention proposes a wind-power-plant reliability modeling method. The method takes correlation of wind speeds between wind power plants and fault rates of wind turbine generators into consideration at the same time and through generation of related wind speeds in a simulating manner, effects of the correlation of the wind speeds between the wind power plants are taken into consideration and through binomial distribution and Monte Carlo simulation, effects of the fault rates of the wind turbine generators are taken into consideration. The reckoning-in method of the fault rates of the wind turbine generators is simple and easy to be realized through a program. A method based on linear division is used to calculate the lasting time of each equivalent state of the wind power plants so that precision of an output-power multi-state probability model of the wind power plants is improved. The wind-power-plant reliability modeling method is not only applicable to reliability modeling of wind power plants formed by combination of wind turbine generators of the same kind, but also applicable to reliability modeling of wind power plants which are formed through combination of wind turbine generators of different kinds so that reference is provided to reliability assessment of a wind power connected-grid power system through use of an analytical method and a non-sequential Monte Carlo method.

Description

A kind of wind energy turbine set Reliability Modeling

Technical field

The invention belongs to electric power system modeling technique field, especially relate to a kind of wind energy turbine set Reliability Modeling.

Background technology

Traditional primary energy is day by day exhausted, environmental problem is outstanding day by day and the background of the support on policy of national governments to Wind Power Development under, wind power generation has worldwide obtained development fast.With conventional power generation systems, compare, wind energy has the feature of intermittent and fluctuation, the continuous increase of wind-powered electricity generation connecting system ratio brings great challenge will to the safe operation of electrical network, therefore be necessary to carry out the assessment of the power system reliability after wind-powered electricity generation access, to provide reference for the development plan of electric power system and the traffic control of system after extensive wind energy access.

The method of Model in Reliability Evaluation of Power Systems can be divided into analytic method and Monte Carlo method two classes, and wherein monte carlo method is further divided into two kinds of sequential and non-sequential Monte Carlo simulations.When utilizing analytic method or non-sequential Monte Carlo simulation to carry out the reliability assessment of the electric power system after wind-electricity integration, conventionally by a multi-mode conventional unit of the equivalent one-tenth of whole wind energy turbine set.The failure rate of the correlation between multiple wind farm wind velocities and wind-powered electricity generation unit itself is two key factors that affect wind energy turbine set reliability model.The wind energy turbine set reliability model of existing bibliographical information or only considered the correlation of wind speed between wind energy turbine set; Only considered the fault of wind-powered electricity generation unit, but counted the wind energy turbine set modeling method complexity of wind-powered electricity generation unit failure rate, and the wind energy turbine set forming at the wind-powered electricity generation unit by dissimilar, counting of wind-powered electricity generation unit failure rate is especially complicated.

Summary of the invention

Technical problem to be solved by this invention is to overcome the deficiencies in the prior art, has proposed a kind of wind energy turbine set Reliability Modeling.

For solving the problems of the technologies described above, the technical solution adopted in the present invention is:

A kind of wind energy turbine set Reliability Modeling, comprises step as follows:

Steps A, parameter initialization, described parameter comprises:

Wind speed simulation year is counted N _y; The number N of wind energy turbine set _wF;

The model number of the dissimilar wind-powered electricity generation unit that each wind energy turbine set comprises, wherein i wind energy turbine set is by n _ithe wind-powered electricity generation unit composition of individual different model, i=1 ..., N _wF;

The number of units of the wind-powered electricity generation unit of various models in each wind energy turbine set, wherein in i wind energy turbine set, the number of units of j type of wind-powered electricity generation unit is m _ij, j=1 ..., n _i;

The technical parameter of each wind energy turbine set apoplexy group of motors, wherein, in i wind energy turbine set, the technical parameter of the wind-powered electricity generation unit of j kind model comprises, rated power P _{r, ij}, wind-powered electricity generation unit incision wind speed V _{ci, ij}, wind-powered electricity generation unit cut-out wind speed be V _{co, ij}, wind-powered electricity generation unit rated wind speed V _{r, ij}, wind-powered electricity generation unit forced outage rate λ _ij;

