CN109034461A - A kind of voltage dip Stochastic prediction method based on actual electric network monitoring information - Google Patents
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Abstract
The invention discloses a kind of voltage dip Stochastic prediction methods based on actual electric network monitoring information for belonging to power quality analysis technical field.The described method includes: reading actual electric network parameter, node admittance matrix is obtained；Temporary drop data is surveyed to each node to handle, and obtains the probabilistic model of fault type and trouble duration；Establish system failure information model；Fault message is generated using Latin Hypercube Sampling, forms fault message raw data base；Fault simulation simulation is carried out, the error of estimation results and measured result is calculated, whether is met the requirements according to error, fault message model is modified, until error meets the requirements or reaches pre-set times of revision, and then obtains final estimation results；The present invention can effectively avoid the problems such as stability is poor, convergence is relatively slow, the used time is longer and estimation results error is larger in existing predictor method, and estimation results are more accurate.
Description
Technical field
The invention belongs to power quality analysis technical field more particularly to a kind of voltages based on actual electric network monitoring information
Temporarily drop Stochastic prediction method.
Background technique
With extensive use of the sensitive equipments such as power electronics in power grid, voltage dip has become most important electric energy matter
One of amount problem.Typical sensitive equipment, such as computer, ac speed regulator, A.C. contactor, are being subjected to voltage Sag Disturbance
After may cause equipment shut down, galloping or error, efficiency decline or the problems such as the lost of life so that industrial
Production process or service activity are even interrupted in production line output and quality decline completely, to cause huge economic loss.For
This, situation occurred is temporarily dropped to network voltage and is estimated, and is found electrical network weak link, is targetedly taken measures to inhibit voltage
Temporarily drop, avoids voltage dip from being of great significance the adverse effect of sensitive equipment as far as possible.
Voltage dip predictor method is broadly divided into actual measurement statistic law and simulation predicting method at present.In view of electric network fault occurs
Randomness, only in the sufficiently long situation of monitoring cycle, the assessment result for surveying statistic law could be accurate enough.It is thus logical
In normal situation, the general method using simulation predicting carries out the assessment of voltage dip.Simulation predicting method be broadly divided into fault position method,
Critical distance method and Monte Carlo Method.Monte Carlo Method initially sets up electric network fault stochastic model, is obtained by simulating short trouble
It to the voltage dip information of the power grid, prescribes a time limit when emulation reaches certain number with year, assessment result can be accurately anti-
The voltage dip situation of the power grid is reflected, therefore Monte Carlo Method is the common method of current voltage dip Stochastic prediction.But it covers
Special Caro method is poor with stability, restrains compared with slow, used time longer defect.In addition, with Electric Power Quality On-line Monitor System
The extensive use of area's power grid throughout our country, part of nodes has been mounted with monitoring device in real system, can get enough
More temporary drop event, in consideration of it, it is proposed that a kind of voltage dip Stochastic prediction method suitable for actual electric network, can either overcome
Monte Carlo Method stability is poor, convergence is slow, with the defect of duration, while estimation results being enabled to be more in line with reality, for electricity
Net company and user provide temporarily drop information, find electrical network weak link, take counter-measure in advance to alleviate voltage dip, reduce
Economic loss.
Summary of the invention
In view of the above-mentioned problems, the invention proposes a kind of voltage dip Stochastic prediction sides based on actual electric network monitoring information
Method, which comprises the following steps:
A, actual electric network parameter is read, obtains node admittance matrix after treatment；
B, the temporary drop data of actual measurement for obtaining actual electric network monitoring each node of system, obtains after processing based on measured data
Fault message model, including fault type information model PtypeWith trouble duration information model Pdur；
C, system failure information model, including faulty line information model P are establishedline, fault location information model PspotWith
Fault impedance information model Pres；
D, fault message is generated using Latin Hypercube Sampling, establishes fault message raw data base, including faulty line
Raw data base, abort situation raw data base, fault type raw data base, duration raw data base and failure resistance
Anti- raw data base；
E, carry out fault simulation simulation, obtain the temporary drop characteristic value of each temporary drop event, including temporary decline, the duration and
Temporarily drop type；It calculates each node and temporarily drops index, estimating for monitoring device node will be installed and temporarily dropped index and power grid actual measurement temporarily drop
Index comparison, is calculated the error of estimation results and measured result；Whether error in judgement is less than 20%, if error is unsatisfactory for
Requirement is stated, fault message model is modified, repeats step E, until error is less than 20% or reaches pre-set amendment
Number obtains final estimation results.
The actual electric network parameter includes: system node number, line parameter circuit value, transformer parameter, system load flow parameter, power generation
Machine parameter.
The step B specifically includes following sub-step:
B1, each node of export survey temporary drop data；
B2, the temporary drop data of actual measurement is screened, obtains the total failare frequency of actual measurement system, method particularly includes:
B2-1, the identical temporary drop event of and temporary drop type short to temporary drop time of origin interval are normalized；
B2-2, the temporary drop event of each node is counted again, obtains the failure frequency in the period from practical measurement, and then obtain
To the total failare frequency F of actual measurement systemnum, its calculation formula is:
In formula, T indicates the period from practical measurement as unit of year, NtIndicate the failure frequency in the period from practical measurement；
It is B3, for statistical analysis to failure occurrence type, establish fault type information model Ptype:
In formula, PtypeIndicate the probability of all types of failures of the generation based on measured data；PLG、P2LG、P2L、P3LGIt respectively indicates
Occur that single-phase earthing, two phase ground, two-phase be alternate and the probability of malfunction of three-phase ground；
B4, trouble duration information model P is establisheddur, method for building up are as follows:
B4-1, period from practical measurement reach 3-5 or when the temporary drop data of actual measurement reach 300 groups or more, extracts and temporarily drops every time
Duration fits the mathematical model of duration using MATLAB, establishes trouble duration information model Pdur；
B4-2, period from practical measurement obey the phase using trouble duration less than 3 years or when the temporary drop data of actual measurement is less than 300 groups
Hope to be 0.06s, standard deviation is the general fault Duration Information model P of the standardized normal distribution of 0.01sdur, calculation formula is such as
Under:
Pdur=N (0.06,0.01)
B5, SARFI is chosen90Index temporarily drops evaluation index as each node, calculates each node and surveys temporary frequency reducing time, wherein
SARFI90The calculation formula of index are as follows:
In formula, DTIndicate that total number of days in period from practical measurement, D indicate index calculating cycle number of days, value 365, NTExpression is being supervised
It surveys the node in cycle T and the temporary decline temporary frequency reducing lower than 90% time occurs.
