CN110247396A  State Estimation for Distribution Network and system based on adaptive robust Unscented kalman filtering  Google Patents
State Estimation for Distribution Network and system based on adaptive robust Unscented kalman filtering Download PDFInfo
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 CN110247396A CN110247396A CN201910646332.XA CN201910646332A CN110247396A CN 110247396 A CN110247396 A CN 110247396A CN 201910646332 A CN201910646332 A CN 201910646332A CN 110247396 A CN110247396 A CN 110247396A
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Classifications

 H—ELECTRICITY
 H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
 H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
 H02J3/00—Circuit arrangements for ac mains or ac distribution networks

 H02J2003/007—
Abstract
The present disclosure proposes State Estimation for Distribution Network and system based on adaptive robust Unscented kalman filtering, comprising: the dynamic model and sampling plan for determining distribution network system establish Unscented kalman filtering model, determine and measure mixed media；The Optimized model for introducing adaptive robust, using timevarying SageHusa Noise statistics extimators, discontinuity surface optimizes processing to distribution network system noise when each, receives most recent data；It establishes Robust filter model to handle bad data existing for system, obtaining has robust effect status predication value and covariance, to carry out the state estimation at next moment.Unscented kalman filtering model used by the disclosure obtains order of information, can satisfy requirement of the system to high nonlinearity by the state distribution of setting multiple groups random symmetric point approximate simulation system.
Description
Technical field
This disclosure relates to which distribution network status estimation technical field, is filtered more particularly to based on adaptive robust Unscented kalman
The State Estimation for Distribution Network and system of wave.
Background technique
Distribution network status estimation is to ensure the important means and electric power dispatching system of power network safety operation
Important component.Traditional static state estimation, can only obtain the data of current time section, filter out big error, low essence
The bad data of degree establishes estimation model, supplements pseudo measuring point to infull point is measured, current time essence is obtained after being calculated
True operating status.But the ability of the limitation of algorithm discontinuity surface when it being caused to be not previously predicted next itself, with power distribution network
The continuous improvement of complexity, the requirement for power distribution network discontinuity surface predictive ability when multiple also step up.Acquisition system connects
The operating status of a period of time of getting off can efficiently control the power output of load, increase economic efficiency, can also find out power distribution network
In will there is a situation where give warning in advance and propose corresponding processing strategie, avoid the generation of accident.Dynamic state estimator goes out
Now solves the problems, such as this, the dynamic state estimator model based on Unscented kalman filtering algorithm utilizes past state estimation
System realtime status is simulated with the quantity of state at current time, and by metric data system analog quantity is modified and
Prediction, but the presence of noise error makes to estimate that accuracy there are also the space further promoted, needs to redesign in filtering algorithm
The mathematical model of state estimation and corresponding derivation algorithm.
Existing Unscented kalman filtering method is difficult to meet well power distribution network for state estimation height both at home and abroad at present
The needs of precision, are mainly manifested in:
1., due to the influence of sampling plan and state equation, can there is certain system inside Unscented kalman filtering
Error is normally set up a constant and carrys out noise error inside simulation system, but general power distribution network carries out system when state estimation
Noise is timevarying, and over time, the system noise for most starting setting is no longer satisfied the requirement of system accuracy, anxious
The stale data for needing most recent data to be left come discontinuity surface when substituting previous, how to be filtered to system noise is also
The problem to be solved such as one.
2. generally obtaining system realtime measurement data using SCADA/PMU hybrid measurement module in power grid, but catching
The generation for, due to a series of influence of environmental factors such as temperature, humidity, having in data procedures and measuring rough error is obtained, how to these
Bad data carries out effective and reasonable processing, and it is to be resolved to reduce influence of the bad data to system prediction precision.
Summary of the invention
The purpose of this specification embodiment is to provide the state of electric distribution network based on adaptive robust Unscented kalman filtering
Estimation method has higher stability and accuracy when system being made to carry out state estimation
This specification embodiment provides the State Estimation for Distribution Network based on adaptive robust Unscented kalman filtering,
It is achieved through the following technical solutions:
Include:
The dynamic model and sampling plan for determining distribution network system establish Unscented kalman filtering model, determine to measure and mix
Conjunction means；
The Optimized model for introducing adaptive robust is interrupted when each using timevarying SageHusa Noise statistics extimators
Processing is optimized in face of distribution network system noise, receives most recent data；
It establishes Robust filter model to handle bad data existing for system, obtaining has robust effect status predication
Value and covariance, to carry out the state estimation at next moment.
Further technical solution, the dynamic model of distribution network system are as follows:
Wherein, x_{k}State vector, and x are tieed up for n_{k}=[U_{k},θ_{k}]∈R^{n}, wherein U_{k}For the voltage magnitude of each node of system, θ_{k}
For the phase angle of each node of system；y_{k}It is tieed up for m and measures vector, and y_{k}=[U_{k},P_{k},Q_{k},P_{i},Q_{i}]^{T}∈R^{m}, wherein P_{k}And Q_{k}Respectively it is
Each node trend active power and reactive power, P in uniting_{i}And Q_{i}Each Branch Power Flow active power and idle function respectively in system
Rate, f are state transition function；H is to measure function；q_{k}And v_{k}The systematic error of respectively n dimension and the error in measurement of m dimension, are obeyed equal
Value is zero, standard deviation is Q and the distribution of the white noise of R.