Step B, calculates the equivalent status number N of wind energy turbine set _s, its computing formula is Sturgis empirical equation, its expression formula is:

N _s=[1+3.322log(8760×N _y)]

Wherein, [] represents rounding operation;

Step C, according to the history of each wind energy turbine set hour air speed data, obtains the wind speed probability distribution of each wind energy turbine set, calculates the coefficient correlation between each wind farm wind velocity, produces hour wind series of each wind energy turbine set with correlation;

Step D, calculates the cumulative probability of same type wind turbine group fault number of units in each wind energy turbine set;

Wherein, in j type of wind-powered electricity generation unit of i wind energy turbine set, there is the cumulative probability CP of k typhoon group of motors fault _{k, ij}for:

${\mathrm{CP}}_{k,\mathrm{ij}}=\underset{f=0}{\overset{k}{\mathrm{\Σ}}}\left(\begin{array}{c}{m}_{\mathrm{ij}}\\ f\end{array}\right){\mathrm{\λ}}_{\mathrm{ij}}^f{(1-{\mathrm{\λ}}_{\mathrm{ij}})}^{m_{\mathrm{ij}}-f}$

Wherein, k=0,1,2 ..., m _ij;

Step e: hour wind series of each wind energy turbine set and the characteristics of output power of wind-powered electricity generation unit that according to step C, produce, calculate in each wind energy turbine set power output hour sequence P when various types of separate unit wind-powered electricity generation units normally move _{wTG, ij}(t);

Step F: consider wind farm wind velocity correlation and wind-powered electricity generation unit failure rate, calculate the little time series of all wind-powered electricity generation unit power output sums of same type in each wind energy turbine set,

Wherein, the little time series of j type of all wind-powered electricity generation unit power output sums of i wind energy turbine set, its concrete calculation procedure is as follows;

Step F-1: produce equally distributed random number r between [0,1];

Step F-2: by the cumulative probability CP in the random number r in step F-1 and step D _{k, ij}compare; If random number r is between accumulated probability CP _{k, ij}and CP _{k+1, ij}between, the number of defects n of j type of wind-powered electricity generation unit of i wind energy turbine set _{fault, ij}for:

$n_{\mathrm{fault},\mathrm{ij}}=k+\frac{r-{\mathrm{CP}}_{k,\mathrm{ij}}}{{\mathrm{CP}}_{k+1,\mathrm{ij}}-{\mathrm{CP}}_{k,\mathrm{ij}}}$

Step F-3: hour sequence P that calculates all wind-powered electricity generation unit power output sums of j type of i wind energy turbine set _{wF, ij}(t), its computing formula is:

P _WF,ij(t)=(m _ij-n _fault,ij)×P _WTG,ij(t)

Step G: calculate the little time series of power output of each wind energy turbine set,

Wherein, the power output of i wind energy turbine set hour sequence P _{wF, i}(t) computing formula is:

$P_{\mathrm{WF},i}\left(t\right)=\underset{j=1}{\overset{n_i}{\mathrm{\Σ}}}{P}_{\mathrm{WF},\mathrm{ij}}\left(t\right)$

Step H: calculate whole N _wFhour sequence P of individual Power Output for Wind Power Field sum _wF(t), its computing formula is:

$P_{\mathrm{WF}}\left(t\right)=\underset{i=1}{\overset{N_{\mathrm{WF}}}{\mathrm{\Σ}}}{P}_{\mathrm{WF},i}\left(t\right)$

Step I: the little time series of the Power Output for Wind Power Field of step H gained is carried out to linear partition, obtain multimode power output probabilistic model, its concrete steps are as follows:

Step I-1: the rated output power sum of all wind-powered electricity generation units of whole wind energy turbine set is divided into N _sindividual state, wherein the rated output power AS of l state _lfor:

${\mathrm{AS}}_l=\frac{\underset{i=1}{\overset{N_{\mathrm{WF}}}{\mathrm{\Σ}}}\underset{j=1}{\overset{n_i}{\mathrm{\Σ}}}{P}_{R,\mathrm{ij}}{m}_{\mathrm{ij}}}{N_s-1}\×(l-1)$

Wherein, l=1 ..., N _s;

Step I-2: by whole Power Output for Wind Power Field sum P of t hour _wF(t) duration is divided into respectively state l and state l+1; Wherein, the duration that is divided into state l is Δ T _l,t, the duration that is divided into state l+1 is Δ T _{l+1, t}; Δ T _l,twith Δ T _{l+1, t}computing formula respectively as follows:

If P _wF(t) ∈ [AS _l, AS _l+1]:

$\mathrm{\Δ}{T}_{l,t}=\frac{{\mathrm{AS}}_{l+1}-P_{\mathrm{WF}}\left(t\right)}{{\mathrm{AS}}_{l+1}-{\mathrm{AS}}_l}$

$\mathrm{\Δ}{T}_{l+1,t}=\frac{P_{\mathrm{WF}}\left(t\right)-{\mathrm{AS}}_l}{{\mathrm{AS}}_{l+1}-{\mathrm{AS}}_l}$

If $P_{\mathrm{WF}}\left(t\right)\∉[{\mathrm{AS}}_l,{\mathrm{AS}}_{l+1}],$ :

ΔT _l,t=0

ΔT _l+1,t=0

The duration T of step I-3: computing mode l _l, its computing formula is:

$T_l=\underset{t=1}{\overset{T}{\mathrm{\Σ}}}\mathrm{\Δ}{T}_{l,t}$

Wherein T refers to the lasting hourage of Power Output for Wind Power Field, i.e. T=8760 × N _y;

Step I-4: calculate the probability that l state occurs

, its computing formula is:

${\mathrm{EP}}_{{\mathrm{AS}}_l}=\frac{T_l}{T}.$

The invention has the beneficial effects as follows: the present invention proposes a kind of wind energy turbine set Reliability Modeling, described method is considered correlation and the wind-powered electricity generation unit failure rate of wind speed between wind energy turbine set simultaneously, the impact that is produced relevant wind speed and counted wind speed correlation between wind energy turbine set by simulation, has counted the impact of wind-powered electricity generation unit failure rate by binomial distribution and Monte Carlo simulation; Wind-powered electricity generation unit failure rate to count method simple, the program that is easy to realizes; Use a kind of method based on linear partition to calculate the duration of each state of value such as grade of wind energy turbine set, improved the precision of Power Output for Wind Power Field multimode probabilistic model.The present invention is not only applicable to the Reliability modeling of the wind energy turbine set being comprised of the wind-powered electricity generation unit of same type, also be applicable to the Reliability modeling of the wind energy turbine set being formed by dissimilar wind-powered electricity generation unit, for the reliability assessment that utilizes analytic method and non-sequential Monte Carlo method to carry out wind-electricity integration electric power system provides reference.

Accompanying drawing explanation

Fig. 1 is wind energy turbine set Reliability Modeling flow chart of the present invention.

Fig. 2 is in specific embodiment, calculates the flow chart of the cumulative probability of same type wind turbine group fault number of units in each wind energy turbine set.

Fig. 3 is while considering wind speed correlation and wind-powered electricity generation unit failure rate, calculates the flow chart of the little time series of whole Power Output for Wind Power Field sums.

Fig. 4 is the flow chart that calculates the probability of each state of value such as grade of wind energy turbine set based on linear partition method.

