CN102842105A - Online transient state stability risk evaluating method for metering wind power uncertainty - Google Patents

Abstract

The invention relates to an online transient state stability risk evaluating method for metering wind power uncertainty. The method comprises the following steps of: determining a wind power ultra short-term prediction value and confidence interval evaluation; determining basic conditions of transient state stability probability calculation; determining the transient state stability fault probability; judging whether the transient state instability happens; evaluating the transient state stability fault consequence severity; evaluating the transient state instability fault consequence severity; calculating transient state stability risk evaluation indexes; judging whether all line transient state risk evaluation indexes are calculated; and outputting the transient state stability risk evaluation index values. Through adoption of the method, the transient state stability evaluation is carried on a power transmission line or even the whole system, the probability of the system transient state stability and the consequence caused are well combined organically, and the system operation decision is carried out by scheduling operation personnel according to various transient state stability risk indexes obtained.

Description

A kind of probabilistic transient stability risk of wind-powered electricity generation online evaluation method of taking into account

Technical field

The present invention relates to the online evaluation method, be specifically related to a kind of probabilistic transient stability risk of wind-powered electricity generation online evaluation method of taking into account.

Background technology

The general Deterministic Methods that adopts of electric system on-line transient stability assessment.On the one hand, Deterministic Methods has been ignored the uncertain factor that exists in the real system, assess the transient stability of current system with major accident standard, and its conclusion is normally conservative; On the other hand, the running status of artificial appointment and event of failure might not cause severest consequences.Particularly after large-scale wind power is concentrated connecting system; Characteristics such as its randomness and undulatory property make electric system be in more under the uncertain background; Cause the transient stability problem of electrical network also to have uncertainty, the operating pressure of electrical network strengthens, and the control difficulty increases.Therefore, press for and introduce new theory and research means, improve the ability of Operation of Electric Systems reply transient stability.

The consequence that the probability that the incident of can taking all factors into consideration risk assessment technology takes place and incident take place, this method has been successfully applied to a plurality of fields such as finance, space flight, nuclear power, as to stock exchange, credit evaluation, the congruent aspect of nuclear power station safety.IEEE is defined as the probability of accident generation and the product of the consequence that accident produces with risk, and the research of assessing for transient stability provides new direction.

The continuous increase of the wind-powered electricity generation installed capacity and the amount of generating electricity by way of merging two or more grid systems, its uncertainty is more obvious to the power system transient stability influence.If can compare prediction exactly to ultra short-term wind power; Then the management and running personnel can exert oneself according to wind-powered electricity generation and predict the outcome and combine ultra-short term; Power systems is carried out online evaluation; Real-time adjustment operation plan is stabilized the impact that uncertainty that wind-powered electricity generation exerts oneself is brought to electrical network.

Summary of the invention

Deficiency to prior art; The present invention provides a kind of probabilistic transient stability risk of wind-powered electricity generation online evaluation method of taking into account; This method is utilized chaos time Phase Space Theory; Foundation is based on the ultrashort phase forecast model of the wind power of chaos time sequence, and based on normal distribution model, sets up the probability distribution function of wind power predicated error; Secondly on the basis of analysis circuit probability of malfunction, set up circuit transient stability probability model; Propose transient stability risk consequence comprehensive estimation method, and provide corresponding Practical Mathematical Model; Provided at last and taken into account the probabilistic electric power system transient stability risk of wind-powered electricity generation online evaluation calculation process, the different running method that wherein forms with wind power prediction fiducial interval is represented the probabilistic influence of wind-powered electricity generation.

The objective of the invention is to adopt following technical proposals to realize:

A kind of probabilistic transient stability risk of wind-powered electricity generation online evaluation method of taking into account, its improvements are that said method comprises the steps:

1) confirms ultrashort phase predicted value of wind power and Estimating Confidence Interval thereof;

2) confirm the basic condition of transient stability probability calculation;

3) confirm the transient stability probability of malfunction;

4) judge whether to take place the transient state unstability;

5) transient stability failure effect severity is assessed

6) the transient state consequence of failure order of severity is assessed;

7) calculate transient stability risk assessment index;

8) judge whether that all circuit transient state risk assessment indexs all calculate;

9) output transient stability risk assessment desired value.

Wherein, in the said step 1),, utilize wide area monitoring system WAMS real-time measurement wind power data and wind power historical data, calculate wind power predicted value P based on the chaos time sequence model _Pred(t); Obtain wind power predicted value P according to power prediction probability of error Distribution calculation _Pred(t) fiducial interval; The said ultrashort phase referred to 15 minutes.

Wherein, said chaos time sequence model is based on phase space reconfiguration, utilizes the weighing first order local area predicted method to obtain the ultrashort phase predictor formula of wind power.

Wherein, adopt the wind energy turbine set relative error statistics power prediction probability of error to distribute; Said wind energy turbine set relative error e _tBe t wind power predicted value P constantly _Meas(t) with wind power measured value P _Pred(t) poor, promptly use following (19) formula to represent:

e _t＝P _pred(t)-P _meas(t) （19）。

Wherein, the said fiducial interval that calculates the ultrashort phase predicted value of wind power comprises the steps:

A, judgement wind power predicted value P _Pred(t) affiliated power range;

B, search the corresponding probability of error densimetric curve of said power range;

C, confirm the power probability density;

D, seek accumulated probability more than or equal to the interval of fiducial interval degree 1-α as fiducial interval.

Wherein, among the said step a, said power range comprises ten sections of five equilibrium.

Wherein, among the said step b, said probability of error densimetric curve is normal distribution curve, and said probability of error density function is represented with following (23) formula:

$f\left(x\right)=\frac{1}{\sqrt{2\mathrm{\π}}{\mathrm{\σ}}_f}{e}^{-\frac{{(x-{\mathrm{\μ}}_f)}^2}{2{{\mathrm{\σ}}_f}^2}}---\left(23\right);$

In the formula: μ _fBe expectation of a random variable; σ _fStandard deviation for stochastic variable.

Wherein, among the said step c, confirm the power probability density of each power range according to (23) formula.

Wherein, said step 2) in, the basic condition of said transient stability probability calculation comprises wind power predicted value P _Pred(t), predicted power fiducial interval higher limit P _Pred(t) _Min, predicted power fiducial interval lower limit P _Pred(t) _Max, ultra-short term, network topology structure and each generator state.

Wherein, transient stability probability packet vinculum road fault probability of happening, line fault type probability and line fault location probability.

Wherein, if the fault that circuit takes place is permanent fault, and the probability that two circuits break down simultaneously disregards, and the probability that then breaks down on the circuit Lk is represented with following (24) formula:

$P_r\left(L_k\right){=1-e}^{-{\mathrm{\λ}}_{k}t}(k=\mathrm{1,2},...,m)---\left(24\right);$

In the formula: P _r(L _k) probability of malfunction that takes place for circuit; λ _kCircuit L in the express time section _kFailure-frequency; T representes trouble duration; M is a natural number.

Wherein, said fault type is divided into three-phase ground connection, two phase ground, phase fault and single-phase earthing by the order of severity; Fault type is C _jThe frequency that takes place of fault be f _i, then the probability of its generation is represented with following (25) formula:

$P_r\left(C_j\right)=\frac{f_j}{\underset{i=1}{\overset{4}{\mathrm{\Σ}}}{f}_j}(j=\mathrm{1,2},...,4)---\left(25\right);$

And have:

$\underset{j=1}{\overset{4}{\mathrm{\Σ}}}{P}_r\left(C_j\right)=1---\left(26\right);$

In the formula: P _r(C _j) be the fault type probability of circuit; f _iFrequency for the line fault generation.

Wherein, if the total length of circuit is L, the trouble spot is L to the distance of circuit head end _d, defining variable D:

$D=\frac{L_d}{L}---\left(27\right);$

Said line fault location probability is represented with following (28) formula:

$\underset{h=1}{\overset{n_d}{\mathrm{\Σ}}}{P}_r\left(D_h\right)=1---\left(28\right);$

In the formula: P _r(D _h) expression circuit on h probability that breaks down in the trouble spot; n _dDiscrete trouble spot sum on the expression circuit.

