CN103928923B - A kind of network stationary power quality method for early warning based on sensitivity analysis - Google Patents

Abstract

The invention discloses a kind of network stationary power quality method for early warning based on sensitivity analysis, comprise the following steps: 1) determine the interstitial content in electrical network and network topology structure; 2) Load flow calculation initial parameter and the stationary power quality warning data of electrical network is gathered; 3) data integrity degree verification; 4) data analysis, comprises exceed standard analysis and anomaly analysis; 5) build sensitivity coefficient discrimination matrix, calculate early warning dynamic threshold; 6) comprehensive pre-warning analysis, obtains early warning result.The inventive method utilizes the dynamic threshold obtained by sensitivity coefficient analysis, comprehensively analyzes, obtain early warning result to the degree that exceeds standard of node warning data each in network and intensity of anomaly.

Description

A kind of network stationary power quality method for early warning based on sensitivity analysis

Technical field

The invention belongs to power quality analysis technical field, relate to a kind of power quality analysis method for electric power system and power consumer transmission and distribution network.

Background technology

The quality of power supply can simply be defined as: be related to normally the work voltage of (or run), each index of electric current of power supply, using electricity system and equipment and depart from the degree of prescribed limit.This shows the importance of the quality of power supply.

At present, mainly concentrate in collecting method or the basic handling to the data after collection to the research of the quality of power supply both at home and abroad, these researchs mainly comprise equipment for monitoring power quality and method, electricity quality evaluation and improvement, and the aspect such as electrical energy power quality disturbance identification, and electric energy quality monitoring data exception is detected and early warning, particularly whole network, multinode the research such as early warning still inadequate.Outliers mining is carried out to the electric energy quality monitoring data of whole network, multinode, and send early warning reliably in time, can find existed in operation of power networks or potential power quality problem, and send early warning information to the functional organizations such as fortune inspection, maintenance and power quality disturbance terminal as early as possible.But the existing research about early warning mainly concentrates on the excavation of the abnormal data of certain node monitors and analyzing, lack the parsing to whole network, multinode and process, early warning can not be made for macroreticular quality of power supply unusual condition, cause network power quality fault to can not get timely solution, thus fault may be caused to expand further.For this defect, the present invention intends on the basis analyzing each node sensitivity in electrical network, provide the dynamic threshold being applicable to different node, realize whole network, the early warning of the multinode quality of power supply, thus accomplished electrical network power quality problem is early found, early solve, improve the fail safe of electrical network overall operation, stability, reliability and economy.

Summary of the invention

Technical problem: the present invention is directed to the deficiencies in the prior art, a kind of quality of power supply state making to understand for electricity consumption both sides each node in affiliated area electrical network a period of time is provided, improves the network stationary power quality method for early warning based on sensitivity analysis of operation of power networks reliability and economy.

Technical scheme: the network stationary power quality method for early warning based on sensitivity analysis of the present invention, comprises the steps:

1) interstitial content in electrical network is determined, then according to the network topology structure of the connected mode determination electrical network between the node space layout in electrical network and node;

2) Load flow calculation initial parameter and the stationary power quality warning data of electrical network is gathered;

3) to described step 2) the stationary power quality warning data that gathers carries out integrity degree verification, if meet integrity degree requirement, then enter step 4), otherwise after abandoning and gathering stationary power quality warning data, return step 2) Resurvey stationary power quality warning data;

4) data analysis: according to existing quality of power supply national Specification value, to step 2) the stationary power quality warning data that gathers exceeds standard detection, counts the times N that exceeds standard; Anomaly analysis is carried out to stationary power quality warning data simultaneously, obtain the statistic S quantizing its wave form varies degree;

5) dynamic threshold is calculated: according to step 2) the Load flow calculation initial parameter that gathers, utilize Continuation Method to analyze network trend, then the sensitivity coefficient of each node in computing network;

Divide the sensitivity coefficient domain of definition interval simultaneously, then build sensitivity coefficient discrimination matrix, analyze the corresponding relation of sensitivity coefficient and dynamic threshold variable quantity in each domain of definition interval;

Last interval belonging to the sensitivity coefficient of node each in network, calculate the threshold value N that dynamically exceeds standard of each node _twith dynamic abnormal threshold value S _t;

6) comprehensive pre-warning analysis: use piecewise nonlinear function to quantize the degree that exceeds standard and the intensity of anomaly of stationary power quality warning data respectively;

Then based on weigthed sums approach, the comprehensive degree that exceeds standard and the intensity of anomaly analyzing stationary power quality warning data, obtains exceeding standard and intensity of anomaly comprehensive quantification value;

Ultimate analysis obtains stationary power quality early warning result.

