CN105488343A - Fault probability calculation method of secondary electric power equipment - Google Patents

Abstract

The invention provides a fault probability calculation method of secondary electric power equipment. The fault probability calculation method of the secondary electric power equipment comprises the following steps of obtaining the quantification value of the operation state of the secondary equipment; obtaining the fault probability of equipment with different state grades; and performing correlation fitting on the quantification value of the operation state of the equipment and the fault probability by a least square method. The invention provides a fault probability resolving method based on secondary equipment state evaluation; and on the basis of completing a state parameter model, a correlation curve of the operation state and the fault probability is fit by the least square method. The method has the advantages that the on-site and historical operation records can be sufficiently used; the fault probability of the secondary equipment is measured and calculated according to the mathematical law; and the data support is provided for the risk quantification evaluation of the secondary equipment.

Description

A kind of second power equipment probability of malfunction computing method

Technical field

The present invention relates to a kind of computing method, particularly relate to a kind of second power equipment probability of malfunction computing method.

Background technology

Probability of malfunction is the important reliability index of equipment one, is also the basis of carrying out equipment risk evaluation.In electrical network, directly decide the production run of delivery of electrical energy for the high pressure primary equipment of power transmission and transformation, therefore come into one's own about the statistics of this type of probability of equipment failure and calculating.And concerning primary equipment carries out the secondary device of protection and measurement and control, the operation of normal reliable is also the basic demand ensureing the stabilization of power grids and power equipment safety, need to obtain equal concern.

At present the relation between working time and probability of equipment failure is considered emphatically to the research of probability of malfunction, and consider that other factors such as running environment etc. is added up again on the basis running the time limit.But the factor affecting equipment failure generation is a lot, only consider that the probability of malfunction obtained when running the factor such as the time limit and running environment has some limitations.

Summary of the invention

The invention provides a kind of second power equipment probability of malfunction computing method, to solve the larger problem of probability of equipment failure computing method limitation of the prior art.

The invention provides a kind of second power equipment probability of malfunction computing method, described second power equipment probability of malfunction computing method comprise: the quantized value obtaining secondary device running status; Obtain the probability of malfunction of different conditions grade equipment; Utilize least square method that the quantized value of described equipment running status is associated matching with described probability of malfunction.

Preferably, the quantized value of described acquisition secondary device running status comprises: selected characteristic quantity of state, and described eigenstate amount is quantized, and asks for eigenstate amount quantized value; Ask for the weighted value of described eigenstate amount; Weighted value according to described eigenstate amount quantized value and described eigenstate amount asks for secondary device running status quantized value.

Preferably, described by described eigenstate amount quantification, ask for eigenstate amount quantized value and comprise: calculated by the property spent together, ask for parameter information matrix.

Preferably, the weighted value asking for described eigenstate amount described in comprises: based on the analytical hierarchy process of 1-9 scale, obtain the weight of each parameter.

Preferably; describedly utilize least square method that the quantized value of described equipment running status is associated matching with described probability of malfunction to comprise: according to described secondary device running status quantized value and described probability of malfunction, seek an objective function by mathematical modeling or summarizing experimental data: $f^{*}\left(S\right)=a_0^{*}{f}_0\left(S\right)+a_1^{*}{f}_0\left(S\right)+...+a_n^{*}{f}_n\left(S\right);$ Make f ^*(S) meet $\underset{i=1}{\overset{m}{\Σ}}{\[f^{*}\left(S_i\right)-P_i\]}^2=\underset{f\left(S\right)=F}{MIN}\underset{i=1}{\overset{m}{\Σ}}{\[f\left(S_i\right)-P_i\]}^2,$ Its mid point the multivariate function $s(a_0,a_1,...,a_n)=\underset{i=1}{\overset{m}{\Σ}}{\[\underset{k=0}{\overset{n}{\Σ}}{a}_k{f}_k\left(S_i\right)-P_i\]}^2$ Minimal point, thus meet system of equations: $\frac{\∂s}{\∂a_k}=\underset{i=1}{\overset{m}{\Σ}}\underset{k=0}{\overset{n}{\Σ}}\[a_k{f}_k\left(S_i\right)-P_i\]{(\Σa_k{f}_k\left(S_i\right)-P_i)}^{\′}=0;$

Introduce mark $(h,g)=\underset{i=1}{\overset{m}{\Σ}}h\left(x_i\right)g\left(x_i\right)=h\left(x_1\right)g\left(x_1\right)+h\left(x_2\right)g\left(x_2\right)+...+h\left(x_m\right)g\left(x_m\right),$ Can obtain

a ₀(f _k,f ₀)+a ₀(f _k,f ₀)+…+a _n(f _k,f _n)＝(f _k,y),(k＝0,1,…,n)

Matrix form is $\left[\begin{array}{cccc}(f_0,f_0)& (f_0,f_1)& ...& (f_0,f_n)\\ (f_1,f_0)& (f_1,f_1)& ...& (f_1,f_n)\\ .& .& & .\\ .& .& ...& .\\ .& .& & .\\ (f_n,f_0)& (f_n,f_1)& ...& (f_n,f_n)\end{array}\right]\left[\begin{array}{c}{a}_0\\ a_1\\ .\\ .\\ .\\ a_n\end{array}\right]=\left[\begin{array}{c}(f_0,y)\\ (f_1,y)\\ .\\ .\\ .\\ (f_n,y)\end{array}\right]$

Work as f ₀(S), f ₁(S) ..., f _n(S), during linear independence, least square solution is obtained

The technical scheme that embodiments of the invention provide can comprise following beneficial effect:

The invention provides a kind of second power equipment probability of malfunction computing method, described second power equipment probability of malfunction computing method comprise: the quantized value obtaining secondary device running status; Obtain the probability of malfunction of different conditions grade equipment; Utilize least square method that the quantized value of described equipment running status is associated matching with described probability of malfunction.Propose the probability of malfunction acquiring method based on secondary device state evaluation herein, improve on the basis of state parameter model, utilize the invariance curve of least square fitting running status and probability of malfunction.The method can make full use of logout that is on-the-spot and history, calculates secondary device probability of malfunction according to mathematical law, for the risk quantification assessment of secondary device provides Data support.

Should be understood that, it is only exemplary and explanatory that above general description and details hereinafter describe, and can not limit the present invention.

Accompanying drawing explanation

Fig. 1 is the process flow diagram of a kind of second power equipment probability of malfunction computing method provided in the embodiment of the present invention;

Fig. 2 is the method flow diagram of the quantized value of the acquisition secondary device running status provided in the embodiment of the present invention;

Fig. 3 is the schematic diagram of the secondary device state evaluation parametric model provided in the embodiment of the present invention.

Embodiment

Here will be described exemplary embodiment in detail, its sample table shows in the accompanying drawings.When description below relates to accompanying drawing, unless otherwise indicated, the same numbers in different accompanying drawing represents same or analogous key element.Embodiment described in following exemplary embodiment does not represent all embodiments consistent with the present invention.On the contrary, they only with as in appended claims describe in detail, the example of device that aspects more of the present invention are consistent.

Each embodiment in this instructions all adopts the mode of going forward one by one to describe, between each embodiment identical similar part mutually see, what each embodiment stressed is the difference with other embodiment.

Please refer to Fig. 1, the process flow diagram of a kind of second power equipment probability of malfunction computing method provided in the embodiment of the present invention is provided.

As shown in Figure 1, the invention provides a kind of second power equipment probability of malfunction computing method, described second power equipment probability of malfunction computing method comprise: the quantized value obtaining secondary device running status; Obtain the probability of malfunction of different conditions grade equipment; Utilize least square method that the quantized value of described equipment running status is associated matching with described probability of malfunction.Propose the probability of malfunction acquiring method based on secondary device state evaluation herein, improve on the basis of state parameter model, utilize the invariance curve of least square fitting running status and probability of malfunction.The method can make full use of logout that is on-the-spot and history, calculates secondary device probability of malfunction according to mathematical law, for the risk quantification assessment of secondary device provides Data support.

Please refer to Fig. 2, the method flow diagram of the quantized value of the acquisition secondary device running status provided in the embodiment of the present invention is provided.

As shown in Figure 2, the quantized value of described acquisition secondary device running status comprises: selected characteristic quantity of state, and described eigenstate amount is quantized, and asks for eigenstate amount quantized value; Ask for the weighted value of described eigenstate amount; Weighted value according to described eigenstate amount quantized value and described eigenstate amount asks for secondary device running status quantized value.

Please refer to Fig. 3, the schematic diagram of the secondary device state evaluation parametric model provided in the embodiment of the present invention is provided.

As shown in Figure 3, equipment state amount choose mainly in conjunction with practical operating experiences, look for the characteristic parameter that can reflect secondary device self-condition, and analyze the comprehensive of parameter and rationality.According to the principle that overall process, systematicness combine with level, in conjunction with the operational management feature of secondary device self, set up secondary device state evaluation parametric model.

Determine the method for weighting of parameter model, adopt 0.1 ~ 0.9 scale, step is as follows:

(1) fuzzy matrix is built (n is the number of the key element will determining weight), such as:

(technical information quality, device workmanship, construction and installation quality, check and acceptance quality, defect situation, familial defect, post-installation review parameter, regular inspection situation, the anti-configuration of situation, equipment redundancy, device performance parameters, real time execution data, running environment, working time, the producer of arranging support that 15 aspects are weight key element, i.e. n=15;

By comparing structure Fuzzy Complementary Judgment Matrices between two between element represent for upper strata criterion, the relative importance degree of this layer with it between Its Related Elements.

Work as a _ijwhen=0.5, u _iand u _jof equal importance; Work as a _ijduring > 0.5, u _icompare u _jimportant; Work as a _ijduring < 0.5, u _jcompare u _iimportant, specifically as shown in table 1.

Table 1: fuzzy judgment matrix

Suppose there be m expert, m fuzzy matrix can be obtained like this, be designated as A ^(m).

(2) Fuzzy consistent matrix R is asked for

Fuzzy matrix wherein:

$\left\{\begin{array}{c}{r}_i=\underset{k=1}{\overset{n}{\&Sigma;}}{a}_{ik}\left(i=1,2,\mathrm{...},n\right)\\ r_{ij}=\frac{r_i-r_j}{2n}+0.5\end{array}\right.$

To m fuzzy matrix A, be designated as A ^(m), m fuzzy complementary matrix R can be obtained, be designated as R ^(m).

(3) polymer matrix of Fuzzy consistent matrix is asked

Suppose that every expert status equal (if distinct root provides this expert's weighting factor according to the importance degree of expert opinion) can obtain thus: $\tilde{R}=\frac{1}{m}\&Sigma;R^{\left(m\right)}.$

(4) weights W=(w is asked for ₁, w ₂..., w _n):

Have weight matrix: $w_i=\frac{1}{n}-\frac{1}{2a}+\frac{1}{na}\underset{k=1}{\overset{n}{\&Sigma;}}{\tilde{r}}_{ik},$ Wherein:

$\left\{\begin{array}{c}a=\frac{n}{2}-\mathrm{\&beta;}+\mathrm{\&xi;}\\ \mathrm{\&beta;}=min\left\{\underset{k=1}{\overset{n}{\&Sigma;}}{\tilde{r}}_{ik}\right\}\\ 0<\mathrm{\&xi;}<\mathrm{\&beta;}-0.5\\ r_i=\underset{k=1}{\overset{n}{\&Sigma;}}{a}_{ik}\left(i=1,2,\mathrm{...},n\right)\end{array}\right.$

Further, described by described eigenstate amount quantification, ask for eigenstate amount quantized value and comprise: calculated by the property spent together, ask for parameter information weight matrix.