The method that the step C establishes system failure information model are as follows:
C1, for faulty line, it is assumed that line fault probability is directly proportional to line length, count each line length, into
And the probability of every line fault is obtained, establish faulty line information model Pline:
In formula, K is route sum；PlineIndicate the probability of line fault；Pj(j=1,2 ..., K) is the event of j-th strip route
Hinder probability, andLjIndicate the length of j-th strip route；
C2, for abort situation, it is assumed that the probability that each point breaks down on route is identical, and abort situation obeys [0,1]
It is uniformly distributed, establishes fault location information model Pspot, calculation formula is as follows:
In formula,Indicate the probability of malfunction in the N number of position section of j-th strip route；
C3, for fault impedance, it is assumed that fault impedance obedience is desired for 5 Ω, and standard deviation is the standardized normal distribution of 1 Ω,
Establish fault impedance information model Pres, calculation formula is as follows:
Pres=N (5,1)
The step D is based on Latin Hypercube Sampling and generates fault message, and establishing fault message raw data base further includes
Following sub-step:
D1, the faulty line information model P established according to step Cline, using Latin Hypercube Sampling, obtain fault wire
Road raw data base；
D2, the fault location information model P established according to step Cspot, using Latin Hypercube Sampling, obtain fault bit
Set raw data base；
D3, the fault type information model P established according to step Btype, using Latin Hypercube Sampling, obtain failure classes
Type raw data base；
D4, the trouble duration information model P established according to step Bdur, using Latin Hypercube Sampling, continued
Time raw data base；
D5, the fault impedance information model P established according to step Cres, using Latin Hypercube Sampling, obtain fault impedance
Raw data base.
The step E further includes following sub-step:
E1, input system parameter carry out fault simulation simulation, obtain according to the fault message raw data base that step D is obtained
To the temporary drop characteristic value of each temporary drop event, including temporary decline, the duration, type temporarily drops；
E2, each mistake for estimating temporarily drop index and the temporary drop index of power grid actual measurement for having installed monitoring device node is calculated separately
Difference, it is assumed that temporarily drop index is respectively I for estimate temporarily drop index and the power grid actual measurement of i-th of nodees,iAnd Irel,i, then error value epsiloni
Calculation formula are as follows:
E3, each node is ranked up by error is descending, the error that monitoring device node has each been installed in setting permits
Perhaps it is worth, respectively allows each error for having installed monitoring device node and the error according to the descending node sequence of error
Value is compared, if error not in the range of error permissible value, obtains each node using pearson correlation analytic approach and temporarily drops
Relevance between index and fault message model, selection and each maximum fault message of node relevance, to fault message mould
Type is modified；
E4, using revised fault message model, regenerate fault message raw data base, carry out fault simulation mould
It is quasi-, each temporarily drop event is counted, each node is calculated and temporarily drops index, provide the temporary drop for not installing all nodes of monitoring device node
The temporary drop characteristic value of event temporarily drops in index and each node every time, including temporarily drops type, temporary decline, temporarily drop duration.
The method that the step E3 is modified fault message model are as follows:
E3-1 data processing
For each node, filters out each node and temporarily drop index and cause each fault message for temporarily dropping event, including therefore
Hinder route, abort situation, fault impedance and trouble duration；
Data are normalized in E3-2
The obtained data of step E3-1 are normalized using deviation standardized method, i.e., initial data are carried out
Linear transformation is mapped to transformation results between [0,1], transfer function are as follows:
In formula, c*After the data normalization mapping obtained for step E3-1 as a result, c is the initial data before mapping, max
For the maximum value of sample data, min is the minimum value of sample data；
E3-3 Modifying model
Using pearson correlation analytic approach, obtains each node and temporarily drop relevance between index and each fault message, and will
The relevance of each node is ranked up, and selection is with the temporarily drop maximum fault message of index relevance, the failure established to step B, C
Information model is modified.
In the step E3-3, Modifying model method particularly includes:
E3-3-1, selection and the maximum fault message of node relevance, by corresponding fault message model additional one
A correction factor α, to correct the fault message model；
E3-3-2, hypothesis and the maximum fault message of 1 relevance of node are faulty line, then to the line connecting with node 1
Road is modified, and the line failure rate connecting with node 1 is added correction factor α respectively1, then the route e connecting with node 1 repairs
Line failure rate after just are as follows:
Pe,re=Pe+α1
In formula, Pe,reFor the revised line failure rate of route e, PeFor the first original circuit failure rate of route e；
E3-3-3, correction factor α is individually subtracted to the farther away route of 1 electrical distance of node1, then it is connect with node 1
The revised line failure rate of route f are as follows:
Pf,re=Pf-α1
In formula, Pf,reFor the revised line failure rate of route f, PfFor the first original circuit failure rate of route f；
E3-3-4, new line fault information model is obtained after above-mentioned amendment, regenerate fault message original number
According to library, fault simulation simulation is carried out, each temporarily drop event is counted, calculates each node and temporarily drop index, the temporarily drop of estimating of node 1 is referred to
Temporarily the comparison of drop index is modified rear error calculation to mark with power grid actual measurement, if error is greater than 20%, continues above-mentioned amendment, until accidentally
Difference is less than 20% or reaches preset times of revision, finally obtains the line failure rate with 1 phase connecting lines of node；
E3-3-5, h node is corrected one by one according to above-mentioned modification method, if there are still not in error range after amendment
Node, then node not in error range is corrected simultaneously according to above-mentioned modification method, is finally obtained and prison has been installed
Survey the failure rate of h node phase connecting lines of device.
The calculation method of the correction factor α is as follows:
1) assume that system shares H node, wherein there are the h nodes that monitoring device is housed, the H node is compiled respectively
Number be 1,2,3 ..., h, h+1, h+2 ..., H, i.e., before h node be the node equipped with monitoring device, rear H-h node is does not pacify
Fill the node of monitoring device；
2) error that temporarily drop index surveys temporarily drop index with power grid of estimating for assuming h node is respectively ε1,ε2,ε3,…,
εh, h node is successively modified, the correction factor of each node is denoted as α respectively1,α2,α3,…,αh；
3) α is arranged to node 1 first1Initial value is 0.01, and each correction amount is 0.01, then the modified correction factor of kth time
For α1=| 0.01+0.01*k |；
Wherein, α1It is positive and negative according to it is following rule choose:
A) it when the temporary drop index of node 1 and fault message are positively correlated, temporarily drops index and increases with the increase of fault message
Greatly, if ε1It is positive, then estimates temporarily drop index and be greater than actual measurement temporarily drop index, fault message correction factor α1It is negative；If ε1It is negative, then
It estimates temporarily drop index and is less than actual measurement temporarily drop index, fault message correction factor α1It is positive；
B) it when the temporary drop index of node 1 and fault message are negatively correlated, temporarily drops index and subtracts with the increase of fault message
It is small, if ε1It is positive, then estimates temporarily drop index and be greater than actual measurement temporarily drop index, fault message correction factor α1It is positive；If ε1It is negative, then
It estimates temporarily drop index and is less than actual measurement temporarily drop index, fault message correction factor α1It is negative.