The step 2 of the inventive method) in, the concrete grammar of data acquisition is:

Gather the Monitoring Data of each node stationary power quality warning index in electrical network, form " stationary power quality warning data ";

Simultaneously the initial parameter of Load flow calculation in collection network, is formed " Load flow calculation initial parameter ".

The step 3 of the inventive method) in the concrete grammar that stationary power quality warning data carries out integrity degree verification be:

If stationary power quality warning data number exceedes the verification threshold value of normal Monitoring Data number, then judge to meet integrity degree requirement, otherwise do not meet integrity degree requirement.

The step 3 of the inventive method) in, verification threshold value can be 50%.

The inventive method step 5) in, the concrete grammar calculating dynamic threshold is:

51) according to step 2) the Load flow calculation initial parameter that gathers, Continuation Method is utilized to analyze network trend, calculate state variables all in electrical network, control variables and relevant parameter, then based on Sensitivity Analysis Method, the sensitivity coefficient of each node in computing network;

Divide the sensitivity coefficient domain of definition interval simultaneously, then build sensitivity coefficient discrimination matrix, analyze the corresponding relation of sensitivity coefficient and dynamic threshold variable quantity in each domain of definition interval;

52) belonging to the sensitivity coefficient judging each node in network, the domain of definition is interval, according to the corresponding relation of sensitivity coefficient and dynamic threshold variable quantity in domain of definition interval, finds out the dynamic threshold variable quantity that each node sensitivity coefficient is corresponding;

53) dynamic threshold of each node is calculated in accordance with the following methods respectively: the changes of threshold amount that dynamically exceeds standard in dynamic threshold variable quantity and dynamic abnormal changes of threshold amount are added with respective static threshold respectively, dynamically exceeded standard threshold value N _twith dynamic abnormal threshold value S _t.

The inventive method step 6) idiographic flow be:

Use following piecewise nonlinear function, quantize the degree that exceeds standard of stationary power quality warning data:

$u(N,N_T)=\left\{\begin{array}{cc}60+(e^{2.4t_1}-1),& t_1>2\\ 40t_1+20,& 1<t_1<2\\ 100e^{4(t_1-2)},& 0<t_1<1\end{array}\right.$

In formula, u (N, N _t) be represent stationary power quality warning data to exceed standard the quantized value of degree; t ₁the number of times ratio value that exceeds standard, t ₁=N/N _t;

Use following piecewise nonlinear function, quantize the intensity of anomaly of stationary power quality warning data:

$v(S,S_T)=\left\{\begin{array}{cc}60+(e^{2.4t}-1),& t>2\\ 40t+20,& 1<t<2\\ 100e^{4(t-2)},& 0<t<1\end{array}\right.$

In formula, v (S, S _t) be the quantized value representing stationary power quality warning data intensity of anomaly; t ₂abnormal coefficient ratio value, t ₂=S/S _t;

Then following formula is utilized to obtain exceeding standard of stationary power quality warning data and intensity of anomaly comprehensive quantification value:

F＝au(N,N _T)+bv(S,S _T)

In formula, F represents stationary power quality warning data to exceed standard and the comprehensive quantification value of intensity of anomaly; A is that stationary power quality warning data exceeds standard the proportionality coefficient of degree quantized value, and b is the proportionality coefficient of stationary power quality warning data intensity of anomaly quantized value;

Finally calculate stationary power quality early warning result according to following formula:

Beneficial effect: compared with prior art, the present invention has the following advantages:

(1) scope improving the application of quality of power supply early warning system is conducive to.The existing research about quality of power supply early warning mainly concentrate on certain node exceeded standard and abnormal data excavation and analyze, lack the overall quality of power supply early warning to whole network, multinode, whole network quality of power supply incipient fault is caused to fail to obtain Timeliness coverage, and solve, thus fault may be made to expand further.This network stationary power quality method for early warning based on sensitivity analysis provides the quality of power supply early warning for localized network entirety, thus effectively can make up the deficiency of pre existing alarm system.

(2) accuracy improving quality of power supply early warning system is conducive to.What the research of existing single node quality of power supply early warning adopted is single certainty static threshold, does not make dynamic conditioning according to the quality of power supply characteristic of each node own, so may cause because threshold value arranges inaccurate and make early warning by mistake and leak early warning.The present invention is based on Continuation Method and sensitivity analysis, to deployment analysis such as each node sensitivity coefficients of electrical network, set up the dynamic threshold being adapted to the different quality of power supply of each node and requiring, thus effectively can improve the precision of pre existing alarm system.