Wherein, in the said step 3), if line fault probability of happening, line fault type probability and line fault location probability are separate and be independent of each other, transient stability probability of malfunction P then _r(E _i) represent with following (29) formula:

$P_r\left(E_i\right)=\underset{j=1}{\overset{4}{\mathrm{\Σ}}}\underset{k=1}{\overset{m}{\mathrm{\Σ}}}\underset{h=1}{\overset{n_d}{\mathrm{\Σ}}}{P}_r\left(D_h\right)P_r\left(C_j\right)P_r\left(L_k\right)---\left(29\right);$

In the formula: P _r(L _k) probability that takes place for line fault; P _r(C _j) be line fault type probability, P _r(D _h) be the line fault location probability.

Wherein, in the said step 4),, the transient state unstability then carries out step 6) if taking place; If the transient state unstability does not take place, but the fault that causes the transient state unstability is arranged, then carry out step 5).

Wherein, in the said step 5), said transient stability failure effect severity comprises merit angle unstability severity, variation severity and frequency shift (FS) severity.

Wherein, establishing electric system has M platform generator, and i kind fault takes place on the j bar circuit, and then unstability severity in merit angle is represented with following (30) formula:

$S_{\mathrm{\Δ\δ}}\left(E_i\right)=\underset{m=1}{\overset{M}{\mathrm{\Σ}}}S(E_i,{\mathrm{\Δ\δ}}_{\mathrm{im},\max })---\left(30\right);$

In the formula: E _iBe i fault model; S (E _i, △ δ _{Im, max}) be m platform generator's power and angle unstability severity; △ δ _{Im, max}Be to depart from the maximum generator's power and angle in the generator center of inertia between i age at failure;

The stable severity function of generator's power and angle is represented with following (31) formula:

$S_{\mathrm{\&Delta;\&delta;}}=\left\{\begin{array}{cc}0,& \mathrm{\&Delta;\&delta;}\&le;0.6\\ 2.5\mathrm{\&Delta;\&delta;}-1.5,& 0.6<\mathrm{\&Delta;\&delta;}\&le;1\\ 1,& \mathrm{\&Delta;\&delta;}\&GreaterEqual;1\end{array}\right.---\left(31\right).$

Wherein, establishing electric system has Y bar bus, and i kind fault takes place on j bar circuit, and then transient voltage skew severity is represented with following (32) formula:

$S_u\left(E_i\right)=\underset{y=1}{\overset{Y}{\mathrm{\&Sigma;}}}S(E_i,u_{\mathrm{iy},\max })---\left(32\right);$

In the formula: E _iBe i fault model; S (E _i, u _{Iy, max}) be y bar busbar voltage skew severity; u _{Iy, max}It is y bar bus peak excursion magnitude of voltage after i the fault clearance;

Transient voltage skew severity function is represented with following (33) formula:

$S_u=\left\{\begin{array}{cc}1,& u\&le;0.75\\ -4u+4,& 0.75<u\&le;1\\ 10u-9,& 1<u\&le;1.1\\ 1,& u>1.1\end{array}\right.$

$\left(33\right).$

Wherein, establishing electric system has S platform generator, and i kind fault takes place on the j bar circuit, then defines the frequency shift (FS) severity and does

$S_f\left(E_i\right)=\underset{s=1}{\overset{S}{\mathrm{\&Sigma;}}}S(E_i,f_{\mathrm{is},\max })---\left(34\right);$

In the formula: E _iBe i fault model; S (E _i, f _{Is, max}) be s platform generator frequency skew severity; f _{Is, max}It is the peak excursion frequency of s platform generator after i the fault clearance;

Generator frequency skew severity function is represented with following (35) formula:

$S_f=\left\{\begin{array}{cc}1,& f\&le;0.95\\ -20f+20,& 0.95<f\&le;1\\ 20f-20,& 1<f\&le;1.05\\ 1,& f>1.05\end{array}\right.---\left(35\right).$

Wherein, in the said step 6), adopt the method for control cost that the transient state consequence of failure order of severity is divided into cutting load loss, unit starting and maintenance cost and three parts of unknown losses.

Wherein, the loss of said cutting load comprises and cuts down the indirect loss that sale of electricity loss that load causes and user's power failure cause; Said cutting load loss is represented with following (36) formula:

Im _L＝P _L×(r ₁+r ₂)×h （36）；

In the formula: Im _LThe loss of expression cutting load; P _LBe the cutting load amount; r ₁And r ₂Represent the indirect loss that sale of electricity loss that cutting load causes and user have a power failure and cause respectively; H is an interruption duration.

Wherein, said unit starting and maintenance cost are represented with following (37) formula:

Im _r＝C _rep+C _start （37）；

In the formula: Im _rExpression unit starting and maintenance cost; C _RepThe expression maintenance cost; C _StartExpression unit starting expense.

Wherein, said unknown losses comprises extreme weather disaster and cascading failure; Represent unknown losses with Im'; The overhead control cost that the transient state unstability causes is represented with following (38) formula:

Im＝Im _L+Im _r+Im' （38）。

Wherein, in the said step 7), said transient stability risk assessment index comprises transient state angle stability risk assessment index, transient voltage skew risk assessment index, transient frequency skew risk assessment index and transient state unstability risk assessment index; Use following (39)-(42) formula to represent respectively:

R _Δδ＝P _r(S)×S _Δδ （39）；

R _u＝P _r(S)×S _u （40）；

R _f＝P _r(S)×S _f （41）；

R _inst＝P _r(S)×Im （42）；

In the formula: R _{Δ δ}, R _uAnd R _fRepresent transient state angle stability risk indicator, transient voltage skew risk indicator and transient frequency skew risk indicator respectively; R _InstBe transient state unstability risk indicator; P _r(S) be the transient stability probability of malfunction; S _{Δ δ}, S _uAnd S _fRepresent transient state angle stability consequence severity, transient voltage skew consequence severity and transient frequency skew consequence severity respectively.

Wherein, in the said step 8),, then carry out step 9) if the transient stability risk of all circuits is all calculated; If the circuit that does not calculate is arranged, then returns step 5).

With the prior art ratio, the beneficial effect that the present invention reaches is:

1, the probabilistic transient stability risk of the wind-powered electricity generation online evaluation method of taking into account provided by the invention; The ultrashort phase forecast model of wind power and its probability of error distribution function are counted transient stability risk assessment calculating; Make assessment result consider the randomness and the undulatory property of wind-powered electricity generation, meet the current general trend of greatly developing clean energy resource.

2, the probabilistic transient stability risk of the wind-powered electricity generation online evaluation method of taking into account provided by the invention; Relate to transient stability consequence comprehensive assessment model; The operation risk of system stability is kept in assessment in the time of can normally moving in system; When the system failure possibly cause unstability, weigh the consequence of unstability with the control cost.

3, the probabilistic transient stability risk of the wind-powered electricity generation online evaluation method of taking into account provided by the invention; Can carry out the transient stability assessment to transmission line of electricity and even total system; Can combine the probability of power system transient stability and the consequence that causes well, the management and running personnel can carry out system's operational decisions according to the multiple transient stability risk indicator that obtains.

Description of drawings

Fig. 1 is the normal distribution curve match power prediction error profile synoptic diagram that utilizes provided by the invention;

Fig. 2 is the predicted value fiducial interval calculation flow chart of ultrashort phase of wind power provided by the invention;

Fig. 3 is the discrete probability distribution synoptic diagram of line fault provided by the invention position;

Fig. 4 is an angle stability severity function synoptic diagram provided by the invention;

Fig. 5 is a variation severity function synoptic diagram provided by the invention;

Fig. 6 is a frequency shift (FS) severity function synoptic diagram provided by the invention;

Fig. 7 is the probabilistic transient stability risk of the wind-powered electricity generation online evaluation method flow diagram of taking into account provided by the invention.

