CN102842105B - 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

One takes into account wind-powered electricity generation probabilistic transient stability risk online evaluation method

Technical field

The present invention relates to online evaluation method, be specifically related to one and take into account wind-powered electricity generation probabilistic transient stability risk online evaluation method.

Background technology

The assessment of electric system on-line transient stability generally adopts Deterministic Methods.On the one hand, Deterministic Methods have ignored the uncertain factor existed in real system, assesses the transient stability of current system with most major accident standard, and its conclusion is normally conservative; On the other hand, the running status of manually specifying and event of failure, might not cause severest consequences.Particularly after large-scale wind power concentrates connecting system, its feature such as randomness and undulatory property makes electric system be under uncertain background more, cause the transient stability problem of electrical network also to have uncertainty, the operating pressure of electrical network strengthens, and controlling difficulty increases.Therefore, in the urgent need to introducing new theory and research means, improve the ability of Operation of Electric Systems reply transient stability.

Risk assessment technology can consider event occur probability and event occur consequence, this method has been successfully applied to multiple fields such as finance, space flight, nuclear power, as to stock exchange, credit evaluation, nuclear power station safety congruence aspect.Risk is defined as the product of the probability of accident generation and the consequence of accident generation by IEEE, and the research for Transient Stability Evaluation provides new direction.

The continuous increase of installed capacity of wind-driven power and the amount of generating electricity by way of merging two or more grid systems, its uncertainty is more obvious on power system transient stability impact.Predict exactly if can compare ultra-short term wind power, then management and running personnel can predict the outcome according to wind power output and in conjunction with ultra-short term, online evaluation is carried out to the transient stability of electric system, real-time adjustment operation plan, the impact that the uncertainty stabilizing wind power output is brought to electrical network.

Summary of the invention

For the deficiencies in the prior art, the invention provides one and take into account wind-powered electricity generation probabilistic transient stability risk online evaluation method, the method utilizes chaotic time Phase Space Theory, set up the ultrashort-term wind power forecast model based on chaos time sequence, and based on normal distribution model, set up the probability distribution function of wind power prediction error; Secondly, on the basis of analysis circuit probability of malfunction, circuit probabilistic transient stability model is set up; Propose transient stability risk schedule comprehensive estimation method, and provide corresponding Practical Mathematical Model; Finally give and take into account wind-powered electricity generation probabilistic electric power system transient stability risk online evaluation calculation process, the different running method wherein formed with wind power prediction fiducial interval represents the probabilistic impact of wind-powered electricity generation.

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

One takes into account wind-powered electricity generation probabilistic transient stability risk online evaluation method, and its improvements are, described method comprises the steps:

1) ultrashort-term wind power predicted value and Estimating Confidence Interval thereof is determined;

2) basic condition that probabilistic transient stability calculates is determined;

3) transient stability probability of malfunction is determined;

4) judge whether Transient Instability occurs;

5) transient stability failure effect severity is assessed

6) Transient Instability severity degree is assessed;

7) transient stability risk assessment index is calculated;

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

9) transient stability risk assessment desired value is exported.

Wherein, in described step 1), based on chaos time sequence model, utilize wide-area monitoring systems WAMS real-time measurement wind power data and wind power historical data, calculate wind power prediction value P _pred(t); Wind power prediction value P is calculated according to the distribution of the power prediction probability of error _predthe fiducial interval of (t); Described ultra-short term refers to 15 minutes.

Wherein, described chaos time sequence model is based on phase space reconfiguration, utilizes weight-plus local-region method to obtain ultrashort-term wind power predictor formula.

Wherein, the distribution of the wind energy turbine set relative error statistics power prediction probability of error is adopted; Described wind energy turbine set relative error e _tfor the wind power prediction value P of t _meas(t) and wind power measured value P _predt the difference of (), namely represents by following (19) formula:

e _t＝P _pred(t)-P _meas(t) （19）。

Wherein, the fiducial interval calculating ultrashort-term wind power predicted value described in comprises the steps:

A, judge wind power prediction value P _predthe affiliated power range of (t);

B, search probability of error densimetric curve corresponding to described power range;

C, determine power probability density;

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

Wherein, in described step a, described power range comprises ten sections of decile.

Wherein, in described step b, described probability of error densimetric curve is normal distribution curve, and described probability of error density function following (23) formula represents:

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

In formula: μ _ffor expectation of a random variable; σ _ffor the standard deviation of stochastic variable.

Wherein, in described step c, determine the power probability density of each power range according to (23) formula.

Wherein, described step 2) in, the basic condition that described probabilistic transient stability calculates comprises wind power prediction value P _pred(t), predicted power fiducial interval higher limit P _pred(t) _min, predicted power lower limit of confidence interval value P _pred(t) _max, ultra-short term, network topology structure and each Generator Status.

Wherein, probabilistic transient stability comprises line fault probability of happening, line fault type probability and Location probability.