(3) practicality and the discrimination that improve quality of power supply early warning system is conducive to.The research of existing quality of power supply early warning according to single data exceed standard or intensity of anomaly makes early warning, cause early warning result only to consider the situation of change in a certain respect of data, to make early warning by mistake due to unilateral characteristic analytical data so may cause and leak early warning.Comprehensive quantification of the present invention analyzes exceeding standard of data and intensity of anomaly, more comprehensively characteristic analytical data.Thus accomplished, while raising pre existing alarm system precision, to reduce the number of plies of advanced warning grade, reasonable arrangement discrimination, make it more meet functional need.

(4) be conducive to the security reliability improving regional power grid operation, according to the thinking that this network power quality pre-alert method provides, cause the quality of power supply early warning event of regional power grid to provide in time counter-measure reliably to a period of time.For for electricity consumption both sides, the operation/production schedule of self can be adjusted in good time, avoid the quality of power supply to worsen further, improve the security reliability of operation of power networks.

Accompanying drawing explanation

Fig. 1 is flow chart of the present invention.

Fig. 2 is the flow chart analyzing dynamic threshold.

Embodiment

Below in conjunction with accompanying drawing, technical scheme of the present invention is described further.

Network stationary power quality method for early warning based on sensitivity analysis of the present invention, flow process as shown in Figure 1, specifically comprises the following steps.

1) interstitial content in electrical network is determined, then according to the network topology structure of the connected mode determination electrical network between the node space layout in electrical network and node.

2) Load flow calculation initial parameter and the stationary power quality warning data of electrical network is gathered.

Gather the Monitoring Data of each node stationary power quality warning index in electrical network, form " quality of power supply warning data ";

Simultaneously the initial parameter of Load flow calculation in collection network, is formed " Load flow calculation initial parameter ".

3) to step 2) the stationary power quality warning data that gathers carries out integrity degree verification, if meet integrity degree requirement, then enter step 4), otherwise abandon after gathering stationary power quality warning data, return step 2) Resurvey stationary power quality warning data, but without the need to Resurvey Load flow calculation initial parameter.

Step 3) in integrity degree verification concrete grammar be:

If stationary power quality warning data number exceedes the verification threshold value of normal Monitoring Data number, then judge to meet integrity degree requirement, otherwise do not meet integrity degree requirement.

The present invention is according to practical operating experiences and user's needs, and setting verification threshold value is 50%.Such as current harmonics produces data in every 3 minutes, within one day, should have 480, if the voltage deviation data then got are lower than 240, then think that this day data of this voltage deviation is invalid.

4) to step 2) the stationary power quality warning data that gathers carries out data analysis.

According to existing quality of power supply national Specification value, to step 2) the stationary power quality warning data that gathers exceeds standard detection, counts the times N that exceeds standard; Anomaly analysis is carried out to stationary power quality warning data simultaneously, obtain the statistic S quantizing its wave form varies degree.

Above-mentioned power quality standard comprises:

GB/T12326-2008 " quality of power supply voltage fluctuation and flicker "

GB/T15945-2008 " quality of power supply power system frequency deviation "

GB/T14549-1993 " quality of power supply utility network harmonic wave "

GB/T18481-2001 " quality of power supply temporary overvoltage and transient overvoltage "

GB/T15543-2008 " quality of power supply imbalance of three-phase voltage "

GB/T12325-2008 " quality of power supply supply power voltage deviation "

Step 4) in the concrete steps of anomaly analysis be:

(1) the length W of sliding window is determined;

(2) sliding window is from first, this sky data, often slides once, and window just moves a numerical value backward.Such as a jth window w ^j=x (k): k=j-W+1, j-W+2 ..., j}, respectively calculate statistical values degree of bias g _{1, j}, g _2jj, and calculate the mean value u of this window, and standard deviation sigma.Wherein the circular of the degree of bias and kurtosis is as follows:

The degree of bias has various definitions, and normal distribution and all symmetrical degrees of bias are all 0, and the computing formula extensively adopted is:

$s_k=\left\{\begin{array}{c}{g}_1=\frac{N\&CenterDot;\underset{n=1}{\overset{N}{\&Sigma;}}{\left(x\right(n)-\hat{m})}^3}{(N-1)\&CenterDot;(N-2)\&CenterDot;{\left(\widehat{\mathrm{\&sigma;}}\right)}^3}\\ \{E\left(g_1\right)-\frac{\sqrt{\mathrm{var}\left(g_1\right)}}{\sqrt{a}}\&le;g_1\&le;E\left(g_1\right)+\frac{\sqrt{\mathrm{var}\left(g_1\right)}}{\sqrt{a}}\}\\ E\left(g_1\right)=0,\\ \{-\mathrm{\&infin;}\&le;g_1\&le;E\left(g_1\right)-\frac{\sqrt{\mathrm{var}\left(g_1\right)}}{\sqrt{a}},E\left(g_1\right)+\frac{\sqrt{\mathrm{var}\left(g_1\right)}}{\sqrt{a}}\&le;g_1\&le;+\mathrm{\&infin;}\}\end{array}\right.---\left(1\right)$

Wherein μ is the mean value of variable X, μ _ibe the i-th center, rank square, E () is for expecting.Wherein represent expectation mean value and the standard deviation of x (n) respectively.

Kurtosis is the statistic that all value distributional patterns of description delay degree suddenly, and its computing formula is:

$k_u=\left\{\begin{array}{c}{g}_2=\frac{N\&CenterDot;(N+1)\&CenterDot;\underset{n=1}{\overset{N}{\&Sigma;}}{\left(x\right(n)-\hat{m})}^4}{(N-1)\&CenterDot;(N-2)\&CenterDot;(N-3)\&CenterDot;{\left(\widehat{\mathrm{\&sigma;}}\right)}^4}-\frac{3\&CenterDot;{(N-1)}^2}{(N-2)\&CenterDot;(N-3)},\\ \{E\left(g_2\right)-\frac{\sqrt{\mathrm{var}\left(g_2\right)}}{\sqrt{a}}\&le;g_2\&le;E\left(g_2\right)+\frac{\sqrt{\mathrm{var}\left(g_2\right)}}{\sqrt{a}}\}\\ E\left(g_2\right)=\frac{-6}{N-1},\\ \{-\mathrm{\&infin;}\&le;g_2\&le;E\left(g_2\right)-\frac{\sqrt{\mathrm{var}\left(g_2\right)}}{\sqrt{a}},E\left(g_2\right)+\frac{\sqrt{\mathrm{var}\left(g_2\right)}}{\sqrt{a}}\&le;g_2\&le;+\mathrm{\&infin;}\}\end{array}\right.---\left(2\right)$

When being distributed as normal distribution, g ₁, g ₂variance be:

$\mathrm{var}\left(g_1\right)=\frac{6\&CenterDot;N\&CenterDot;(N-1)}{(N-2)\&CenterDot;(N+1)\&CenterDot;(N+3)}---\left(3\right)$

$\mathrm{var}\left(g_2\right)=\frac{24\&CenterDot;N\&CenterDot;{(N-1)}^2}{(N-3)\&CenterDot;(N-2)\&CenterDot;(N+3)\&CenterDot;(N+5)}---\left(4\right)$

(3) repeat the 2nd step, slide into next window, calculate two statistical values, so just obtain the statistical value of all windows;

(4) calculate the degree of bias of each window and the product of kurtosis, and get its maximum as the statistic describing the total intensity of anomaly of Monitoring Data.

5) each node quality of power supply warning data dynamic threshold is analyzed.

51) according to step 2) the Load flow calculation initial parameter that gathers, utilize Continuation Method to analyze network trend;

52) based on Sensitivity Analysis Method, the sensitivity coefficient of each node in computing network;

53) divide the sensitivity coefficient domain of definition interval, then build sensitivity coefficient discrimination matrix, analyze the corresponding relation of sensitivity coefficient and dynamic threshold variable quantity in each domain of definition interval;

54) interval belonging to the sensitivity coefficient of node each in network, calculate the threshold value N that dynamically exceeds standard of each node _twith dynamic abnormal threshold value S _t;

Step 51) in the concrete steps of Continuation Method be:

(1) parameter is introduced

Indicated output, the increasing of load is come with parameter lambda:

0≤λ≤λ _cr(5)

Wherein: the load level of exerting oneself that λ=0 is corresponding basic; λ=λ _crthe load level of exerting oneself of corresponding critical point.

