CN104077493A - Method for constructing state evaluation index system of electric relaying protection system - Google Patents

Abstract

Disclosed is a method for constructing a state evaluation index system of an electric relaying protection system. The method is characterized by comprising the steps that a hierarchical level model is constructed based on a level analysis method; a candidate evaluation factor set of a relaying protection device and a secondary loop is determined through a group decision-making method based on entropy, then the candidate evaluation factor set is further extracted by applying a kernel principal component analysis method, and an optimal evaluation factor set is obtained; different weight coefficients are given to different factors to reflect the importance degree and influence of the factors, subjective weighting based on a fuzzy evaluation weighting method is carried out on the optimal factor set, then objective weighting based on the kernel principal component analysis method is carried out on the optimal factor set, and the comprehensive weight of the optimal factor set is obtained by combining the two kinds of weighting; the model is hierarchically divided according to the built relaying protection system, the extracted optimal factor set is combined, a weight system is obtained through a subjective and objective comprehensive weighting method, the complete state evaluation index system of the electric relaying protection system is built, and objective, accurate and comprehensive state evaluation is carried out on the electric relaying protection system.

Description

A kind of construction method of system of relay protection State Assessment Index System

Technical field

The present invention relates to POWER SYSTEM STATE evaluation index technical field, is a kind of construction method of system of relay protection State Assessment Index System.

Background technology

System of relay protection is as a huge complication system; have comprise that equipment is numerous, between each equipment and the feature that between equipment and system, relevance is strong, run time behaviour is complicated; the relation affecting between these factors of many factors of its state is intricate; during relay protection generation state transition, be often attended by the variation of a plurality of quantity of states.

To carry out state estimation comprehensively and accurately to the running status of relay protection, need extract the most representatively and can the objective quantity of state that reflects definitely duty, set up relay protection State Assessment Index System.

At present relevant for the correlative study achievement of system of relay protection State Assessment Index System; wherein a kind of method of main flow is for adopting principal component analysis (PCA); this method is by research index system immanent structure relation; thereby a plurality of indexs are converted into a few separate overall target; its advantage is that the definite of its weights is based on data; acceptor's viewing does not ring; there is good objectivity; and separate between the overall target drawing; the intersection of minimizing information, this is favourable to analysis and evaluation.

But the shortcoming of this method is to process nonlinear data poor effect; and the data of this bulky complex system of system of relay protection to have be nonlinear data greatly; and there is no system, comprehensively consider the effect of each state estimation factor; must improve principal component analysis (PCA); can process nonlinear datas a large amount of in power protection system; objective and accurately provide conclusion, but so far, there is not yet relevant bibliographical information and practical application.

Summary of the invention

The object of the invention is to overcome the shortcoming of prior art; a kind of construction method of objective, accurate and comprehensive system of relay protection State Assessment Index System is provided; the method can be processed preferably to nonlinear datas a large amount of in system of relay protection, thereby obtains more objective, accurate and comprehensive conclusion.

The object of the invention is to be realized by following technical scheme: a kind of construction method of system of relay protection State Assessment Index System, it is characterized in that, it comprises the following steps:

(1) adopt based on analytical hierarchy process and build Recurison order hierarchy model, relay protection system is divided according to equipment, equipment is divided according to protective relaying device and secondary circuit, relay protection system state estimation being decomposed into simple, maneuverable multi-level model, carries out assessment and analysis;

(2) first use Group Decision Method based on entropy to determine candidate's appraisal parameters of protective relaying device and secondary circuit, and then use core principle component analysis method to carry out more comprehensive and objective further extraction to candidate's appraisal parameters, obtain best-evaluated set of factors;

(3) by giving different weight coefficients to different factors, embody its significance level and influence power, best set of factors is carried out to the subjectivity of the enabling legislation based on fuzzy evaluation and compose power, again best set of factors is carried out to the Objective Weight based on core principle component method, compose the comprehensive weight that power obtains best set of factors for comprehensive two kinds;

(4), according to the relay protection system level partitioning model of setting up, in conjunction with the best set of factors of extracting, the weight system of using subjective and objective comprehensive tax power method to obtain, sets up complete relay protection State Assessment Index System,