Wherein, if the fault that circuit occurs is permanent fault, and the probability that two circuits break down simultaneously is disregarded, then probability circuit Lk broken down following (24) formula represents:

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

In formula: P _r(L _k) for circuit occur probability of malfunction; λ _krepresent the circuit L in the time period _kfailure-frequency; T represents trouble duration; M is natural number.

Wherein, described fault type is divided into three-phase ground, two phase ground, phase fault and single-phase earthing by the order of severity; Fault type is C _jfault occur frequency be f _i, then its probability occurred represents by 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 formula: P _r(C _j) be the fault type probability of circuit; f _ifor the frequency that line fault occurs.

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

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

Described Location probability following (28) formula represents:

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

In formula: P _r(D _h) represent the probability that on circuit, break down in h trouble spot; n _drepresent discrete trouble spot sum on circuit.

Wherein, in described step 3), if line fault probability of happening, line fault type probability and Location probability are separate and be independent of each other, then transient stability probability of malfunction P _r(E _i) represent by 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 formula: P _r(L _k) for line fault occur probability; P _r(C _j) be line fault type probability, P _r(D _h) be Location probability.

Wherein, in described step 4), if there is Transient Instability, carry out step 6); If not there is Transient Instability, but there is the fault causing Transient Instability, then carry out step 5).

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

Wherein, if electric system has M platform generator, i-th kind of fault occurs jth bar circuit, then merit angle unstability severity following (30) formula represents:

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

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

The stable severity function of generator's power and angle represents by 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, if electric system has Y bar bus, i-th kind of fault occurs jth bar circuit, then transient voltage skew severity following (32) formula represents:

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

In formula: E _ibe i-th fault model; S (E _i, u _{iy, max}) be y article of busbar voltage skew severity; u _{iy, max}be y article of bus peak excursion magnitude of voltage after i-th fault clearance;

Transient voltage skew severity function following (33) formula represents:

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

$\left(33\right).$

Wherein, if electric system has S platform generator, i-th kind of fault occurs jth bar circuit, then defining frequency shift (FS) severity is

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

In formula: E _ibe i-th 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-th fault clearance;

Generator frequency skew severity function following (35) formula represents:

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

Wherein, in described step 6), the method controlling cost is adopted Transient Instability severity degree to be divided into cutting load loss, unit starting and maintenance cost and unknown losses three parts.

Wherein, the loss of described cutting load comprises sale of electricity loss that reduction plans causes and user and to have a power failure the indirect loss caused; Described cutting load loss represents by following (36) formula:

Im _L＝P _L×(r ₁+r ₂)×h （36）；

In formula: Im _lrepresent cutting load loss; P _lfor cutting load amount; r ₁and r ₂represent that the sale of electricity loss that causes of cutting load and user have a power failure the indirect loss caused respectively; H is interruption duration.

Wherein, described unit starting and maintenance cost following (37) formula represent:

Im _r＝C _rep+C _start（37）；

In formula: Im _rrepresent unit starting and maintenance cost; C _reprepresent maintenance cost; C _startrepresent unit starting expense.

Wherein, described unknown losses comprises extreme weather disaster and cascading failure; Unknown losses is represented with Im'; The overhead control cost that Transient Instability causes following (38) formula represents:

Im＝Im _L+Im _r+Im' （38）。

Wherein, in described step 7), described transient stability risk assessment index comprises transient rotor angle stability risk assessment index, transient voltage skew risk assessment index, transient frequency skew risk assessment index and Transient Instability risk assessment index; Represent by following (39)-(42) formula 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 formula: R _{Δ δ}, R _uand R _frepresent transient rotor angle stability risk indicator, transient voltage skew risk indicator and transient frequency skew risk indicator respectively; R _instfor Transient Instability risk indicator; P _r(S) be transient stability probability of malfunction; S _{Δ δ}, S _uand S _frepresent transient rotor angle stability sequence severity, transient voltage skew sequence severity and transient frequency skew sequence severity respectively.

Wherein, in described step 8), if the transient stability risk of all circuits calculates all, then carry out step 9); The circuit do not calculated if having, then return step 5).

Compared with the prior art, the beneficial effect that the present invention reaches is:

1, provided by the inventionly wind-powered electricity generation probabilistic transient stability risk online evaluation method is taken into account, ultrashort-term wind power forecast model and its probability of error distribution function are counted transient stability risk assessment calculate, make assessment result consider randomness and the undulatory property of wind-powered electricity generation, meet the current general trend greatly developing clean energy resource.

2, provided by the inventionly wind-powered electricity generation probabilistic transient stability risk online evaluation method is taken into account, relate to transient stability consequence Integrated Evaluation Model, the operation risk maintaining system stability can be assessed when system is normally run, when the system failure may cause unstability, to control cost to weigh the consequence of unstability.