The beneficial effects of the present invention are:
A kind of voltage dip Stochastic prediction method based on actual electric network monitoring information proposed by the present invention, according to practical electricity
Temporary drop event is netted to establish failure random information model, can more accurately reflect the fault message of the actual electric network；Using
Latin Hypercube Sampling method is instead of traditional Monte Carlo Method, can be more quickly steady under the premise of guaranteeing accuracy
Surely it estimates to obtain each node of the actual electric network and temporarily drops situation；Estimation results will temporarily be dropped and assessment result progress temporarily drops in actual electric network
Error calculation is chosen stronger temporarily with each node relevance if error is unsatisfactory for required precision using pearson correlation analytic approach
Information is dropped, temporary drop information model is modified until error meets the requirements or reaches pre-set times of revision, so that in advance
It is more accurate to estimate result, can effectively avoid in existing predictor method that stability is poor, convergence is relatively slow, the used time is longer and estimates knot
The problems such as fruit error is larger makes counter-measure for user and grid company to alleviate voltage dip bring economy damage in time
Frustrated justice is obvious.
Detailed description of the invention
Attached drawing 1 is the flow chart of the voltage dip Stochastic prediction method based on actual electric network monitoring information；
Attached drawing 2 is Latin Hypercube Sampling schematic diagram in the specific embodiment of the invention；
Attached drawing 3 is China's urban distribution network real system topological diagram in the specific embodiment of the invention；
Specific embodiment
The present invention is described in detail with reference to the accompanying drawings and examples.
The detailed reasoning analysis method of following discloses and demonstration analysis example.However, specific reasoning disclosed herein and analysis
Procedural details are demonstrated merely for the sake of description analyzes the purpose of example.
It should be appreciated, however, that the present invention is not limited to disclosed particular exemplary embodiment, but covering falls into disclosure model
Enclose interior all modifications, equivalent and alternative.In the description to whole attached drawings, identical appended drawing reference indicates identical member
Part.
The index and method used in several present invention are introduced first below:
(1) SARFI index
SARFI index is system average rms value vibration frequency index, for describing single-measurement in specific time
Point root-mean-square valve fluctuates situation.SARFI index includes two kinds of forms: one is the statistical indicators based on a certain threshold voltage
SARFIx, another kind is the statistical indicator SARFI based on sensitive equipment curvecurve。
For SARFIxIndex, x be root-mean-square valve voltage threshold, indicated with hundred-mark system form, possible value be 180,
140,120,110,90,80,70,50 or 10 etc.；For SARFIcurveIndex indicates to exceed certain class sensitive equipment reference curve
The frequency of the voltage dip event of range, and the SARFI that different reference curve mapping is differentcurveIndex.Due to real system
In the more difficult acquisition of each specific sensitive equipment of node, sensitive equipment tolerance curve can not determine, mainly to each node in the present invention
Voltage dip situation is estimated, therefore x value is 90 in the present invention, that is, uses SARFI90Index temporarily drops assessment as each node
Index, the SARFI of a certain node90Index calculation formula is as follows:
In formula, DTIndicate that total number of days in period from practical measurement, D indicate index calculating cycle number of days, here value 365, with year
For unit, NTIndicate that the temporary decline temporary frequency reducing lower than 90% time occurs for the node in monitoring cycle T.
(2) Latin Hypercube Sampling
Latin Hypercube Sampling schematic diagram as shown in connection with fig. 2, it is assumed that total M stochastic variable X in a certain probability problem1、
X2、…、XM, XmFor any stochastic variable, and XmCumulative distribution function are as follows: Ym=Fm(Xm)。
Assuming that sampling number is N, by its cumulative distribution function YmThe longitudinal axis be divided into N number of Deng sections, width 1/N.If
Each stochastic variable is mutually indepedent, xmnFor the n-th sample value of m-th of variable.
Latin Hypercube Sampling method basic step is as follows:
1) M × N-dimensional matrix L is generatedM×N, every a line of the matrix is the random sequence of (1, N) integer, amnFor it
M row n column element；
2) M × N-dimensional matrix U is generatedM×N, each element of the matrix obeys [0,1] and is uniformly distributed, umnFor its m row n
Column element；
3) M × N-dimensional sampling matrix X is calculatedM×N, xmnFor its m row n column element, then:
In formula, m=1,2 ..., M；N=1,2 ..., N.
(3) Pearson correlation coefficients analytic approach
The basic principle of pearson correlation analytic approach is assumed that there are two variables a, b, then the Pearson of two variables
Related coefficient can be calculated by the following formula to obtain:
In formula, Ra,bIndicate the correlation of variable a and b,Respectively indicate the mathematical expectation of variable a and b.
The range of Pearson correlation coefficients is [- 1,1], and for absolute value closer to 1, relevance is stronger；Absolute value closer to
0, relevance is weaker, indicates that 2 variables are negatively correlated when related coefficient is less than 0, indicates that 2 variables are in when related coefficient is greater than 0
It is positively correlated.Pearson correlation coefficients absolute value and the corresponding relationship of relevance power are as shown in table 1.
The corresponding relationship of table 1 Pearson correlation coefficients absolute value and relevance power
| Related coefficient | Relevance |
| 0≤|R|<0.2 | It is very weak |
| 0.2≤|R|<0.4 | It is weak |
| 0.4≤|R|<0.6 | It is medium |
| 0.6≤|R|<0.8 | By force |
| 0.8≤|R|≤1.0 | It is very strong |
Attached drawing 1 is the flow chart of the voltage dip Stochastic prediction method based on actual electric network monitoring information, as shown in Figure 1,
Described method includes following steps:
A, actual electric network parameter is read, obtains node admittance matrix after treatment；
B, the temporary drop data of actual measurement for obtaining actual electric network monitoring each node of system, obtains after processing based on measured data
Fault message model, including fault type information model PtypeWith trouble duration information model Pdur；
C, system failure information model, including faulty line information model P are establishedline, fault location information model PspotWith
Fault impedance information model Pres；
D, fault message is generated using Latin Hypercube Sampling, establishes fault message raw data base, including faulty line
Raw data base, abort situation raw data base, fault type raw data base, duration raw data base and failure resistance
Anti- raw data base；
E, carry out fault simulation simulation, obtain the temporary drop characteristic value of each temporary drop event, including temporary decline, the duration and
Temporarily drop type；It calculates each node and temporarily drops index, estimating for monitoring device node will be installed and temporarily dropped index and power grid actual measurement temporarily drop
Index comparison, is calculated the error of estimation results and measured result；Whether error in judgement is less than 20%, if error is unsatisfactory for
Requirement is stated, fault message model is modified, repeats step E, until error is less than 20% or reaches pre-set amendment
Number obtains final estimation results.