Wherein, in core principle component analysis method, select suitable kernel function and parameter thereof, according to polynomial kernel function:

K ₁＝(＜x _i,x _j＞+c) ^d

With RBF kernel function:

K ₂＝exp(-||x _i-x _j|| ²/2σ ²)，(i，j＝1,2，…q)

By these two kernel functions, create a new kernel function, utilize the closure of kernel function to create a kernel function:

$K=\sqrt{\frac{{(k_1+k_2)}^2}{2}}=\sqrt{\frac{{({(<x_i,x_j>+c)}^d+\exp (-||x_i-x_j{\left|\right|}^2/2{\mathrm{\&sigma;}}^2))}^2}{2}}$

In the time of that step of comprehensive weight, choose formula

$\mathrm{\&alpha;}={\mathrm{\&lambda;}}^{\&prime;}+\mathrm{\&theta;}(R^{\&prime;}-{\mathrm{\&lambda;}}^{\&prime;})={\mathrm{\&lambda;}}^{\&prime;}+(1-\frac{R^{\&prime;}\&CenterDot;{\mathrm{\&lambda;}}^{\&prime;}}{\left|\right|R^{\&prime;}\left|\right|\left|\right|{\mathrm{\&lambda;}}^{\&prime;}\left|\right|})/2(R^{\&prime;}-{\mathrm{\&lambda;}}^{\&prime;})$

As the method for comprehensive weight,

α is comprehensive weight, and λ ' is the weights that core principle component analysis obtains, and R' is the weights based on fuzzy evaluation.

The construction method of a kind of system of relay protection State Assessment Index System of the present invention, carried out level decomposition by system before this, was decomposed into multi-level model.And then obtain candidate's appraisal parameters by the group's plan method based on entropy, and then by core principle component method, candidate's appraisal parameters is extracted and to obtain best set of factors, avoid the large and nonlinear data of sample data amount to cause finding out the set of factors that is not best.By based on fuzzy evaluation enabling legislation and the comprehensive tax power method based on core principle component enabling legislation, obtain assessment factor comprehensive weight more objective, science.Build system of relay protection State Assessment Index System and can carry out objective, accurate and comprehensive state estimation to system of relay protection.

Accompanying drawing explanation

Fig. 1 is a kind of construction method block diagram of system of relay protection State Assessment Index System;

The membership function figure of the temperature in Fig. 2 running environment;

The membership function figure of Fig. 3 humidity;

The membership function figure of the failure condition of Fig. 4 software and hardware;

The anti-implementation of conditions subordinate function figure that arranges of Fig. 5;

The membership function figure of Fig. 6 protective device action accuracy;

Fig. 7 insulate and is greater than the membership function figure of 10M Ω insulation status when loop;

There is not the membership function figure of the insulation status of DC earthing in Fig. 8;

Fig. 9 works as the membership function figure that analog quantity error is no more than 5% data sampling;

Figure 10 communication condition subordinate function figure;

The membership function figure of Figure 11 channel communications situation;

Figure 12 operating ambient temperature scoring figure.

Embodiment

Below in conjunction with Fig. 1, the embodiment of a kind of system of relay protection State Assessment Index System method of the present invention is described in detail.

With reference to Fig. 1, the construction method of a kind of system of relay protection State Assessment Index System of the present invention, comprises the following steps:

(1) relay protection system is decomposed into a plurality of relay protection devices: relay protection system is carried out to level decomposition, set up the Recurison order hierarchy model of relay protection system;

(2) incite somebody to action index for selection factor from protective relaying device and secondary circuit as requested, the index factor of choosing is processed and obtained best set of factors and weight thereof, finally build with regard to evaluation index system: first according to relay protection state estimation and the regulation of inspection and repair, in conjunction with expert advice, quality from equipment self, preventive trial, operating condition and historical data image data, set up assessment candidate set of factors, the group's plan method re-using based on entropy is extracted assessment candidate set of factors, reject unessential factor, obtain candidate's appraisal parameters, then utilize core principle component analysis method to extract the Main composition factor of above-mentioned candidate's appraisal parameters, set up its best-evaluated set of factors,