3, provided by the inventionly wind-powered electricity generation probabilistic transient stability risk online evaluation method is taken into account, Transient Stability Evaluation can be carried out to transmission line of electricity and even whole system, well the probability of power system transient stability and the consequence caused can be combined, management and running personnel can carry out system cloud gray model decision-making according to the multiple transient stability risk indicator obtained.

Accompanying drawing explanation

Fig. 1 provided by the inventionly utilizes normal distribution curve fitting power predicated error distribution schematic diagram;

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

Fig. 3 is the discrete probability distribution schematic diagram of Location provided by the invention;

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

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

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

Fig. 7 provided by the inventionly takes into account wind-powered electricity generation probabilistic transient stability risk online evaluation method flow diagram.

Embodiment

Below in conjunction with accompanying drawing, the specific embodiment of the present invention is described in further detail.

Provided by the inventionly take into account wind-powered electricity generation probabilistic transient stability risk online evaluation method, first chaotic time Phase Space Theory is utilized, set up the ultrashort-term wind power forecast model based on chaos time sequence, and based on normal distribution model, set up the probability distribution function of wind power prediction error; Secondly, on the basis of analysis circuit probability of malfunction, circuit probabilistic transient stability model is set up; Propose transient stability risk schedule comprehensive estimation method, and provide corresponding Practical Mathematical Model; Finally give and take into account wind-powered electricity generation probabilistic electric power system transient stability risk online evaluation calculation process, the different running method wherein formed with wind power prediction fiducial interval represents the probabilistic impact of wind-powered electricity generation.

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

1) ultrashort-term wind power predicted value and Estimating Confidence Interval thereof is determined:

Based on chaos time sequence model, utilize wide-area monitoring systems WAMS real-time measurement wind power data and wind power historical data, calculate wind power prediction value P _pred(t); Wind power prediction value P is calculated according to the distribution of the power prediction probability of error _predthe fiducial interval of (t); Ultra-short term refers to 15 minutes.

One, the ultrashort-term wind power based on chaos time sequence model is predicted:

Based on chaology, utilize wide-area monitoring systems WAMS real-time measurement wind power data, set up ultrashort-term wind power forecast model, the data basis of system running state is provided for analyzing the online risk assessment of the grid-connected transient stability caused in large-scale wind power field.

(1) Phase-space Reconstruction introduction:

Takens theorem: M is D ₂dimension stream shape, be a smooth differomorphism, y:M → R, y have Second Order Continuous derivative, an embedding.

A suitable embedding dimension can be found according to Takens theorem; if the dimension postponing coordinate is ATTRACTOR DIMENSIONS; so regular track can be recovered at this embedded space; namely under homeomorphic meaning, recover the dynamics of chaotic attractor, and then establish solid theoretical foundation for the prediction of chaos time sequence.For time series { x _k: k=1,2 ..., n}, if can choose rightly Embedded dimensions m and time delay t ₀, reconstructing its phase space is:

$X_1={[x_1,x_{t_0+1},x_{2t_0+1},\&CenterDot;\&CenterDot;\&CenterDot;,x_{(m-1)t_0+1}]}^T,$

$X_2={[x_2,x_{t_0+2},x_{2t_0+2},\&CenterDot;\&CenterDot;\&CenterDot;,x_{(m-1)t_0+2}]}^T,---\left(1\right)$

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

$X_{n-(m-1)t_0}={[x_{n-(m-1)t_0},x_{t_0+n-(m-1)t_0},x_{2t_0+n-(m-1)t_0},\&CenterDot;\&CenterDot;\&CenterDot;,x_{(m-1)t_0+n-(m-1)t_0}]}^T$

Wherein, N=n-(m-1) t ₀for reconstructing the length of time vector serial.

Therefore, in phase space reconfiguration, the selection of time delay and Embedded dimensions is the most important, and the choose reasonable of these two parameters and optimization directly have influence on authenticity and the reliability of approached attractor.

(2) based on the choosing method of C-C method Parameters for Phase Space Reconstruction:

Time delay in phase space reconfiguration, it is independently little as far as possible value that its selection principle normally makes it in statistical significance.Research shows, affects the principal element of phase space reconstruction quality, is not only to choose separately t time delay ₀with Embedded dimensions m, the more important thing is by with the embedding window width t joined together _w=(m-1) t ₀determination, the C-C method proposed on this basis is combined and is considered t time delay ₀with Embedded dimensions m, time delay can be calculated simultaneously and embed time window width.The method forms statistic by the correlation integral of sequence, and statistic represents the correlativity of Nonlinear Time Series.T is determined by the graph of a relation of statistic and time delay ₀(m-1) t ₀, thus determine Embedded dimensions m.

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

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 formula: m represents Embedded dimensions; N is length of time series; R is time series radius; C _s(m, N, r, t) is for embedding time series correlation integral; for embedding time series correlation integral when Embedded dimensions is m.