The available formula of process (6) for generator and load represents:

$\left\{\begin{array}{c}{P}_{Gi}=P_{Gi0}\left(1+{\mathrm{\&lambda;K}}_{PGi}\right)\\ P_{Li}=P_{Li0}\left(1+{\mathrm{\&lambda;K}}_{PLi}\right)\\ Q_{Li}=Q_{Li0}\left(1+{\mathrm{\&lambda;K}}_{QLi}\right)\end{array}\right.---\left(6\right)$

Wherein: to refer to corresponding to λ=0 basic exerts oneself for subscript 0, load level; K _pGi, K _pLi, K _qLifor the node of specifying is exerted oneself or the growth factor of load;

The expression formula of the load of exerting oneself of change is substituted into conventional Load Flow equation, then has:

$\left\{\begin{array}{c}{P}_{Gi0}(1+{\mathrm{\&lambda;K}}_{PGi})-P_{Li0}(1+{\mathrm{\&lambda;K}}_{PLi})-\underset{j=1}{\overset{n}{\&Sigma;}}{V}_i{V}_j(G_{ij}{\mathrm{cos\&theta;}}_{ij}+B_{ij}{\mathrm{sin\&theta;}}_{ij})=0\\ Q_{Gi}-Q_{Li0}(1+{\mathrm{\&lambda;K}}_{QLi})-\underset{j=1}{\overset{n}{\&Sigma;}}{V}_i{V}_j(G_{ij}{\mathrm{sin\&theta;}}_{ij}-B_{ij}{\mathrm{cos\&theta;}}_{ij})=0\end{array}\right.---\left(7\right)$

Wherein: i=1 ..., n; P _gi, Q _gifor the generator output of node; P _li, Q _lifor the load of node; V _i, θ _ifor voltage magnitude and the phase angle of node i; G _ij, B _ijfor real part and the imaginary part of node admittance battle array the i-th, j element.

Conveniently represent, by (7) by the form being write as matrix and vector, such as formula (8)

H(x,λ)＝00≤λ≤λ _cr(8)

(2) link is predicted

If there is the trend solution of corresponding exerting oneself substantially, load level (λ=0), then from it, tangentially can to select suitable step-length (make it correct step and have solution), obtain down the predicted value of some trend solutions.

First calculate tangent vector, total differential got to power flow equation (8), and is write as matrix form namely:

$\left[\begin{array}{cc}\frac{\&part;H}{\&part;x}& \frac{\&part;H}{\&part;\mathrm{\&lambda;}}\end{array}\right]\left[\begin{array}{c}dx\\ d\mathrm{\&lambda;}\end{array}\right]=0---\left(9\right)$

Wherein: for the Jacobian matrix of conventional Load Flow equation; [dxd λ] ^tfor the tangent vector required.

Note, amount number to be asked more than equation number 1, need increase one-dimensional equation here, has determine to separate to make tangent vector, for this reason, specifies tangent vector [dxd λ] ^tk component d _xkmodulus value is 1, then (9) formula becomes:

$\left[\begin{array}{c}\frac{\&part;H}{\&part;x}\frac{\&part;H}{\&part;\mathrm{\&lambda;}}\\ e_k\end{array}\right]\&lsqb;\begin{array}{c}dx\\ \&part;\mathrm{\&lambda;}\end{array}\&rsqb;=\&lsqb;\begin{array}{c}0\\ \&PlusMinus;1\end{array}\&rsqb;---\left(10\right)$

Wherein, e _krepresent row vector, except K element is 1, other elements all equal 0, $\left[\begin{array}{c}\frac{\&part;H}{\&part;x}\frac{\&part;H}{\&part;\mathrm{\&lambda;}}\\ e_k\end{array}\right]$ Be the Jacobian matrix of the rear power flow equation of expansion.

If it is suitable that K value is chosen, then above-mentioned assignment procedure can make the Jacobian matrix after expanding nonsingular in critical point place.After trying to achieve tangent vector by formula (10), the predicted value of some trend solutions can be obtained down according to formula (11),

$\left[\begin{array}{c}{x}^{\&prime;}\\ {\mathrm{\&lambda;}}^{\&prime;}\end{array}\right]=\left[\begin{array}{c}{x}^{\left(0\right)}\\ {\mathrm{\&lambda;}}^{\left(0\right)}\end{array}\right]+\mathrm{\&sigma;}\left[\begin{array}{c}dx\\ d\mathrm{\&lambda;}\end{array}\right]---\left(11\right)$

Wherein, $\left[\begin{array}{c}{x}^{\left(0\right)}\\ {\mathrm{\&lambda;}}^{\left(0\right)}\end{array}\right]$ For the trend solution of seeking point, σ is step-length, and its value should make down the predicted value of any drop within its convergence radius.