Embodiment

Below in conjunction with accompanying drawing, a kind of wind energy turbine set Reliability Modeling that the present invention is proposed is elaborated:

As shown in Figure 1, be the flow chart of wind energy turbine set Reliability Modeling, a kind of wind energy turbine set Reliability Modeling, comprises that step is as follows:

Steps A, parameter initialization, described parameter comprises:

Wind speed simulation year is counted N _y; The number N of wind energy turbine set _wF;

The model number of the dissimilar wind-powered electricity generation unit that each wind energy turbine set comprises, wherein i wind energy turbine set is by n _ithe wind-powered electricity generation unit composition of individual different model, i=1 ..., N _wF;

The number of units of the wind-powered electricity generation unit of various models in each wind energy turbine set, wherein in i wind energy turbine set, the number of units of the wind-powered electricity generation unit of j type is m _ij, j=1 ..., n _i;

The technical parameter of each wind energy turbine set apoplexy group of motors, wherein in i wind energy turbine set, the technical parameter of the wind-powered electricity generation unit of j kind model comprises, rated power P _{r, ij}, wind-powered electricity generation unit incision wind speed V _{ci, ij}, wind-powered electricity generation unit cut-out wind speed be V _{co, ij}, wind-powered electricity generation unit rated wind speed V _{r, ij}, wind-powered electricity generation unit forced outage rate λ _ij;

Step B, calculates the equivalent status number N of wind energy turbine set _s, its computing formula is Sturgis empirical equation, its expression formula is:

N _s=[1+3.322log(8760×N _y)] （1）

Wherein, [] represents rounding operation;

Step C, according to the history of each wind energy turbine set hour air speed data, obtains the wind speed probability distribution of each wind energy turbine set, calculates the coefficient correlation between each wind farm wind velocity, produces hour wind series of each wind energy turbine set with correlation;

An embodiment of step C, its concrete steps are as follows:

Step C-1: the wind speed of supposing each wind energy turbine set is all obeyed Weibull distribution, according to the history of each wind energy turbine set hour air speed data, estimates the Weibull distribution parameters c of each wind farm wind velocity _iand k _ithereby, obtaining the Weibull distribution model of each wind farm wind velocity, expression is

$f\left(v_i\right)=\left(\frac{k_i}{c_i}\right){\left(\frac{v_i}{c_i}\right)}^{k_i-1}\exp [-{\left(\frac{v_i}{c_i}\right)}^{k_i}]---\left(2\right)$

$F\left(v_i\right)={\∫}_0^{v_x}f\left(v_i\right){\mathrm{dv}}_i=1-\exp [-{\left(\frac{v_i}{c_i}\right)}^{k_i}]---\left(3\right)$

Wherein v _ibe the wind speed variable of i wind energy turbine set, f (v _i), F (v _i) be respectively v _iprobability density function and cumulative distribution function.

Step C-2: use inverse transformation method, the cumulative distribution function of each wind farm wind velocity is carried out to inverse transformation and obtain without wind speed correlation properties, that obey each wind energy turbine set of Weibull distribution, the wherein wind series v of i wind energy turbine set _icomputing formula be

$v_i=c_i{[-\ln (1-R_i)]}^{1/k_i}---\left(4\right)$

R in formula _ifor equally distributed random number sequence between [0,1] that generates, the length of sequence is 8760N _y.

Step C-3: according to the history of each wind energy turbine set hour air speed data, calculate the Pearson correlation coefficient of the wind speed between wind energy turbine set, obtain the correlation matrix A of wind farm wind velocity.

Step C-4: to the wind speed column vector producing in step C-2

carry out standardization and obtain the wind speed column vector z after standardization, computing formula is:

$z=\frac{x-u}{\mathrm{\σ}}---\left(5\right)$

In formula, u is N _wFindividual wind energy turbine set 8760N _ythe column vector that the mean value of individual hour wind series forms, σ is N _wFindividual wind energy turbine set 8760N _ythe column vector that the standard deviation of individual hour wind series forms.