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

Define the maximum deviation about r:

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

In formula: △ S (m, N, t) has measured the maximum deviation about radius r.General N, the selection of r, t has certain limit when 2≤m≤5, σ/2≤r≤2 σ, and during N >=500 (σ is seasonal effect in time series mean square deviation or standard deviation), progressive distribution can obtain well approximate by 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:

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

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

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

From above formula, optimum delay time is corresponding first zero point, or first minimal value; S _cort time corresponding to () minimum value is best embedding time window width t _w, by embedding time window formula t _w=(m-1) t ₀embedded dimensions m can be obtained.

(3) weight-plus local-region method:

First order local area method refers to the small neighbourhood carrying out matching n-th surrounding with X (t+1)=aX (t)+b.

If the field of n-th comprises a t ₁, t ₂..., t _p, then above formula can be expressed as

$\left|\begin{array}{c}X(t_1+1)\\ X(t_2+1)\\ \&CenterDot;\&CenterDot;\&CenterDot;\\ X(t_p+1)\end{array}\right|=a\left|\begin{array}{c}X\left(t_1\right)\\ X\left(t_2\right)\\ \&CenterDot;\&CenterDot;\&CenterDot;\\ X\left(t_p\right)\end{array}\right|+b---\left(7\right);$

If central point X _kneighbor point be X _ki, i=1,2 ..., q, and to X _kdistance be D _iif, D _md _iin minimum value, defining point X _kiweights be

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

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

X _ki+1＝aX _ki+be i＝1,2,...,q （9）；

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 adding-weight one-rank local-region method.

The specific algorithm flow process of adding-weight one-rank local-region method is as follows:

I, phase space reconstruction.

Calculate seasonal effect in time series Embedded dimensions and time delay according to C-C method, obtaining phase space reconstruction is

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 phase space reconstruction, N=n-(m-1) t ₀.

Ii, searching point of proximity.

In phase space, calculate the space length between each point to central point X (N), find out X _kreference vector collection X _ki, i=1,2 ..., q, and put X _kito X _kdistance be D _iif, D _md _iin minimum value, defining point X _kiweights be

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

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

$\left|\begin{array}{c}{X}_{k1+1}\\ X_{k2+1}\\ \&CenterDot;\&CenterDot;\&CenterDot;\\ X_{\mathrm{kq}+1}\end{array}\right|=a\left|\begin{array}{cc}{X}_{k1}& e\\ X_{k2}& e\\ \&CenterDot;\&CenterDot;\&CenterDot;& \\ X_{\mathrm{kq}}& e\end{array}\right|\left[\begin{array}{c}q\\ b\end{array}\right],$ Wherein $e=\left[\begin{array}{c}1\\ 1\\ \&CenterDot;\\ \&CenterDot;\\ \&CenterDot;\\ 1\end{array}\right]---\left(13\right);$

Application least square method has

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

Above formula is regarded as the binary function about unknown number a, b, both sides ask local derviation to obtain

$\left\{\begin{array}{c}a\underset{i=1}{\overset{q}{\mathrm{\&Sigma;}}}{P}_i{x_{\mathrm{ki}}}^2+b\underset{i=1}{\overset{q}{\mathrm{\&Sigma;}}}{P}_i{x}_{\mathrm{ki}}=\underset{i=1}{\overset{q}{\mathrm{\&Sigma;}}}{P}_i{x}_{\mathrm{ki}}{x}_{\mathrm{ki}+1}\\ a\underset{i=1}{\overset{q}{\mathrm{\&Sigma;}}}{P}_i{x}_{\mathrm{ki}}+b=\underset{i=1}{\overset{q}{\mathrm{\&Sigma;}}}{P}_i{x}_{\mathrm{ki}+1}\end{array}\right.---\left(15\right);$

Solving equations formula (15), obtains a, b, then substitutes into formula (13) and obtains predictor formula.

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

(4) predicated error Probabilistic Analysis:

I, predicated error evaluation index

Conventional predicated error evaluation method has root-mean-square error, mean absolute error and mean absolute percentage error.

Root-mean-square error is:

$e_{\mathrm{RMSE}}=\sqrt{\frac{1}{n+1}\underset{t=t_0}{\overset{t_0+n}{\mathrm{\&Sigma;}}}{(P_{\mathrm{pred}}\left(t\right)-P_{\mathrm{meas}}\left(t\right))}^2}---\left(16\right);$

Mean absolute error is:

$e_{\mathrm{MAE}}=\frac{1}{n+1}\underset{t=t_0}{\overset{t_0+n}{\mathrm{\&Sigma;}}}|P_{\mathrm{pred}}\left(t\right)-P_{\mathrm{meas}}\left(t\right)|---\left(17\right);$

Mean absolute percentage error is:

$e_{\mathrm{MAPE}}=\frac{1}{n+1}\underset{t=t_0}{\overset{t_0+n}{\mathrm{\&Sigma;}}}\left|\frac{P_{\mathrm{pred}}\left(t\right)-P_{\mathrm{meas}}\left(t\right)}{P_{\mathrm{meas}}\left(t\right)}\right|---\left(18\right);$

In formula: P _predt predicted value that () is t; P _meast measured value that () is t; t ₀for prediction start time; N counts for always predicting.