Different eigenstate amount define equipment accidents develops or reflects that the capacity of water of health status is distinct, needs to be calculated by the property spent together to make equipment state parameter intuitively comparable.Secondary device state parameter is evaluated, and carries out quantum chemical method, try to achieve parameter information weight matrix to parameter information:

R＝[w ₁,w ₂,…,w _n]

Further, the weighted value asking for described eigenstate amount described in comprises: based on the analytical hierarchy process of 1-9 scale, obtain the weight of each parameter.

The difference of each parameter of equipment role in evaluation process, should give its different weight objective according to the significance level of each parameter, exactly respectively.Adopt the analytical hierarchy process based on 1-9 scale herein, simply compare carrying out between each parameter between two and calculate, drawing the weight of each parameter:

A＝[a ₁,a ₂,…,a _n]

By information quantization value and the weighted value of state parameter, weighting the quantized value of secondary device running status can be asked for:

S＝AoR ^T＝[b ₁,b ₂,L,b _n]

Further, herein the equipment number of units being in different conditions grade is added up, the situation of this grade device fails is added up simultaneously.Can adopt least square method that equipment running status and probability of malfunction (S, P) are carried out association matching.

Utilize the concrete steps of least square fitting probability of malfunction curve as follows:

Given equipment running status and probability of malfunction (S, P), by mathematical modeling or summarizing experimental data, determine the functional relation P=f (S) meeting certain type between S and P substantially.By m group experimental data (S ₁, P ₁), (S ₂, P ₂) ..., (S _m, P _m) at certain Class F={ f ₀(S), f ₀(S) ..., f _n(S) }, a function is sought in (n < m):

$f^{*}\left(S\right)=a_0^{*}{f}_0\left(S\right)+a_1^{*}{f}_0\left(S\right)+...+a_n^{*}{f}_n\left(S\right)$

Make f ^*(S) at state S _iprobability of malfunction value and the sum of square of deviations of empirical value at place are minimum, namely

$\underset{i=1}{\overset{m}{\&Sigma;}}{\&lsqb;f^{*}\left(S_i\right)-P_i\&rsqb;}^2=\underset{f\left(S\right)=F}{MIN}\underset{i=1}{\overset{m}{\&Sigma;}}{\&lsqb;f\left(S_i\right)-P_i\&rsqb;}^2$

Its mid point the multivariate function $s(a_0,a_1,...,a_n)=\underset{i=1}{\overset{m}{\&Sigma;}}{\&lsqb;\underset{k=0}{\overset{n}{\&Sigma;}}{a}_k{f}_k\left(S_i\right)-P_i\&rsqb;}^2$ Minimal point.

Thus meet system of equations:

$\frac{\&part;s}{\&part;a_k}=\underset{i=1}{\overset{m}{\&Sigma;}}\underset{k=0}{\overset{n}{\&Sigma;}}\&lsqb;a_k{f}_k\left(S_i\right)-P_i\&rsqb;{(\&Sigma;a_k{f}_k\left(S_i\right)-P_i)}^{\&prime;}=0$

Namely $\underset{i=1}{\overset{m}{\&Sigma;}}{f}_k\left(S_i\right)\&lsqb;a_0{f}_0\left(S\right)+a_1{f}_1\left(S\right)+...+a_n{f}_n\left(S\right)-P_i\&rsqb;=0$

That is $a_0\underset{i=1}{\overset{m}{\&Sigma;}}{f}_k\left(S_i\right)f_0\left(S_i\right)+a_1\underset{i=1}{\overset{m}{\&Sigma;}}{f}_k\left(S_i\right)f_1\left(S_i\right)+\mathrm{...}+a_n\underset{i=1}{\overset{m}{\&Sigma;}}{f}_k\left(S_i\right)f_n\left(S_i\right)=\underset{i=1}{\overset{m}{\&Sigma;}}{f}_k\left(S_i\right)P_i$

