CN109087014A - A kind of electric system Robust filter method - Google Patents
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Abstract
The invention discloses a kind of electric system Robust filter methods, it is a kind of electric system fuzzy self-adaption Robust filter (Fuzzy Adaptive Robust Estimation, FARE) method, meter and the uncertainty of measurement weight, with the superiority and inferiority of continuous fuzzy membership evaluation measuring point, the non-optimum i.e. bad problem of measuring point is well solved, with the sum of Weighted Fuzzy degree of membership for minimizing measuring point poor quality for optimization aim, using prim al- dual interior point m ethod (Primal-Dual Interior Point Method, PDIPM it) solves, and it realizes to the adaptive of measurement rough error.Multiple ieee standard examples and the simulation results of Polish system show that the method for the present invention has good robustness.
Description
Technical field
The present invention relates to a kind of electric system Robust filter methods, belong to electric power system power source dispatching technique field.
Background technique
As the foundation stone of Energy Management System, Power system state estimation estimates power train according to the life data of telemetering
The real-time running state of system.Influence of the estimated accuracy of traditional weighted least-squares vulnerable to bad data, a kind of practical place
Reason method be before state estimation addition raw data detection identification program, maximum regularization residual detection be it is most common not
Good Data Detection discrimination method, but this processing method is disadvantageous in that relating to a large amount of matrix inversions calculates, even with
Efficiently Sparse technology, computation complexity increase also with scale equal proportion is measured.
In comparison, Robust filter device is with certain estimation criterion, realized in estimation procedure to bad data from
It adapts to, without additional raw data detection identification program, thus causes the research of a large amount of scholar both at home and abroad.Including adding
Weigh least absolute value, Non quadratic criteria, index targets type, maximum qualification rate etc..
Summary of the invention
Technical problem to be solved by the invention is to provide a kind of electric system Robust filter methods, first with continuous mould
The relative merits for pasting subordinating degree function evaluation measuring point, avoid the non-optimum i.e. bad problem of measuring point；Secondly in view of with equipment
Aging, running environment at the time of change, the accuracy in measurement of instrument is difficult to continue to keep to stablize, and carries out school again to measuring instrument
Core must pay very big economic cost, thus be difficult systematically to check measuring instrument again, that is to say, that measure variance be with
Time change, it is difficult to accurately estimate, i.e., measurement weight exists uncertain, and the present invention is based on measuring point fuzzy membership is online
Correct measuring standard it is poor, realize to measure rough error it is adaptive, with minimize measuring point poor quality Weighted Fuzzy degree of membership it
With for optimization aim, solved using prim al- dual interior point m ethod.The present invention preferably can provide optimal policy scheme for policymaker, more
The simulation results of a ieee standard example and Polish system, demonstrate the validity of the method for the present invention.
The present invention uses following technical scheme to solve above-mentioned technical problem:
The present invention provides a kind of electric system Robust filter method, comprising the following steps:
Step 1, the superiority and inferiority that measuring point is evaluated with continuous fuzzy membership, solve the problems, such as that measuring point is non-optimum i.e. bad；
Step 2, with the sum of Weighted Fuzzy degree of membership for minimizing measuring point poor quality for optimization aim, establish bad based on measuring point
The state estimation model of matter fuzzy membership；
Step 3, using the state estimation model in prim al- dual interior point m ethod PDIPM solution procedure 2, be based in solution procedure
Measuring point poor quality fuzzy membership amendment measuring standard is poor, to complete electric system Robust filter.
As further technical solution of the present invention, i-th of measuring point poor quality fuzzy membership in step 1 are as follows:Wherein, riFor the estimation residual error of i-th of measuring point, σiMeasuring standard for i-th of measuring point is poor,
A, b is the fuzzy membership characteristic parameter greater than 0, and i=1,2 ..., m, m are measuring point sum.
As further technical solution of the present invention, the state estimation based on measuring point poor quality fuzzy membership in step 2
Model are as follows:Wherein, r is that m ties up residual vector, x*For the estimated value of state vector x, h
() is measurement function vectors.
M is introduced as further technical solution of the present invention, in step 3 and ties up non-negative loose the vector factor l and u, will be based on
The state estimation model of measuring point poor quality fuzzy membership is equivalent toAfterwards, it adopts
The model is solved with prim al- dual interior point m ethod PDIPM.