Incomplete same due to the installed capacity of each wind energy turbine set, formula (16) and formula (17) compare the predicated error between each wind energy turbine set; Formula (18) is very low or when being approximately zero in wind power output, e _mAPEvery large even infinitely great, be unfavorable for statistical study.Therefore, the inventive method adopts relative error to carry out statistical forecast error characteristics, definition wind energy turbine set predicated error e _tfor the wind power prediction value P of t _meas(t) and wind power measured value P _predthe difference of (t), namely

e _t＝P _pred(t)-P _meas(t) （19）；

Because the amplitude of variation difference of predicated error under different wind power level is very large, particularly very low at power or close to zero time, therefore, with the specified installed capacity of wind energy turbine set for reference value, normalization predicated error e _tfor

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

In formula: e _t' be wind power prediction error perunit value; P _wGfor the specified installed capacity of 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 _meast () is normalized,

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

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

In order to further refinement is analyzed, the wind energy turbine set installed capacity after normalization is divided into n section, the scope of each section is error statistics analysis is carried out to each section.

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

Wind power prediction certainly exists certain error, its error can present certain probability density characteristics on the whole, certain wind energy turbine set 2010.1 ~ 2010.12 wind power prediction relative error is analyzed, its distribution character meets normal distribution matching substantially, therefore adopt the distribution of normal distribution curve matching wind power prediction relative error, as shown in Figure 1.

By known to the analysis of Fig. 1, there is certain probability density characteristics in wind power prediction error on the whole, and this distribution character can describe with normal distribution, and therefore, its probability density function expression formula is

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

In formula: μ _ffor expectation of a random variable; σ _ffor the standard deviation of stochastic variable.

Formula (23) is utilized to can be calculated the probability density of predicated error in each power range.For the predicted power value P of t _predt (), given α (0 < α < 1), meets P (P _pred(t) _min< P _pred(t) < P _pred(t) _max)=1-α, then claim interval [P _pred(t) _min, P _pred(t) _max] be power P _predt the degree of confidence of () is the fiducial interval of 1-α, wherein P _pred(t) _minand P _pred(t) _maxcertain section scope upper lower limit value of wind energy turbine set installed capacity after expression normalization respectively.

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

Concrete calculation procedure is described as follows:

1. to a certain wind power prediction value P _predt (), judges after normalization which power range it belongs to; Power range comprises ten sections of decile;

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

3. distribute according to the power probability of this section, find 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 _predthe fiducial interval of (t).

2) basic condition that probabilistic transient stability calculates is determined:

The basic condition that probabilistic transient stability calculates comprises wind power prediction value P _pred(t), predicted power fiducial interval higher limit P _pred(t) _min, predicted power lower limit of confidence interval value P _pred(t) _max, ultra-short term, network topology structure and each Generator Status.

Two, transient stability risk evaluation model:

Probability model:

Under this method employing analytic calculation method of operation, the power system transient stability probability of certain period, comprises the enchancement factors such as fault rate, fault type and abort situation.Study a question to simplify; when computational scheme probability of malfunction index; assuming that relay protection system action; disregard circuit breaker failure; do not consider the uncertainty impact of power load distributing; do not consider transient fault, generator and transformer fault, and suppose separate between different faults (event) and be independent of each other.

A, line fault probability of happening:

When not considering weather conditions to transmission line of electricity reliability effect, line failure rate can represent with the circuit number of stoppages in a period of time.Because line fault number of times can be obtained by historical reliability data analysis, so line fault occurrence probability function represents by Poisson distribution, parameter is frequency lambda _k.Assuming that the fault that circuit occurs is permanent fault, and the probability that two circuits break down simultaneously is negligible, therefore circuit L _kon the probability that breaks down as the formula (24).

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

In formula: P _r(L _k) for circuit occur probability of malfunction; λ _krepresent the circuit L in certain hour section _kfailure-frequency; T represents trouble duration.

B, fault type probability:

Suppose that circuit has four kinds of mutual independently fault types, by order of severity sequence be: three-phase ground, two phase ground, phase fault, single-phase earthing.To certain specific circuit, type is C _jfault occur frequency 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 formula: P _r(C _j) be the fault type probability of circuit; f _ifor the frequency that line fault occurs.

Reference the probability that the above-mentioned Four types fault that table 1 provides for working group of IEEE electric system relay special commission occurs, the fault of other type is ignored because the probability of generation is very little or consequence is very little.