(3) correction link

With predicted value ［ x ' λ ' ］ ^titerative is made for initial value substitutes into power flow equation (9).Now, also can run into the problem of amount number to be asked more than equation number 1, solution specifies state variable [x λ] ^tk component x _kvalue be predicted value, thus increase an equation, the power flow equation be expanded:

$\left[\begin{array}{c}H(x,\mathrm{\&lambda;})\\ x_k-\mathrm{\&eta;}\end{array}\right]=0---\left(12\right)$

Wherein: x _kfor amount to be asked [x λ ］ ^tk component, because its value is designated, therefore be called as continuous parameter; η is K component of predicted value;

By predicted value ［ x ' λ ' ］ ^tsubstitute into as initial value in the power flow equation (12) of expansion and solve with Newton iterative method, then can obtain new trend solution.

More than be the concrete solution procedure of continuous tide prediction-aligning step.By constantly prediction-trimming process, can reach and cross critical point to draw out complete PV curve.

Step 52) concrete steps of medium sensitivity analytic approach are:

When quick estimating system voltage stabilization, the conventional U-Q sensitivity relation simplified.Known linear static system power voltage equation has following form:

$\left[\begin{array}{c}\mathrm{\&Delta;}P\\ \mathrm{\&Delta;}Q\end{array}\right]=\left[\begin{array}{cc}{J}_{P\mathrm{\&theta;}}& J_{PU}\\ J_{Q\mathrm{\&theta;}}& J_{QU}\end{array}\right]\left[\begin{array}{c}\mathrm{\&Delta;}\mathrm{\&theta;}\\ \mathrm{\&Delta;}U\end{array}\right]=\&lsqb;J\&rsqb;\left[\begin{array}{c}\mathrm{\&Delta;}\mathrm{\&theta;}\\ \mathrm{\&Delta;}U\end{array}\right]---\left(13\right)$

In formula: P is active power vector; Q is reactive power vector; θ is node voltage angle vector; U is node voltage amplitude vector.

From formula (13), system voltage stabilizes affects by active power and reactive power.But, at each operating point, active power can be kept constant, even Δ P=0, then

$\mathrm{\&Delta;}Q=\&lsqb;J_{QU}-J_{Q\mathrm{\&theta;}}{J}_{P\mathrm{\&theta;}}^{-1}{J}_{PU}\&rsqb;\mathrm{\&Delta;}U=J_R\mathrm{\&Delta;}U---\left(14\right)$

$\mathrm{\&Delta;}U=J_R^{-1}\mathrm{\&Delta;}Q---\left(15\right)$

In formula, matrix J _rit is the U-Q Jacobian matrix simplified.Its i-th diagonal element is the U-Q sensitivity of node i.

Therefore, the sensitivity coefficient of whole node can be obtained by the analysis of this step.

Step 53) in analyze the corresponding relation of sensitivity coefficient and dynamic threshold variable quantity in each domain of definition interval concrete steps be:

(1) to sensitivity coefficient place domain of definition demarcation interval.

The sensitivity coefficient place domain of definition, according to practical operation situation, is divided into 7 intervals by the present invention.

When sensitivity coefficient is timing, these 7 intervals are [0,0.05] respectively, [0.05,0.1], [0.1,0.15], [0.15,0.2], [0.2,0.25], [0.25,0.3], [0.3 ,+∞];

When sensitivity coefficient is for time negative, these 7 intervals are [-∞ ,-0.3] respectively, [-0.3 ,-0.25], [-0.25 ,-0.2], [-0.2 ,-0.15], [-0.15 ,-0.1], [-0.1 ,-0.05], [-0.05 ,-0];

(2) ratio of establishing these 7 interval dynamic threshold variable quantities corresponding to sensitivity coefficient to occupy all variable quantities separately is respectively respectively w ₁, w ₂..., w ₇.According to the relative importance size of these variable quantity proportions, obtain one 7 dimension discrimination matrix;

Table 1 discrimination matrix constitution element

In table, a _ijwhat represent is relative importance size, and its value can adopt 1 ~ 9 scale.

(3) maximal eigenvector calculating this discrimination matrix as shown in the formula (16) is utilized:

AW＝λ _maxW(16)

In formula, λ _maxit is eigenvalue of maximum; W is corresponding maximal eigenvector; A is discrimination matrix.

Then, the variable quantity ratio in each interval will can be obtained after the maximal eigenvector normalization calculated.