As further technical solution of the present invention, base during using prim al- dual interior point m ethod PDIPM solving model
It is poor in measuring point poor quality fuzzy membership amendment measuring standard, even σi (k+1)=σi (k)·f(vi)(k), σi (k+1)Repeatedly for kth+1 time
For when i-th of measuring point measuring standard it is poor, σi (k)The measuring standard of i-th of measuring point is poor when iteration secondary for kth, f (vi)(k)For kth
Measuring standard difference correction function when secondary iteration.
As further technical solution of the present invention, measuring standard difference correction function f (v when kth time iterationi)(k)=[1-
(1/(vi(|ri|,σi))(k))]1/b, wherein(ri)(k)When iteration secondary for kth
The estimation residual error of i-th of measuring point.
The invention adopts the above technical scheme compared with prior art, has following technical effect that calculation proposed by the present invention
Method avoids the non-optimum i.e. bad problem of measuring point with the relative merits of fuzzy membership quantitative assessment measuring point, and based on measuring point
Fuzzy membership automatically updates measurement weight, realizes to the adaptive of rough error is measured, there is good robustness.The present invention
The method of proposition has following features:
1) without carrying out bad data verification, without subjective setting weight, it is easy to debugging and maintenance；
2) bad data is recognized automatically according to fuzzy membership during iteration, realize to the adaptive of measurement rough error
It answers, state estimation result is mainly influenced by high-quality measurement；
If 3) measurement weight of some measuring point is much smaller than remaining measuring point under continuous multiple sections, then the measuring point
Measuring instrument is likely to damage or data transmission channel breaks down, thus the method for the present invention is easy to find out the event in measuring
Barrier.Multiple Example Verifications validity of the method for the present invention, thus the method for the present invention has certain engineering practical value.
Specific embodiment
Embodiments of the present invention are described below in detail, described embodiment is exemplary, and is only used for explaining this
Invention, and be not construed as limiting the claims.
Those skilled in the art can understand that unless otherwise defined, all terms used herein (including skill
Art term and scientific term) there is meaning identical with the general understanding of those of ordinary skill in fields of the present invention.Also
It should be understood that those terms such as defined in the general dictionary should be understood that have in the context of the prior art
The consistent meaning of meaning will not be explained in an idealized or overly formal meaning and unless defined as here.
Technical solution of the present invention is described in further detail below with reference to specific example:
The invention discloses a kind of electric system fuzzy self-adaption Robust filter (Fuzzy Adaptive Robust
Estimation, FARE) method, meter and the uncertainty of measurement weight, with the excellent of continuous fuzzy membership evaluation measuring point
It is bad, the non-optimum i.e. bad problem of measuring point has been well solved, has been excellent with the sum of the Weighted Fuzzy degree of membership for minimizing measuring point poor quality
Change target, is solved using prim al- dual interior point m ethod (Primal-Dual Interior Point Method, PDIPM), and realize
To measuring the adaptive of rough error.
Power system state estimation measurement equation are as follows:
Z=h (x)+e (1)
In formula: x is state vector, if system node number is n, then x includes the amplitude and phase angle of node voltage, and dimension is
2n-1；Z is that m dimension measures vector；H (x) is measurement function vectors；E is error in measurement vector, and assumes that error in measurement obeys normal state
Distribution, m are measuring point sum.
The residual equation of state estimation are as follows:
R=z-h (x*) (2)
In formula: r is that m ties up residual vector, x*For the estimated value of state vector x.
Then for i-th of measuring point, if it estimates residual error riVery little, the then it is believed that measuring point is high-quality measuring point；Conversely, if its
Estimate residual error riIt is very big, then it is believed that the measuring point is measuring point inferior.Since the accuracy in measurement of each measuring point is not quite similar, σ is definedi
Measuring standard for i-th measuring point is poor, thus with the σ of the weighted residual of measuring pointi/riSize discrimination superiority and inferiority measuring point is more reasonable.
Definition event y is the superiority and inferiority of measuring point, if set Y contains all event y, the relationship of that identical element element y and set Y can
With a characteristic function --- subordinating degree function v (y) is indicated, theoretical for classical data acquisition system, is had:
But the superiority and inferiority of practical measuring point is opposite concept, that is, absolute superiority and inferiority are not present, compared to classical data
Sets theory, fuzzy set allow degree of membership to take any value on [0,1], and the present invention chooses continuously differentiable bell membership degree letter
Number, for i-th of measuring point, the fuzzy membership function of poor quality are as follows:
In formula: a, b are the fuzzy membership characteristic parameter greater than 0.