Embodiment

Be described in further detail below in conjunction with the accompanying drawing specific embodiments of the invention.

The probabilistic transient stability risk of the wind-powered electricity generation online evaluation method of taking into account provided by the invention; At first utilize chaos time Phase Space Theory; Foundation is based on the ultrashort phase forecast model of the wind power of chaos time sequence; And, set up the probability distribution function of wind power predicated error based on normal distribution model; Secondly on the basis of analysis circuit probability of malfunction, set up circuit transient stability probability model; Propose transient stability risk consequence comprehensive estimation method, and provide corresponding Practical Mathematical Model; Provided at last and taken into account the probabilistic electric power system transient stability risk of wind-powered electricity generation online evaluation calculation process, the different running method that wherein forms with wind power prediction fiducial interval is represented the probabilistic influence of wind-powered electricity generation.

As shown in Figure 7, Fig. 7 is that Fig. 7 of the present invention is the probabilistic transient stability risk of the wind-powered electricity generation online evaluation method flow diagram of taking into account provided by the invention, and this method comprises the steps:

1) confirm ultrashort phase predicted value of wind power and Estimating Confidence Interval thereof:

Based on the chaos time sequence model, utilize wide area monitoring system WAMS real-time measurement wind power data and wind power historical data, calculate wind power predicted value P _Pred(t); Obtain wind power predicted value P according to power prediction probability of error Distribution calculation _Pred(t) fiducial interval; The ultrashort phase referred to 15 minutes.

One, based on the ultrashort phase prediction of the wind power of chaos time sequence model:

Based on chaology, utilize wide area monitoring system WAMS real-time measurement wind power data, set up the ultrashort phase forecast model of wind power, for analyzing the be incorporated into the power networks online risk assessment of transient stability that causes of large-scale wind power field the data basis of system running state is provided.

(1) phase space reconfiguration is theoretical introduces:

Takens theorem: M is D ₂Dimension stream shape,

Be a smooth differomorphism, y:M → R, y have the Second Order Continuous derivative,

An embedding.

Can find a suitable embedding dimension according to the Takens theorem; If postponing the dimension of coordinate is ATTRACTOR DIMENSIONS; Can recover track clocklike out at this embedded space so; Promptly under the homeomorphic meaning, recover the dynamics of chaotic attractor, and then established solid theory for the prediction of chaos time sequence.For time series { x _k: k=1,2 ..., n}, if can choose rightly embed dimension m and time delay t ₀, its phase space of reconstruct is:

${\mathrm{<figure-callout\; id="1"\; label="X"\; filenames="HDA00001863797500021.png,HDA00001863797500022.png"\; state="\{\{state\}\}">X</figure-callout>}}_1={[x_1,{\mathrm{<figure-callout\; id="0"\; label="x\; t"\; filenames="HDA00001863797500011.png,HDA00001863797500022.png"\; state="\{\{state\}\}">x</figure-callout>}}_{t_{}},x_{2{\mathrm{<figure-callout\; id="0"\; label="t"\; filenames="HDA00001863797500011.png,HDA00001863797500022.png"\; state="\{\{state\}\}">t</figure-callout>}}_0+1},\&CenterDot;\&CenterDot;\&CenterDot;,x_{(m-1){\mathrm{<figure-callout\; id="0"\; label="t"\; filenames="HDA00001863797500011.png,HDA00001863797500022.png"\; state="\{\{state\}\}">t</figure-callout>}}_0+1}]}^T,$

${\mathrm{<figure-callout\; id="2"\; label="X"\; filenames="HDA00001863797500021.png"\; state="\{\{state\}\}">X</figure-callout>}}_2={[x_2,{\mathrm{<figure-callout\; id="0"\; label="x\; t"\; filenames="HDA00001863797500011.png,HDA00001863797500022.png"\; state="\{\{state\}\}">x</figure-callout>}}_{t_{}},x_{2{\mathrm{<figure-callout\; id="0"\; label="t"\; filenames="HDA00001863797500011.png,HDA00001863797500022.png"\; state="\{\{state\}\}">t</figure-callout>}}_0+2},\&CenterDot;\&CenterDot;\&CenterDot;,x_{(m-1){\mathrm{<figure-callout\; id="0"\; label="t"\; filenames="HDA00001863797500011.png,HDA00001863797500022.png"\; state="\{\{state\}\}">t</figure-callout>}}_0+2}]}^T,---\left(1\right)$

$\&CenterDot;\&CenterDot;\&CenterDot;\&CenterDot;\&CenterDot;\&CenterDot;$

$X_{n-(m-1){\mathrm{<figure-callout\; id="0"\; label="t"\; filenames="HDA00001863797500011.png,HDA00001863797500022.png"\; state="\{\{state\}\}">t</figure-callout>}}_0}={[x_{n-(m-1){\mathrm{<figure-callout\; id="0"\; label="t"\; filenames="HDA00001863797500011.png,HDA00001863797500022.png"\; state="\{\{state\}\}">t</figure-callout>}}_0},{\mathrm{<figure-callout\; id="0"\; label="x\; t"\; filenames="HDA00001863797500011.png,HDA00001863797500022.png"\; state="\{\{state\}\}">x</figure-callout>}}_{t_{}},x_{2{\mathrm{<figure-callout\; id="0"\; label="t"\; filenames="HDA00001863797500011.png,HDA00001863797500022.png"\; state="\{\{state\}\}">t</figure-callout>}}_0+n-(m-1){\mathrm{<figure-callout\; id="0"\; label="t"\; filenames="HDA00001863797500011.png,HDA00001863797500022.png"\; state="\{\{state\}\}">t</figure-callout>}}_0},\&CenterDot;\&CenterDot;\&CenterDot;,x_{(m-1){\mathrm{<figure-callout\; id="0"\; label="t"\; filenames="HDA00001863797500011.png,HDA00001863797500022.png"\; state="\{\{state\}\}">t</figure-callout>}}_0+n-(m-1)t_0}]}^T$

Wherein, N=n-(m-1) t ₀Length for reconstruct time arrow sequence.

Therefore, in phase space reconfiguration time delay the most important with the selection that embeds dimension, the choose reasonable of these two parameters with optimize authenticity and the reliability that directly has influence on the attractor that is approached.

(2) based on the choosing method of C-C method phase space reconfiguration parameter:

Time delay in the phase space reconfiguration, its selection principle normally makes it on statistical significance, be independently as far as possible little value.Research shows, influences the principal element of phase space reconstruction quality, not only is to choose separately t time delay ₀With embed dimension m, the more important thing is with the embedding window width t that joins together _w=(m-1) t ₀Confirm that the C-C method that proposes is on this basis united and considered t time delay ₀With embedding dimension m, can calculate time delay simultaneously and embed the time window width.This method constitutes statistic through the correlation integral of sequence, and statistic has been represented the correlativity of Nonlinear Time Series.Graph of a relation through statistic and time delay is confirmed t ₀(m-1) t ₀Thereby, determine and embed dimension m.

If X _i(i=1,2 ..., be M) according to the point in the phase space after the reconstruct of front introduction:

The definition statistic:

Time series { x _iBe divided into t disjoint time series: To t disjoint subsequence S (m, N, r, t), then

$S(m,N,r,t)=\frac{1}{t}\underset{s=1}{\overset{t}{\mathrm{\&Sigma;}}}[C_s(m,N,r,t)-C_s^m(1,\frac{N}{t},r,t)]---\left(2\right);$

In the formula: m representes to embed dimension; N is a length of time series; R is the time series radius; C _s(r is t) for embedding the time series correlation integral for m, N;

Embedding time series correlation integral when being m for the embedding dimension.

If sequence is independent identically distributed, for fixing m and t, when N → ∞, to all r, S is all arranged, and (t) perseverance is zero for m, r.But sequence is limited usually, and therefore general S (m, N, r, t) non-vanishing.

Definition is about the maximum deviation of r:

△S(m,N,t)＝max{S(m,N,r _i,t)}-min{S(m,N,r _j,t)} i≠j （3）；

In the formula: (m, N t) have measured maximum deviation about radius r to △ S.General N, r, the selection of t has certain limit when 2≤m≤5, σ/2≤r≤2 σ, N>=500 o'clock (σ is seasonal effect in time series mean square deviation or standard deviation), progressive distribution can obtain well approximate through finite sequence.During concrete calculating, get m=2,3,4,5, r _i=i σ/2, i=1,2,3,4.Calculate following three statistics:

$\stackrel{\&OverBar;}{S}\left(t\right)=\frac{1}{16}\underset{m=2}{\overset{5}{\mathrm{\&Sigma;}}}\underset{j=1}{\overset{4}{\mathrm{\&Sigma;}}}S(m,r_j,t)---\left(4\right);$

$\mathrm{\&Delta;}\stackrel{\&OverBar;}{S}\left(t\right)=\frac{1}{4}\underset{m=2}{\overset{5}{\mathrm{\&Sigma;}}}\mathrm{\&Delta;S}(m,t)---\left(5\right);$

$S_{\mathrm{cor}}\left(t\right)=\mathrm{\&Delta;}\stackrel{\&OverBar;}{S}\left(t\right)+\left|\stackrel{\&OverBar;}{S}\left(t\right)\right|---\left(6\right);$

Can know that by following formula optimum delay time is corresponding

First zero point, or First minimal value; S _Cor(t) the minimum value time corresponding is best embedding time window width t _w, by embedding time window formula t _w=(m-1) t ₀Can embed dimension m.

(3) weighing first order local area predicted method:

The first order local area method is meant with X (t+1)=aX (t)+b comes match n point small neighbourhood on every side.

If the field that n is ordered comprises a t ₁, t ₂..., t _p, then following formula can be expressed as

$\left|\begin{array}{c}X({\mathrm{<figure-callout\; id="1"\; label="t"\; filenames="HDA00001863797500021.png,HDA00001863797500022.png"\; state="\{\{state\}\}">t</figure-callout>}}_1+1)\\ X({\mathrm{<figure-callout\; id="2"\; label="t"\; filenames="HDA00001863797500021.png"\; state="\{\{state\}\}">t</figure-callout>}}_2+1)\\ \&CenterDot;\&CenterDot;\&CenterDot;\\ X(t_p+1)\end{array}\right|=a\left|\begin{array}{c}X\left(t_1\right)\\ X\left(t_2\right)\\ \&CenterDot;\&CenterDot;\&CenterDot;\\ X\left(t_p\right)\end{array}\right|+b---\left(7\right);$

If central point X _kNeighbor point be X _Ki, i=1,2 ..., q, and to X _kDistance be D _i, establish D _mBe D _iIn minimum value, defining point X _KiWeights do

$P_i=\frac{\exp (-a(D_i-D_m))}{\underset{i=1}{\overset{q}{\mathrm{\&Sigma;}}}\exp (-a(D_i-D_m))}---\left(8\right);$

A is a parameter, generally gets a=1.Then the first order local area linear fit does

X _ki+1＝aX _ki+be i＝1,2,...,q （9）；

The application weighted least-squares method has

$\underset{i=1}{\overset{q}{\mathrm{\&Sigma;}}}P{(X_{\mathrm{ki}+1}-{\mathrm{aX}}_{\mathrm{ki}}-b)}^2=\min ---\left(10\right);$

Be translated into common LEAST SQUARES MODELS FITTING, be the weighing first order local area method.

The specific algorithm flow process of weighing first order local area method is following:

I, phase space reconstruction.

Calculate seasonal effect in time series according to the C-C method and embed dimension and time delay, obtain phase space reconstruction and do

X(t)＝(x(t),x(t+t ₀),...,x(t+(m-1)t ₀))∈R ^d,t＝1,2,...,N （11）；

Wherein N is the number of the point in the phase space reconstruction, N=n-(m-1) t ₀

Ii, searching point of proximity.

In phase space, calculate each point to the space length between the central point X (N), find out X _kReference vector collection X _Ki, i=1,2 ..., q, and some X _KiTo X _kDistance be D _i, establish D _mBe D _iIn minimum value, defining point X _KiWeights do

$P_i=\frac{\exp (-a(D_i-D_m))}{\underset{i=1}{\overset{q}{\mathrm{\&Sigma;}}}\exp (-a(D_i-D_m))}---\left(12\right);$

Iii, carry out prediction and calculation.The single order weighted linear fits to

$\left|\begin{array}{c}{\mathrm{<figure-callout\; id="1"\; label="X\; k"\; filenames="HDA00001863797500021.png,HDA00001863797500022.png"\; state="\{\{state\}\}">X</figure-callout>}}_k\\ {\mathrm{<figure-callout\; id="2"\; label="X\; k"\; filenames="HDA00001863797500021.png"\; state="\{\{state\}\}">X</figure-callout>}}_k\\ \&CenterDot;\&CenterDot;\&CenterDot;\\ X_{\mathrm{Kq}+1}\end{array}\right|=a\left|\begin{array}{cc}{X}_{k1}& e\\ X_{k2}& e\\ \&CenterDot;\&CenterDot;\&CenterDot;& \\ X_{\mathrm{Kq}}& e\end{array}\right|\left[\begin{array}{c}q\\ b\end{array}\right],$ Wherein $e=\left[\begin{array}{c}1\\ 1\\ \&CenterDot;\\ \&CenterDot;\\ \&CenterDot;\\ 1\end{array}\right]---\left(13\right);$

The application least square method has

$\underset{i=1}{\overset{q}{\mathrm{\&Sigma;}}}P{(X_{\mathrm{ki}+1}-{\mathrm{aX}}_{\mathrm{ki}}-b)}^2=\min ---\left(14\right);$

Regard following formula as about unknown number a, b binary function, both sides ask local derviation to obtain

$\left\{\begin{array}{c}a\underset{i=1}{\overset{q}{\mathrm{\&Sigma;}}}{P}_i{x_{\mathrm{<figure-callout\; id="2"\; label="ki"\; filenames="HDA00001863797500021.png"\; state="\{\{state\}\}">ki</figure-callout>}}}^2+b\underset{i=1}{\overset{q}{\mathrm{\&Sigma;}}}{P}_i{x}_{\mathrm{ki}}=\underset{i=1}{\overset{q}{\mathrm{\&Sigma;}}}{P}_i{x}_{\mathrm{ki}}{x}_{\mathrm{ki}+1}\\ a\underset{i=1}{\overset{q}{\mathrm{\&Sigma;}}}{P}_i{x}_{\mathrm{ki}}+b=\underset{i=1}{\overset{q}{\mathrm{\&Sigma;}}}{P}_i{x}_{\mathrm{ki}+1}\end{array}\right.---\left(15\right);$

The group of solving an equation formula (15) obtains a, b, and substitution formula then (13) obtains predictor formula.

Predict according to predictor formula.Obviously, the reference vector collection is X _Ki, i=1,2 ..., the one-step prediction of q is X _Ki+1, i=1,2 ..., q.

(4) the predicated error probability nature is analyzed:

I, predicated error evaluation index

Predicated error evaluation method commonly used has root-mean-square error, mean absolute error and mean absolute percentage error.