(4) according to the history of the data judging threshold value in stationary power quality early warning setting experience, and by seeking the opinion of the primary demand for electricity consumption both sides, static threshold being set, comprising static state and to exceed standard threshold value N _t1with static outlier threshold S _t1.Be multiplied by these ratios by the static threshold of 50% again, the dynamic threshold variable quantity in each interval can be obtained.

In sum, the corresponding relation of sensitivity coefficient and dynamic threshold variable quantity in each domain of definition interval can be obtained.

Step 54) in calculate the concrete steps of dynamic threshold and be:

(1) according to step 52) analyze the sensitivity coefficient obtaining each node, judge that the domain of definition belonging to it is interval.Again according to the corresponding relation of sensitivity coefficient and dynamic threshold variable quantity in each domain of definition interval, judge the dynamic threshold variable quantity △ N of each node _t1with △ S _t1.

(2) each node dynamic threshold is:

$\left\{\begin{array}{c}{N}_T=N_{T1}+{\mathrm{\&Delta;N}}_{T1}\\ S_T=S_{T1}+{\mathrm{\&Delta;S}}_{T1}\end{array}\right.---\left(17\right)$

In formula, N _tand S _tbe respectively exceed standard threshold value and the outlier threshold of each node.

6) comprehensive pre-warning analysis: quantize exceeding standard and intensity of anomaly of warning data, obtain early warning result.

Step 6) in quantize the exceeding standard and intensity of anomaly of warning data, show that the concrete steps of early warning result are:

(1) use following piecewise nonlinear function, quantize the degree that exceeds standard of stationary power quality warning data:

$u(N,N_T)=\left\{\begin{array}{cc}60+(e^{2.4t_1}-1),& t_1>2\\ 40t_1+20,& 1<t_1<2\\ 100e^{4(t_1-2)},& 0<t_1<1\end{array}\right.---\left(18\right)$

In formula, u (N, N _t) be represent stationary power quality warning data to exceed standard the quantized value of degree; t ₁be the number of times ratio value that exceeds standard, it equals N/N _t;

Use following piecewise nonlinear function, quantize the intensity of anomaly of stationary power quality warning data:

$v(S,S_T)=\left\{\begin{array}{cc}60+(e^{2.4t_2}-1),& t_2>2\\ 40t_2+20,& 1<t_2<2\\ 100e^{4(t_2-2)},& 0<t_2<1\end{array}\right.---\left(19\right)$

In formula, v (S, S _t) be the quantized value representing stationary power quality warning data intensity of anomaly; t ₂be abnormal coefficient ratio value, it equals S/S _t;

(2) following formula is then utilized to obtain exceeding standard of stationary power quality warning data and intensity of anomaly comprehensive quantification value:

F＝au(N,N _T)+bv(S,S _T)(20)

In formula, F represents stationary power quality warning data to exceed standard and the comprehensive quantification value of intensity of anomaly; A is that stationary power quality warning data exceeds standard the proportionality coefficient of degree quantized value, and b is the proportionality coefficient of stationary power quality warning data intensity of anomaly quantized value; According to actual commissioning experience, a and b can be set and be 0.5.

(3) stationary power quality early warning result is calculated according to following formula.

Claims (6)

1., based on a network stationary power quality method for early warning for sensitivity analysis, it is characterized in that, the method comprises the following steps:

1) interstitial content in electrical network is determined, then according to the network topology structure of the connected mode determination electrical network between the node space layout in electrical network and node;

2) Load flow calculation initial parameter and the stationary power quality warning data of electrical network is gathered;

3) to described step 2) the stationary power quality warning data that gathers carries out integrity degree verification, if meet integrity degree requirement, then enter step 4), otherwise after abandoning and gathering stationary power quality warning data, return step 2) Resurvey stationary power quality warning data;

4) data analysis: according to existing quality of power supply national Specification value, to step 2) the stationary power quality warning data that gathers exceeds standard detection, counts the times N that exceeds standard; Anomaly analysis is carried out to stationary power quality warning data simultaneously, obtain the statistic S quantizing its wave form varies degree;

5) dynamic threshold is calculated: according to step 2) the Load flow calculation initial parameter that gathers, utilize Continuation Method to analyze network trend, then the sensitivity coefficient of each node in computing network;

Divide the sensitivity coefficient domain of definition interval simultaneously, then build sensitivity coefficient discrimination matrix, analyze the corresponding relation of sensitivity coefficient and dynamic threshold variable quantity in each domain of definition interval;

Last interval belonging to the sensitivity coefficient of node each in network, calculate the threshold value N that dynamically exceeds standard of each node _twith dynamic abnormal threshold value S _t;

6) comprehensive pre-warning analysis: use piecewise nonlinear function to quantize the degree that exceeds standard and the intensity of anomaly of stationary power quality warning data respectively;

Then based on weigthed sums approach, the comprehensive degree that exceeds standard and the intensity of anomaly analyzing stationary power quality warning data, obtains exceeding standard and intensity of anomaly comprehensive quantification value;

Ultimate analysis obtains stationary power quality early warning result.