Step C-5: the correlation matrix A between the wind energy turbine set of step C-3 gained is carried out to Choleskey decomposition, and its expression formula is:

A=LL ^T （6）

Wherein, L is upper triangular matrix, and diagonal element be on the occasion of;

Step C-6: the wind speed column vector ν that calculates each wind energy turbine set with associate feature _re, its expression formula is:

ν _re=Lz+u （7）

Another embodiment of step C is that its concrete steps are as follows based on the theoretical wind speed of being correlated with that produces of copula:

Step C-1: according to the history of each wind energy turbine set hour air speed data, the method that adopts norm of nonparametric kernel density to estimate is determined the edge distribution of each wind farm wind velocity, calculates the coefficient correlation between wind farm wind velocity;

Step C-2: based on the beeline method of experience copula function and theoretical copula function, select optimum copula function;

Step C-3: according to the coefficient correlation between the wind farm wind velocity of step C-1 calculating gained, adopt Maximum Likelihood Estimation Method, the unknown parameter in copula function is carried out to parameter Estimation.

Step C-4: according to constructed optimum copula function, adopt condition sampling to produce each wind energy turbine set hour wind series with correlation.

Step D, calculates the cumulative probability of same type wind turbine group fault number of units in each wind energy turbine set; As shown in Figure 2, calculate in the wind-powered electricity generation unit of j type of i wind energy turbine set, occur k platform (k=0,1,2 ..., m _ij) the cumulative probability CP of wind-powered electricity generation unit fault _{k, ij}detailed process as follows;

Step D-1, calculates the definite probability EP that has k typhoon group of motors to break down in the wind-powered electricity generation unit of j type of i wind energy turbine set _{k, ij}, its computing formula is as follows:

${\mathrm{EP}}_{k,\mathrm{ij}}=\left(\begin{array}{c}{m}_{\mathrm{ij}}\\ k\end{array}\right){\mathrm{\λ}}_{\mathrm{ij}}^k{(1-{\mathrm{\λ}}_{\mathrm{ij}})}^{m_{\mathrm{ij}}-k}---\left(8\right)$

Step D-2: the EP of gained in determining step D-1 _{k, ij}whether be less than the minimum ε of appointment, if not, proceed to step D-3; If so, ignore the failure condition that wind-powered electricity generation unit CCS casual clearing station number is greater than k.

Step D-3: calculated the cumulative probability CP that has k typhoon group of motors to break down in the wind-powered electricity generation unit of j type of i wind energy turbine set by formula (9) _{k, ij};

CP _k,ij=CP _k-1,ij+EP _k,ij （9）

Especially, ${\mathrm{<figure-callout\; id="0"\; label="CP"\; filenames="HDA0000442619270000021.png,HDA0000442619270000031.png"\; state="\{\{state\}\}">CP</figure-callout>}}_{0,\mathrm{ij}}={\mathrm{<figure-callout\; id="0"\; label="EP"\; filenames="HDA0000442619270000021.png,HDA0000442619270000031.png"\; state="\{\{state\}\}">EP</figure-callout>}}_{0,\mathrm{ij}}={(1-{\mathrm{\&lambda;}}_{\mathrm{ij}})}^{m_{\mathrm{ij}}}$

Step e: according to the characteristics of output power of hour wind series of each wind energy turbine set of step C gained and wind-powered electricity generation unit, calculate in each wind energy turbine set power output hour sequence P when various types of separate unit wind-powered electricity generation units normally move _{wTG, ij}(t), concrete computing formula is

$P_{\mathrm{WTG},\mathrm{ij}}\left(t\right)=\left\{\begin{array}{cc}0,& 0\&le;v_{\mathrm{re},i}\left(t\right)<V_{\mathrm{ci},\mathrm{ij}}\\ P_{R,\mathrm{ij}}\frac{v_{\mathrm{re},i}\left(t\right)-V_{\mathrm{ci},\mathrm{ij}}}{V_{R,\mathrm{ij}}-V_{\mathrm{ci},\mathrm{ij}}},& V_{\mathrm{ci},\mathrm{ij}}\&le;v_{\mathrm{re},i}\left(t\right)<V_{R,\mathrm{ij}}\\ P_{R,\mathrm{ij}},& V_{R,\mathrm{ij}}\&le;v_{\mathrm{re},i}\left(t\right)<V_{\mathrm{co},\mathrm{<figure-callout\; id="0"\; label="ij"\; filenames="HDA0000442619270000021.png,HDA0000442619270000031.png"\; state="\{\{state\}\}">ij</figure-callout>}}\\ 0,& v_{\mathrm{re},i}\left(t\right)>V_{\mathrm{co},\mathrm{ij}}\end{array}\right.---\left(10\right)$

V in formula _{re, i}(t) wind speed in i the wind energy turbine set t moment with associate feature of expression step C gained.