(3), to best-evaluated set of factors, first adopt the enabling legislation based on fuzzy evaluation to obtain subjective weight, then by core principle component analysis method, best-evaluated set of factors is carried out to Objective Weight and obtain a new weight, last comprehensive two weights;

(4) according to the relay protection system level partitioning model of setting up, best-evaluated set of factors and the power of tax method, obtain the weight of each factor, according to Fig. 2-Figure 12, obtain the corresponding factor scoring of institute again, according to getting minimum scoring principle, obtain the scoring of protective relaying device again, in like manner can obtain the scoring of secondary circuit, the scoring of equipment, thus the scoring that finally obtains relay protection system builds relay protection system State Assessment Index System;

The step that described group's plan method based on entropy is extracted best-evaluated factor is:

Hypothesis evaluation candidate's set of factors has m factor, has n expert to compose weight to each factor, so, obtains a weight matrix F,

$F=\left[\begin{array}{cccc}{r}_{11}& r_{12}& ...& r_{1n}\\ r_{21}& r_{22}& ...& r_{2n}\\ .& .& & .\\ .& .& ...& .\\ .& .& & .\\ r_{m1}& r_{m2}& ...& r_{\mathrm{mn}}\end{array}\right]$

R _ijrepresent the tax weights of j expert to i index, i=1 wherein, 2 ... m, j=1,2 ... n,

First the weights of each row being asked to entropy computing, is to ask the entropy of each expert to m Index Weights value, and $\underset{j=1}{\overset{m}{\mathrm{\&Sigma;}}}{r}_{\mathrm{ij}}=1.$

The formula of row entropy is:

$H_j=\underset{i=1}{\overset{m}{\mathrm{\&Sigma;}}}-r_{\mathrm{ij}}\ln r_{\mathrm{ij}}---\left(1\right)$

H _jwhat represent is the entropy of j expert to m Index Weights value.

When the larger expression of row entropy expert j is almost more or less the same for the tax weights of m index, represent that j expert is not deep, authoritative low for composing weights consideration, give up this expert's tax weights,

Above-mentioned row entropy, according to arranging from small to large, is set to a threshold value 80%, get its front q row entropy weights that corresponding expert composes, allow $\frac{\underset{j=1}{\overset{q}{\mathrm{\&Sigma;}}}{H}_j}{\underset{i=1}{\overset{m}{\mathrm{\&Sigma;}}}{H}_j}\&GreaterEqual;80\%;$

Re-construct get q expert to m index factor compose weights matrix F ':

$F^{\&prime;}=\left[\begin{array}{cccc}{r}_{11}& r_{12}& ...& r_{1q}\\ r_{21}& r_{22}& ...& r_{2q}\\ .& .& & .\\ .& .& ...& .\\ .& .& & .\\ r_{m1}& r_{m2}& ...& r_{\mathrm{mq}}\end{array}\right]$

Asking the entropy of q expert to the tax weights of m index, is row entropy again, asks for row entropy and first the weights of every a line of F' is normalized before, is:

$r_{\mathrm{ij}}^{\&prime;}=\frac{r_{\mathrm{ij}}}{\underset{j=1}{\overset{q}{\mathrm{\&Sigma;}}}{r}_{\mathrm{ij}}}---\left(2\right)$

R' _ijr _ijvalue after normalization.

I=1 wherein, 2 ... m j=1,2 ... q.

