CN110348114A - A kind of non-precision fault recognition method of power grid completeness status information reconstruct - Google Patents
A kind of non-precision fault recognition method of power grid completeness status information reconstruct Download PDFInfo
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Abstract
The invention discloses a kind of non-precision fault recognition methods of power grid completeness status information reconstruct, phasor measurement is synchronized using the WAMS of RTU, PMU, and provide wide area fault message, meet the real-time and requirement of overall importance of trend fingerprint collecting as far as possible, and under fault sample and the incomplete situation of information, it introduces non-precision principle of probability to go to study, meets the requirement of fault-tolerance as far as possible；In addition, the fault message that the trend fingerprint of RTU, PMU obtain is bound to non-precision principle, to carry out fault identification in power grid uncertainty situation using wide area measurement as background.
Description
Technical field
The invention belongs to power system failure diagnostic technical fields, more specifically, are related to a kind of complete character of power grid
The non-precision fault recognition method of state signal reconstruct.
Background technique
With the formation of China's " smart grid " implementation and extra-high voltage, bulk power grid interconnection pattern, it is badly in need of being promoted
Fault diagnosis and fault location technology escort safely for bulk power grid.SCADA/EMS system is current domestic power grids at different levels
The on-line monitoring and data collection system that company generally uses, while EMS is most complete applied to field of power system function at present
Kind Energy Management System.RTU is basis and the core of SCADA system, main data acquisition, data communication and executes control
The functions such as central command processed, the RTU data acquired are transmitted to control centre by communication network in time, but information space is local,
Time irreversibility, real-time be not high.And WAMS can observe electric system simultaneously under time-space-amplitude three-dimensional system of coordinate
The overall picture of global electromechanical dynamic process, therefore have wide regional coverage and temporal synchronism spatially, providing has unification
When target high-precision real when wide area measurement information.PMU is based on synchronous phase angle measuring technology, can be surveyed by being gradually laid out the whole network key
The Wide Area Measurement System of the synchronous phase angle test cell PMU of point is able to achieve the monitoring to dynamic process of electrical power system, measurement
Data can reflect the dynamic behaviour feature of system, ensure that real-time.Therefore application of the WAMS in electric network failure diagnosis field is ground
Study carefully with realistic meaning and practical value.
Many methods are proposed to the fault diagnosis of electric system both at home and abroad at present, are mainly had based on expert system principle
Power system failure diagnostic, application of the artificial neural network in fault diagnosis, the electric power system fault based on optimisation technique are examined
It is disconnected, the power system failure diagnostic based on rough set theory, the power system failure diagnostic based on fuzzy set theory, based on heredity
The power system failure diagnostic of algorithm, the fault diagnosis etc. of the electric system based on Petri network.These methods have their own characteristics,
It goes to solve the problems, such as electric network failure diagnosis from different approach, but also all there is respective defect.
It is largely to carry out failure on the basis of obtaining complete information based on control centre to examine for current method
It is disconnected, and these information are completely reliable.But in actual moving process, due to electrical equipments such as protective device, breakers
Malfunction or be failure to actuate equal error messages or useful information missing often will have a direct impact on fault diagnosis as a result, and
The status information of all relay protections is all sent into control centre and there is very big difficulty, therefore is many in this case
Method is all unable to satisfy.So us is needed to carry out in-depth study in terms of method for diagnosing faults in the imperfect situation of information,
Seek it is a kind of can preferably overcome this difficulty under Incomplete information situation, the method for making reasonable diagnostic result, and
And consider a variety of diagnostic methods of use in conjunction, improve the diagnosis capability of system.And non-precision principle can enhance fault-tolerance,
His conventional method compares can be with the deficiency before compensating for a certain extent.Therefore in fault sample and information it is not possible that complete
Under standby objective conditions, introduces non-precision probability description failure and make great sense.
Summary of the invention
It is an object of the invention to overcome the deficiencies of the prior art and provide a kind of the non-of power grid completeness status information reconstruct
The fault message that the trend fingerprint of PMU, RTU obtain is dissolved into introducing using wide area measurement as background by accurate fault recognition method
Non-precision principle of probability in, so that it is determined that fault diagnosis result.
