CN103793854A - Multiple combination optimization overhead transmission line operation risk informatization assessment method - Google Patents

Abstract

The invention discloses a multiple combination optimization overhead transmission line operation risk informatization assessment method which comprises the steps that 1. risk identification is carried out, wherein by data collecting, a fault source is found, a risk accident set is generated, an uncertain risk factor set is analyzed in a statistic mode, and a risk evaluation index system is established; 2. an integrated risk value is determined, a transmission line operation risk assessment model is established, information entropy and an analytic hierarchy process are fused, risk probability, consequences generated by risks and risk weight are considered in a comprehensive mode, risk consequence severity is quantized through a mutual information value, and a transmission line operation integrated risk value is computed in a layer-by-layer-reasoning mode and a multiple-combination mode; and 3. risk determining is carried out, risk grades are divided, analyzing is carried out on the integrated risk weight, the risk grades are determined, and the results of risk assessment are output. According to the method, the overhead transmission line operation risk is assessed in a comprehensive mode and a multilevel mode, and the accuracy of transmission line operation risk assessment and the reliability of transmission line operation are improved.

Description

The overhead transmission line operation risk informatization evaluation method that Multiple Combination is optimized

Technical field

The present invention relates to a kind of Operation of Electric Systems safety technique, be specifically related to a kind of overhead transmission line operation risk informatization evaluation method that Multiple Combination is optimized.

Background technology

The risk assessment of Operation of Electric Systems safety has reflected the process that people deepen continuously to the understanding of power system stability and safe operation.Overhead transmission line is being born the main power transmission mode of power construction, be accompanied by the continuous increase of power grid construction scale, the safety and stablization problem of transmission line of electricity operation is also outstanding gradually, and the impact of electric system is also day by day increased, in the face of thus in order to ensure the healthy and stable operation of circuit, improve safety and the reliability of operation of power networks, the risk assessment of overhead transmission line operation is also just arisen at the historic moment.At present, about the risk assessment of Operation of Electric Systems has become the focus of domestic and international research, along with the concept of Modern Risk Leading assessment is introduced in the system of Power System Security Assessment, risk assessment to overhead transmission line operation also becomes a large focal spot, how to use rationally effective means to assess overhead transmission line operation risk, also become a problem in the urgent need to considering.

In the face of the ever-increasing complicacy of transmission line of electricity, uncertainty, failure accident be difficult to expect, often bring heavier and complicated maintenance decision, study overhead transmission line operation risk assessment method, accurately hold the risk status of circuit, for the aid decision making of transmission line of electricity, reasonably optimizing maintenance resource distribution, guarantees circuit stable and high effective operation, has very important directive function.The risk assessment of overhead transmission line operation is exactly to set up risk evaluation system according to the feature of overhead transmission line operation, multi-angle, multi-level analytical calculation transmission line of electricity operation integrated risk weights, for follow-up maintenance and aid decision making provide support, thereby its assess effectiveness and accuracy safeguard that to circuit Decision Making Effect is larger.

At present, can be divided into Deterministic Methods and the large class of uncertain method two for transmission line of electricity security of operation methods of risk assessment.Deterministic Methods is full-fledged, is widely used, and margin of safety is large, and reliability is high, but it has ignored complicacy and the randomness of transmission system operation, and risk is lacked to quantitative test, can cause margin of safety excessive, cannot conscientiously ensure the security of circuit operation.Uncertain method considers that transmission system moves the randomness of each risk accidents and the probability of generation thereof, by all accidents being carried out to the comprehensive assessment realizing total system security of analyzing, but probabilistic analysis method has only been considered randomness and the uncertainty of line fault, do not consider the consequence influence degree that line fault causes, therefore do not consider the safety case of circuit yet.

In the face of nowadays increasingly sophisticated transmission system, traditional system stability analysis and safety evaluation method have showed the deficiency of existence.The present invention is based on the security risk assessment of Modern Risk Leading theory, overcome the deficiency of above-mentioned appraisal procedure, a kind of overhead transmission line operation risk informatization evaluation method that Multiple Combination is optimized is disclosed, fully take into account internal factor and the impact of external factor on line security operation of circuit, Multiple Combination, successively reasoning and calculation transmission line of electricity operation integrated risk weights, thereby have more science and practicality, not only improve accuracy and the transmission line of electricity reliability of operation of transmission line of electricity risk assessment, be also conducive to analysis, the management and decision of risk.