Thereby can obtain row entropy H _ifor:

$H_i=\underset{j=1}{\overset{q}{\mathrm{\&Sigma;}}}-r_{\mathrm{ij}}^{\&prime;}\ln r_{\mathrm{ij}}^{\&prime;}---\left(3\right)$

Work as H _ilarger explanation q expert have no objection to the tax weights of i index, and recognition rate is high, and accuracy is higher, represents that i index can not cause assessment result to occur larger error, otherwise make assessment result error large, so H _ithe significance level that simultaneously represents index, then, to H _ibe normalized $H_i^{\&prime;}=\frac{H_i}{\underset{i=1}{\overset{m}{\mathrm{\&Sigma;}}}{H}_i},$

(r _i1, r _i2... r _iq) _ithe row of F' matrix, according to (r _i1, r _i2... r _iq) _iconstruct a judgment matrix X _i,

$X_i=\left[\begin{array}{cccc}1& w_1/w_2& ...& w_1/w_q\\ w_2/w_1& 1& ...& w_2/w_q\\ ...& ...& & \\ w_q/w_1& & ...& 1\end{array}\right],$ Reference numerals 1-9 and inverse thereof are regarded matrix X as scale _ielement, X _icorresponding by matrix F ' the capable judgment matrix obtaining of i, then ask for X _ieigenvalue of maximum λ ⁱ _max,

The definition of judgment matrix scale, table 1:

J wherein, k=1,2 ... q, i=1,2 ... m, W _awith W _bbe exactly scale, be the element of matrix Xi,

Then calculating coincident indicator:

$\mathrm{CI}=\frac{{{\mathrm{\&lambda;}}^i}_{\max }-q}{q-1},---\left(4\right)$

λ wherein ⁱ _maxfor judgment matrix X _ieigenvalue of maximum.

Ask for coincident indicator ratio:

CR＝CI/RI, (5)

Wherein RI is mean random coincident indicator, and in the time of CR<0.10, the consistance of matrix can be accepted, otherwise to judgment matrix X _irevise,

So, finally obtain the concrete weights of each index factor, be:

R _i'＝λ ⁱ _maxH _i'(r _i1,r _i2,…r _iq) _i(6)

R wherein _i' what represent is the weights of i index, (r _i1, r _i2... r _iq) _irepresent the block median of q expert to i Index Weights value, be: R _i=median (r _i1+ r _i2+ ... + r _iq), R _iexpression is the weights after stage median is processed to the weights of i index of q expert,

Then set a threshold value, get 80% as its threshold value, by R _i' according to descending sort, get its front q value and make R _i', make 80%, finally extract this q R _i' corresponding factor, obtain candidate's appraisal parameters.

Described core principle component analysis method, the key step of KPCA is:

When processing candidate's appraisal parameters x _i, (i=1,2 ..., while q) running into nonlinear data, be mapped in feature space, in feature space, become linear data, introduce nonlinear mapping function realize input space Rm to the mapping of feature space F,

Select suitable kernel function and parameter thereof, select polynomial kernel function:

K ₁＝(＜x _i,x _j＞+c) ^d， (7)

RBF kernel function:

K ₂＝exp(-||x _i-x _j|| ²/2σ ²)，(i，j＝1,2，…q)。(8)

Choose two kernel functions and create a new kernel function, utilize the closure of kernel function to create a kernel function:

$K=\sqrt{\frac{{(k_1+k_2)}^2}{2}}.---\left(9\right)$

If data are zero-mean, be the data covariance matrix on feature space F is:

Now by solving down, establish an equation:

C ^F V＝λV (11)

Obtain eigenvalue λ (λ >=0) and corresponding proper vector V (V ∈ F/{0})

According to formula (11) build both members is done simultaneously and the equation of inner product:

Consider that all proper vectors all can be by linear expression, and there is a _i(i=1,2 ... q), make:

Formula (13) and (10) are brought into formula (12) to be obtained:

Define q rank matrix k', wherein an element of k' so, substitution (5) formula, obtains:

qλKa＝K ²a (15)

A=(a wherein ₁, a ₂a _q) ^t,

Abbreviation (15) formula obtains:

qλa＝K a (16)

Solve the eigenwert of the non-zero of (16) formula,

The value of non-zero λ is carried out to descending sort and obtain, λ ₁, λ ₂... λ _p, 1≤p≤q wherein,

Due to formula:

Therefore by standardization a ^k, carry out the proper vector in standardized feature space, i.e. (V _iv _i)=1,

(17) formula is derived and is obtained:

a ^k·a ^k＝1/λ _k (18)