Specifically, reading actual electric network parameter in step A, and handle above-mentioned parameter, obtaining node admittance matrix
It specifically includes:
A1, according to actual electric network input system number of nodes, line parameter circuit value, transformer parameter, system load flow parameter, generator
Parameter；
A2, above-mentioned parameter is handled, obtains node admittance matrix.
Specifically, obtaining the temporary drop data of actual measurement of actual electric network monitoring each node of system, after processing in the step B
Obtain the fault message model based on measured data, including fault type information model PtypeWith trouble duration information model
PdurIncluding following sub-step:
B1, each node of export survey temporary drop data；
B2, the temporary drop data of actual measurement is screened, obtains the total failare frequency of actual measurement system, specifically includes following sub-step
It is rapid:
B2-1, time of origin interval shorter (within several seconds) and the temporarily identical temporary drop event progress normalizing of drop type drop to temporary
Change processing, it is believed that it causes for same event of failure；
B2-2, the temporary drop event of each node is counted again, obtains the failure frequency in the period from practical measurement, and then obtain
To the annual total failare frequency F of actual measurement systemnum:
In formula, T indicates the period from practical measurement as unit of year, NtIndicate the failure frequency in the period from practical measurement；
B3, difference is temporarily dropped due to single-phase, two-phase, three-phase mainly as caused by single-phase, two-phase, three-phase fault, to electricity
Net, which surveys the temporary drop type probability model that temporary drop data counts, can be used as the probabilistic model of each fault type.Existing temporarily drop
In predictor method, the generation for fault type generally uses probabilistic model more general both at home and abroad, but works as and estimate a certain reality
When the power grid of border, the probability which occurs all types of failures may have the characteristics that its own, might not be with universal model
Unanimously, when therefore being directed to actual electric network, the present invention models it again, to meet the actual conditions of the power grid.To failure
The type of generation is for statistical analysis, establishes the fault type information model P based on measured datatype:
In formula, PtypeIndicate the probability of all types of failures of the generation based on measured data；PLG、P2LG、P2L、P3LGIt respectively indicates
Occur that single-phase earthing, two phase ground, two-phase be alternate and the probability of malfunction of three-phase ground.
B4, trouble duration information model P is establisheddur, method for building up are as follows:
B4-1, in the case where measured data is more, as period from practical measurement can reach 3-5 or the temporary drop data of actual measurement reaches
At 300 groups or more, the extractable duration temporarily dropped every time out, fitted in MATLAB using function (such as Gaussian function)
The mathematical model of duration establishes trouble duration information model Pdur；
B4-2, in the case where measured data is less, as when period from practical measurement is shorter or the temporary drop data of actual measurement is less than 300 groups,
Still using trouble duration information model P more general at presentdur, i.e., hypothesis trouble duration, which is obeyed, is desired for
0.06s, standard deviation are the standardized normal distribution of 0.01s, calculation formula are as follows:
Pdur=N (0.06,0.01)
B5, SARFI is chosen90Index calculates each node and surveys temporary frequency reducing time, wherein SARFI90The calculation formula of index
Are as follows:
In formula, DTIndicate that total number of days in period from practical measurement, D indicate index calculating cycle number of days, value 365, NTExpression is being supervised
It surveys the node in cycle T and the temporary decline temporary frequency reducing lower than 90% time occurs.
Specifically, establishing system failure information model includes following sub-step in the step C:
C1, for faulty line, commonly assume that line fault probability is directly proportional to line length, statistics each route it is long
Degree, and then the probability of every line fault is obtained, establish faulty line information model Pline:
In formula, K is route sum；The probability of Pline expression line fault；Pj(j=1,2 ..., K) it is j-th strip route
Probability of malfunction, andLjIndicate the length of j-th strip route.
C2, for abort situation, the probability for commonly assuming that each point breaks down on route is identical, thus abort situation obey
[0,1] is uniformly distributed, and establishes fault location information model Pspot:
In formula,Indicate the probability of malfunction in the N number of position section of j-th strip route；
C3, for fault impedance, due to being difficult to indicate fault impedance with accurate number, it is assumed that fault impedance obey the phase
Hope to be 5 Ω, standard deviation is the standardized normal distribution of 1 Ω, establishes fault impedance information model P with thisres, calculation formula is as follows:
Pres=N (5,1)
Specifically, generating fault message in the step D based on Latin Hypercube Sampling, establishing fault message original number
Further include following sub-step according to library:
D1, the faulty line information model P established according to step C1line, using Latin Hypercube Sampling, obtain fault wire
Road raw data base；
For faulty line, it is assumed that random number y obeys [0,1] and is uniformly distributed, and is generated using Latin Hypercube Sampling random
Y is counted, then corresponding faulty line FlineIt indicates are as follows:
In formula, K is route sum；Pj(j=1,2 ..., K) is the probability of malfunction of j-th strip route, and
D2, the fault location information model P established according to step C2spot, since the information model is continuous probability distribution,
Using Latin Hypercube Sampling, abort situation raw data base is obtained；
D3, the fault type information model P established according to step B3type, using Latin Hypercube Sampling, obtain failure classes
Type raw data base；
For fault type, it is assumed that random number z obeys [0,1] and is uniformly distributed, and is generated using Latin Hypercube Sampling random
Z is counted, then corresponding fault type FtypeIt is expressed as
In formula, PLG、P2LG、P2L、P3LIt respectively indicates and occurs that single-phase earthing, two phase ground, two-phase be alternate and three-phase ground
Probability of malfunction.
D4, the trouble duration information model P established according to step B4dur, since it is continuous probability distribution, use
Latin Hypercube Sampling obtains duration raw data base；
D5, the fault impedance information model P established according to step C3res, since it is continuous probability distribution, using Latin
Hypercube sampling, obtains fault impedance raw data base.