For achieving the above object, a kind of non-precision fault identification side of power grid completeness status information reconstruct of the present invention
Method, which comprises the following steps:
(1), the fault feature vector of the operating parameter and topological parameter and power grid of power grid in a failure mode is obtained；
(2), operating parameter, topological parameter and the fault feature vector that will acquire are input to Wide Area Measurement System WAMS；
(3), allocation optimum model is solved, realizes that the RTU of grid nodes or PMU are distributed rationally；
Wherein, X=[x1,x2,…,xi,…,xn] indicating that the RTU or PMU of grid nodes configure vector, n is grid nodes
Number；A is node incidence matrix；Y1,Y2It is the subset of Y, Y1It indicates and zero note
The ingress not corresponding vector of associated nodes, Y2Indicate that the associated nodes of zero injection node correspond to vector set；Assuming that there is m node
It is not associated with zero injection node, then I=[1,1 ..., 1]T m×1It is the matrix of m × 1, TinjIndicate zero injection node and its association
The relational matrix of node, BinjIt is corresponding vector set；
(4), the grid nodes after distributing realization RTU or PMU rationally are labeled as tidal characteristics point；
(5), preset failure data fingerprint library is constructed
(5.1), be 1~n to each route number consecutively in power grid, using every route independent failure or combined fault as
A kind of failure, to construct preset failure collection；
(5.2), preset failure concentration any type failure is acted on tidal characteristics point, then according to following formula meter
Calculate the average voltage amplitude U on each tidal characteristics pointi；
Wherein, T indicates that failure acts on time, u on tidal characteristics pointi(t) the voltage letter of i-th of tidal characteristics point is indicated
Number；
Then by all UiConstitutive characteristic vector x=[U1,U2,…,Ui,...]；
(5.3), every group of average voltage amplitude one-to-one forecast failure data are formed with corresponding fault category to refer to
Line library；
(6), nonlinear model is solved
(6.1), in f (x │ Aj) and in P (Ai) on introduce non-precision model respectively, wherein f (x │ Aj) indicate characteristic quantity x
In failure AjUnder Gauss joint density function, P (Aj) indicate failure AjThe probability of generation, AjIndicate jth class failure；
(6.2), it enablesVariance isThe non-precision section of expectation and variance is expanded by parameter beta；Extension
The non-precision section with variance it is expected out；
Wherein,Indicate desired maximum likelihood estimation,Indicate the maximum likelihood estimation of variance；
(6.3), non-precision Operations of Interva Constraint condition is established；
According to the Dirichlet model of non-precision, and combine P (Aj) regression nature, nonnegativity condition, collectively form constraint
Condition；
Wherein, if M=∑ mjFor the total degree of all line failures under all failure classes in certain actual electric network 1 year；
mjFor the number of all line failures under jth class failure；S is the non-precision parameter of every line failure；E[·]
Desired minimum value is sought in expression,Desired maximum value is sought in expression；
(6.4), according to constraint condition, the non-precision constraint condition that step (6.3) are established is substituting in step (6.1)
f(x│Aj)With P (Aj) on, obtain objective function F (x, P (Aj)), and form following formula nonlinear optimization equation group:
Wherein, j=j', j, j' ∈ [1, K], K are fault category sum；
(7), the non-precision interval probability value under each failure classes, reselection probability value highest are calculated by step (6)
Corresponding fault category is as fault diagnosis result, i.e. completion fault identification.
Goal of the invention of the invention is achieved in that
A kind of non-precision fault recognition method of power grid completeness status information reconstruct of the present invention, utilizes RTU, PMU
WAMS synchronizes phasor measurement, and provides wide area fault message, meets the real-time of trend fingerprint collecting and of overall importance as far as possible
It is required that and under fault sample and the incomplete situation of information, introduce non-precision principle of probability and go to study, meet fault-tolerance as far as possible
It is required that；In addition, the fault message that the trend fingerprint of RTU, PMU obtain is bound to non-precision original using wide area measurement as background
Reason, to carry out fault identification in power grid uncertainty situation.
Meanwhile a kind of non-precision fault recognition method of present invention power grid completeness status information reconstruct also has and following has
Beneficial effect:
(1), compared with existing traditional power grid fault diagnosis, to RTU, PMU of WAMS as the spy for extracting entire trend
The non-precision probability that non-precision theoretical description failure occurs is levied and quotes, the present invention has strong real-time, and fault-tolerance is high, theoretical
Basic strong, practical feature.
(2), with it is existing RTU, PMU are layouted to entire power grid compared with, the present invention optimizes it using integer programming method
Configuration can reduce cost, improve monitoring efficiency.
(3), compared with existing expert system and petri method etc., KNOWLEDGE BASE IN EXPERT SYSTEM does not have learning by imitation
Ability and fault-tolerant ability is low and petri net is low to wrong identification ability, non-precision probability theory avoid these lack
Point obtains diagnostic result with more rigorous probabilistic method.
(4), the present invention realizes electric network swim using RTU, PMU configuration node of WAMS as electric network swim fingerprint feature point
The wide area of fingerprint, synchronization, online acquisition；Secondly, collected fault data combination non-precision principle is realized power network line
The probability calculation of failure, so quickly, accurately identify fail result.
Detailed description of the invention
Fig. 1 is a kind of non-precision fault recognition method flow chart of power grid completeness status information reconstruct of the present invention；
Fig. 2 is IEEE11 node system；
Fig. 3 is characteristic point simulated failure voltage magnitude simulation waveform；
Fig. 4 is all kinds of probability of malfunction interval graphs of corresponding different β value；
Fig. 5 is corresponding all kinds of fault section figures of different s values.
Specific embodiment
A specific embodiment of the invention is described with reference to the accompanying drawing, preferably so as to those skilled in the art
Understand the present invention.Requiring particular attention is that in the following description, when known function and the detailed description of design perhaps
When can desalinate main contents of the invention, these descriptions will be ignored herein.
Embodiment
For the convenience of description, being first illustrated to the relevant speciality term occurred in specific embodiment:
WAMS (Wide-Area Measurement System): Wide Area Measurement System；
RTU (Remote Terminal Unit): remote terminal control system
PMU (Phasor Measurement Unit): synchronous phase measuring in power system；
ODP (Optimal Device Placement): device allocation optimum；
Fig. 1 is a kind of non-precision fault recognition method flow chart of power grid completeness status information reconstruct of the present invention.
In the present embodiment, as shown in Figure 1, a kind of non-precision failure of power grid completeness status information reconstruct of the present invention is known
Other method, comprising the following steps:
S1, the fault feature vector of the operating parameter and topological parameter and power grid of power grid in a failure mode is obtained；
S2, the operating parameter that will acquire, topological parameter and fault feature vector are input to Wide Area Measurement System WAMS；
S3, allocation optimum model is solved, realizes that the RTU of grid nodes or PMU are distributed rationally；
RTU and PMU based on WAMS are distributed rationally, and power grid is distributed before and after failure with different trends, trend fingerprint
It is able to reflect its distribution character, there is mapping to close for electric network swim fingerprint and topological structure, the method for operation, the fault condition of power grid etc.
System, this point are similar to the uniqueness of mankind's fingerprint.Therefore key node is chosen as characteristic point, these characteristic points can react
The Partial Load Flow of power grid can carry out global observation by choosing multiple characteristic points to power grid, reflect entire characteristics of tidal flow.Often
The attribute of a characteristic point is characterized by fingerprint characteristic amount, electric current phasor including characteristic point voltage phasor and its relative branch,
Active and reactive trend.
For a node system, consider that the ODP problem of power grid observability can be described with following mathematical model:
Wherein, X=[x1,x2,…,xi..., xn] indicate that the RTU or PMU of grid nodes configure vector, ωiIt is arranged to this
Coefficient is not thought to change within the same time, xiFor binary variable, meet:
F (X) is a phasor function, and f (X)=AX, I are the column vectors that element is 1；A is node incidence matrix,
Main diagonal element is all 1, and if two nodes are interrelated, corresponding element 1, is otherwise 0 to element.
In traditional Zero-one integer programming method, if not considering Branch Power Flow and node injection data as constraint item
When part, ODP problem is exactly linear integer programming problem, convenient for solving, but by Branch Power Flow data known in network or power
When injecting node as constraint condition, RTU, the PMU largely reduced under the premise of meeting power grid observability matches
Set number；But it is allocation optimum problem being converted into nature of nonlinear integral programming problem in place of Shortcomings, to influence to ask
Solve speed and quality.
Assuming that the information of electrical quantity is based entirely on RTU, PMU data source, Branch Power Flow data are unknown, only consider zero energy
Inject node.Equally using the objective function in above-mentioned model, and in order to study conveniently, it is assumed that in objective function RTU, PMU
Deployment cost coefficient ωi(i=1,2 ..., n)=1, then improved model is as follows:
Wherein, X=[x1,x2,…,xi,…,xn] indicating that the RTU or PMU of grid nodes configure vector, n is grid nodes
Number；A is node incidence matrix；Y1,Y2It is the subset of Y, Y1It indicates and zero note
The ingress not corresponding vector of associated nodes, Y2Indicate that the associated nodes of zero injection node correspond to vector set；Assuming that there is m node
It is not associated with zero injection node, then I=[1,1 ..., 1]T m×1It is the matrix of m × 1, TinjIndicate zero injection node and its association
The relational matrix of node, BinjIt is corresponding vector set；
RTU, PMU that grid nodes may be implemented in we in this way are distributed rationally.