Root-mean-square error is:

$e_{\mathrm{RMSE}}=\sqrt{\frac{1}{n+1}\underset{t=t_0}{\overset{{\mathrm{<figure-callout\; id="0"\; label="t"\; filenames="HDA00001863797500011.png,HDA00001863797500022.png"\; state="\{\{state\}\}">t</figure-callout>}}_0+n}{\mathrm{\&Sigma;}}}{(P_{\mathrm{pred}}\left(t\right)-P_{\mathrm{meas}}\left(t\right))}^2}---\left(16\right);$

Mean absolute error is:

$e_{\mathrm{MAE}}=\frac{1}{n+1}\underset{t=t_0}{\overset{{\mathrm{<figure-callout\; id="0"\; label="t"\; filenames="HDA00001863797500011.png,HDA00001863797500022.png"\; state="\{\{state\}\}">t</figure-callout>}}_0+n}{\mathrm{\&Sigma;}}}|P_{\mathrm{pred}}\left(t\right)-P_{\mathrm{meas}}\left(t\right)|---\left(17\right);$

Mean absolute percentage error is:

$e_{\mathrm{MAPE}}=\frac{1}{n+1}\underset{t=t_0}{\overset{{\mathrm{<figure-callout\; id="0"\; label="t"\; filenames="HDA00001863797500011.png,HDA00001863797500022.png"\; state="\{\{state\}\}">t</figure-callout>}}_0+n}{\mathrm{\&Sigma;}}}\left|\frac{P_{\mathrm{pred}}\left(t\right)-P_{\mathrm{meas}}\left(t\right)}{P_{\mathrm{meas}}\left(t\right)}\right|---\left(18\right);$

In the formula: P _Pred(t) be t predicted value constantly; P _Meas(t) be t measured value constantly; t ₀For predicting the zero hour; N counts for total prediction.

Because the installed capacity of each wind energy turbine set is also incomplete same, formula (16) and formula (17) be the predicated error between each wind energy turbine set relatively; Formula (18) when wind-powered electricity generation is exerted oneself very low or be approximately zero, e _MAPEVery big even infinitely great, be unfavorable for statistical study.Therefore, the inventive method adopts relative error to come the statistical forecast error characteristics, definition wind energy turbine set predicated error e _tBe t wind power predicted value P constantly _Meas(t) with wind power measured value P _Pred(t) poor, promptly

e _t＝P _pred(t)-P _meas(t) （19）；

Because the amplitude of variation difference of predicated error under different wind power levels be very big, particularly very low or when approaching zero at power, therefore, be reference value with the specified installed capacity of wind energy turbine set, normalization predicated error e _tFor

${e_t}^{\&prime;}=\frac{e_t}{P_{\mathrm{WG}}}---\left(20\right);$

In the formula: e _t' be wind power predicated error perunit value; P _WGSpecified installed capacity for wind energy turbine set.

In like manner, to wind-powered electricity generation predicted power P _Pred(t) and wind-powered electricity generation measured power P _Meas(t) carry out normalization,

$P_{\mathrm{pred}}{\left(t\right)}^{\&prime;}=\frac{P_{\mathrm{pred}}\left(t\right)}{P_{\mathrm{WG}}}---\left(21\right);$

$P_{\mathrm{meas}}{\left(t\right)}^{\&prime;}=\frac{P_{\mathrm{meas}}\left(t\right)}{P_{\mathrm{WG}}}---\left(22\right);$

For further refinement analysis; Wind energy turbine set installed capacity after the normalization is divided into n section, and the scope of each section is carried out the error statistics analysis for

to each section.

II, the power prediction probability of error distribute and Estimating Confidence Interval:

The wind power prediction certainly exists certain error; Its error can demonstrate certain probability density characteristics on the whole; Certain wind energy turbine set 2010.1 ~ 2010.12 wind powers prediction relative error is analyzed; Its distribution character satisfies the normal distribution match basically, thus adopt normal distribution curve match wind power prediction relative error to distribute, as shown in Figure 1.

Analysis through to Fig. 1 can know that there is certain probability density characteristics on the whole in the wind power predicated error, and this distribution character can be described in enough normal distributions, and therefore, its probability density function expression formula does

$f\left(x\right)=\frac{1}{\sqrt{2\mathrm{\&pi;}}{\mathrm{\&sigma;}}_f}{e}^{-\frac{{(x-{\mathrm{\&mu;}}_f)}^2}{2{{\mathrm{\&sigma;}}_f}^2}}---\left(23\right);$

In the formula: μ _fBe expectation of a random variable; σ _fStandard deviation for stochastic variable.

Utilize formula (23) to calculate the probability density that can get predicated error in each power range.For t predicted power value P constantly _Pred(t), given α (0＜α＜1) satisfies P (P _Pred(t) _Min＜P _Pred(t)＜P _Pred(t) _Max)=1-α then claims interval [P _Pred(t) _Min, P _Pred(t) _Max] be power P _Pred(t) degree of confidence is the fiducial interval of 1-α, wherein P _Pred(t) _MinAnd P _Pred(t) _MaxCertain section scope upper lower limit value of representing wind energy turbine set installed capacity after the normalization respectively.

According to the prediction of wind power predicted value and power probability density curve, can this wind power predicted value estimation under a certain confidence level, thus can reflect the fluctuation range of this wind power predicted value, its calculation flow chart is as shown in Figure 2:

Concrete calculation procedure is described as follows:

1. to a certain wind power predicted value P _Pred(t), judge after the normalization which power range it belongs to; Power range comprises ten sections of five equilibrium;

2. find out the corresponding probability of error densimetric curve of this section, obtain corresponding power probability density curve through conversion;

3. according to the power probability distribution of this section, seek the interval that cumulative probability is greater than or equal to degree of confidence 1-α, choose the minimum interval of length as this power prediction value P _Pred(t) fiducial interval.

2) confirm the basic condition of transient stability probability calculation:

The basic condition of transient stability probability calculation comprises wind power predicted value P _Pred(t), predicted power fiducial interval higher limit P _Pred(t) _Min, predicted power fiducial interval lower limit P _Pred(t) _Max, ultra-short term, network topology structure and each generator state.

Two, transient stability risk evaluation model:

Probability model:

This method adopts the power system transient stability probability of certain period under the analytic calculation method of operation, comprises enchancement factors such as fault probability of happening, fault type and abort situation.Study a question in order to simplify; When computational scheme probability of malfunction index; Suppose the relay protection system action, disregard circuit breaker failure, the uncertainty influence of not considering to load and distributing; Do not consider transient fault, generator and transformer fault, and separate and be independent of each other between hypothesis different faults (incident).

A, line fault probability of happening:

When not considering weather conditions to the transmission line of electricity reliability effect, line failure rate can be represented with the circuit number of stoppages in a period of time.Because the line fault number of times can be obtained by the historical reliability data analysis, so line fault probability of happening function can represent that parameter is a frequency lambda through Poisson distribution _kSuppose that the fault that circuit takes place is permanent fault, and the probability that two circuits break down simultaneously can ignore, so circuit L _kOn the probability that breaks down suc as formula shown in (24).

$P_r\left(L_k\right)=1-e^{-{\mathrm{\&lambda;}}_{k}t}(k=\mathrm{1,2},...,m)---\left(24\right);$

In the formula: P _r(L _k) probability of malfunction that takes place for circuit; λ _kCircuit L in the expression certain hour section _kFailure-frequency; T representes trouble duration.

B, fault type probability:

Suppose that circuit has four kinds of fault types independently mutually, by order of severity ordering is: three-phase ground connection, two phase ground, phase fault, single-phase earthing.To certain bar specific circuit, type is C _jThe frequency that takes place of fault be f _i, then its probability of happening is:

$P_r\left(C_j\right)=\frac{f_j}{\underset{i=1}{\overset{4}{\mathrm{\&Sigma;}}}{f}_j}(j=\mathrm{1,2},...,4)---\left(25\right);$

And have,

$\underset{j=1}{\overset{4}{\mathrm{\&Sigma;}}}{P}_r\left(C_j\right)=1---\left(26\right);$

In the formula: P _r(C _j) be the fault type probability of circuit; f _iFrequency for the line fault generation.

The reference probability that above-mentioned four types of faults that table 1 provides for working group of IEEE electric system relay special commission take place, the fault of other type are very little or consequence is very little ignores because of the probability that takes place.