The probability that the dissimilar fault of table 1 occurs

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

C, abort situation probability:

Generally speaking, the information on failure point of power transmission line is less, is difficult to determine the impact on line fault such as weather, geographical conditions.According to Bayes---Laplce's criterion (Bayes-Laplace Criterion): if better distribution can not have been proved, adopt univesral distribution, thus it is identical for thinking on circuit that each position probability breaks down, and therefore the position of fault is on the line in unified discrete distribution.If the total length of certain circuit is L, trouble spot is L to the distance of circuit head end _d, defining variable D:

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

From above formula, D above changes in interval [0,1], in order to the convenience studied a question, usually the abort situation of circuit is simplified further.Suppose circuit to be divided into three sections, every section of use typical fault point replaces, then obtain the Location discrete probability distribution shown in Fig. 3, and have

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

In formula: P _r(D _h) represent the probability that on circuit, break down in h trouble spot; n _drepresent discrete trouble spot sum on circuit.

3) transient stability probability of malfunction is determined:

Assuming that fault rate, 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) be

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

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

4) judge whether Transient Instability occurs: if there is Transient Instability, carry out step 6); If not there is Transient Instability, but there is the fault causing Transient Instability, then carry out step 5).

5) transient stability failure effect severity is assessed:

This method establishes electric power system transient stability consequence Integrated Evaluation Model, gives the definition of failure effect severity, proposes transient stability order of severity Consequence Assessment index, and gives respective function expression formula; Cause the fault of Transient Instability if having, then adopt control cost to weigh Transient Instability consequence, realize assessing all sidedly electric power system transient stability consequence problem.Based on existing Transient Stability Evaluation index, propose three kinds herein and estimation of transient can stablize the consequence index of the order of severity, comprise merit angle unstability severity, voltage deviation severity and frequency shift (FS) severity, the order of severity of Power System Transient Stability Contingency is stated.

1. merit angle unstability severity:

Merit angular difference between generator is the basic index of electric power system transient stability criterion.When electric system suffers large disturbance, the input mechanical output of generator and output electromagnetic power out of trim, cause the change of rotor angle, occur between each group relatively to wave, when this wave finally make the relative angle between some generators constantly increase time, can not keep synchronous between generator, namely system loses transient stability.

If system has M platform generator, i-th kind of fault occurs jth bar circuit, then defining merit angle unstability severity 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 formula: E _ibe i-th fault model; S (E _i, △ δ _{im, max}) be m platform generator's power and angle unstability severity; △ δ _{im, max}be depart from the maximum generator's power and angle in the generator center of inertia between i-th age at failure.

Fig. 4 is angle stability severity function, represents that 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 generator's power and angle unstability severity, and unit all adopts perunit value.When merit angular difference is more than or equal to △ δ _maxtime (system unstability criterion angle), its severity is 1; When merit angular difference is less than 0.6 △ δ _maxtime, its severity is approximately 0.The stable severity function of generator's power and angle is

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

2. variation severity:

Voltage stabilization refer to electric system under normal operation or after suffering disturbance in system all buses maintain the ability of acceptable voltage.At voltage unsafe condition, system voltage departs from normal voltage level, collapse of voltage even occurs and causes whole power system collapse.What variation severity reflected is the extent of injury causing generator bus variation in system after electric system is broken down.

If system has Y bar bus, i-th kind of fault occurs jth bar circuit, then defining transient voltage skew severity is

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

In formula: E _ibe i-th fault model; S (E _i, u _{iy, max}) be y article of busbar voltage skew severity; u _{iy, max}be y article of bus peak excursion magnitude of voltage after i-th fault clearance.

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

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

3. frequency shift (FS) severity:

After electric system suffers large disturbances, there is large imbalance in generating and workload demand, causes frequency shift (FS).When system frequency exceeds generator allowed band, if deal with improperly, will chain reaction be caused, even cause large-area power-cuts.What frequency shift (FS) severity reflected is the extent of injury that after electric system is broken down, generator frequency offsets.

If system has S platform generator, i-th kind of fault occurs jth bar circuit, then defining frequency shift (FS) severity is

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

In formula: E _ibe i-th 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-th fault clearance.

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

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

6) Transient Instability severity degree is assessed:

In order to weigh the consequence of Transient Instability more intuitively, adopt the method controlling cost that the relevant consequence of Transient Instability is divided into 3 parts herein: cutting load loses; Unit starting and maintenance cost and unknown losses.

1. cutting load loss:

First generator failure can cause meritorious minimizing of exerting oneself, and frequency reduces.If under frequency is reduced to certain level, then low frequency Automatic Load device starting operation, makes partial line circuit breaker tripping cut-out load.Usual way only considered sale of electricity loss, does not consider that user has a power failure caused indirect loss, and cutting load loss defined herein comprises two parts: a part is the sale of electricity loss that reduction plans causes; Another part is that user has a power failure the indirect loss caused.