S4, by realize RTU, PMU distribute rationally after grid nodes labeled as tidal characteristics point；
Following two points should be met for collocation point as trend fingerprint feature point:
1. guaranteeing the complete observability to power grid in grid collapses, to effectively track and record electric network fault
Dynamic behaviour；
2. being needed in view of economic technology condition, while in view of the increase of number will will lead to the increase of fault information volume
Under the premise of guaranteeing diagnostic accuracy, RTU, PMU of minimal number are configured, to improve the fast throughput of diagnostic method.
S5, building preset failure data fingerprint library
When grid collapses, RTU, PMU after distributing rationally collect fault characteristic value first, all to be arranged in the section
The RTU or PMU (i.e. trend fingerprint) of point (bus) keep ornamental to the whole network, and collected data have real time and dynamic, energy
The state of enough power grids of reflection well.X=[U A will be chosen in practical applicationU I AIP Q] six-vector as characteristic quantity,
In, U: node voltage amplitude AU: node voltage phase angle I: branch current magnitudes AI: branch current phase angle P: node injects wattful power
Rate, Q: node injects reactive power, and characteristic quantity covers that information is complete, and fault-tolerance is higher, and precision is higher.
Illustrate by taking the amplitude of collected voltage phasor as an example, all RTU or PMU are with a collected voltage magnitude, structure
At feature vector.For last fingerprint recognition, a mapping relations should be established first, through a large number of experiments (to route
Each position carries out fault setting), empirical data is obtained, it is formed a set by we, by establishing with preset failure collection
After mapping relations, group is built into forecast failure data fingerprint library.
Forecast failure data fingerprint library is characterized in that empirical and mapping, empirical to be in library have largely
Characteristic fingerprint information, with above-mentioned feature vector x, since RTU, PMU are there are measurement error and the objective influence on tidal flow of actual electric network,
For same fault category, the feature vector taken has error.It is considered that within a certain error range feature
Vector and fault category have mapping.Trend and feature vector after mapping guarantee failure are one-to-one relationships, and
Different types of faults classification (single or cascading failure) causes changes in distribution after trend, it is assumed that relay protection movement excision failure electricity
This period of state before network recovery failure is denoted as Δ t, the characteristic fingerprint vector and the failure of during this period of time institute's typing
Combination is also one-to-one.
The specific practice of building is described in we below, as follows:
It S5.1, is 1~n to each route number consecutively in power grid, each route can occur multiple types failure, mainly include
Basic fault type (broken string [F in 51], single-line to ground fault [F2], line to line fault [F3], two-phase grounding fault [F4], three-phase is short
Road [F5], using every route independent failure or combined fault as a kind of failure, to construct preset failure collection；
S5.2, preset failure concentration any type failure is acted on tidal characteristics point, when an error occurs, is collected
The voltage phasor of tidal characteristics point, it is assumed that tidal characteristics point is set as Y1, Y2..., YM, the voltage measured is U1, U2...,
UMForm feature vector x=[U1,U2,...,UM], short circuit is set in the different location of route respectively by each failure classes.Failure
It being set since the 2s moment, continues 0.2s and cut off failure, system emulation obtains the wavy curve of characteristic point bus voltage amplitude,
Add test every n% from 0 to 100% in surveyed range, total 100/n test point is denoted as N1~N100/n, then obtain 100/
N group test data.This period before reaching stable state is denoted as T, i.e. trouble duration, using average voltage amplitude algorithm,
As formula obtains U11~U1,100/n, U21~U2,100/n..., UM1~UM,100/nIt is calculated with following formula flat on each tidal characteristics point
Equal voltage magnitude Ui；
Wherein, T indicates that failure acts on time, u on tidal characteristics pointi(t) the voltage letter of i-th of tidal characteristics point is indicated
Number；
Then by all UiConstitutive characteristic vector x=[U1,U2,…,Ui,...]；
S5.3, every group of average voltage amplitude is formed into one-to-one forecast failure data fingerprint with corresponding fault category
Library；
S6, nonlinear model is solved
S6.1, in f (x │ Aj) and in P (Ai) on introduce non-precision model respectively, wherein f (x │ Aj) indicate characteristic quantity x