After bell fuzzy membership function, the relative superior or inferior degree of membership problem of measuring point is normalized to section well
On [0,1], the non-optimum i.e. bad problem of measuring point has been well solved.
Length Factor Method in Power System State based on weighted least-squares method is optimization with minimum weight residual sum of squares (RSS)
Target, similarly, the present invention is based on the fuzzy memberships of measuring point quality, are subordinate to minimizing the Weighted Fuzzy of measuring point poor quality
The sum of degree is optimization aim, proposes following Optimized model:
In formula:For the weight of i-th of measuring point.
Most state estimation algorithm assume that measurement variances sigma both at home and abroad at present2It is known that and in practical projects,
There are numerous measuring instruments in large-scale electrical power system, and very big economic generation must be paid by check again to measuring instrument
Valence, thus be difficult systematically to check measuring instrument again；Furthermore change at the time of with the aging of equipment, running environment, instrument
Accuracy in measurement be difficult to continue keep stablize.It is changed over time that is, measuring variance, it is difficult to accurately estimate, i.e.,
Measurement weight exists uncertain.Measurement weight is estimated offline with the measurement residuals of continuous multiple sections based on WLS, but works as network
Under topology change and set time window, online updating weight is needed, and need special raw data detection identification program thing
Bad data is first rejected, the calculating time of state estimation algorithm is considerably increased.And the state estimation of domestic real system is general
The weight factor of each measurement is arranged in subjectivity, and debugging and maintenance are complicated, and the weight factor of subjective setting may not be with practical measurement side
Difference is coincide.
For this purpose, the present invention is based on measuring point fuzzy membership amendment measuring standard is poor, i.e., for solving the during formula (5)
K+1 iteration enables:
Measuring standard difference correction function f (v) should follow 2 principles: 1) as measuring point fuzzy membership viWhen close to 1, at this time
The measuring point is high-quality measuring point, should keep approximate constant；2) as measuring point fuzzy membership viWhen close to 0, the measuring point is poor quality at this time
Measuring point should increase σi, that is, when occurring measuring rough error, weight of the measuring point in iteration should be reduced, reduces it to state estimation knot
The influence of fruit.
Based on above-mentioned criterion, correction function f (v can be choseni)(k)=[1- (1/ (vi(|ri|,σi))(k))]1/b, wherein(ri)(k)The estimation residual error of i-th of measuring point when iteration secondary for kth.
Algorithm of the present invention on the basis of considering measuring point quality fuzzy membership, realize to measure rough error from
It adapts to, therefore the algorithm is known as fuzzy self-adaption Robust filter.
Due to the non-differentiability of direct solution objective function, it is difficult to which direct solution formula (5) introduces m and ties up non-negative loose vector
The factor l and u, then formula (5) can be of equal value are as follows:
Formula (7) belongs to optimal power flow problems, and modern interior point method has convergence good, and calculating speed is fast, small by initial value affecting,
The advantages that suitable for solving extensive problem, thus the present invention selects original-dual interior point to solve formula (7), and for kth+1 time
Iteration, it is poor according to formula (6) amendment measuring standard.
Test example of the invention includes the node of IEEE14,30,57,118, and Polish 2383,2746 nodes are (hereafter with WP-
2383, WP-2746 indicate), metric data adds random error in measurement by stringent power flow solutions and obtains, and considers that measuring standard is poor
Uncertainty, voltage measuring standard difference obeys [0.002,0.005] and is uniformly distributed, and power measurement standard deviation is obeyed
[0.004,0.01's] is uniformly distributed.Redundancy is measured between 3.5~4.5.And the bad data of addition 10% at random,
It is poor that bad data adds and subtracts the maximum measuring standard of [5,20] again on the basis of metric data.
The superiority that algorithm is proposed for the verifying present invention, by with the state estimation algorithm that proposed in the past, including WLS, WLAV,
QL carries out robustness comparison.The voltage measurement weight of WLS, WLAV, QC algorithm takes 4, and active and reactive measurement weight takes 1.It is right
In the measuring standard difference initial value σ of inventive algorithm(0), since the amendment of formula (6) is to incrementally increase σ in iteration, therefore σ(0)'s
The measuring standard that selection should be less than actual capabilities is poor, and this paper voltage measures σ(0)0.001 is taken, active and reactive measurement σ(0)It takes
0.002；Fuzzy membership parameter takes a=2.5, b=3.
Different conditions algorithm for estimating is evaluated to the estimation performance of quantity of state using following 2 indexs:
1) mean square error of voltage magnitude:
In formula: VjFor the true value of node j voltage magnitude,For the estimated value of node j voltage magnitude.