The probability that the dissimilar faults of table 1 take place

Fault type	The probability (%) that fault takes place
		Three-phase ground connection	1
Two phase ground	2
		Phase fault	4
Single-phase earthing	93

C, abort situation probability:

Generally speaking, the information on the failure point of power transmission line is less, is difficult to confirm the influence to line fault such as weather, geographical conditions.According to Bayes---Laplce's criterion (Bayes-Laplace Criterion): if can not prove better distribution then adopt unified the distribution; Thereby think on the circuit that it is identical that each location probability breaks down, so the position of fault is unified Discrete Distribution on the line.If the total length of certain bar circuit is L, the trouble spot is L to the distance of circuit head end _d, defining variable D:

$D=\frac{L_d}{L}---\left(27\right);$

Can know that by following formula D goes up in interval [0,1] to change, and for the convenience that studies a question, usually the abort situation of circuit is further simplified.Suppose circuit is divided into three sections, put with a typical fault for every section and replace, then obtain line fault position discrete probability distribution shown in Figure 3, and have

$\underset{h=1}{\overset{n_d}{\mathrm{\&Sigma;}}}{P}_r\left(D_h\right)=1---\left(28\right);$

In the formula: P _r(D _h) expression circuit on h probability that breaks down in the trouble spot; n _dDiscrete trouble spot sum on the expression circuit.

3) confirm the transient stability probability of malfunction:

Suppose that fault probability of happening, fault type probability and abort situation probability are separate and be independent of each other, then transient stability probability of malfunction P _r(E _i) do

$P_r\left(E_i\right)=\underset{j=1}{\overset{4}{\mathrm{\&Sigma;}}}\underset{k=1}{\overset{m}{\mathrm{\&Sigma;}}}\underset{h=1}{\overset{n_d}{\mathrm{\&Sigma;}}}{P}_r\left(D_h\right)P_r\left(C_j\right)P_r\left(L_k\right)---\left(29\right);$

In the formula: P _r(L _k) probability that takes place for line fault; P _r(C _j) be the fault type probability, P _r(D _h) be the abort situation probability.

4) judge whether to take place the transient state unstability: then carry out step 6) if the transient state unstability takes place; If the transient state unstability does not take place, but the fault that causes the transient state unstability is arranged, then carry out step 5).

5) transient stability failure effect severity is assessed:

This method has been set up electric power system transient stability consequence comprehensive assessment model, has provided the definition of failure effect severity, has proposed transient stability order of severity consequence evaluation index, and has provided the respective function expression formula; If the fault that causes the transient state unstability is arranged, then adopt the control cost to weigh the transient state consequence of failure, realize electric power system transient stability consequence problem is assessed all sidedly.Based on existing transient stability evaluation index; This paper has proposed three kinds of consequence indexs of can estimation of transient stablizing the order of severity; Comprise merit angle unstability severity, voltage deviation severity and frequency shift (FS) severity, the order of severity of electric power system transient stability fault is explained.

1. merit angle unstability severity:

Merit angular difference between the generator is the basic index of electric power system transient stability criterion.When electric system suffers big disturbance; The input mechanical output of generator and output electromagnetic power out of trim; Cause the variation of rotor angle, take place to wave relatively between each group, when this relative angle that finally makes between some generators that waves when constantly increasing; Can not keep between the generator synchronously, promptly system loses transient stability.

The system of setting up departments has M platform generator, and i kind fault takes place on the j bar circuit, then defines merit angle unstability severity and is:

$S_{\mathrm{\&Delta;\&delta;}}\left(E_i\right)=\underset{m=1}{\overset{M}{\mathrm{\&Sigma;}}}S(E_i,{\mathrm{\&Delta;\&delta;}}_{\mathrm{im},\max })---\left(30\right);$

In the formula: E _iBe i fault model; S (E _i, △ δ _{Im, max}) be m platform generator's power and angle unstability severity; △ δ _{Im, max}Be to depart from the maximum generator's power and angle in the generator center of inertia between i age at failure.

Fig. 4 is an angle stability severity function, and the expression generator's power and angle departs from the degree in the center of inertia.Transverse axis △ δ is that generator's power and angle is poor, and the longitudinal axis is a generator's power and angle unstability severity, and unit all adopts perunit value.When the merit angular difference more than or equal to △ δ _MaxWhen (system INSTABILITY CRITERION angle), its severity is 1; When the merit angular difference less than 0.6 △ δ _MaxThe time, its severity is approximately 0.The stable severity function of generator's power and angle does

$S_{\mathrm{\&Delta;\&delta;}}=\left\{\begin{array}{cc}0,& \mathrm{\&Delta;\&delta;}\&le;0.6\\ 2.5\mathrm{\&Delta;\&delta;}-1.5,& 0.6<\mathrm{\&Delta;\&delta;}\&le;1\\ 1,& \mathrm{\&Delta;\&delta;}\&GreaterEqual;1\end{array}\right.---\left(31\right);$

2. variation severity:

Voltage is stable be meant electric system under normal operation or after suffering disturbance in the system all buses keep the ability of acceptable voltage.At the voltage unsafe condition, system voltage departs from the normal voltage level, even the generation collapse of voltage causes whole power system collapse.What the variation severity reflected is the extent of injury that causes generator bus variation in the system after electric system is broken down.

The system of setting up departments has Y bar bus, and i kind fault takes place on j bar circuit, then defines transient voltage skew severity to do

$S_u\left(E_i\right)=\underset{y=1}{\overset{Y}{\mathrm{\&Sigma;}}}S(E_i,u_{\mathrm{iy},\max })---\left(32\right);$

In the formula: E _iBe i fault model; S (E _i, u _{Iy, max}) be y bar busbar voltage skew severity; u _{Iy, max}It is y bar bus peak excursion magnitude of voltage after i the fault clearance.

Fig. 5 is transient voltage skew severity function, the extent of injury of expression busbar voltage skew.Transverse axis u is a busbar voltage, and the longitudinal axis is a transient voltage skew severity, and unit all adopts perunit value.For every bus, when voltage was ratings, transient voltage skew severity was 0, when its skew smaller or equal to 0.75 or more than or equal to 1.1 the time, transient voltage skew severity is 1.The severity function of transient voltage skew does

$S_u=\left\{\begin{array}{cc}1,& u\&le;0.75\\ -4u+4,& 0.75<u\&le;1\\ 10u-9,& 1<u\&le;1.1\\ 1,& u>1.1\end{array}\right.---\left(33\right);$

3. frequency shift (FS) severity:

After electric system suffered big disturbance, big imbalance appearred in generating and workload demand, causes frequency shift (FS).When system frequency exceeds the generator allowed band, if deal with improperly, will cause chain reaction, even cause large-area power-cuts.What the frequency shift (FS) severity reflected is the extent of injury of generator frequency skew after electric system is broken down.

The system of setting up departments has S platform generator, and i kind fault takes place on the j bar circuit, then defines the frequency shift (FS) severity and does

$S_f\left(E_i\right)=\underset{s=1}{\overset{S}{\mathrm{\&Sigma;}}}S(E_i,f_{\mathrm{is},\max })---\left(34\right);$

In the formula: E _iBe i fault model; S (E _i, f _{Is, max}) be s platform generator frequency skew severity; f _{Is, max}It is the peak excursion frequency of s platform generator after i the fault clearance.

Fig. 6 is generator frequency skew severity function, the extent of injury of expression generator frequency skew.Transverse axis f is a generator frequency, longitudinal axis S _fBe generator frequency skew severity, unit all adopts perunit value.When voltage is ratings, promptly during 50Hz, generator frequency skew severity is 0, when its skew smaller or equal to 0.95 or more than or equal to 1.05 the time, transient voltage skew severity is 1.The severity function of generator frequency skew does

$S_f=\left\{\begin{array}{cc}1,& f\&le;0.95\\ -20f+20,& 0.95<f\&le;1\\ 20f-20,& 1<f\&le;1.05\\ 1,& f>1.05\end{array}\right.---\left(35\right).$

6) the transient state consequence of failure order of severity is assessed:

In order to weigh the consequence of transient state unstability more intuitively, the method that this paper adopts the control cost is divided into 3 parts with the relevant consequence of transient state unstability: the cutting load loss; Unit starting and maintenance cost and unknown losses.