In failure AjUnder Gauss joint density function, P (Aj) indicate failure AjThe probability of generation, AjIndicate jth class failure；
S6.2, orderVariance isThe non-precision section of expectation and variance is expanded by parameter beta；It expands
It is expected that the non-precision section with variance；
Wherein,Indicate desired maximum likelihood estimation,Indicate the maximum likelihood estimation of variance；
S6.3, non-precision Operations of Interva Constraint condition is established；
According to the Dirichlet model of non-precision, and combine P (Aj) regression nature, nonnegativity condition, collectively form constraint
Condition；
Wherein, if M=∑ mjFor the total degree of all line failures under all failure classes in certain actual electric network 1 year；
mjFor the number of all line failures under jth class failure；S is the non-precision parameter of every line failure, is determined
Prior information has been determined for the influence degree of statistical result, s is bigger, it is necessary to which more samples eliminate prior information for system
The influence for counting result, usually takes [1,2].；EDesired minimum value is sought in [] expression,Desired maximum value is sought in expression；
β describes the non-precision of each line fault characteristic quantity distribution, and s describes the non-precision of every line failure,
The two parameters suffer from specific physical significance.
S6.4, according to constraint condition, the non-precision constraint condition that step S6.3 is established is substituting to the f (x in step S6.1
│Aj) and P (Aj) on, obtain objective function F (x, P (Aj)), and form following formula nonlinear optimization equation group:
Wherein, j=j', j, j' ∈ [1, K], K are fault category sum；
S7, the non-precision interval probability value under each failure classes, reselection probability value highest institute are calculated by step S6
Corresponding fault category is as fault diagnosis result, i.e. completion fault identification.
Example
By taking IEEE11 system as an example, emulated with PSASP.
Individually respectively plus line to line fault ground fault by every route in system, and the 0% of route, 10%,
20% ..., 90%, 100% this 10 position measurements are divided into 10 fault categories.It is denoted as Ai(i=1,2 ..., 10).This 10
Successively serial number is denoted as A to route1~A10.As shown in Fig. 2, when an error occurs, the PMU after distributing rationally collects attachment point
Voltage phasor, it is assumed that RTU the or PMU attachment point after optimization is set as 3,5, No. 6 buses, and the voltage measured is U1、U2、U3,
Form feature vector x=[U1U2U3]。
As shown in figure 3, add AB line to line fault ground fault on 5-7 route midpoint, fault time setting 2~
2.2s, the voltage magnitude waveform for the characteristic point (3,5,6) that system emulation comes out,
Assuming that Ui, i=1,2 ..., n Gaussian distributed, and each Gaussian random variable is independent of one another, then and it is linear
Combination must be Gauss, Ui, i=1,2 ..., n obey Joint Gaussian distribution, it may be assumed that
10 groups of data under every a kind of failure are found out to the Maximum-likelihood estimation of its mean failure rate voltage expectation and variance, such as
Shown in table 1；
Table 1 is the maximum likelihood estimation of mean failure rate voltage expectation and variance under each failure classes；
Table 1
In this example because each failure between be it is independent,
And M=21 is set, mi∈ { 1,2,3 }, i=1,2,3 when, take 1；I=4,5,6, when take 2；I=7,8,9,10 when, take 3,
The influence of parameter beta and s to distribution is discussed below.
1), the influence (s=1) of parameter beta
β mainly influences the non-precision of each line fault characteristic quantity distribution, s=1 is arranged, with A5For class failure, when out
When existing x=(0.2436,0.2776,0.2128) characteristic quantity, by the way that the minimum and maximum of corresponding failure class lower probability is calculated
Value.As shown in figure 4, all kinds of probability of malfunction sections that β is obtained under taking 0.05,0.10,0.15,0.20 respectively.
See that faulty line probability interval length is gradually widened as β value increases from Fig. 4, maximum value increases, minimum value
Reduce, non-fault line probability interval length reduces with β increase, and maximum value reduces, and minimum value increases.
2), the influence (β=0.05) of parameter s
Fixing Beta=0.05, takes s=1 respectively, and 5,10,20,
From Fig. 5 it can be observed that increasing with s, faulty line probability interval length is gradually widened, and maximum value increases, minimum value
Reduce, non-fault line probability interval length reduces with s increase, and maximum value reduces, and minimum value increases.