2) mean square error of voltage phase angle:
In formula: θjFor the true value of node j voltage phase angle,For the estimated value of node j phase angle, default node 1 is balance section
Point.
In the case where containing 10% bad data in 6 systems, the index of MSE1, MSE2 of 4 kinds of state estimation algorithms
As shown in table 1.
Table 1 contains MSE1 the and MSE2 index performance of different algorithm for estimating under 10% bad data
The test macro of multiple and different scales it can be seen from the test result of table 1, when containing 10% bad data, no
With estimator quantity of state estimation aspect of performance: by the size of MSE1 and MSE2 in table 1, it is known that each algorithm estimates quantity of state
Performance: FARE > WLAV > QC > WLS is counted, and FARE is substantially better than remaining 3 kinds of algorithm.In addition, FARE algorithm estimates voltage phase angle
Meter performance is better than the estimation performance to voltage magnitude.
The above, the only specific embodiment in the present invention, but scope of protection of the present invention is not limited thereto, appoints
What is familiar with the people of the technology within the technical scope disclosed by the invention, it will be appreciated that expects transforms or replaces, and should all cover
Within scope of the invention, therefore, the scope of protection of the invention shall be subject to the scope of protection specified in the patent claim.
Claims (6)
1. a kind of electric system Robust filter method, which comprises the following steps:
Step 1, the superiority and inferiority that measuring point is evaluated with continuous fuzzy membership, solve the problems, such as that measuring point is non-optimum i.e. bad；
Step 2, with the sum of Weighted Fuzzy degree of membership for minimizing measuring point poor quality for optimization aim, establish and be based on measuring point poor quality
The state estimation model of fuzzy membership；
Step 3, using the state estimation model in prim al- dual interior point m ethod PDIPM solution procedure 2, in solution procedure be based on measuring point
Poor quality fuzzy membership amendment measuring standard is poor, to complete electric system Robust filter.
2. a kind of electric system Robust filter method according to claim 1, which is characterized in that i-th of measuring point in step 1
Poor quality fuzzy membership are as follows:Wherein, riFor the estimation residual error of i-th of measuring point, σiIt is i-th
The measuring standard of a measuring point is poor, and a, b are the fuzzy membership characteristic parameter greater than 0, and i=1,2 ..., m, m are measuring point sum.
3. a kind of electric system Robust filter method according to claim 2, which is characterized in that be based on measuring point in step 2
The state estimation model of poor quality fuzzy membership are as follows:Wherein, r be m tie up residual error to
Amount, x*For the estimated value of state vector x, h () is measurement function vectors.
4. a kind of electric system Robust filter method according to claim 5, which is characterized in that it is non-to introduce m dimension in step 3
Negative relaxation vector factor l and u, the state estimation model based on measuring point poor quality fuzzy membership is equivalent toAfterwards, which is solved using prim al- dual interior point m ethod PDIPM.
5. a kind of electric system Robust filter method according to claim 4, which is characterized in that the point in using former antithesis
It is poor based on measuring point poor quality fuzzy membership amendment measuring standard during method PDIPM solving model, even σi (k+1)=
σi (k)·f(vi)(k), σi (k+1)The measuring standard of i-th of measuring point is poor when for+1 iteration of kth, σi (k)I-th when iteration secondary for kth
The measuring standard of measuring point is poor, f (vi)(k)Measuring standard difference correction function when iteration secondary for kth.
6. a kind of electric system Robust filter method according to claim 5, which is characterized in that measured when kth time iteration
Standard deviation correction function f (vi)(k)=[1- (1/ (vi(|ri|,σi))(k))]1/b, wherein(ri)(k)The estimation residual error of i-th of measuring point when iteration secondary for kth.
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| CN112905958A (en) * | 2021-01-27 | 2021-06-04 | 南京国电南自电网自动化有限公司 | Short-time data window telemetry data state identification method and system based on measurement and control device |
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| CN103886193B (en) * | 2014-03-13 | 2017-05-24 | 河海大学 | Fuzzy self-adaptation robust estimation method of electric power system |
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| CN112905958A (en) * | 2021-01-27 | 2021-06-04 | 南京国电南自电网自动化有限公司 | Short-time data window telemetry data state identification method and system based on measurement and control device |
| CN112905958B (en) * | 2021-01-27 | 2024-04-19 | 南京国电南自电网自动化有限公司 | Short-time data window telemetry data state identification method and system based on measurement and control device |
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