1. cutting load loss:

Generator failure at first can cause meritorious minimizing of exerting oneself, and frequency reduces.If frequency is reduced under the certain level, then low frequency Automatic Load device starts action, makes partial line circuit breaker tripping cut-out load.Usual way has only been considered the sale of electricity loss, does not consider user's caused indirect loss that has a power failure, and the cutting load loss of this paper definition comprises two parts: a part is to cut down the sale of electricity loss that load causes; Another part is the indirect loss that the user has a power failure and causes.

Im _L＝P _L×(r ₁+r ₂)×h （36）；

Im in the formula _LThe loss of expression cutting load; P _LBe the cutting load amount; r ₁And r ₂Represent direct loss and indirect loss that cutting load causes respectively; H is an interruption duration.

2. maintenance cost and unit starting cost:

When generator and circuit break down, need could restore electricity again after the maintenance out of service, maintenance cost is defined as C _RepThe stoppage in transit unit must restart again and be incorporated into the power networks, and the start-up cost that needs is C _StartSo maintenance cost and unit starting cost are suc as formula shown in (37).

Im _r＝C _rep+C _start （37）；

3. unknown losses:

Be difficult to definite loss,, be defined as Im' like extreme weather disaster, cascading failure etc.

Therefore, the overhead control cost that caused of transient state unstability does

Im＝Im _L+Im _r+Im' （38）。

7) calculate transient stability risk assessment index:

In sum, transient stability risk assessment index can be expressed as formula (39)-(42):

R _Δδ＝P _r(S)×S _Δδ （39）；

R _u＝P _r(S)×S _u （40）；

R _f＝P _r(S)×S _f （41）；

R _inst＝P _r(S)×Im （42）；

In the above-mentioned formula, R _{Δ δ}, R _uAnd R _fRepresent transient state angle stability risk indicator, transient voltage skew risk indicator and transient frequency skew risk indicator respectively; R _InstBe transient state unstability risk indicator; P _r(S) be the transient stability probability of malfunction; S _{Δ δ}, S _uAnd S _fRepresent transient state angle stability consequence severity, transient voltage skew consequence severity and transient frequency skew consequence severity respectively.

8) judge whether that all circuit transient state risk assessment indexs all calculate:, then carry out step 9) if the transient stability risk of all circuits is all calculated; If the circuit that does not calculate is arranged, then returns step 5).

9) output transient stability risk assessment desired value.

The probabilistic transient stability risk of the wind-powered electricity generation online evaluation method of taking into account provided by the invention; Can carry out the transient stability assessment to transmission line of electricity and even total system; Can combine the probability of power system transient stability and the consequence that causes well, the management and running personnel can carry out system's operational decisions according to the multiple transient stability risk indicator that obtains.

Should be noted that at last: above embodiment is only in order to technical scheme of the present invention to be described but not to its restriction; Although the present invention has been carried out detailed explanation with reference to the foregoing description; Under the those of ordinary skill in field be to be understood that: still can specific embodiments of the invention make amendment or be equal to replacement; And do not break away from any modification of spirit and scope of the invention or be equal to replacement, it all should be encompassed in the middle of the claim scope of the present invention.

Claims (25)

1. take into account the probabilistic transient stability risk of wind-powered electricity generation online evaluation method for one kind, it is characterized in that said method comprises the steps:

1) confirms ultrashort phase predicted value of wind power and Estimating Confidence Interval thereof;

2) confirm the basic condition of transient stability probability calculation;

3) confirm the transient stability probability of malfunction;

4) judge whether to take place the transient state unstability;

5) transient stability failure effect severity is assessed;

6) the transient state consequence of failure order of severity is assessed;

7) calculate transient stability risk assessment index;

8) judge whether that all circuit transient state risk assessment indexs all calculate;

9) output transient stability risk assessment desired value.

2. transient stability risk online evaluation method as claimed in claim 1; It is characterized in that, in the said step 1), based on the chaos time sequence model; Utilize wide area monitoring system WAMS real-time measurement wind power data and wind power historical data, calculate wind power predicted value P _Pred(t); Obtain wind power predicted value P according to power prediction probability of error Distribution calculation _Pred(t) fiducial interval; The said ultrashort phase referred to 15 minutes.

3. transient stability risk online evaluation method as claimed in claim 2 is characterized in that said chaos time sequence model is based on phase space reconfiguration, utilizes the weighing first order local area predicted method to obtain the ultrashort phase predictor formula of wind power.

4. transient stability risk online evaluation method as claimed in claim 2 is characterized in that, adopts the wind energy turbine set relative error statistics power prediction probability of error to distribute; Said wind energy turbine set relative error e _tBe t wind power predicted value P constantly _Meas(t) with wind power measured value P _Pred(t) poor, promptly use following (19) formula to represent:

e _t＝P _pred(t)-P _meas(t) （19）。

5. transient stability risk online evaluation method as claimed in claim 2 is characterized in that the said fiducial interval that calculates the ultrashort phase predicted value of wind power comprises the steps:

A, judgement wind power predicted value P _Pred(t) affiliated power range;

B, search the corresponding probability of error densimetric curve of said power range;

C, confirm the power probability density;

D, seek accumulated probability more than or equal to the interval of fiducial interval degree 1-α as fiducial interval.

6. transient stability risk online evaluation method as claimed in claim 3 is characterized in that, among the said step a, said power range comprises ten sections of five equilibrium.

7. transient stability risk online evaluation method as claimed in claim 3 is characterized in that among the said step b, said probability of error densimetric curve is normal distribution curve, and said probability of error density function is represented with following (23) formula:

$f\left(x\right)=\frac{1}{\sqrt{2\mathrm{\&pi;}}{\mathrm{\&sigma;}}_f}{e}^{-\frac{{(x-{\mathrm{\&mu;}}_f)}^2}{2{{\mathrm{\&sigma;}}_f}^2}}---\left(23\right);$

In the formula: μ _fBe expectation of a random variable; σ _fStandard deviation for stochastic variable.

8. transient stability risk online evaluation method as claimed in claim 3 is characterized in that, among the said step c, confirms the power probability density of each power range according to (23) formula.

9. transient stability risk online evaluation method as claimed in claim 1 is characterized in that said step 2) in, the basic condition of said transient stability probability calculation comprises wind power predicted value P _Pred(t), predicted power fiducial interval higher limit P _Pred(t) _Min, predicted power fiducial interval lower limit P _Pred(t) _Max, ultra-short term, network topology structure and each generator state.

10. transient stability risk online evaluation method as claimed in claim 1 is characterized in that, transient stability probability packet vinculum road fault probability of happening, line fault type probability and line fault location probability.

11. transient stability risk online evaluation method as claimed in claim 10 is characterized in that, if the fault that circuit takes place is permanent fault, and the probability that two circuits break down simultaneously disregards, then circuit L _kOn the probability that breaks down represent with following (24) formula:

$P_r\left(L_k\right){=1-e}^{-{\mathrm{\&lambda;}}_{k}t}(k=\mathrm{1,2},...,m)---\left(24\right);$

In the formula: P _r(L _k) probability of malfunction that takes place for circuit; λ _kCircuit L in the express time section _kFailure-frequency; T representes trouble duration; M is a natural number.

12. transient stability risk online evaluation method as claimed in claim 10 is characterized in that said fault type is divided into three-phase ground connection, two phase ground, phase fault and single-phase earthing by the order of severity; Fault type is C _jThe frequency that takes place of fault be f _i, then the probability of its generation is represented with following (25) formula:

$P_r\left(C_j\right)=\frac{f_j}{\underset{i=1}{\overset{4}{\mathrm{\&Sigma;}}}{f}_j}(j=\mathrm{1,2},...,4)---\left(25\right);$

And have:

$\underset{j=1}{\overset{4}{\mathrm{\&Sigma;}}}{P}_r\left(C_j\right)=1---\left(26\right);$

In the formula: P _r(C _j) be the fault type probability of circuit; f _iFrequency for the line fault generation.

13. transient stability risk online evaluation method as claimed in claim 10 is characterized in that, if the total length of circuit is L, the trouble spot is L to the distance of circuit head end _d, defining variable D:

$D=\frac{L_d}{L}---\left(27\right);$

Said line fault location probability is represented with following (28) formula:

$\underset{h=1}{\overset{n_d}{\mathrm{\&Sigma;}}}{P}_r\left(D_h\right)=1---\left(28\right);$

In the formula: P _r(D _h) expression circuit on h probability that breaks down in the trouble spot; n _dDiscrete trouble spot sum on the expression circuit.

14. transient stability risk online evaluation method as claimed in claim 1; It is characterized in that; In the said step 3), if line fault probability of happening, line fault type probability and line fault location probability are separate and be independent of each other, transient stability probability of malfunction P then _r(E _i) represent with following (29) formula:

$P_r\left(E_i\right)=\underset{j=1}{\overset{4}{\mathrm{\&Sigma;}}}\underset{k=1}{\overset{m}{\mathrm{\&Sigma;}}}\underset{h=1}{\overset{n_d}{\mathrm{\&Sigma;}}}{P}_r\left(D_h\right)P_r\left(C_j\right)P_r\left(L_k\right)---\left(29\right);$

In the formula: P _r(L _k) probability that takes place for line fault; P _r(C _j) be line fault type probability, P _r(D _h) be the line fault location probability.

15. transient stability risk online evaluation method as claimed in claim 1 is characterized in that, in the said step 4), then carries out step 6) if the transient state unstability takes place; If the transient state unstability does not take place, but the fault that causes the transient state unstability is arranged, then carry out step 5).

16. transient stability risk online evaluation method as claimed in claim 1 is characterized in that, in the said step 5), said transient stability failure effect severity comprises merit angle unstability severity, variation severity and frequency shift (FS) severity.

17. transient stability risk online evaluation method as claimed in claim 16 is characterized in that establishing electric system has M platform generator, and i kind fault takes place on the j bar circuit, then unstability severity in merit angle is represented with following (30) formula:

$S_{\mathrm{\&Delta;\&delta;}}\left(E_i\right)=\underset{m=1}{\overset{M}{\mathrm{\&Sigma;}}}S(E_i,{\mathrm{\&Delta;\&delta;}}_{\mathrm{im},\max })---\left(30\right);$

In the formula: E _iBe i fault model; S (E _i, △ δ _{Im, max}) be m platform generator's power and angle unstability severity; △ δ _{Im, max}Be to depart from the maximum generator's power and angle in the generator center of inertia between i age at failure;

The stable severity function of generator's power and angle is represented with following (31) formula:

$S_{\mathrm{\&Delta;\&delta;}}=\left\{\begin{array}{cc}0,& \mathrm{\&Delta;\&delta;}\&le;0.6\\ 2.5\mathrm{\&Delta;\&delta;}-1.5,& 0.6<\mathrm{\&Delta;\&delta;}\&le;1\\ 1,& \mathrm{\&Delta;\&delta;}\&GreaterEqual;1\end{array}\right.---\left(31\right).$

18. transient stability risk online evaluation method as claimed in claim 16 is characterized in that establishing electric system has Y bar bus, and i kind fault takes place on j bar circuit, then transient voltage skew severity is represented with following (32) formula:

$S_u\left(E_i\right)=\underset{y=1}{\overset{Y}{\mathrm{\&Sigma;}}}S(E_i,u_{\mathrm{iy},\max })---\left(32\right);$

In the formula: E _iBe i fault model; S (E _i, u _{Iy, max}) be y bar busbar voltage skew severity; u _{Iy, max}It is y bar bus peak excursion magnitude of voltage after i the fault clearance;

Transient voltage skew severity function is represented with following (33) formula:

$S_u=\left\{\begin{array}{cc}1,& u\&le;0.75\\ -4u+4,& 0.75<u\&le;1\\ 10u-9,& 1<u\&le;1.1\\ 1,& u>1.1\end{array}\right.$

$\left(33\right).$

19. transient stability risk online evaluation method as claimed in claim 16 is characterized in that establishing electric system has S platform generator, and i kind fault takes place on the j bar circuit, then defines the frequency shift (FS) severity and does

$S_f\left(E_i\right)=\underset{s=1}{\overset{S}{\mathrm{\&Sigma;}}}S(E_i,f_{\mathrm{is},\max })---\left(34\right);$

In the formula: E _iBe i fault model; S (E _i, f _{Is, max}) be s platform generator frequency skew severity; f _{Is, max}It is the peak excursion frequency of s platform generator after i the fault clearance;

Generator frequency skew severity function is represented with following (35) formula:

$S_f=\left\{\begin{array}{cc}1,& f\&le;0.95\\ -20f+20,& 0.95<f\&le;1\\ 20f-20,& 1<f\&le;1.05\\ 1,& f>1.05\end{array}\right.---\left(35\right).$

20. transient stability risk online evaluation method as claimed in claim 1; It is characterized in that; In the said step 6), adopt the method for control cost that the transient state consequence of failure order of severity is divided into cutting load loss, unit starting and maintenance cost and three parts of unknown losses.

21. transient stability risk online evaluation method as claimed in claim 20 is characterized in that, the loss of said cutting load comprises cuts down the indirect loss that sale of electricity loss that load causes and user's power failure cause; Said cutting load loss is represented with following (36) formula:

Im _L＝P _L×(r ₁+r ₂)×h （36）；

In the formula: Im _LThe loss of expression cutting load; P _LBe the cutting load amount; r ₁And r ₂Represent the indirect loss that sale of electricity loss that cutting load causes and user have a power failure and cause respectively; H is an interruption duration.

22. transient stability risk online evaluation method as claimed in claim 20 is characterized in that, said unit starting and maintenance cost are represented with following (37) formula:

Im _r＝C _rep+C _start （37）；

In the formula: Im _rExpression unit starting and maintenance cost; C _RepThe expression maintenance cost; C _StartExpression unit starting expense.

23. transient stability risk online evaluation method as claimed in claim 20 is characterized in that said unknown losses comprises extreme weather disaster and cascading failure; Represent unknown losses with Im'; The overhead control cost that the transient state unstability causes is represented with following (38) formula:

Im＝Im _L+Im _r+Im' （38）。

24. transient stability risk online evaluation method as claimed in claim 1; It is characterized in that; In the said step 7), said transient stability risk assessment index comprises transient state angle stability risk assessment index, transient voltage skew risk assessment index, transient frequency skew risk assessment index and transient state unstability risk assessment index; Use following (39)-(42) formula to represent respectively:

R _△δ＝P _r(S)×S _△δ （39）；

R _u＝P _r(S)×S _u （40）；

R _f＝P _r(S)×S _f （41）；

R _inst＝P _r(S)×Im （42）；

In the formula: R _{△ δ}, R _uAnd R _fRepresent transient state angle stability risk indicator, transient voltage skew risk indicator and transient frequency skew risk indicator respectively; R _InstBe transient state unstability risk indicator; P _r(S) be the transient stability probability of malfunction; S _{Δ δ}, S _uAnd S _fRepresent transient state angle stability consequence severity, transient voltage skew consequence severity and transient frequency skew consequence severity respectively.

25. transient stability risk online evaluation method as claimed in claim 1 is characterized in that, in the said step 8), if the transient stability risk of all circuits is all calculated, then carries out step 9); If the circuit that does not calculate is arranged, then returns step 5).

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