Specifically, the step E specifically includes the following steps:
E1, input system parameter carry out fault simulation simulation, obtain according to the fault message raw data base that step D is obtained
To the temporary drop characteristic value of each temporary drop event, including temporary decline, the duration, type temporarily drops；
E2, the obtained temporary drop event of statistical analysis E1, calculate each node and temporarily drop index, calculate separately and each have installed monitoring
The error amount for estimating temporarily drop index and the temporary drop index of power grid actual measurement of device node, it is assumed that index temporarily drops in i-th of estimating for node
Surveying temporarily drop index with power grid is respectively Ies,iAnd Irel,i, then error value epsiloniCalculation formula are as follows:
E3, each node is ranked up by error is descending, the error that monitoring device node has each been installed in setting permits
Perhaps it is worth, respectively allows each error for having installed monitoring device node and the error according to the descending node sequence of error
Value is compared, if error not in the range of error permissible value, obtains each node using pearson correlation analytic approach and temporarily drops
Relevance between index and fault message model (such as faulty line, abort situation, fault impedance and trouble duration), choosing
It takes and is modified with each maximum fault message of node relevance, the fault message model established to step B, C.
E4, the revised fault message model obtained using E3 regenerate fault message raw data base, carry out event
Hinder analogue simulation, counts each temporarily drop event, calculate each node and temporarily drop index, obtain final estimation results, provide and prison is not installed
Survey device node all nodes temporary drop index and each node every time temporarily drop event temporary drop characteristic value, including temporarily drop type,
Temporary decline, temporarily drop duration.
Wherein, the method that step E3 is modified fault message model are as follows:
E3-1 data processing
For each node, filters out each node and temporarily drop index and cause each fault message for temporarily dropping event, including therefore
Hinder route, abort situation, fault impedance and trouble duration；
Data obtained above are normalized E3-2, are converted between [0,1]；
It is normalized using min-max standardization (also referred to as deviation standardization), i.e., line is carried out to initial data
Property transformation, be mapped to end value between [0,1], transfer function are as follows:
In formula, c*After the data normalization mapping obtained for step E3-1 as a result, c is the initial data before mapping, max
For the maximum value of sample data, min is the minimum value of sample data；
E3-3 Modifying model
Using pearson correlation analytic approach, obtains each node and temporarily drop relevance and progress between index and each fault message
Sequence, selection and the temporarily drop maximum fault message of index relevance, to the corresponding failure mathematical model established in step B, C
It is modified, modification method are as follows:
E3-3-1, selection and the maximum fault message of node relevance, by corresponding fault message model additional one
A correction factor α, to correct the fault message model；
E3-3-2, hypothesis and the maximum fault message of 1 relevance of node are faulty line, then to the line connecting with node 1
Road is modified, and the line failure rate connecting with node 1 is added correction factor α respectively1, then the route e connecting with node 1 repairs
Line failure rate after just are as follows:
Pe,re=Pe+α1
In formula, Pe,reFor the revised line failure rate of route e, PeFor the first original circuit failure rate of route e；
E3-3-3, correction factor α is individually subtracted to the farther away route of 1 electrical distance of node1, then it is connect with node 1
The revised line failure rate of route f are as follows:
Pf,re=Pf-α1
In formula, Pf,reFor the revised line failure rate of route f, PfFor the first original circuit failure rate of route f；
E3-3-4, new line fault information model is obtained after above-mentioned amendment, regenerate fault message original number
According to library, fault simulation simulation is carried out, each temporarily drop event is counted, calculates each node and temporarily drop index, the temporarily drop of estimating of node 1 is referred to
Temporarily the comparison of drop index is modified rear error calculation to mark with power grid actual measurement, if error is greater than 20%, continues above-mentioned amendment, until accidentally
Difference is less than 20% or reaches preset times of revision, finally obtains the line failure rate with 1 phase connecting lines of node；
E3-3-5, h node is corrected one by one according to above-mentioned modification method, if there are still not in error range after amendment
Node, then node not in error range is corrected simultaneously according to above-mentioned modification method, is finally obtained and prison has been installed
Survey the failure rate of h node phase connecting lines of device.
The calculation method of the correction factor α is as follows:
1) assume that system shares H node, wherein there are the h nodes that monitoring device is housed, the H node is compiled respectively
Number be 1,2,3 ..., h, h+1, h+2 ..., H, i.e., before h node be the node equipped with monitoring device, rear H-h node is does not pacify
Fill the node of monitoring device；
2) error that temporarily drop index surveys temporarily drop index with power grid of estimating for assuming h node is respectively ε1,ε2,ε3,…,
εh, using the method for " correcting one by one ", h node is successively modified, the correction factor of each node is denoted as α respectively1,α2,
α3,…,αh；
3) α is arranged to node 1 first1Initial value is 0.01, and each correction amount is 0.01, then the modified correction factor of kth time
For α1=| 0.01+0.01*k |；
Wherein, α1It is positive and negative according to it is following rule choose:
A) it when the temporary drop index of node 1 and fault message are positively correlated, temporarily drops index and increases with the increase of fault message
Greatly, if ε1It is positive, illustrates to estimate temporarily drop index and be greater than actual measurement temporarily drop index, need the smaller size for estimating temporarily drop index, therefore
Fault message correction factor α1It is negative；If ε1It is negative, then estimates temporarily drop index and be less than actual measurement temporarily drop index, fault message amendment system
Number α1It is positive；
B) it when the temporary drop index of node 1 and fault message are negatively correlated, temporarily drops index and subtracts with the increase of fault message
It is small, if ε1It is positive, illustrates to estimate temporarily drop index and be greater than actual measurement temporarily drop index, need to reduce the size for estimating temporarily drop index, therefore
Fault message correction factor α1It is positive；If ε1It is negative, then estimates temporarily drop index and be less than actual measurement temporarily drop index, fault message amendment system
Number α1It is negative.
It is assumed that with the maximum fault message of 1 relevance of node be faulty line, then to the route being connect with node 1 into
Row amendment, positive correction factor α is added to the line failure rate connecting with node 1 respectively1, the route e that is to sum up connect with node 1
Amendment after line failure rate are as follows:
Pe,re=Pe+α1
In formula, Pe,reFor line failure rate after route e amendment, PeFor route e first original circuit failure rate.
Since the sum of all line failure rates are 1, amendment is individually subtracted to the farther away route of 1 electrical distance of node
Factor alpha1, i.e., line failure rate after the amendment for the route f being connect with node 1 are as follows:
Pf,re=Pf-α1
In formula, Pf,reFor line failure rate after route f amendment, PfFor route f first original circuit failure rate.