Step F: consider wind farm wind velocity correlation and wind-powered electricity generation unit failure rate, calculate the little time series of all wind-powered electricity generation unit power output sums of same type in each wind energy turbine set,

Wherein, the calculation procedure of the little time series of all wind-powered electricity generation unit power output sums of j type of i wind energy turbine set is as follows;

Step F-1: produce equally distributed random number r between [0,1].

Step F-2: by the cumulative probability CP in the random number r in step F-1 and step D-3 _{k, ij}compare; If random number r is between accumulated probability CP _{k, ij}and CP _{k+1, ij}between, the number of defects n of the wind-powered electricity generation unit of j type of i wind energy turbine set _{fault, ij}for:

$n_{\mathrm{fault},\mathrm{ij}}=k+\frac{r-{\mathrm{CP}}_{k,\mathrm{ij}}}{{\mathrm{CP}}_{k+1,\mathrm{ij}}-{\mathrm{CP}}_{k,\mathrm{ij}}}---\left(11\right)$

Wherein, k be in step D-3 with cumulative probability CP _{k, ij}corresponding wind-powered electricity generation unit CCS casual clearing station number.

Step F-3: hour sequence P that calculates all wind-powered electricity generation unit power output sums of j type of i wind energy turbine set _{wF, ij}(t), its computing formula is:

P _WF,ij(t)=(m _ij-n _fault,ij)×P _WTG,ij(t) （12）

Step G: calculate the little time series of power output of each wind energy turbine set,

Wherein, i (i=1 ..., N _wF) hour sequence P of Power Output for Wind Power Field _{wF, i}(t) computing formula is:

$P_{\mathrm{WF},i}\left(t\right)=\underset{j=1}{\overset{n_i}{\mathrm{\&Sigma;}}}{P}_{\mathrm{WF},\mathrm{ij}}\left(t\right)---\left(13\right)$

Step H: calculate whole N _wFhour sequence P of individual Power Output for Wind Power Field sum _wF(t), its computing formula is:

$P_{\mathrm{WF}}\left(t\right)=\underset{i=1}{\overset{N_{\mathrm{WF}}}{\mathrm{\&Sigma;}}}{P}_{\mathrm{WF},i}\left(t\right)---\left(14\right)$

As shown in Figure 3, provided the 8760 × N that is obtained whole Power Output for Wind Power Field sums by step e to step H _ythe flow chart of individual one hour rated output sequential value.

Step I: as shown in Figure 4, the little time series of the Power Output for Wind Power Field of step H gained is carried out to linear partition, obtain multimode power output probabilistic model, its concrete steps are as follows:

Step I-1: the rated output power sum of all wind-powered electricity generation units of whole wind energy turbine set is divided into N _sindividual state, wherein the rated output power AS of l state _lfor:

Step I-2: by whole Power Output for Wind Power Field sum P of t hour _wF(t) duration is divided into respectively state l and state l+1; Wherein, the duration that is divided into state l is Δ T _l,t, the duration that is divided into state l+1 is Δ T _{l+1, t}; Δ T _l,twith Δ T _{l+1, t}computing formula respectively as follows:

If P _wF(t) ∈ [AS _l, AS _l+1]:

$\mathrm{\&Delta;}{T}_{l,t}=\frac{{\mathrm{AS}}_{l+1}-P_{\mathrm{WF}}\left(t\right)}{{\mathrm{AS}}_{l+1}-{\mathrm{AS}}_l}---\left(16\right)$

$\mathrm{\&Delta;}{T}_{l+1,t}=\frac{P_{\mathrm{WF}}\left(t\right)-{\mathrm{AS}}_l}{{\mathrm{AS}}_{l+1}-{\mathrm{AS}}_l}---\left(17\right)$