Im _L＝P _L×(r ₁+r ₂)×h （36）；

Im in formula _lrepresent the loss of cutting load; P _lfor cutting load amount; r ₁and r ₂represent the direct loss that cutting load causes and indirect loss respectively; H is interruption duration.

2. maintenance cost and unit starting cost:

When generator and circuit break down, again could restore electricity after needing maintenance out of service, maintenance cost is defined as C _rep; Stoppage in transit unit must restart and be incorporated into the power networks, and the start-up cost of needs is C _start, therefore maintenance cost and unit starting cost are such as formula shown in (37).

Im _r＝C _rep+C _start（37）；

3. unknown losses:

Very doubt loss, as extreme weather disaster, cascading failure etc., is defined as Im'.

Therefore, the overhead control cost that Transient Instability causes is

Im＝Im _L+Im _r+Im' （38）。

7) transient stability risk assessment index is calculated:

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 above-mentioned formula, R _{Δ δ}, R _uand R _frepresent transient rotor angle stability risk indicator, transient voltage skew risk indicator and transient frequency skew risk indicator respectively; R _instfor Transient Instability risk indicator; P _r(S) be transient stability probability of malfunction; S _{Δ δ}, S _uand S _frepresent transient rotor angle stability sequence severity, transient voltage skew sequence severity and transient frequency skew sequence severity respectively.

8) judge whether that all circuit transient state risk assessment indexs calculate all: if the transient stability risk of all circuits calculates all, then carry out step 9); The circuit do not calculated if having, then return step 5).

9) transient stability risk assessment desired value is exported.

Provided by the inventionly take into account wind-powered electricity generation probabilistic transient stability risk online evaluation method, Transient Stability Evaluation can be carried out to transmission line of electricity and even whole system, well the probability of power system transient stability and the consequence caused can be combined, management and running personnel can carry out system cloud gray model decision-making according to the multiple transient stability risk indicator obtained.

Finally should be noted that: above embodiment is only in order to illustrate that technical scheme of the present invention is not intended to limit, although with reference to above-described embodiment to invention has been detailed description, those of ordinary skill in the field are to be understood that: still can modify to the specific embodiment of the present invention or equivalent replacement, and not departing from any amendment of spirit and scope of the invention or equivalent replacement, it all should be encompassed in the middle of right of the present invention.

Claims (14)

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

1) ultrashort-term wind power predicted value and Estimating Confidence Interval thereof is determined;

2) basic condition that probabilistic transient stability calculates is determined;

3) transient stability probability of malfunction is determined;

4) judge whether Transient Instability occurs;

5) transient stability failure effect severity is assessed;

6) Transient Instability severity degree is assessed;

7) transient stability risk assessment index is calculated;

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

9) transient stability risk assessment desired value is exported;

Described step 1) in, based on chaos time sequence model, utilize wide-area monitoring systems WAMS real-time measurement wind power data and wind power historical data, calculate wind power prediction value P _pred(t); Wind power prediction value P is calculated according to the distribution of the power prediction probability of error _predthe fiducial interval of (t); Described ultra-short term refers to 15 minutes;

Described chaos time sequence model is based on phase space reconfiguration, utilizes weight-plus local-region method to obtain ultrashort-term wind power predictor formula;

Adopt the distribution of the wind energy turbine set relative error statistics power prediction probability of error; Described wind energy turbine set relative error e _tfor the wind power prediction value P of t _pred(t) and wind power measured value P _meast the difference of (), namely represents by following (19) formula:

e _t＝P _pred(t)-P _meas(t) (19)；

The described fiducial interval calculating ultrashort-term wind power predicted value comprises the steps:

A, judge wind power prediction value P _predthe affiliated power range of (t);

B, search probability of error densimetric curve corresponding to described power range;

C, determine power probability density;

D, searching accumulated probability are more than or equal to the interval of fiducial interval degree 1-α as fiducial interval;

In described step a, described power range comprises ten sections of decile;

In described step b, described probability of error densimetric curve is normal distribution curve, and described probability of error density function following (23) formula represents:

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

In formula: μ _ffor expectation of a random variable; σ _ffor the standard deviation of stochastic variable;

In described step c, determine the power probability density of each power range according to (23) formula;

Probabilistic transient stability comprises line fault probability of happening, line fault type probability and Location probability;

If the fault that circuit occurs is permanent fault, and the probability that two circuits break down simultaneously is disregarded, then circuit L _kon the probability that breaks down represent by following (24) formula:

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

In formula: P _r(L _k) for circuit occur probability of malfunction; λ _krepresent the circuit L in the time period _kfailure-frequency; T represents trouble duration; M is natural number;

Described fault type is divided into three-phase ground, two phase ground, phase fault and single-phase earthing by the order of severity; Fault type is C _jfault occur frequency be f _j, then its probability occurred represents by 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 formula: P _r(C _j) be the fault type probability of circuit; f _jfor the frequency that line fault occurs;

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

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

Described Location probability following (28) formula represents:

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

In formula: P _r(D _h) represent the probability that on circuit, break down in h trouble spot; n _drepresent discrete trouble spot sum on circuit.