By obtaining new line fault information model after above-mentioned amendment, fault message initial data is then regenerated
Library carries out fault simulation simulation, counts each temporarily drop event, calculates each node and temporarily drop index, and node 1 is estimated temporarily drop index
Temporarily drop index comparison, which is surveyed, with power grid is modified rear error calculation, if error not within the allowable range of values, continues above-mentioned amendment,
Until error meets the requirements or reach times of revision, the line failure rate with 1 phase connecting lines of node is finally obtained.
Similarly, for node 2, according to identical modification method, the line failure rate being connected with node 2 is obtained；With this
Analogize, can finally obtain the failure rate with h node phase connecting lines for having installed monitoring device.
If each node error still has node not in error range after " one by one correct ", then to section not in error range
Point does whole amendment according to the method described above, i.e., is corrected simultaneously to node, finally obtain and installed h node of monitoring device
The failure rate of phase connecting lines.
If not being line failure rate with a certain strongest fault message of node relevance, then modification method and line fault
The modification method of rate is similar.
By the above method, revised fault message model can be obtained.
Embodiment 1
Illustrate technical effect of the invention below by way of a specific embodiment.
Attached drawing 3 is China's urban distribution network real system topological diagram.As shown in figure 3, the urban distribution network shares 2 500kV
Substation's (node 1,2) and 19 220kV substations (node 3~21), are formed by 48 connections.
It is obtained with the urban distribution network Electric Power Quality On-line Monitor System from January, 2017 in June, 2017 part monitoring node
The temporary drop measured data taken is research object, and the present invention is to temporary drop time of origin interval very short (within several seconds) and temporary drop type phase
Same temporary drop event is screened, it is believed that caused by it is same event of failure, is carried out after counting again to the urban distribution network
As a result as follows: in half a year, total temporarily drop event number is 38 times, and caused event of failure is 26 times.Table 2 is that the urban distribution network respectively monitors
Temporary frequency reducing time occurs for node, and simultaneous selection SARFI90 temporarily drops assessment result and be shown in Table 2 as temporarily drop index, each node.To each event
Barrier is statisticallyd analyze, and it is as shown in table 3 to obtain all types of fault rates.
Assessment result temporarily drops in each node of table 2
| Number | Monitoring node | Temporary frequency reducing time | SARFI90 index |
| 5 | Pu County | 4 | 8 |
| 3 | Tao Tang | 3 | 6 |
| 21 | Xing Tang | 2 | 4 |
| 19 | It is eternally happy | 8 | 16 |
| 18 | Qiao Bei | 6 | 12 |
| 10 | Ancient city | 3 | 6 |
| 16 | Zhang Li | 9 | 18 |
| 14 | Zheng Zhuan | 3 | 6 |
| It amounts to | 38 | 76 |
The all types of fault rates of table 3
| Fault type | Failure rate |
| LG | 53% |
| 2L | 14% |
| 2LG | 3% |
| 3LG | 30% |
Real system is carried out by two step of E1, E2 and temporarily drops simulation analysis, the simulation value of node monitoring device node will be installed
It is as shown in table 4 with measured value Comparative result.As can be seen from Table 4, in addition to node 3 and node 18, the temporary frequency reducing of other nodes
Secondary simulation value is larger with measured value error, and the temporary drop predictor error of node 10 is even more to have reached 80.00%, it is therefore desirable to original
Have temporarily drop information model be modified, with get be more suitable for the real system it is temporary drop estimate stochastic model.
Table 4 installs the simulation value and measured value Comparative result of node monitoring device node
| Number | SARFI90Measured value | SARFI90Simulation value | Error (%) |
| 3 | 6 | 5.50 | -8.33 |
| 5 | 8 | 13.97 | 74.62 |
| 10 | 6 | 10.80 | 80.00 |
| 14 | 6 | 8.40 | 40.00 |
| 16 | 18 | 8.83 | -50.94 |
| 18 | 12 | 10.58 | -11.83 |
| 19 | 16 | 12.04 | -24.75 |
| 21 | 4 | 7.18 | 79.50 |
In view of having obtained the mathematical modulo of the fault type based on measured data in the foundation of aforementioned fault message model
Type, therefore no longer it is modified.Node SARFI each to this area90Index and fault message model have carried out related coefficient
It calculates, related coefficient of the table 5 between each node SARFI90 index and fault message model.As shown in Table 5, each node
SARFI90 index and temporarily drop route relevance maximum, therefore the mentioned method needle of E2.2 step will be used in following model amendment
" correcting one by one " is first carried out to line failure rate and carries out " whole amendment " again, and error permissible value takes 20%.
Related coefficient between each node SARFI90 index of table 5 and fault message model
| Node number | Faulty line | Abort situation | Fault impedance | Trouble duration |
| 1 | -0.5521 | 0.1081 | 0.1434 | 0.0018 |
| 2 | -0.4923 | 0.0636 | 0.2392 | -0.0087 |
| 3 | 0.5048 | -0.0049 | 0.1122 | 0.0066 |
| 4 | -0.1339 | -0.0876 | -0.0489 | -0.0396 |
| 5 | -0.1942 | -0.0099 | 0.0986 | 0.0141 |
| 6 | 0.3419 | 0.1756 | 0.1214 | -0.0044 |
| 7 | -0.2147 | -0.1613 | 0.0464 | 0.0160 |
| 8 | -0.4379 | -0.2388 | 0.0359 | 0.0128 |
| 9 | -0.4602 | -0.0147 | -0.1257 | -0.0216 |
| 10 | -0.5275 | 0.3374 | 0.1058 | -0.0015 |
| 11 | -0.3345 | 0.0191 | 0.1702 | -0.0113 |
| 12 | -0.3490 | -0.0154 | 0.1163 | 0.0028 |
| 13 | -0.2648 | -0.0530 | 0.0642 | 0.0031 |
| 14 | -0.2416 | 0.0027 | 0.1568 | -0.0004 |
| 15 | -0.3345 | -0.0027 | 0.1444 | -0.0107 |
| 16 | -0.1592 | -0.0288 | 0.0853 | 0.0185 |
| 17 | -0.1508 | -0.0802 | 0.0122 | 0.0110 |
| 18 | -0.3496 | -0.0738 | 0.0656 | 0.0002 |
| 19 | -0.3744 | -0.3169 | -0.0596 | -0.0092 |
| 20 | 0.3576 | -0.2897 | -0.0241 | 0.0057 |
| 21 | -0.5096 | -0.3445 | 0.0592 | -0.0038 |
Table 6 is the comparing result that monitoring device node simulation value and measured value have been installed after correcting.Table 4 and table 6 are compared
It is found that revised temporary drop estimation results decrease with measured value error, most node all meet error 20% with
It is interior, it is contemplated that the influence of monitoring cycle and certain accidentalia (weather, artificial maloperation etc.), thus while being estimated after amendment
Value still has certain error with value, but can satisfy forecast demand substantially.