If $P_{\mathrm{WF}}\left(t\right)\&NotElement;[{\mathrm{AS}}_l,{\mathrm{AS}}_{l+1}],$ :

ΔT _l,t=0 （18）

ΔT _l+1,t=0 （19）

The duration T of step I-3: computing mode l _l, its computing formula is:

$T_l=\underset{t=1}{\overset{T}{\mathrm{\&Sigma;}}}\mathrm{\&Delta;}{T}_{l,t}---\left(20\right)$

Wherein T refers to the lasting hourage of Power Output for Wind Power Field, i.e. T=8760 × N _y.

Step I-4: calculate the probability that l state occurs , its computing formula is:

${\mathrm{EP}}_{{\mathrm{AS}}_l}=\frac{T_l}{T}---\left(21\right)$

Claims (1)

1. a wind energy turbine set Reliability Modeling, is characterized in that, comprises step as follows:

Steps A, parameter initialization, described parameter comprises:

Wind speed simulation year is counted N _y; The number N of wind energy turbine set _wF;

The model number of the dissimilar wind-powered electricity generation unit that each wind energy turbine set comprises, wherein i wind energy turbine set is by n _ithe wind-powered electricity generation unit composition of individual different model, i=1 ..., N _wF;

The number of units of the wind-powered electricity generation unit of various models in each wind energy turbine set, wherein in i wind energy turbine set, the number of units of j type of wind-powered electricity generation unit is m _ij, j=1 ..., n _i;

The technical parameter of each wind energy turbine set apoplexy group of motors, wherein, in i wind energy turbine set, the technical parameter of the wind-powered electricity generation unit of j kind model comprises, rated power P _{r, ij}, wind-powered electricity generation unit incision wind speed V _{ci, ij}, wind-powered electricity generation unit cut-out wind speed be V _{co, ij}, wind-powered electricity generation unit rated wind speed V _{r, ij}, wind-powered electricity generation unit forced outage rate λ _ij;

Step B, calculates the equivalent status number N of wind energy turbine set _s, its computing formula is Sturgis empirical equation, its expression formula is:

N _s=[1+3.322log(8760×N _y)]

Wherein, [] represents rounding operation;

Step C, according to the history of each wind energy turbine set hour air speed data, obtains the wind speed probability distribution of each wind energy turbine set, calculates the coefficient correlation between each wind farm wind velocity, produces hour wind series of each wind energy turbine set with correlation;

Step D, calculates the cumulative probability of same type wind turbine group fault number of units in each wind energy turbine set;

Wherein, in j type of wind-powered electricity generation unit of i wind energy turbine set, there is the cumulative probability CP of k typhoon group of motors fault _{k, ij}for:

${\mathrm{CP}}_{k,\mathrm{ij}}=\underset{f=0}{\overset{k}{\mathrm{\&Sigma;}}}\left(\begin{array}{c}{m}_{\mathrm{ij}}\\ f\end{array}\right){\mathrm{\&lambda;}}_{\mathrm{ij}}^f{(1-{\mathrm{\&lambda;}}_{\mathrm{ij}})}^{m_{\mathrm{ij}}-f}$

Wherein, k=0,1,2 ..., m _ij;

Step e: hour wind series of each wind energy turbine set and the characteristics of output power of wind-powered electricity generation unit that according to step C, produce, calculate in each wind energy turbine set power output hour sequence P when various types of separate unit wind-powered electricity generation units normally move _{wTG, ij}(t);

Step F: consider wind farm wind velocity correlation and wind-powered electricity generation unit failure rate, calculate the little time series of all wind-powered electricity generation unit power output sums of same type in each wind energy turbine set,

Wherein, the little time series of all wind-powered electricity generation unit power output sums that i wind energy turbine set is j type, its concrete calculation procedure is as follows;

Step F-1: produce equally distributed random number r between [0,1];

Step F-2: by the cumulative probability CP in the random number r in step F-1 and step D _{k, ij}compare; If random number r is between accumulated probability CP _{k, ij}and CP _{k+1, ij}between, the number of defects n of j type of wind-powered electricity generation unit of i wind energy turbine set _{fault, ij}for:

$n_{\mathrm{fault},\mathrm{ij}}=k+\frac{r-{\mathrm{CP}}_{k,\mathrm{ij}}}{{\mathrm{CP}}_{k+1,\mathrm{ij}}-{\mathrm{CP}}_{k,\mathrm{ij}}}$

Step F-3: hour sequence P that calculates all wind-powered electricity generation unit power output sums of j type of i wind energy turbine set _{wF, ij}(t), P _{wF, ij}(t) computing formula is:

P _WF,ij(t)=(m _ij-n _fault,ij)×P _WTG,ij(t)

Step G: calculate the little time series of power output of each wind energy turbine set,

Wherein, the power output of i wind energy turbine set hour sequence P _{wF, i}(t) computing formula is:

$P_{\mathrm{WF},i}\left(t\right)=\underset{j=1}{\overset{n_i}{\mathrm{\&Sigma;}}}{P}_{\mathrm{WF},\mathrm{ij}}\left(t\right)$

Step H: calculate whole N _wFhour sequence P of individual Power Output for Wind Power Field sum _wF(t), its computing formula is:

$P_{\mathrm{WF}}\left(t\right)=\underset{i=1}{\overset{N_{\mathrm{WF}}}{\mathrm{\&Sigma;}}}{P}_{\mathrm{WF},i}\left(t\right)$

Step I: the little time series of the Power Output for Wind Power Field of step H gained is carried out to linear partition, obtain multimode power output probabilistic model, its concrete steps are as follows:

Step I-1: the rated output power sum of all wind-powered electricity generation units of whole wind energy turbine set is divided into N _sindividual state, wherein the rated output power AS of l state _lfor:

${\mathrm{AS}}_l=\frac{\underset{i=1}{\overset{N_{\mathrm{WF}}}{\mathrm{\&Sigma;}}}\underset{j=1}{\overset{n_i}{\mathrm{\&Sigma;}}}{P}_{R,\mathrm{ij}}{m}_{\mathrm{ij}}}{N_s-1}\&times;(l-1)$

Wherein, l=1 ..., N _s;

Step I-2: by whole Power Output for Wind Power Field sum P of t hour _wF(t) duration is divided into respectively state l and state l+1; Wherein, the duration that is divided into state l is Δ T _l,t, the duration that is divided into state l+1 is Δ T _{l+1, t}; Δ T _l,twith Δ T _{l+1, t}computing formula respectively as follows:

If P _wF(t) ∈ [AS _l, AS _l+1]:

$\mathrm{\&Delta;}{T}_{l,t}=\frac{{\mathrm{AS}}_{l+1}-P_{\mathrm{WF}}\left(t\right)}{{\mathrm{AS}}_{l+1}-{\mathrm{AS}}_l}$

$\mathrm{\&Delta;}{T}_{l+1,t}=\frac{P_{\mathrm{WF}}\left(t\right)-{\mathrm{AS}}_l}{{\mathrm{AS}}_{l+1}-{\mathrm{AS}}_l}$

If $P_{\mathrm{WF}}\left(t\right)\&NotElement;[{\mathrm{AS}}_l,{\mathrm{AS}}_{l+1}],$ :

ΔT _l,t=0

ΔT _l+1,t=0

The duration T of step I-3: computing mode l _l, its computing formula is:

$T_l=\underset{t=1}{\overset{T}{\mathrm{\&Sigma;}}}\mathrm{\&Delta;}{T}_{l,t}$

Wherein T refers to the lasting hourage of Power Output for Wind Power Field, i.e. T=8760 × N _y;

Step I-4: calculate the probability that l state occurs , its computing formula is:

${\mathrm{EP}}_{{\mathrm{AS}}_l}=\frac{T_l}{T}.$

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