2. transient stability risk online evaluation method as claimed in claim 1, is characterized in that, described step 2) in, the basic condition that described probabilistic transient stability calculates comprises wind power prediction value P _pred(t), predicted power fiducial interval higher limit P _pred(t) _min, predicted power lower limit of confidence interval value P _pred(t) _max, ultra-short term, network topology structure and each Generator Status.

3. transient stability risk online evaluation method as claimed in claim 1, it is characterized in that, described step 3) in, if line fault probability of happening, line fault type probability and Location probability are separate and be independent of each other, then transient stability probability of malfunction P _r(E _i) represent by 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 formula: P _r(L _k) for line fault occur probability; P _r(C _j) be line fault type probability, P _r(D _h) be Location probability.

4. transient stability risk online evaluation method as claimed in claim 1, is characterized in that, described step 4) in, if there is Transient Instability, carry out step 6); If not there is Transient Instability, but there is the fault causing Transient Instability, then carry out step 5).

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

6. transient stability risk online evaluation method as claimed in claim 5, is characterized in that, if electric system has M platform generator, i-th kind of fault occurs jth bar circuit, then merit angle unstability severity following (30) formula represents:

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

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

The stable severity function of generator's power and angle represents by following (31) formula:

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

7. transient stability risk online evaluation method as claimed in claim 5, is characterized in that, if electric system has Y bar bus, i-th kind of fault occurs jth bar circuit, then transient voltage skew severity following (32) formula represents:

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

In formula: E _ibe i-th fault model; S (E _i, u _{iy, max}) be y article of busbar voltage skew severity; u _{iy, max}be y article of bus peak excursion magnitude of voltage after i-th fault clearance;

Transient voltage skew severity function following (33) formula represents:

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

8. transient stability risk online evaluation method as claimed in claim 5, is characterized in that, if electric system has S platform generator, i-th kind of fault occurs jth bar circuit, then define frequency shift (FS) severity and be

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

In formula: E _ibe i-th 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-th fault clearance;

Generator frequency skew severity function following (35) formula represents:

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

9. transient stability risk online evaluation method as claimed in claim 1, it is characterized in that, described step 6) in, adopt the method controlling cost Transient Instability severity degree to be divided into cutting load loss, unit starting and maintenance cost and unknown losses three parts.

10. transient stability risk online evaluation method as claimed in claim 9, is characterized in that, described cutting load loss comprises sale of electricity loss that reduction plans causes and user and to have a power failure the indirect loss caused; Described cutting load loss represents by following (36) formula:

Im _L＝P _L×(r ₁+r ₂)×h (36)；

In formula: Im _lrepresent cutting load loss; P _lfor cutting load amount; r ₁and r ₂represent that the sale of electricity loss that causes of cutting load and user have a power failure the indirect loss caused respectively; H is interruption duration.

11. transient stability risk online evaluation methods as claimed in claim 9, is characterized in that, described unit starting and maintenance cost following (37) formula represent:

Im _r＝C _rep+C _start(37)；

In formula: Im _rrepresent unit starting and maintenance cost; C _reprepresent maintenance cost; C _startrepresent unit starting expense.

12. transient stability risk online evaluation methods as claimed in claim 9, it is characterized in that, described unknown losses comprises extreme weather disaster and cascading failure; Unknown losses is represented with Im'; The overhead control cost that Transient Instability causes following (38) formula represents:

Im＝Im _L+Im _r+Im' (38)；

Wherein: Im _lrepresent cutting load loss; Im _rrepresent unit starting and maintenance cost.

13. transient stability risk online evaluation methods as claimed in claim 1, it is characterized in that, described step 7) in, described transient stability risk assessment index comprises transient rotor angle stability risk assessment index, transient voltage skew risk assessment index, transient frequency skew risk assessment index and Transient Instability risk assessment index; Represent by following (39)-(42) formula 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 formula: R _{Δ δ}, R _uand R _frepresent transient rotor angle stability risk indicator, transient voltage skew risk indicator and transient frequency skew risk indicator respectively; R _instfor Transient Instability risk assessment index; P _r(S) be transient stability probability of malfunction; S _{Δ δ}, S _uand S _frepresent transient rotor angle stability sequence severity, transient voltage skew sequence severity and transient frequency skew sequence severity respectively; Im represents the overhead control cost that Transient Instability causes.

14. transient stability risk online evaluation methods as claimed in claim 1, is characterized in that, described step 8) in, if the transient stability risk of all circuits calculates all, then carry out step 9); The circuit do not calculated if having, then return step 5).