After to fault message Modifying model, temporarily drop Stochastic prediction is re-started, other is obtained shown in table 7 and does not install
The temporary drop estimation results of monitoring device node.
Table 6 has installed the comparing result of monitoring device node simulation value and measured value after correcting
| Node number | SARFI90Measured value | SARFI90Correct simulation value | Error (%) |
| 3 | 6 | 5.06 | -15.67 |
| 5 | 8 | 10.57 | 32.12 |
| 10 | 6 | 4.65 | -22.50 |
| 14 | 6 | 6.60 | 10.00 |
| 16 | 18 | 7.54 | -58.11 |
| 18 | 12 | 12.68 | 5.67 |
| 19 | 16 | 13.42 | -16.12 |
| 21 | 4 | 4.75 | 18.75 |
Table 7 does not install the temporary drop estimation results of monitoring device node
| Node number | Estimate SARFI90Value | Node number | Estimate SARFI90Value |
| 1 | 5.50 | 11 | 6.59 |
| 2 | 6.60 | 12 | 3.67 |
| 4 | 1.88 | 13 | 1.50 |
| 6 | 9.62 | 15 | 6.60 |
| 7 | 9.57 | 17 | 9.39 |
| 8 | 9.25 | 20 | 13.42 |
| 9 | 0.64 | - | - |
This embodiment is merely preferred embodiments of the present invention, but scope of protection of the present invention is not limited thereto,
In the technical scope disclosed by the present invention, any changes or substitutions that can be easily thought of by anyone skilled in the art,
It should be covered by the protection scope of the present invention.Therefore, protection scope of the present invention should be with scope of protection of the claims
Subject to.
Claims (9)
1. a kind of voltage dip Stochastic prediction method based on actual electric network monitoring information, which comprises the following steps:
A, actual electric network parameter is read, obtains node admittance matrix after treatment；
B, the temporary drop data of actual measurement for obtaining actual electric network monitoring each node of system, obtains the failure based on measured data after processing
Information model, including fault type information model PtypeWith trouble duration information model Pdur；
C, system failure information model, including faulty line information model P are establishedline, fault location information model PspotAnd failure
Impedance information model Pres；
D, fault message is generated using Latin Hypercube Sampling, establishes fault message raw data base, including faulty line is original
Database, abort situation raw data base, fault type raw data base, duration raw data base and fault impedance are former
Beginning database；
E, fault simulation simulation is carried out, the temporary drop characteristic value of each temporary drop event, including temporary decline, duration and temporary drop are obtained
Type；It calculates each node and temporarily drops index, estimating for monitoring device node will be installed and temporarily dropped index and power grid actual measurement temporarily drop index
Comparison, is calculated the error of estimation results and measured result；Whether error in judgement is less than 20%, if error is unsatisfactory for above-mentioned want
It asks, fault message model is modified, repeat step E, until error is less than 20% or reaches pre-set times of revision,
Obtain final estimation results.
2. a kind of voltage dip Stochastic prediction method based on actual electric network monitoring information according to claim 1, special
Sign is that the actual electric network parameter includes: system node number, line parameter circuit value, transformer parameter, system load flow parameter, power generation
Machine parameter.
3. a kind of voltage dip Stochastic prediction method based on actual electric network monitoring information according to claim 1, special
Sign is that the step B specifically includes following sub-step:
B1, each node of export survey temporary drop data；
B2, the temporary drop data of actual measurement is screened, obtains the total failare frequency of actual measurement system, method particularly includes:
B2-1, the identical temporary drop event of and temporary drop type short to temporary drop time of origin interval are normalized；
B2-2, the temporary drop event of each node is counted again, obtains the failure frequency in the period from practical measurement, and then obtain reality
The total failare frequency F of examining systemnum, its calculation formula is:
In formula, T indicates the period from practical measurement as unit of year, NtIndicate the failure frequency in the period from practical measurement；
It is B3, for statistical analysis to failure occurrence type, establish fault type information model Ptype:
In formula, PtypeIndicate the probability of all types of failures of the generation based on measured data；PLG、P2LG、P2L、P3LGRespectively indicate generation
Single-phase earthing, two phase ground, two-phase be alternate and the probability of malfunction of three-phase ground；
B4, trouble duration information model P is establisheddur, method for building up are as follows:
B4-1, period from practical measurement reach 3-5 or when the temporary drop data of actual measurement reach 300 groups or more, extract continuing of temporarily dropping every time
Time fits the mathematical model of duration using MATLAB, establishes trouble duration information model Pdur；
B4-2, period from practical measurement are desired for less than 3 years or when the temporary drop data of actual measurement is less than 300 groups using trouble duration obedience
0.06s, standard deviation are the general fault Duration Information model P of the standardized normal distribution of 0.01sdur, calculation formula is as follows:
Pdur=N (0.06,0.01)
B5, SARFI is chosen90Index temporarily drops evaluation index as each node, calculates each node and surveys temporary frequency reducing time, wherein
SARFI90The calculation formula of index are as follows:
In formula, DTIndicate that total number of days in period from practical measurement, D indicate index calculating cycle number of days, value 365, NTIt indicates in monitoring week
The temporary decline temporary frequency reducing lower than 90% time occurs for the node in phase T.
4. a kind of voltage dip Stochastic prediction method based on actual electric network monitoring information according to claim 1, special
Sign is, the method that the step C establishes system failure information model are as follows:
C1, for faulty line, it is assumed that line fault probability is directly proportional to line length, count each line length, and then
To the probability of every line fault, faulty line information model P is establishedline:
In formula, K is route sum；PlineIndicate the probability of line fault；Pj(j=1,2 ..., K) is that the failure of j-th strip route is general
Rate, andLjIndicate the length of j-th strip route；
C2, for abort situation, it is assumed that the probability that each point breaks down on route is identical, abort situation obey [0,1] it is uniform
Distribution, establishes fault location information model Pspot, calculation formula is as follows:
In formula, PspotjIndicate the probability of malfunction in the N number of position section of j-th strip route；
C3, for fault impedance, it is assumed that fault impedance obedience is desired for 5 Ω, and standard deviation is the standardized normal distribution of 1 Ω, establishes
Fault impedance information model Pres, calculation formula is as follows:
Pres=N (5,1).