λ wherein _k(k=1,2 ... p)

Above-mentioned λ ₁, λ ₂... λ _pcorresponding index factor is exactly finally to obtain best-evaluated set of factors, then this p factor is reduced and is returned to adopt formula from feature space:

Wherein the value that Non-linear Principal Component is mapped to feature space,

If the non-vanishing average of data, tackles K matrix center and turns to with remove to replace K:

$\stackrel{\&OverBar;}{K}=(I-\frac{1}{q}{1}_q{1}_q^T)K(I-\frac{1}{q}{1}_q{1}_q^T)---\left(20\right)$

Through type (20) reduction, thus best-evaluated set of factors X={x finally obtained ₁, x ₂... x _p.

In the described enabling legislation step based on fuzzy evaluation:

It is X={x that formula (20) is obtained to smallest evaluation set of factors ₁, x ₂... x _p, the importance according to expert to each factor weight, adopts method of expertise to give each factor a value, thereby can construct the fuzzy subset A=[a of an X _i] i=1,2 ... p, and $\underset{i=1}{\overset{p}{\mathrm{\&Sigma;}}}{a}_i=1.$

Then expert adopts basic tax power method to obtain the weights set B of best set of factors, and this weights set B is carried out dividing based on K mean cluster, marks off 5 intervals, { U ₁, U ₂, U ₃, U ₄, U ₅.For each factor, a rational membership function F (X) is set, calculate its each interval degree of membership, get the corresponding interval of maximum degree of membership, above-mentioned step of dividing based on K mean cluster:

(1) to given weights set B, choose 5 initial cluster center points, be { U ₁, U ₂, U ₃, U ₄, U ₅.

(2) a distance metric d (U of definition _i)=| U _i-r _j|, j=1,2 ... p, i=1,2 ... 5.S _jbe j the corresponding weights of factor in data B, it is to weigh s _jdistance length to each cluster centre.

(3) calculate successively s _jto each U _idistance be d (U _i) value, each d (U relatively _i) size, d (U _i) minimum corresponding s _j, its corresponding factor j is put into corresponding U _icluster interval, finally form five initial cluster intervals.

(4) for five above-mentioned cluster intervals, again ask for their cluster centre, using formula s is the number of the weights in the i interval of answering, S _iit is the sum of all weights in i interval, place.

Thereby obtain 5 new cluster centres according to said method.

Then repeat above-mentioned (2), (3), the step of (4), until cluster centre no longer changes.

Five have finally correctly been divided with U _ifor the interval of cluster centre, thus meet for the method based on fuzzy evaluation provide five correct objectively interval.

According to Fig. 1, can learn that appraisal parameters finally has 8 classes, running environment, the failure condition of software and hardware, the anti-implementation of conditions of arranging, performance factor, insulation status, data sampling, communication situation, lane testing situation.

Then each factor is analyzed.

For the temperature in running environment, its membership function figure is described as shown in Figure 2.

What its horizontal ordinate was corresponding is environment temperature, and what ordinate was corresponding is its degree of membership, its membership function F (x ₁) as follows:

By membership function above, can obtain, when given temperature factor collection, according to this membership function, calculating its degree of membership value.By F (x ₁) value follow divided cluster is interval correspondingly determines which interval, its place, and then the cluster centre value of getting between its location replaces F (x ₁).

Then the F (x after replacement value ₁) with its corresponding fuzzy subset, multiply each other, all results that finally obtain, after being normalized, drawn result is exactly its weights.

In like manner for other factors, adopting identical method is given membership function difference, and we only list membership function figure and the function of each factor here.

The membership function figure of corresponding humidity as shown in Figure 3.

Its membership function F (x ₂) as follows:

The membership function figure of the failure condition of software and hardware as shown in Figure 4.

Its membership function is F (x ₃) as follows:

Instead arrange implementation of conditions subordinate function figure as shown in Figure 5.

The anti-implementation of conditions subordinate function F (x that arranges ₄) as follows:

The membership function figure of protective device action accuracy as shown in Figure 6.