Introduce mark $(h,g)=\underset{i=1}{\overset{m}{\&Sigma;}}h\left(x_i\right)g\left(x_i\right)=h\left(x_1\right)g\left(x_1\right)+h\left(x_2\right)g\left(x_2\right)+...+h\left(x_m\right)g\left(x_m\right)$

That is: a ₀(f _k, f ₀)+a ₀(f _k, f ₀)+... + a _n(f _k, f _n)=(f _k, y), (k=0,1 ..., n)

Matrix form is $\left[\begin{array}{cccc}(f_0,f_0)& (f_0,f_1)& ...& (f_0,f_n)\\ (f_1,f_0)& (f_1,f_1)& ...& (f_1,f_n)\\ .& .& & .\\ .& .& ...& .\\ .& .& & .\\ (f_n,f_0)& (f_n,f_1)& ...& (f_n,f_n)\end{array}\right]\left[\begin{array}{c}{a}_0\\ a_1\\ .\\ .\\ .\\ a_n\end{array}\right]=\left[\begin{array}{c}(f_0,y)\\ (f_1,y)\\ .\\ .\\ .\\ (f_n,y)\end{array}\right]$

Work as f ₀(S), f ₁(S) ..., f _n(S), during linear independence, existence and unique solution can be proved: i.e. f ^*(S) being exactly least square solution, is namely utilize least square method under variance meaning to the best-fit that experimental data realizes.

Distribution curve Inversion Calculation:

Secondary device fault rate calculates checking

P＝f(S)＝K×e ^-C×S

In formula, K is scale-up factor, and C is coefficient of curvature, S operational outfit state.

Rule of thumb analyze, give the raw data of secondary device: Proportional coefficient K is 1.78E – 3, coefficient of curvature C is 0.522.In addition, this kind equipment is carried out to malfunction evaluation and the fault statistics of 2 years, calculating and statistics distribute as shown in table 2:

Table 2 secondary device condition grading and state grade relation table data

In table, malfunction evaluation result is 0 ~ 100, wherein 100 represent state optimizations, 0 represent state the poorest, every 10 points as an opinion rating.

Determine condition grading and state grade corresponding relation as shown in table 2:

Table 3: secondary device condition grading and state grade relation table

Malfunction grade i has 10 classes, and corresponding equipment number of units is respectively M1 ~ M10, is N, then has following formula in this evaluation cycle internal fault number of units:

$N=\underset{i=1}{\overset{10}{\&Sigma;}}{M}_i{P}_i=\underset{i=1}{\overset{10}{\&Sigma;}}{M}_i{\mathrm{Ke}}^{-C\&times;S_i}---\left(15\right)$

Table 2 data are substituted into formula (15) can in the hope of the inverting value of K and C.

By comparing original value and inverting value, Inversion Calculation precision can be satisfied with substantially, and error is mainly to the difference after condition grading radix point.As long as therefore obtain the state score of same category of device then and total number of units of concrete fault generation, a nonlinear equation about K and C just can be obtained.There is the statistics of more than 2 years just can verify K and C.

Above-described embodiment of the present invention, does not form limiting the scope of the present invention.Any amendment done within the spirit and principles in the present invention, equivalent replacement and improvement etc., all should be included within protection scope of the present invention.

The above is only the specific embodiment of the present invention, those skilled in the art is understood or realizes the present invention.To be apparent to one skilled in the art to the multiple amendment of these embodiments, General Principle as defined herein can without departing from the spirit or scope of the present invention, realize in other embodiments.Therefore, the present invention can not be restricted to these embodiments shown in this article, but will meet the widest scope consistent with principle disclosed herein and features of novelty.

Claims (5)

1. second power equipment probability of malfunction computing method, is characterized in that, described second power equipment probability of malfunction computing method comprise:

Obtain the quantized value of secondary device running status;

Obtain the probability of malfunction of different conditions grade equipment;

Utilize least square method that the quantized value of described equipment running status is associated matching with described probability of malfunction.

2. second power equipment probability of malfunction computing method according to claim 1, is characterized in that, the quantized value of described acquisition secondary device running status comprises:

Selected characteristic quantity of state, and described eigenstate amount is quantized, ask for eigenstate amount quantized value;

Ask for the weighted value of described eigenstate amount;

Weighted value according to described eigenstate amount quantized value and described eigenstate amount asks for secondary device running status quantized value.

3. second power equipment probability of malfunction computing method according to claim 2, is characterized in that, described by described eigenstate amount quantification, ask for eigenstate amount quantized value and comprise: calculated by the property spent together, ask for parameter information matrix.

4. second power equipment probability of malfunction computing method according to claim 2, is characterized in that, described in ask for described eigenstate amount weighted value comprise: based on the analytical hierarchy process of 1-9 scale, obtain the weight of each parameter.

5. second power equipment probability of malfunction computing method according to claim 1, is characterized in that, describedly utilize least square method that the quantized value of described equipment running status is associated matching with described probability of malfunction to comprise:

According to described secondary device running status quantized value and described probability of malfunction, seek an objective function by mathematical modeling or summarizing experimental data: $f^{*}\left(S\right)=a_0^{*}{f}_0\left(S\right)+a_1^{*}{f}_0\left(S\right)+...+a_n^{*}{f}_n\left(S\right);$

Make f ^*(S) meet $\underset{i=1}{\overset{m}{\&Sigma;}}{\&lsqb;f^{*}\left(S_i\right)-P_i\&rsqb;}^2=\underset{f\left(S\right)=F}{MIN}\underset{i=1}{\overset{m}{\&Sigma;}}{\&lsqb;f\left(S_i\right)-P_i\&rsqb;}^2,$ Its mid point the multivariate function $s(a_0,a_1,...,a_n)=\underset{i=1}{\overset{m}{\&Sigma;}}{\&lsqb;\underset{k=0}{\overset{n}{\&Sigma;}}{a}_k{f}_k\left(S_i\right)-P_i\&rsqb;}^2$ Minimal point, thus meet system of equations: $\frac{\&part;s}{\&part;a_k}=\underset{i=1}{\overset{m}{\&Sigma;}}\underset{k=0}{\overset{n}{\&Sigma;}}\&lsqb;a_k{f}_k\left(S_i\right)-P_i\&rsqb;{\left({\mathrm{\&Sigma;a}}_k{f}_k\right(S_i)-P_i)}^{\&prime;}=0;$

Introduce mark $(h,g)=\underset{i=1}{\overset{m}{\mathrm{\&Sigma;}}}h\left(x_i\right)g\left(x_i\right)=h\left(x_1\right)g\left(x_1\right)+h\left(x_2\right)g\left(x_2\right)+...+h\left(x_m\right)g\left(x_m\right),$ A can be obtained ₀(f _k, f ₀)+a ₀(f _k, f ₀)+... + a _n(f _k, f _n)=(f _k, y), (k=0,1 ..., n)

Matrix form is $\left[\begin{array}{cccc}(f_0,f_0)& (f_0,f_1)& ...& (f_0,f_n)\\ (f_1,f_0)& (f_1,f_1)& ...& (f_1,f_n)\\ \&CenterDot;& \&CenterDot;& & \&CenterDot;\\ \&CenterDot;& \&CenterDot;& ...& \&CenterDot;\\ \&CenterDot;& \&CenterDot;& & \&CenterDot;\\ (f_n,f_0)& (f_n,f_1)& ...& (f_n,f_n)\end{array}\right]\left[\begin{array}{c}{a}_0\\ a_1\\ \&CenterDot;\\ \&CenterDot;\\ \&CenterDot;\\ a_n\end{array}\right]=\left[\begin{array}{c}(f_0,y)\\ (f_1,y)\\ \&CenterDot;\\ \&CenterDot;\\ \&CenterDot;\\ (f_n,y)\end{array}\right]$

Work as f ₀(S), f ₁(S) ..., f _n(S), during linear independence, least square solution is obtained