The present invention, under National 863 planning item fund (2012AA050209) is subsidized, has proposed " the overhead transmission line operation risk informatization evaluation method that Multiple Combination is optimized ".

Summary of the invention

For above deficiency, the object of this invention is to provide the overhead transmission line operation risk informatization evaluation method that Multiple Combination is optimized, on the basis of tradition stratum fractional analysis, information entropy and analytical hierarchy process are merged, consider risk probability, consequence and risk weight that risk produces, application mutual information quantizes risk sequence severity, recycling level-entropy combined method replaces tradition stratum fractional analysis and calculates weights, and optimize weights by least square method, Multiple Combination, successively reasoning and calculation transmission line of electricity operation integrated risk weights, form a kind of assessment models of autonomous optimization.The present invention overcomes pure subjective analysis thought greatly, accuracy and the transmission line of electricity reliability of operation of transmission line of electricity risk assessment are improved, provide powerful support for for finding that early the risk of loss of the potential risk of circuit operation, the possible risk that reduces circuit operation, the operation of minimizing circuit provides, be conducive to analysis, the management and decision of risk.

For realizing above object, the technical scheme that the present invention has taked is:

The overhead transmission line operation risk informatization evaluation method that Multiple Combination is optimized, it comprises the following steps:

Step 1, risk identification, find out the source of trouble by data acquisition and generate risk accidents collection, and risk accidents collection described in statistical study, determines that transmission line of electricity moves the uncertain factor that may exist, and sets up risk factors collection; And according to uncertain risk factors and the operation characteristic thereof of transmission line of electricity operation, set up Risk Assessment Index System;

Step 2, value-at-risk are determined, build transmission line of electricity operation risk assessment model, utilize multilayer, multiple methods of risk assessment to estimate the value-at-risk of each index of transmission line of electricity, further the integrated risk value of computing electric power line;

Step 3, risk are judged, described integrated risk value is analyzed, and determine risk class, the result of output risk assessment.

The method that generates risk accidents collection described in described step 1 is:

By operation patrol and examine, on-line monitoring, preventive trial and account obtain the related data of overhead transmission line operation, produces typical risk accidents collection by Enumeration Method, described risk accidents collection comprises tripping operation stoppage in transit risk accidents and system risk accident; Analyze these risk accidents, determine the uncertain factor that causes risk accidents to occur, production risk set of factors, as the index of risk assessment.

Described step 2 comprises:

Step 2.1, the valuation of risk indicator probability;

The quantification of step 2.2, risk consequence;

Step 2.3, value-at-risk are calculated.

Described risk indicator probability is the size of the possibility of circuit risk factors appearance, and according to the property calculation risk indicator probability of risk accidents collection and risk factors collection, its computing method specifically comprise:

(1) according to the historical failure statistics of circuit operation, calculate the average probability of occurrence of each risk factors, as risk probability, be specially:

$P\left(x_i\right)=\frac{m_i}{N}$

Wherein, m _ifor risk factors x in a period of time _icause the number of times of risk accidents, the total degree that N is risk accidents,

(2) adopt Enumeration Method to generate risk accidents collection, the probability that arbitrary risk indicator i occurs is:

$p_i=\underset{k=1}{\overset{M}{\mathrm{\Π}}}{\mathrm{\μ}}_k\underset{k=1}{\overset{L}{\mathrm{\Π}}}(1-{\mathrm{\μ}}_k)$

Wherein, M is fault element number, and L is normal operation element number, p _ifor risk probability, μ _kfor the dependability parameter of element.

The dependability parameter of described element is to obtain the representative value of emergency shut-down coefficient by inquiry related data, or obtains according to the statistical conditions of element idle time in recent years; The method that is obtained the dependability parameter of element by the statistical conditions of the described idle time of element is in recent years:

wherein, MTTR be in statistical time range total idle time of element; PRD is the length of statistical time range.

Described step 2.2 risk consequence is that risk factors are while occurring, degree and scope that the impact that risk accidents is occurred or harm may involve, cause the generation of risk accidents can be considered to the minimizing process of risk entropy by risk factors effect, by the information entropy of accident that the statistical study of risk factors is reduced risks, risk information entropy is less think its impact or harm larger, be that mutual information represents measuring of risk consequence with cross entropy.Specifically comprise:

Step 2.2.1, according to the conditional probability between sample training maximum-likelihood method calculation risk factor and risk accidents, described conditional probability is at risk factors x _ithe probability that lower risk accidents S occurs;

Step 2.2.2, suppose H (x _i) represent certain risk factors x _iinformation entropy, H (S|x _i) represent certain risk factors x _ithe conditional entropy of risk accidents S under condition, I (x _i, S) and be x _iwith the mutual information of S, represent the consequence of risk generation with mutual information, and mutual information is quantized, be shown below:

I(x _i，S)＝H(S)-H(S|x _i)

Wherein: H (S) represents the information entropy of risk S, E (y) represents the expectation value of parameter y, and from shannon entropy, the information entropy of risk S is:

H(S)＝-E(lnp(S))

H (S|x _i) expression risk factors x _iunder condition, the conditional information entropy of risk S is:

H(S|x _i)＝-E(lnp(S|x _i))

Thereby determine mutual information I (x _i, S) numerical value.

Described step 2.3 risk value adopts level-entropy combined method to calculate, and risk weight is optimized by least square method, specifically comprises the following steps:

Step 2.3.1, determine experience weights; Weights by analytical hierarchy process for calculation risk index, according to risk accidents collection and risk indicator system, set up risk assessment hierarchical structure, size risk indicator being compared between any two with nine grades of scaling laws is carried out scale, set up risk indicator Judgement Matrix, solve maximum characteristic root characteristic of correspondence vector as experience weights, and check consistency;

Step 2.3.2, determine support entropy power; Information entropy is applied to and determines that risk indicator weights are to weigh according to the extremum property of entropy the degree of impact that a certain risk factors produce risk, the degree of support quantification of the possible value that each risk indicator is occurred risk accidents, sets up index and evaluates matrix A=(a _ij) _{n × m}; The degree of support of the possible value that risk accidents is occurred quantizes as shown in the table:

Mark	Very high	High	Medium	Low	Very low
						Index may value	0.8～1.0	0.6～0.8	0.4～0.6	0.2～0.4	0.0～0.2

Solve described index and evaluate matrix, utilize the extremum property of entropy to carry out standardization, obtain characterizing the entropy of risk indicator importance degree, these weights are defined as to support entropy power, be specially:

$w_j=\frac{1-e\left(a_j\right)}{m-{\mathrm{\Σ}}_{j=1}^{m}e\left(a_j\right)}$

Wherein: $e\left(a_j\right)=\frac{H\left(a_j\right)}{\ln n}=-\frac{1}{\ln n}{\mathrm{\Σ}}_{i=1}^n\frac{1+a_{\mathrm{ij}}}{a_j}\ln \frac{1+a_{\mathrm{ij}}}{a_j}$ It is the entropy that obtains characterizing risk indicator importance degree after standardization;

W=(w ₁... w _m) ^t, 0≤w _j≤ 1,

w is the support entropy power set of certain risk indicator collection, and m is risk indicator number;

The optimization of step 2.3.3, combination weights; Experience weights and support entropy weights are merged, introduce least square method and optimize the definite comprehensive weights of risk indicator of level-entropy combined method, be specially:

Wherein, F (Ψ) is the Lagrangian function of the comprehensive weights Ψ of risk indicator,

for the comprehensive weights of every risk indicator, U _jfor analytical hierarchy process obtains experience weights, V _jfor support entropy power, w _jthe relative Link Importance of the each factor obtaining for analytical hierarchy process, s.t. is constraint condition;

By introducing Lagrangian coefficient lambda, the problems referred to above are reduced to unconstrained optimization problem, solve Lagrangian function and obtain Ψ.

Described step 3 comprises:

The calculating of step 3.1, integrated risk value; Consider risk probability, risk consequence and the comprehensive weights of risk indicator, hierarchically structured successively, the multiple calculating integrated risk of risk indicator value, is specially:

${\mathrm{Risk}}_j=\underset{i=1}{\overset{m}{\mathrm{\Σ}}}{p}_i*I(x_i,S_j)*{\mathrm{\ψ}}_i$

Wherein: P _ifor risk probability; I (x _i, S _j) be risk consequence; Ψ _ifor the comprehensive weights of risk indicator;

The division of step 3.2, risk class; Risk is divided into 5 grades, i.e. " very high, high, medium and low, very low ", according to transmission line of electricity operating experience and actual conditions, sets risk class and divides foundation, determines that risk class judgement is interval;

Step 3.3, risk class are judged; Each Risk Calculation result is carried out to hierarchical classification, judge interval division according to above-mentioned different brackets risk, the value-at-risk calculating and risk class are judged to interval matches, determine final risk class.

The present invention, from the influence factor of the various risk accidents of transmission line of electricity operation, in conjunction with the actual conditions of transmission line of electricity operation, has carried out analysis and assessment to overhead transmission line operation risk.Than prior art, beneficial effect of the present invention comprises:

1, all sidedly, at many levels overhead transmission line operation risk is assessed, not only consider randomness and the uncertainty of fault, also considered the consequence influence degree that fault causes, compared the more perfect adequacy that has more of traditional assessment technology;

2, the present invention is the risk assessment technology take probability as core, can take into full account the uncertain factor of circuit operation, to carrying out quantitative test to the uncertainty of various risk factors;

3, the present invention uses the Weighting of Combinatorial Optimization, has greatly overcome dependence and subjectivity to expertise, considers the support of risk indicator itself simultaneously, after subjective and objective combination, optimizes, and obtains more scientific comprehensive weights;

4, the present invention is applied to the theory of information entropy in risk assessment, not only carrys out the severity of measure of risk consequence with mutual information, and solves objective weight-values with the rule of entropy;

5, according to overhead transmission line operation risk assessment result, operations staff can take corresponding measure to patrol and examine or maintenance etc. targetedly, the transmission line malfunction that prevention may cause, all has important engineering using value for efficient, reliable, the safe operation etc. that ensures circuit operation.

Accompanying drawing explanation

Fig. 1 is the estimation flow schematic block diagram of the overhead transmission line operation risk informatization evaluation method of Multiple Combination optimization of the present invention;

Fig. 2 shows the hierarchical structure of overhead transmission line operation risk assessment.

Embodiment

Below in conjunction with the drawings and specific embodiments, content of the present invention is described in further details.

Embodiment

The overhead transmission line operation risk informatization evaluation method that a kind of Multiple Combination of the present embodiment is optimized, shows its estimation flow schematic block diagram referring to Fig. 1, specifically comprises following 3 stages:

The risk identification stage: two parts of setting up that specifically comprise data acquisition and pre-service, Risk Assessment Index System; This stage is also the input stage of risk assessment;

Risk weight is determined the stage: specifically comprise the quantification of risk probability valuation, risk consequence and definite three parts of value-at-risk; Finally carry out calculation risk value according to above-mentioned value-at-risk formula;

Risk decision stage: specifically comprise that risk class is divided, risk is sorted out, the output of risk result.

Wherein:

One, the risk identification stage

1. data acquisition and pre-service, by operation patrol and examine, on-line monitoring, preventive trial and account etc. obtain the related data of overhead transmission line operation, adopt Enumeration Method to collect fault data, find out the source of trouble and generate risk accidents collection;

2. set up Risk Assessment Index System;

Statistical study risk accidents collection, determines that transmission line of electricity moves the uncertain factor that may exist, and sets up risk factors collection; According to uncertain risk factors and the operation characteristic thereof of transmission line of electricity operation, set up Risk Assessment Index System.Shown in being listed in the table below:

Two, risk weight is determined the stage

1. risk indicator probability valuation

According to the historical failure statistics of circuit operation, calculate the average probability of occurrence (failure rate) of each risk factors, as the risk probability of risk indicator, be specially:

$P\left(x_i\right)=\frac{m_i}{N}$

Wherein, m _ifor risk factors x in a period of time _icause the number of times of risk accidents, N is the total degree that risk accidents occurs.

The following sheet format of risk indicator probability:

2. the quantification of risk consequence

1) first will be according to the conditional probability between sample training calculated risk factor and risk accidents, at risk factors x _ithe probability that lower risk S occurs; The method calculation risk conditional probability that adopts maximum likelihood to estimate;

2) suppose H (x _i) represent certain risk factors x _iinformation entropy, H (S|x _i) represent certain risk factors x _ithe conditional entropy of risk accidents S under condition, I (x _i, S) and be x _iwith the mutual information of S, represent the consequence of risk generation with mutual information, and mutual information is quantized, be shown below:

I(x _i，S)＝H(S)-H(S|x _i)

Wherein:

H (S)---the information entropy of risk S, E (y) represents the expectation value of parameter y, from Shannon information entropy, the entropy of risk S is:

H(S)＝-E(lnp(S))

H (S|x _i)---risk factors x _iunder condition, the conditional information entropy of risk S is:

H(S|x _i)＝-E(lnp(S|x _i))

Thereby can determine mutual information I (x _i, S) numerical value.

3. risk weight

1) determine experience weights; Weights by analytical hierarchy process for calculation risk index, according to risk accidents collection and risk indicator system, set up the hierarchical structure of risk assessment index, and Fig. 2 shows the hierarchical structure of overhead transmission line operation risk assessment index; Size risk indicator being compared between any two by Method of nine marks is carried out scale, sets up risk indicator Judgement Matrix, and solve maximum characteristic root characteristic of correspondence vector, as lower floor, the importance on upper strata is to experience weights, and check consistency;

2) determine support entropy power; Information entropy is applied to and determines that risk indicator weights are to weigh according to the extremum property of entropy the degree of impact that a certain risk factors produce risk.The degree of support quantification of the possible value that each risk indicator is occurred risk accidents, sets up index and evaluates matrix A=(a _ij) _{n × m}.Wherein, n represents the possible value mark number of risk indicator degree of support, and m represents risk indicator number.The degree of support of the possible value that risk accidents is occurred quantizes as shown in the table:

The possible value mark of various risks index degree of support

Solve index and evaluate matrix, utilize the extremum property of entropy to carry out standardization, obtain characterizing the entropy of risk indicator importance degree, these weights are defined as to support entropy power; Due to the influence of risk factors, the uncertainty of the relative Link Importance of risk factors j to risk indicator i probable value is measured by following formula:

$H\left(a_j\right)=-\underset{i=1}{\overset{n}{\mathrm{\Σ}}}\frac{1+a_{\mathrm{ij}}}{a_j}\ln \frac{1+a_{\mathrm{ij}}}{a_j}$

Wherein: $a_j={\mathrm{\&Sigma;}}_{i=1}^{n}1+a_{\mathrm{ij}},\frac{1+a_{\mathrm{ij}}}{a_j}<1,n=5,i=\mathrm{1,2},\&CenterDot;\&CenterDot;\&CenterDot;6,j=\mathrm{1,2},\&CenterDot;\&CenterDot;\&CenterDot;m.$

According to the extremum property of entropy, above formula standardization, obtain characterizing the entropy of risk factors importance degree:

$e\left(a_j\right)=\frac{H\left(a_j\right)}{\ln n}=-\frac{1}{\ln n}\underset{i=1}{\overset{n}{\mathrm{\&Sigma;}}}\frac{1+a_{\mathrm{ij}}}{a_j}\ln \frac{1+a_{\mathrm{ij}}}{a_j}$

Wherein: 0≤e (a _j)≤1.

According to the definition of entropy and character, e (a _j) value less, the relative Link Importance of risk factors j is just larger.For the ease of comprehensive evaluation, by e (a _j) determine the evaluation weights θ of risk factors j _jfor:

${\mathrm{\&theta;}}_j=\frac{1-e\left(a_j\right)}{m-{\mathrm{\&Sigma;}}_{j=1}^{m}e\left(a_j\right)}$

Wherein: Θ=(θ ₁... θ _...) ^t, 0≤θ _j≤ 1,

Θ is the support entropy power set of certain risk indicator collection, and m is risk indicator number;

3) comprehensive weight of supposing every risk factors is

use the process of least square method optimization weight as follows:

Wherein, w _jthe relative Link Importance of the each factor obtaining for analytical hierarchy process.By introducing Lagrangian coefficient lambda, the problems referred to above are reduced to unconstrained optimization problem, obtain Lagrangian function expression formula and be:

By above formula pair

ask local derviation with λ and make equation the right equal 0:

In order to solve conveniently, be translated into matrix form:

$\left[\begin{array}{cc}{D}_{\mathrm{mm}}& e_m\\ e_m^T& 0\end{array}\right]\left[\begin{array}{c}\mathrm{\&Psi;}\\ \mathrm{\&lambda;}\end{array}\right]=\left[\begin{array}{c}{C}_m\\ 1\end{array}\right]$

Wherein:

$D_{\mathrm{mm}}=\mathrm{diag}(\underset{i=1}{\overset{n}{\mathrm{\&Sigma;}}}{4a}_{i1}^2,\underset{i=1}{\overset{n}{\mathrm{\&Sigma;}}}{4a}_{i2}^2,\&CenterDot;\&CenterDot;\&CenterDot;,\underset{i=1}{\overset{n}{\mathrm{\&Sigma;}}}{4a}_{\mathrm{im}}^2)$

e _m＝(1?1?…?1) ^T

$C_m={(\underset{i=1}{\overset{n}{\mathrm{\&Sigma;}}}2(w_1+{\mathrm{\&theta;}}_1)a_{i1}^2,\underset{i=1}{\overset{n}{\mathrm{\&Sigma;}}}2(w_2+{\mathrm{\&theta;}}_2)a_{i2}^2,\&CenterDot;\&CenterDot;\&CenterDot;,\underset{i=1}{\overset{n}{\mathrm{\&Sigma;}}}2(w_m+{\mathrm{\&theta;}}_m)a_{\mathrm{im}}^2)}^T$

Dematrix equation:

$\mathrm{\&lambda;}=-\frac{1-e_m^T{D}_{\mathrm{mm}}^{-1}{C}_m}{e_m^T{D}_{\mathrm{mm}}^{-1}{e}_m}$

$\mathrm{\&Psi;}=D_{\mathrm{mm}}^{-1}(C_m-{\mathrm{\&lambda;e}}_m)=D_{\mathrm{mm}}^{-1}(C_m+\frac{1-e_m^T{D}_{\mathrm{mm}}^{-1}{C}_m}{e_m^T{D}_{\mathrm{mm}}^{-1}{e}_m})$

4. integrated risk value is calculated

Consider risk probability, risk consequence and value-at-risk, hierarchically structured successively, the multiple calculating integrated risk of risk indicator value, is specially:

${\mathrm{Risk}}_i=\underset{i=1}{\overset{m}{\mathrm{\&Sigma;}}}{P}_i\&times;C_i\&times;W_i$

Wherein: P _i---risk probability; C _i---risk consequence; W _i---value-at-risk;

Therefore, in sum, being calculated as of a certain sub-risk:

${\mathrm{Risk}}_j=\underset{i=1}{\overset{m}{\mathrm{\&Sigma;}}}{p}_i*I(x_i,S_j)*{\mathrm{\&Psi;}}_i$

Wherein: P _ifor risk probability; I (x _i, S _j) be risk consequence; Ψ _ifor the comprehensive weights of risk indicator.

Three, risk decision stage

1. risk class is divided

For overhead transmission line operation risk is carried out to quantitative test, risk is divided into 5 grades, i.e. " very high, high, medium and low, very low ".According to transmission line of electricity operating experience and actual conditions, set risk class and divide foundation, table specific as follows:

The division of transmission line of electricity operation risk grade

Wherein, the division principle in the judgement interval of risk class is: analyze the result that value-at-risk is calculated, according to the concrete ruuning situation of different circuits and expertise, divide in conjunction with FCM algorithm simultaneously;

2. risk is sorted out and result judgement

According to the division in the judgement interval of above-mentioned different brackets risk, by judging that with risk class interval matches after the value-at-risk normalized calculating, determine final risk class.

Above-listed detailed description is for the illustrating of possible embodiments of the present invention, and this embodiment is not in order to limit the scope of the claims of the present invention, and the equivalence that all the present invention of disengaging do is implemented or changed, and all should be contained in the scope of the claims of this case.

Claims (8)

1. the overhead transmission line operation risk informatization evaluation method that Multiple Combination is optimized, is characterized in that, it comprises the following steps:

Step 1, risk identification, find out the source of trouble by data acquisition and generate risk accidents collection, and risk accidents collection described in statistical study, determines that transmission line of electricity moves the uncertain factor that may exist, and sets up risk factors collection; And according to uncertain risk factors and the operation characteristic thereof of transmission line of electricity operation, set up Risk Assessment Index System;

Step 2, value-at-risk are determined, build transmission line of electricity operation risk assessment model, utilize multilayer, multiple methods of risk assessment to estimate the value-at-risk of each index of transmission line of electricity, further the integrated risk value of computing electric power line;

Step 3, risk are judged, described integrated risk value is analyzed, and determine risk class, the result of output risk assessment.

2. the overhead transmission line operation risk informatization evaluation method that Multiple Combination according to claim 1 is optimized, is characterized in that, the method that generates risk accidents collection described in described step 1 is:

By operation patrol and examine, on-line monitoring, preventive trial and account obtain the related data of overhead transmission line operation, produces typical risk accidents collection by Enumeration Method, described risk accidents collection comprises tripping operation stoppage in transit risk accidents and system risk accident; Analyze these risk accidents, determine the uncertain factor that causes risk accidents to occur, production risk set of factors, as the index of risk assessment.

3. the overhead transmission line operation risk informatization evaluation method that Multiple Combination according to claim 2 is optimized, is characterized in that, described step 2 comprises:

Step 2.1, the valuation of risk indicator probability;

The quantification of step 2.2, risk consequence;

Step 2.3, value-at-risk are calculated.

4. the overhead transmission line operation risk informatization evaluation method that Multiple Combination according to claim 3 is optimized, it is characterized in that, described risk indicator probability is the size of the possibility of circuit risk factors appearance, according to the property calculation risk indicator probability of risk accidents collection and risk factors collection, concrete grammar comprises:

(1) according to the historical failure statistics of circuit operation, calculate the average probability of occurrence of each risk factors, as risk probability, be specially:

$P\left(x_i\right)=\frac{m_i}{N}$

Wherein, m _ifor risk factors x in a period of time _icause the number of times of risk accidents, N is the total degree that risk occurs,

(2) adopt Enumeration Method to generate risk accidents collection, the probability that arbitrary risk indicator i occurs is:

$p_i=\underset{k=1}{\overset{M}{\mathrm{\&Pi;}}}{\mathrm{\&mu;}}_k\underset{k=1}{\overset{L}{\mathrm{\&Pi;}}}(1-{\mathrm{\&mu;}}_k)$

Wherein, M is fault element number, and L is normal operation element number, p _ifor risk indicator probability, μ _kfor the dependability parameter of element.

5. the overhead transmission line operation risk informatization evaluation method that Multiple Combination according to claim 4 is optimized, it is characterized in that, the dependability parameter of described element is to obtain the representative value of emergency shut-down coefficient by inquiry related data, or obtains according to the statistical conditions of element idle time in recent years; The method that is obtained the dependability parameter of element by the statistical conditions of the described idle time of element is in recent years:

wherein, MTTR be in statistical time range total idle time of element; PRD is the length of statistical time range.

6. the overhead transmission line operation risk informatization evaluation method that Multiple Combination according to claim 4 is optimized, is characterized in that, described step 2.2 comprises:

Step 2.2.1, according to the conditional probability between sample training maximum-likelihood method calculation risk factor and risk accidents, described conditional probability is at risk factors x _ithe probability that lower risk accidents S occurs;

Step 2.2.2, suppose H (x _i) represent certain risk factors x _iinformation entropy, H (S|x _i) represent certain risk factors x _ithe conditional entropy of risk accidents S under condition, I (x _i, S) and be x _iwith the mutual information of S, represent the consequence of risk generation with mutual information, and mutual information is quantized, be shown below:

I(x _i，S)＝H(S)-H(S|x _i)

Wherein: H (S) represents the information entropy of risk S, E (y) represents the expectation value of parameter y, and from shannon entropy, the information entropy of risk S is:

H(S)＝-E(lnp(S))

H (S|x _i) expression risk factors x _iunder condition, the conditional information entropy of risk S is:

H(S|x _i)＝-E(lnp(S|x _i))

Determine mutual information I (x _i, S) numerical value.

7. the overhead transmission line operation risk informatization evaluation method that Multiple Combination according to claim 6 is optimized, it is characterized in that, described step 2.3 risk value adopts level-entropy combined method to calculate, and this value-at-risk is optimized by least square method, specifically comprises the following steps:

Step 2.3.1, determine experience weights; Weights by analytical hierarchy process for calculation risk index, according to risk accidents collection and risk indicator system, set up risk assessment hierarchical structure, size risk indicator being compared between any two with nine grades of scaling laws is carried out scale, set up risk indicator Judgement Matrix, solve maximum characteristic root characteristic of correspondence vector as experience weights, and check consistency;

Step 2.3.2, determine support entropy power; Information entropy is applied to and determines that risk indicator weights are to weigh according to the extremum property of entropy the degree of impact that a certain risk factors produce risk, the degree of support quantification of the possible value that each risk indicator is occurred risk accidents, sets up index and evaluates matrix A=(a _ij) _{n × m};

Solve described index and evaluate matrix, utilize the extremum property of entropy to carry out standardization, obtain characterizing the entropy of risk indicator importance degree, these weights are defined as to support entropy power, be specially:

$w_j=\frac{1-e\left(a_j\right)}{m-{\mathrm{\&Sigma;}}_{j=1}^{m}e\left(a_j\right)}$

Wherein: $e\left(a_j\right)=\frac{H\left(a_j\right)}{\ln n}=-\frac{1}{\ln n}{\mathrm{\&Sigma;}}_{i=1}^n\frac{1+a_{\mathrm{ij}}}{a_j}\ln \frac{1+a_{\mathrm{ij}}}{a_j}$ It is the entropy that obtains characterizing risk indicator importance degree after standardization; W=(w ₁... w _...) ^t, 0≤w _j≤ 1, w is the support entropy power set of certain risk indicator collection, and m is risk indicator number;

The optimization of step 2.3.3, risk combination weights; Experience weights and support entropy weights are merged, introduce least square method and optimize the definite risk combination weights of level-entropy combined method, be specially:

Wherein, F (Ψ) is the Lagrangian function of the comprehensive weights Ψ of risk indicator,

for the comprehensive weights of every risk indicator, U _jfor analytical hierarchy process obtains experience weights, V _jfor support entropy power, w _jthe relative Link Importance of the each factor obtaining for analytical hierarchy process, s.t. is constraint condition;

By introducing Lagrangian coefficient lambda, the problems referred to above are reduced to unconstrained optimization problem, solve Lagrangian function and obtain Ψ.

8. the overhead transmission line operation risk informatization evaluation method that Multiple Combination according to claim 7 is optimized, is characterized in that, described step 3 comprises:

The calculating of step 3.1, integrated risk value; Consider risk probability, risk consequence and the comprehensive weights of risk indicator, hierarchically structured successively, the multiple calculating integrated risk of risk indicator value, is specially:

${\mathrm{Risk}}_j=\underset{i=1}{\overset{m}{\mathrm{\&Sigma;}}}{p}_i*I(x_i,S_j)*{\mathrm{\&psi;}}_i$

Wherein: P _ifor risk probability; I (x _i, S _j) be risk consequence; Ψ _ifor the comprehensive weights of risk indicator;

The division of step 3.2, risk class; Risk is divided into 5 grades, i.e. " very high, high, medium and low, very low ", according to transmission line of electricity operating experience and actual conditions, sets risk class and divides foundation, determines that risk class judgement is interval;

Step 3.3, risk class are judged; Each Risk Calculation result is carried out to hierarchical classification, judge interval division according to above-mentioned different brackets risk, the value-at-risk calculating and risk class are judged to interval matches, determine final risk class.

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