Its membership function is F (x ₅) as follows:

$F\left(x_5\right)=\left\{\begin{array}{cc}0& 0\&le;x_5\&le;98.5\\ 10x_5-85& x_5\&GreaterEqual;98.5\end{array}\right.$

The membership function figure that is greater than 10M Ω insulation status when loop insulation as shown in Figure 7.

Its membership function F (x ₆) as follows:

$F\left(x_6\right)=\left\{\begin{array}{cc}0& 0=<x_6<10\\ 0.05x_6& 10=<x_6<=20\\ 1& x_6>=20\end{array}\right.$

Do not occur DC earthing insulation status membership function figure as shown in Figure 8.

Its membership function F (x ₇) as follows:

The membership function figure that is no more than 5% data sampling when analog quantity error as shown in Figure 9.

the sampled value that electric current, voltage channel are respectively organized measured value is respectively CTi, PTi.

When analog quantity error is no more than 5% its membership function F (x ₈):

$F\left(x_8\right)=\left\{\begin{array}{cc}1& 0=<x_8<3\\ -0.5x_8+2.5& 3>x_8<=5\end{array}\right.$

Switching value sampling is correct, without opening into abnormal data sampling membership function:

Communication condition subordinate function figure as shown in figure 10.

Communication condition membership function F (x ₁₀) as follows:

The membership function figure of channel communications situation as shown in figure 11.

Membership function F (the x of channel communications situation ₁₁) as follows:

By above-mentioned membership function, determine between each factor location, get interval cluster centre value and substitute F (x _i) value, F (x then _i) and its corresponding fuzzy subset multiply each other and be worth, adopt formula:

R _i＝a _i·F(x _i) (21)

R wherein _ibe the weight of factor i, then it be normalized and obtained:

$R_i^{\&prime;}=\frac{R_i}{\underset{i=1}{\overset{p}{\mathrm{\&Sigma;}}}{R}_i}---\left(22\right)$

I=1 wherein, 2 ..., p, R _i' be R _ithe last weights of i factor after normalization, weight vectors R'=(R ₁', R' ₂..., R' _p).

Re-use core principle component again and best-evaluated set of factors is carried out to the operation of Objective Weight, step is as follows:

The pivot extracting by core principle component analysis method has represented the size of its entrained primitive character variation information to the contribution of feature set, contribution rate is larger, to characteristic information, explains stronger.

The weight of asking for so best set of factors is equal to asks for eigenwert collection.

According to above-mentioned core principle component analysis method, the known formula that passes through:

Q λ a=K a and a ^ka ^k=1/ λ _k

Ask for λ _kvalue, k=1 wherein, 2 ..., p.

Again to λ _kbe normalized, use formula:

${\mathrm{\&lambda;}}_k^{\&prime;}=\frac{{\mathrm{\&lambda;}}_k}{\underset{k=1}{\overset{p}{\mathrm{\&Sigma;}}}{\mathrm{\&lambda;}}_k}---\left(23\right)$

Resulting λ '=(λ ' ₁, λ ' ₂... λ ' _p) value be exactly the (x corresponding to best-evaluated set of factors the inside ₁, x ₂..., x _p) weights.

Then the result of comprehensive above-mentioned two kinds of weights, adopts aggregative weighted disposal route, and comprehensive weight vector α is:

$\mathrm{\&alpha;}={\mathrm{\&lambda;}}^{\&prime;}+\mathrm{\&theta;}(R^{\&prime;}-{\mathrm{\&lambda;}}^{\&prime;})={\mathrm{\&lambda;}}^{\&prime;}+(1-\frac{R^{\&prime;}\&CenterDot;{\mathrm{\&lambda;}}^{\&prime;}}{\left|\right|R^{\&prime;}\left|\right|\left|\right|{\mathrm{\&lambda;}}^{\&prime;}\left|\right|})/2(R^{\&prime;}-{\mathrm{\&lambda;}}^{\&prime;})---\left(24\right)$

θ is coefficient of colligation, and generalized case value is 0.5, and i=1,2 ..., p, α _ithe comprehensive weights of i factor.

To the best set of factors X={x of relay protection device state estimation ₁, x ₂... x _pcarry out similar division, and will be correlated with or similar each factor is divided classification, in abstract same division, each factor is high-level factor class.State estimation set of factors comprises some assessment factor classes, and each factor class consists of some state estimation factors.The factor class set M={AC setting up ₁, AC ₂..., AC _t, factor class AC wherein _kcomprise | AC _k| individual factor, is expressed as by factor k=1 wherein, 2 ..., T.

Calculating factor class AC _kweighted value in the factor class class of interior each factor according to each factor weight ratio in state estimation factor class class, calculate weight in the class of each factor, in the factor class class of factor, compose power computing formula as follows:

$w_{k_j}=w_{k_j}/\underset{i=1}{\overset{\left|{\mathrm{AC}}_k\right|}{\mathrm{\&Sigma;}}}{w}_{k_i}---\left(25\right)$

And $\underset{j=1}{\overset{\left|{\mathrm{AC}}_k\right|}{\mathrm{\&Sigma;}}}{w}_{k_j}=1,k=\mathrm{1,2},...,T.$

According to subjective and objective comprehensive tax power result of calculation, to carry out the tax power of relay protection State Assessment Index System factor class hierarchy and calculate, it is as follows that assessment factor class weight is composed power computing formula:

$w_k=\underset{i=1}{\overset{\left|{\mathrm{AC}}_k\right|}{\mathrm{\&Sigma;}}}{w}_{k_i}/\underset{k=1}{\overset{T}{\mathrm{\&Sigma;}}}\underset{i=1}{\overset{\left|{\mathrm{AC}}_k\right|}{\mathrm{\&Sigma;}}}{w}_{k_i}---\left(26\right)$

W wherein _kk factor class weights, and

Finally build the step of relay protection system evaluation index system:

Relay protection system is adopted based on analytical hierarchy process and sets up hierarchical model, relay protection system state estimation is decomposed into simple maneuverable multi-level model and carries out assessment and analysis, form evaluation system.

Relay protection system is divided according to equipment, and relay protection system can be divided into several relay protection devices.

Then continue each relay protection device to carry out step analysis, relay protection device is divided into several protective relaying devices and several secondary circuit two classes.

Finally again protective relaying device is decomposed, adopt above-mentioned best-evaluated set of factors extracting method, protective relaying device comprises a plurality of assessment factor classes, according to factor class, composes power method, the corresponding factor class of each factor class weighted value, the factor class that forms protective relaying device is assessed level; Each assessment factor class comprises again similar subset of factors, uses the interior weight of factor class class of assessment factor to compose power method, obtains the interior weighted value of class of each factor in factor class, forms the factors assessment level of protective relaying device.The final assessment hierarchical system that forms protective relaying device.

In like manner obtain the assessment hierarchical system of secondary circuit.

Finally built the evaluation index system of comprehensive, an objective and complete relay protection system.

Use the evaluation index system of relay protection system to assess, according to assessing and comprehensively, finally obtain the scoring of relay protection system to upper level by low.First according to factor standards of grading, carry out factor scoring, in conjunction with weighted value in the factor class class of factor, mark comprehensively, calculate factor class comprehensive grading; On this basis, according to factor class weighted value, COMPREHENSIVE CALCULATING obtains the overall score of protective device.In like manner obtain the overall score of secondary circuit.Get the minimum value in each secondary circuit that relay protection device is corresponding and the scoring of each protective relaying device, as the final scoring of relay protection device.Get the scoring of equipment in relay protection system minimum value as the final scoring of relay protection system.

The process of using evaluation index system to carry out relay protection system assessment below in conjunction with case introduction.

According to the figure shown in Figure 12, operating ambient temperature in protective relaying device is marked:

When ought obtain the temperature of running environment as shown in Figure 12, can obtain the scoring gk of this factor _i, and then according to above-mentioned best-evaluated factor with compose power method, and weights in factor class class, obtain in corresponding running environment factor class class in the weights of temperature the in like manner scoring for middle humidity in running environment factor class class is weights adopt formula

$g_k=g_{k_i}\&CenterDot;w_{k_1}+g_{k_2}\&CenterDot;w_{k_2}---\left(27\right)$

G wherein _kit is the scoring of running environment.

In like manner can obtain successively the failure condition of software and hardware, the anti-implementation of conditions of arranging, performance factor (RCO), insulation status, data sampling, communication situation, lane testing situation factor class is finally marked.

Overall score G for i protective relaying device _i, by the above-mentioned g that obtains each factor class scoring _k, in conjunction with factor class weight w _k, adopt formula:

$G_i=\underset{k=1}{\overset{T}{\mathrm{\&Sigma;}}}{w}_k\&CenterDot;g_k---\left(28\right)$

In like manner can obtain the overall score of each secondary circuit.

For the final scoring of j relay protection device, the minimum value min (G in each secondary circuit that taking equipment is corresponding and the scoring of each protective relaying device _i), as the final scoring G of relay protection device ^j.The final scoring G of relay protection system so, gets the minimum value min (G that marks in system equipment ^j) as the scoring of relay protection system.By above-mentioned relay protection system evaluation index system; can be by the system of this bulky complex of electric relay protection by decomposing by different level; thereby each level of analysis that can be comprehensively careful; each level is carried out to more objective comprehensive analysis; obtain the correct and objective state estimation result of relay protection system, be convenient to carry out repair based on condition of component.

Claims (1)

1. a construction method for system of relay protection State Assessment Index System, is characterized in that, it comprises the following steps:

(1) adopt based on analytical hierarchy process and build Recurison order hierarchy model, relay protection system is divided according to equipment, equipment is divided according to protective relaying device and secondary circuit, relay protection system state estimation being decomposed into simple, maneuverable multi-level model, carries out assessment and analysis;

(2) first use Group Decision Method based on entropy to determine candidate's appraisal parameters of protective relaying device and secondary circuit, and then use core principle component analysis method to carry out more comprehensive and objective further extraction to candidate's appraisal parameters, obtain best-evaluated set of factors;

(3) by giving different weight coefficients to different factors, embody its significance level and influence power, best set of factors is carried out to the subjectivity of the enabling legislation based on fuzzy evaluation and compose power, again best set of factors is carried out to the Objective Weight based on core principle component method, compose the comprehensive weight that power obtains best set of factors for comprehensive two kinds;

(4), according to the relay protection system level partitioning model of setting up, in conjunction with the best factor of evaluation collection extracting, the weight system of using subjective and objective comprehensive tax power method to obtain, sets up complete relay protection State Assessment Index System,

Wherein, in core principle component analysis method, select suitable kernel function and parameter thereof, according to polynomial kernel function:

K ₁＝(＜x _i,x _j＞+c) ^d

With RBF kernel function:

K ₂＝exp(-||x _i-x _j|| ²/2σ ²)，(i，j＝1,2，…q)

By these two kernel functions, create a new kernel function, utilize the closure of kernel function to create a kernel function:

$K=\sqrt{\frac{{(k_1+k_2)}^2}{2}}=\sqrt{\frac{{({(<x_i,x_j>+c)}^d+\exp (-||x_i-x_j{\left|\right|}^2/2{\mathrm{\&sigma;}}^2))}^2}{2}}$

In the time of that step of comprehensive weight, choose formula

$\mathrm{\&alpha;}={\mathrm{\&lambda;}}^{\&prime;}+\mathrm{\&theta;}(R^{\&prime;}-{\mathrm{\&lambda;}}^{\&prime;})={\mathrm{\&lambda;}}^{\&prime;}+(1-\frac{R^{\&prime;}\&CenterDot;{\mathrm{\&lambda;}}^{\&prime;}}{\left|\right|R^{\&prime;}\left|\right|\left|\right|{\mathrm{\&lambda;}}^{\&prime;}\left|\right|})/2(R^{\&prime;}-{\mathrm{\&lambda;}}^{\&prime;})$

As the method for comprehensive weight,

α is comprehensive weight, and λ ' is the weights that core principle component analysis obtains, and R' is the weights based on fuzzy evaluation.