From Fig. 4,5 it can be seen that β is roughly the same with influencing characterisitic of the s to distribution.It can be seen that, work as appearance from Fig. 2,3
When (0.2436,0.2776,0.2128) characteristic quantity x=, by being scanned in forecast failure data fingerprint library, it is calculated
Each data, obtaining occurring the probability on 5-7 route is 0.6582~0.7364, and the probability on All other routes occurs very
It is small, less than 0.2, therefore there is very big assurance to think that failure occurs in 5-7 route.And by the setting of different faults route
And divide different faults class (based on different fingerprint bases) and obtain the knot of similar upper table, to demonstrate just truly having for this method
Effect property.
Although the illustrative specific embodiment of the present invention is described above, in order to the technology of the art
Personnel understand the present invention, it should be apparent that the present invention is not limited to the range of specific embodiment, to the common skill of the art
For art personnel, if various change the attached claims limit and determine the spirit and scope of the present invention in, these
Variation is it will be apparent that all utilize the innovation and creation of present inventive concept in the column of protection.
Claims (1)
1. a kind of non-precision fault recognition method of power grid completeness status information reconstruct, which comprises the following steps:
(1), the fault feature vector of the operating parameter and topological parameter and power grid of power grid in a failure mode is obtained；
(2), operating parameter, topological parameter and the fault feature vector that will acquire are input to Wide Area Measurement System WAMS；
(3), allocation optimum model is solved, realizes that the RTU of grid nodes or PMU are distributed rationally；
Wherein, X=[x1,x2,…,xi,…,xn] indicate that the RTU or PMU of grid nodes configure vector；A is node incidence matrix；Y1,Y2It is the subset of Y, Y1It indicates and zero injection node
The not corresponding vector of associated nodes, Y2Indicate that the associated nodes of zero injection node correspond to vector set；Assuming that having m node and zero note
Ingress is not associated with, then I=[1,1 ..., 1]T m×1It is the matrix of m × 1, TinjIndicate zero injection node and its associated nodes
Relationship square matrix, BinjIt is corresponding vector set；
(4), the grid nodes after distributing realization RTU or PMU rationally are labeled as tidal characteristics point；
(5), preset failure data fingerprint library is constructed
It (5.1), is 1~n to each route number consecutively in power grid, using every route independent failure or combined fault as one kind
Failure, to construct preset failure collection；
(5.2), preset failure concentration any type failure is acted on tidal characteristics point, is then calculated according to following formula every
Average voltage amplitude U on a tidal characteristics pointi；
Wherein, T indicates that failure acts on time, u on tidal characteristics pointi(t) voltage signal of i-th of tidal characteristics point is indicated；
Then by all UiConstitutive characteristic vector x=[U1,U2,…,Ui,...]；
(5.3), every group of average voltage amplitude is formed into one-to-one forecast failure data fingerprint library with corresponding fault category；
(6), nonlinear model is solved
(6.1), in f (x │ Aj) and in P (Ai) on introduce non-precision model respectively, wherein f (x │ Aj) indicate characteristic quantity x in event
Failure AjUnder Gauss joint density function, P (Aj) indicate failure AjThe probability of generation, AjIndicate jth class failure；
(6.2), it enablesVariance isThe non-precision section of expectation and variance is expanded by parameter beta；Expand the phase
Hope the non-precision section with variance；
Wherein,Indicate desired maximum likelihood estimation,Indicate the maximum likelihood estimation of variance；
(6.3), non-precision Operations of Interva Constraint condition is established；
According to the Dirichlet model of non-precision, and combine P (Aj) regression nature, nonnegativity condition, collectively form constraint condition；
Wherein, if M=∑ mjFor the total degree of all line failures under all failure classes in certain actual electric network 1 year；mjFor
The number of all line failures under jth class failure；S is the non-precision parameter of every line failure；E [] is indicated
Desired minimum value is sought,Desired maximum value is sought in expression；
(6.4), according to constraint condition, the non-precision constraint condition that step (6.3) are established is substituting to f (the x │ in step (6.1)
Aj) and P (Aj) on, obtain objective function F (x, P (Aj)), and form following formula nonlinear optimization equation group:
Wherein, j=j', j, j' ∈ [1, K], K are fault category sum；
(7), the non-precision interval probability value under each failure classes is calculated by step (6), reselection probability value highest is right
The fault category answered is as fault diagnosis result, i.e. completion fault identification.
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