5. a kind of voltage dip Stochastic prediction method based on actual electric network monitoring information according to claim 1, special
Sign is, the step D is based on Latin Hypercube Sampling and generates fault message, establish fault message raw data base further include with
Lower sub-step:
D1, the faulty line information model P established according to step Cline, using Latin Hypercube Sampling, it is original to obtain faulty line
Database；
D2, the fault location information model P established according to step Cspot, using Latin Hypercube Sampling, it is original to obtain abort situation
Database；
D3, the fault type information model P established according to step Btype, using Latin Hypercube Sampling, it is original to obtain fault type
Database；
D4, the trouble duration information model P established according to step Bdur, using Latin Hypercube Sampling, obtain the duration
Raw data base；
D5, the fault impedance information model P established according to step Cres, using Latin Hypercube Sampling, it is original to obtain fault impedance
Database.
6. a kind of voltage dip Stochastic prediction method based on actual electric network monitoring information according to claim 1, special
Sign is that the step E further includes following sub-step:
E1, input system parameter carry out fault simulation simulation according to the fault message raw data base that step D is obtained, and obtain each
The temporary drop characteristic value of secondary temporary drop event, including temporary decline, the duration, type temporarily drops；
E2, each error for estimating temporarily drop index and the temporary drop index of power grid actual measurement for having installed monitoring device node is calculated separately
Value, it is assumed that temporarily drop index is respectively I for estimate temporarily drop index and the power grid actual measurement of i-th of nodees,iAnd Irel,i, then error value epsiloni's
Calculation formula are as follows:
E3, each node being ranked up by error is descending, the error permissible value of monitoring device node has each been installed in setting,
According to the descending node sequence of error respectively by each error for having installed monitoring device node and the error permissible value into
Row compares, if error not in the range of error permissible value, obtains each node using pearson correlation analytic approach and temporarily drops index
With the relevance between fault message model, choose with each maximum fault message of node relevance, to fault message model into
Row amendment；
E4, using revised fault message model, regenerate fault message raw data base, carry out fault simulation simulation,
Each temporary drop event is counted, each node is calculated and temporarily drops index, the temporary drop for providing all nodes for not installing monitoring device node refers to
The temporary drop characteristic value of event temporarily drops in mark and each node every time, including temporarily drops type, temporary decline, temporarily drop duration.
7. a kind of voltage dip Stochastic prediction method based on actual electric network monitoring information according to claim 6, special
Sign is, the method that the step E3 is modified fault message model are as follows:
E3-1 data processing
For each node, filters out each node and temporarily drop index and cause each fault message for temporarily dropping event, including fault wire
Road, abort situation, fault impedance and trouble duration；
Data are normalized in E3-2
The obtained data of step E3-1 are normalized using deviation standardized method, i.e., initial data are carried out linear
Transformation, is mapped to transformation results between [0,1], transfer function are as follows:
In formula, c*After the data normalization mapping obtained for step E3-1 as a result, c is the initial data before mapping, max is sample
The maximum value of notebook data, min are the minimum value of sample data；
E3-3 Modifying model
It using pearson correlation analytic approach, obtains each node and temporarily drops relevance between index and each fault message, and by each section
The relevance of point is ranked up, and selection is with the temporarily drop maximum fault message of index relevance, the fault message established to step B, C
Model is modified.
8. a kind of voltage dip Stochastic prediction method based on actual electric network monitoring information according to claim 7, special
Sign is, in the step E3-3, Modifying model method particularly includes:
E3-3-1, selection and the maximum fault message of node relevance repair additional one, corresponding fault message model
Positive coefficient α, to correct the fault message model；
E3-3-2, hypothesis and the maximum fault message of 1 relevance of node be faulty line, then to the route being connect with node 1 into
Row amendment, adds correction factor α for the line failure rate connecting with node 1 respectively1, then after the route e amendment being connect with node 1
Line failure rate are as follows:
Pe,re=Pe+α1
In formula, Pe,reFor the revised line failure rate of route e, PeFor the first original circuit failure rate of route e；
E3-3-3, correction factor α is individually subtracted to the farther away route of 1 electrical distance of node1, then the route f that is connect with node 1
Revised line failure rate are as follows:
Pf,re=Pf-α1
In formula, Pf,reFor the revised line failure rate of route f, PfFor the first original circuit failure rate of route f；
E3-3-4, new line fault information model is obtained after above-mentioned amendment, regenerate fault message raw data base,
Carry out fault simulation simulation, count each time temporarily drop event, calculate each node and temporarily drop index, by node 1 estimate temporarily drop index and
Temporarily the comparison of drop index is modified rear error calculation for power grid actual measurement, if error is greater than 20%, continues above-mentioned amendment, until error is small
In 20% or reaching preset times of revision, the line failure rate with 1 phase connecting lines of node is finally obtained；
E3-3-5, h node is corrected one by one according to above-mentioned modification method, if there are still sections not in error range after amendment
Point, then node not in error range is corrected simultaneously according to above-mentioned modification method, finally obtain and monitoring dress has been installed
The failure rate for the h node phase connecting lines set.
9. a kind of voltage dip Stochastic prediction method based on actual electric network monitoring information according to claim 8, special
Sign is that the calculation method of the correction factor α is as follows:
1) assume that system shares H node, wherein there are the nodes of h equipped with monitoring device, the H node is numbered respectively be
1,2,3 ..., h, h+1, h+2 ..., H, i.e., preceding h node are the node equipped with monitoring device, and rear H-h node is not install prison
Survey the node of device；
2) error that temporarily drop index surveys temporarily drop index with power grid of estimating for assuming h node is respectively ε1,ε2,ε3,…,εh, to h
A node is successively modified, and the correction factor of each node is denoted as α respectively1,α2,α3,…,αh；
3) α is arranged to node 1 first1Initial value is 0.01, and each correction amount is 0.01, then the modified correction factor of kth time is α1
=| 0.01+0.01*k |；
Wherein, α1It is positive and negative according to it is following rule choose:
A) it when the temporary drop index of node 1 and fault message are positively correlated, temporarily drops index and increases with the increase of fault message, if
ε1It is positive, then estimates temporarily drop index and be greater than actual measurement temporarily drop index, fault message correction factor α1It is negative；If ε1It is negative, then estimates temporarily
It drops index and is less than actual measurement temporarily drop index, fault message correction factor α1It is positive；
B) it when the temporary drop index of node 1 and fault message are negatively correlated, temporarily drops index and reduces with the increase of fault message, if
ε1It is positive, then estimates temporarily drop index and be greater than actual measurement temporarily drop index, fault message correction factor α1It is positive；If ε1It is negative, then estimates temporarily
It drops index and is less than actual measurement temporarily drop index, fault message correction factor α1It is negative.
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