2. a kind of network stationary power quality method for early warning based on sensitivity analysis according to claim 1, is characterized in that, described step 2) in, the concrete grammar of data acquisition is:

Gather the Monitoring Data of each node stationary power quality warning index in electrical network, form " stationary power quality warning data ";

Simultaneously the initial parameter of Load flow calculation in collection network, is formed " Load flow calculation initial parameter ".

3. a kind of network stationary power quality method for early warning based on sensitivity analysis according to claim 1, is characterized in that, described step 3) in the concrete grammar that stationary power quality warning data carries out integrity degree verification be:

If stationary power quality warning data number exceedes the verification threshold value of normal Monitoring Data number, then judge to meet integrity degree requirement, otherwise do not meet integrity degree requirement.

4. a kind of network stationary power quality method for early warning based on sensitivity analysis according to claim 3, it is characterized in that, described verification threshold value is 50%.

5. a kind of network stationary power quality method for early warning based on sensitivity analysis according to claim 1,2,3 or 4, is characterized in that, described step 5) in, the concrete grammar calculating dynamic threshold is:

51) according to step 2) the Load flow calculation initial parameter that gathers, Continuation Method is utilized to analyze network trend, calculate state variables all in electrical network, control variables and relevant parameter, then based on Sensitivity Analysis Method, the sensitivity coefficient of each node in computing network;

Divide the sensitivity coefficient domain of definition interval simultaneously, then build sensitivity coefficient discrimination matrix, analyze the corresponding relation of sensitivity coefficient and dynamic threshold variable quantity in each domain of definition interval;

52) belonging to the sensitivity coefficient judging each node in network, the domain of definition is interval, according to the corresponding relation of sensitivity coefficient and dynamic threshold variable quantity in domain of definition interval, finds out the dynamic threshold variable quantity that each node sensitivity coefficient is corresponding;

53) dynamic threshold of each node is calculated in accordance with the following methods respectively: the changes of threshold amount that dynamically exceeds standard in dynamic threshold variable quantity and dynamic abnormal changes of threshold amount are added with respective static threshold respectively, dynamically exceeded standard threshold value N _twith dynamic abnormal threshold value S _t.

6. a kind of network stationary power quality method for early warning based on sensitivity analysis according to claim 5, is characterized in that, described step 6) idiographic flow be:

Use following piecewise nonlinear function, quantize the degree that exceeds standard of stationary power quality warning data:

$u(N,N_T)=\left\{\begin{array}{cc}60+(e^{2.4t_1}-1),& t_1>2\\ 40t_1+20,& 1<t_1<2\\ 100e^{4(t_1-2)},& 0<t_1<1\end{array}\right.$

In formula, u (N, N _t) be represent stationary power quality warning data to exceed standard the quantized value of degree; t ₁the number of times ratio value that exceeds standard, t ₁=N/N _t;

Use following piecewise nonlinear function, quantize the intensity of anomaly of stationary power quality warning data:

$u(S,S_T)=\left\{\begin{array}{cc}60+(e^{2.4t_2}-1),& t_2>2\\ 40t_2+20,& 1<t_2<2\\ 100e^{4(t_2-2)},& 0<t_2<1\end{array}\right.$

In formula, v (S, S _t) be the quantized value representing stationary power quality warning data intensity of anomaly; t ₂abnormal coefficient ratio value, t ₂=S/S _t;

Then following formula is utilized to obtain exceeding standard of stationary power quality warning data and intensity of anomaly comprehensive quantification value:

F＝au(N,N _T)+bv(S,S _T)

In formula, F represents stationary power quality warning data to exceed standard and the comprehensive quantification value of intensity of anomaly; A is that stationary power quality warning data exceeds standard the proportionality coefficient of degree quantized value, and b is the proportionality coefficient of stationary power quality warning data intensity of anomaly quantized value;

Finally calculate stationary power quality early warning result according to following formula: