CN103793854B - The overhead transmission line operation risk informatization evaluation method that Multiple Combination is optimized - Google Patents

Abstract

The invention discloses a kind of 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, the uncertain risk revulsion of statistical study, sets up Risk Assessment Index System; The determination of step 2, integrated risk value, build transmission line of electricity operation risk assessment model, information entropy and analytical hierarchy process are merged, consider risk probability, risk produce consequence and risk weight, application association relationship quantizes risk schedule severity, and Multiple Combination, successively reasoning and calculation transmission line of electricity run integrated risk value; Step 3, risk judge, divide risk class, analyze, determine risk class to integrated risk weights, export the result of risk assessment.The present invention assesses overhead transmission line operation risk all sidedly, at many levels, improves accuracy and the transmission line of electricity reliability of operation of transmission line of electricity risk assessment.

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 reflects the process that the understanding of people to power system stability and safe operation deepens continuously.Overhead transmission line carries the main power transmission mode of power construction, in the continuous increase along with power grid construction scale, the safety and stablization sex chromosome mosaicism that transmission line of electricity runs also is given prominence to 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 that overhead transmission line runs also just is arisen at the historic moment.At present, risk assessment about Operation of Electric Systems has become the focus of research both at home and abroad, along with the concept of Modern Risk Leading assessment is introduced in the system of Power System Security Assessment, one large focal spot is also become to the risk assessment that overhead transmission line runs, how using rationally effective means to assess overhead transmission line operation risk, also becoming 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, research overhead transmission line operation risk assessment method, the risk status of accurate assurance circuit, for the aid decision making of transmission line of electricity, reasonably optimizing maintenance resource distribution, ensures circuit stable and high effective operation, has very important directive function.The risk assessment that overhead transmission line runs is exactly set up risk evaluation system according to the feature that overhead transmission line runs, multi-angle, multi-level simulation tool computing electric power line run integrated risk weights, for follow-up maintenance and aid decision making provide support, thus its assess effectiveness and accuracy affect larger on the maintenance measures of circuit.

At present, Deterministic Methods and the large class of uncertain method two can be divided into 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 have ignored complicacy and the randomness of transmission system operation, lacks quantitative test, margin of safety can be caused excessive, conscientiously cannot ensure the security that circuit runs risk.Uncertain method considers that transmission system runs the randomness of each risk accidents and the probability of generation thereof, by carrying out all accidents comprehensively analyzing the assessment realized total system security, but probabilistic analysis method only considered randomness and the uncertainty of line fault, do not consider the consequence influence degree that line fault causes therefore 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, disclose a kind of overhead transmission line operation risk informatization evaluation method that Multiple Combination is optimized, fully take into account the impact that the internal factor of circuit and external factor are run line security, Multiple Combination, successively reasoning and calculation transmission line of electricity run integrated risk weights, thus have more science and practicality, not only increase accuracy and the transmission line of electricity reliability of operation of transmission line of electricity risk assessment, be also conducive to the analysis of risk, management and decision.

The present invention, under National 863 planning item fund (2012AA050209) is subsidized, proposes " 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, the consequence that risk produces and risk weight, application mutual information quantizes risk schedule 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 runs integrated risk weights, form a kind of assessment models of autonomous optimization.The present invention overcomes pure subjective analysis thought greatly, improve accuracy and the transmission line of electricity reliability of operation of transmission line of electricity risk assessment, for finding potential risk that circuit runs early, reduce possible risk that circuit runs, the risk of loss that reduces circuit operation provides and provides powerful support for, and is conducive to the analysis of risk, management and decision.

For realizing above object, the technical scheme that this invention takes 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, risk accidents collection described in statistical study, determines that transmission line of electricity runs the uncertain factor that may exist, sets up risk revulsion; And according to transmission line of electricity run uncertain risk factors and operation characteristic, 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, the integrated risk value of further computing electric power line;

Step 3, risk judge, analyze, determine risk class to described integrated risk value, export the result of risk assessment.

The method generating risk accidents collection described in described step 1 is:

Patrol and examine by running, on-line monitoring, preventive trial and account obtain the related data that overhead transmission line runs, produce 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 causing risk accidents to occur, production risk set of factors, as the index of risk assessment.

Described step 2 comprises:

Step 2.1, risk indicator probabilistic estimation;

The quantification of step 2.2, risk schedule;

Step 2.3, value-at-risk calculate.

Described risk indicator probability is the property calculation risk indicator probability of the size of the possibility that circuit risk factors occur, foundation risk accidents collection and risk revulsion, and its computing method specifically comprise:

(1) according to the historical failure statistics that circuit runs, 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 of 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 the representative value being obtained emergency shut-down coefficient by inquiry related data, or obtains according to the statistical conditions of element idle time in recent years; The method being 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 element total idle time; PRD is the length of statistical time range.

Described step 2.2 risk consequence is that risk factors are when occurring, the degree that the impact occur risk accidents or harm may involve and scope, cause the generation of risk accidents can be considered to the minimizing process of risk entropy by risk factors effect, namely by the information entropy of accident of reducing risks to the statistical study of risk factors, risk information entropy is less, thinks that it affects or endangers larger, represents measuring of risk schedule with cross entropy and mutual information.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 with mutual information the consequence that risk produces, and mutual information quantized, be shown below:

I(x _i，S)＝H(S)-H(S|x _i)

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

H(S)＝-E(lnp(S))

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

H(S|x _i)＝-E(lnp(S|x _i))

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

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

Step 2.3.1, determine experience weights; Analytical hierarchy process is used for the weights of calculation risk index, according to risk accidents collection and risk indicator system, set up risk assessment hierarchical structure, with nine grades of scaling laws, scale is carried out to the size that risk indicator compares between any two, set up risk indicator Judgement Matrix, solve Maximum characteristic root characteristic of correspondence vector empirically weights, and check consistency;

Step 2.3.2, determine that support entropy is weighed; Information entropy is applied to and determines that risk indicator weights weigh according to the extremum property of entropy the disturbance degree that a certain risk factors produce risk, by the degree of support quantification of each risk indicator to the possible value that risk accidents occurs, set up metrics evaluation matrix A=(a _ij) _{n × m}; The degree of support of possible value risk accidents 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 metrics evaluation matrix, utilize the extremum property of entropy to carry out standardization, obtain the entropy characterizing risk indicator importance degree, these weights are defined as support entropy power, are 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 obtaining 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, Combining weights; Experience weights and support entropy weight are merged, introducing least square method optimizes the comprehensive weights of risk indicator that level-entropy combined method is determined, is 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 _jfor the relative Link Importance of each factor that analytical hierarchy process obtains, s.t. is constraint condition;

By introducing Lagrange coefficient λ, the problems referred to above being reduced to unconstrained optimization problem, solving Lagrangian function and obtaining Ψ.

Described step 3 comprises:

The calculating of step 3.1, integrated risk value; Consider the comprehensive weights of risk probability, risk schedule and risk indicator, hierarchically structured successively, the multiple calculating integrated risk value of risk indicator, 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 schedule; 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, setting risk class partitioning standards, determines that risk class judges interval;

Step 3.3, risk class judge; Hierarchical classification is carried out to each Risk Calculation result, judges interval division according to above-mentioned different brackets risk, the value-at-risk calculated and risk class are judged that interval matches, determines final risk class.

The influence factor of the various risk accidents that the present invention runs from transmission line of electricity, in conjunction with the actual conditions that transmission line of electricity runs, has carried out analysis and assessment to overhead transmission line operation risk.Compared to 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, have also contemplated that the consequence influence degree that fault causes, compare that Traditional measurements technology is more perfect has more adequacy;

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

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

4, the theory of information entropy is applied in risk assessment by the present invention, 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 carry out patrolling and examining or maintenance etc. targetedly, the transmission line malfunction that prevention may cause, for ensureing that efficient, reliable, the safe operation that circuit runs etc. all has important engineer applied and is worth.

Accompanying drawing explanation

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

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 see Fig. 1, specifically comprises following 3 stages:

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

Risk weight determines the stage: specifically comprise risk probability valuation, the quantification of risk schedule and determination 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 risk class division, risk is sorted out, Risk Results exports.

Wherein:

One, the risk identification stage

1. data acquisition and pre-service, patrols and examines by running, on-line monitoring, preventive trial and account etc. obtains the related data that overhead transmission line runs, 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 runs the uncertain factor that may exist, sets up risk revulsion; The uncertain risk factors run according to transmission line of electricity and operation characteristic thereof, set up Risk Assessment Index System.Shown in being listed in the table below:

Two, risk weight determines the stage

1. risk indicator probabilistic estimation

According to the historical failure statistics that circuit runs, 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 schedule

1) first will according to the conditional probability between sample training calculated risk factor and risk accidents, namely at risk factors x _ithe probability that lower risk S occurs; Adopt the method calculation risk conditional probability of Maximum-likelihood estimation;

2) H (x is supposed _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 with mutual information the consequence that risk produces, and mutual information 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, and 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))

Thus mutual information I (x can be determined _i, S) numerical value.

3. risk weight

1) experience weights are determined; Analytical hierarchy process is used for the weights of calculation risk index, according to risk accidents collection and risk indicator system, set up the hierarchical structure of risk assessment index, Fig. 2 shows the hierarchical structure of overhead transmission line operation risk assessment index; By Method of nine marks, scale is carried out to the size that risk indicator compares between any two, set up risk indicator Judgement Matrix, solve Maximum characteristic root characteristic of correspondence vector and be experience weights as the importance of lower floor to upper strata, and check consistency;

2) determine that support entropy is weighed; Information entropy is applied to and determines that risk indicator weights weigh according to the extremum property of entropy the disturbance degree that a certain risk factors produce risk.By the degree of support quantification of each risk indicator to the possible value that risk accidents occurs, set up metrics evaluation 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 possible value risk accidents occurred quantizes as shown in the table:

The possible value mark of various risks index degree of support

Solve metrics evaluation matrix, utilize the extremum property of entropy to carry out standardization, obtain the entropy characterizing risk indicator importance degree, these weights are defined as support entropy power; Due to the influence of risk factors, the uncertainty of risk factors j to the relative Link Importance of 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 the entropy characterizing 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 definition and the character of entropy, e (a _j) value less, the relative Link Importance of risk factors j is 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) suppose that the comprehensive weight of every risk factors is use the process of least square method optimization weight as follows:

Wherein, w _jfor the relative Link Importance of each factor that analytical hierarchy process obtains.By introducing Lagrange coefficient λ, the problems referred to above are reduced to unconstrained optimization problem, obtaining Lagrangian function expression formula is:

By above formula pair ask local derviation with λ and make to equal 0 on the right of equation:

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 calculates

Consider risk probability, risk schedule and value-at-risk, hierarchically structured successively, the multiple calculating integrated risk value of risk indicator, 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 schedule; 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 schedule; Ψ _ifor the comprehensive weights of risk indicator.

Three, risk decision stage

1. risk class divides

In order to carry out quantitative test to overhead transmission line operation risk, 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, setting risk class partitioning standards, 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 calculates, according to different circuit carrying out practically situation and expertise, divide in conjunction with FCM algorithm simultaneously;

2. risk classification judges with result

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

Above-listed detailed description is illustrating for possible embodiments of the present invention, and this embodiment is also not used 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 (3)

1. an overhead transmission line operation risk informatization evaluation method for Multiple Combination optimization, it 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, risk accidents collection described in statistical study, determines that transmission line of electricity runs the uncertain factor that may exist, sets up risk revulsion; And according to transmission line of electricity run uncertain risk factors and operation characteristic, 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, the integrated risk value of further computing electric power line;

Step 3, risk judge, analyze, determine risk class to described integrated risk value, export the result of risk assessment;

Wherein: the method generating risk accidents collection described in described step 1 is:

Patrol and examine by running, on-line monitoring, preventive trial and account obtain the related data that overhead transmission line runs, produce 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 causing risk accidents to occur, production risk set of factors, as the index of risk assessment;

Described step 2 comprises:

Step 2.1, risk indicator probabilistic estimation;

The quantification of step 2.2, risk schedule;

Step 2.3, value-at-risk calculate;

Described risk indicator probability is the property calculation risk indicator probability of the size of the possibility that circuit risk factors occur, foundation risk accidents collection and risk revulsion, and concrete grammar comprises:

(1) according to the historical failure statistics that circuit runs, 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 is the dependability parameter of element;

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

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 with mutual information the consequence that risk produces, and mutual information quantized, be shown below:

I(x _i，S)＝H(S)-H(S|x _i)

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

H(S)＝-E(ln p(S))

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

H(S|x _i)＝-E(ln p(S|x _i))

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

2. the overhead transmission line operation risk informatization evaluation method of Multiple Combination optimization according to claim 1, it is characterized in that, described step 2.3 risk value adopts level-entropy combined method to calculate, and is optimized this value-at-risk by least square method, specifically comprises the following steps:

Step 2.3.1, determine experience weights; Analytical hierarchy process is used for the weights of calculation risk index, according to risk accidents collection and risk indicator system, set up risk assessment hierarchical structure, with nine grades of scaling laws, scale is carried out to the size that risk indicator compares between any two, set up risk indicator Judgement Matrix, solve Maximum characteristic root characteristic of correspondence vector empirically weights, and check consistency;

Step 2.3.2, determine that support entropy is weighed; Information entropy is applied to and determines that risk indicator weights weigh according to the extremum property of entropy the disturbance degree that a certain risk factors produce risk, by the degree of support quantification of each risk indicator to the possible value that risk accidents occurs, set up metrics evaluation matrix A=(a _ij) _{n × m};

Solve described metrics evaluation matrix, utilize the extremum property of entropy to carry out standardization, obtain the entropy characterizing risk indicator importance degree, these weights are defined as support entropy power, are 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 obtaining 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, risk Combining weights; Experience weights and support entropy weight are merged, introducing least square method optimizes the risk Combining weights that level-entropy combined method is determined, is 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 _jfor the relative Link Importance of each factor that analytical hierarchy process obtains, s.t. is constraint condition;

By introducing Lagrange coefficient λ, the problems referred to above being reduced to unconstrained optimization problem, solving Lagrangian function and obtaining Ψ.

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

The calculating of step 3.1, integrated risk value; Consider the comprehensive weights of risk probability, risk schedule and risk indicator, hierarchically structured successively, the multiple calculating integrated risk value of risk indicator, 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 schedule; 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, setting risk class partitioning standards, determines that risk class judges interval;

Step 3.3, risk class judge; Hierarchical classification is carried out to each Risk Calculation result, judges interval division according to above-mentioned different brackets risk, the value-at-risk calculated and risk class are judged that interval matches, determines final risk class.