The state equation of further technical solution, distribution network system uses two exponential smoothing of Holt, by being arranged not
Same smoothing parameter comes two kinds of factors in smooth original state time series, state equation are as follows:
Wherein, x_{k}Indicate that the k moment is directed to the status predication value of power distribution network；Indicate k moment state estimation；α_{k}And β_{k}For
Smoothing parameter, between 0~1；s_{k}Indicate the smoothed out value of current state；b_{k}Indicate the smoothed out trend of current state.
Further technical solution is corrected symmetric sampling method using ratio to the sampling plan of state of electric distribution network amount, is passed through
It is sampled to being added after the different weights of the multiple sigma points impartings of state measurement, is the nonlinear system of higherdimension for power distribution network,
Obtain more accurate quantity of state random distribution information.
Further technical solution establishes Unscented kalman filtering for established state equation and sampling plan
Model:
According to the state equation of system, the prior state of Unscented kalman filtering model is obtained；
After acquisition system prior state, the measurement prediction of system is calculated；
After obtaining metric data, system is filtered.
Adaptive robust mould is added to the Unscented kalman filtering algorithm based on Kalman's theory in further technical solution
Type solves the state estimation of electric system.
Timevarying noise statistics valuation mould is added in further technical solution after Unscented kalman filtering model filtering
Type, the system noise data of discontinuity surface recently when receiving each forget the stale data for influencing system accuracy.
Further technical solution, the measurement covariance P of Unscented kalman filtering model_{y}A judgment basis is added before
Determine the presence or absence of bad data, if it does not exist bad data, then μ_{k}=1, otherwise introduce robust model；
After substituting into new measurement covariance, being obtained using Unscented kalman filtering model has robust effect status predication value
And covariance, to carry out the state estimation at next moment.
This specification embodiment provides the distribution network status estimation system based on adaptive robust Unscented kalman filtering,
It is achieved through the following technical solutions:
Include:
Model building module determines the dynamic model and sampling plan of distribution network system, establishes Unscented kalman filtering mould
Type determines and measures mixed media；
Optimization processing module introduces the Optimized model of adaptive robust, utilizes timevarying SageHusa noise statistics valuation
Device, discontinuity surface optimizes processing to distribution network system noise when each, receives most recent data；
State estimation module is established Robust filter model and is handled bad data existing for system, obtains with anti
Poor effect status predication value and covariance, to carry out the state estimation at next moment.
Compared with prior art, the beneficial effect of the disclosure is:
State equation and sampling plan used by the disclosure have multiple adjustment parameters, and the selection of multiple parameters is so that be
System model has very strong adaptability and flexibility and status predication can be kept more accurate with smooth state amount.
Unscented kalman filtering model used by the disclosure passes through setting multiple groups random symmetric point approximate simulation system
State distribution, obtains order of information, can satisfy requirement of the system to high nonlinearity.
Adaptive robust model used by the disclosure concludes error caused by system modelling the simulation system to timevarying
In system noise, and its influence to system stability can be effectively resisted there are bad data, antilocking system hair
It dissipates.
Detailed description of the invention
The Figure of description for constituting a part of this disclosure is used to provide further understanding of the disclosure, and the disclosure is shown
Meaning property embodiment and its explanation do not constitute the improper restriction to the disclosure for explaining the disclosure.
Fig. 1 is the overall design drawing of the adaptive robust Unscented kalman filtering model of embodiment of the present disclosure；
Fig. 2 is the Unscented kalman filtering flow chart of embodiment of the present disclosure；
Fig. 3 is the adaptive noise statistical estimation device model schematic of embodiment of the present disclosure；
Fig. 4 is the robust model schematic of embodiment of the present disclosure.
Specific embodiment
It is noted that following detailed description is all illustrative, it is intended to provide further instruction to the disclosure.Unless another
It indicates, all technical and scientific terms used herein has usual with disclosure person of an ordinary skill in the technical field
The identical meanings of understanding.
It should be noted that term used herein above is merely to describe specific embodiment, and be not intended to restricted root
According to the illustrative embodiments of the disclosure.As used herein, unless the context clearly indicates otherwise, otherwise singular
Also it is intended to include plural form, additionally, it should be understood that, when in the present specification using term "comprising" and/or " packet
Include " when, indicate existing characteristics, step, operation, device, component and/or their combination.
Examples of implementation one
This embodiment disclose the State Estimation for Distribution Network based on adaptive robust Unscented kalman filtering, referring to attached
Shown in Fig. 1, it is first determined the dynamic model and sampling plan of system establish Unscented kalman filtering model, determine and measure mixing
Means.The Optimized model of adaptive robust is subsequently introduced, using timevarying SageHusa Noise statistics extimators, in each time
Section optimizes processing to system noise, receives most recent data, improves the accuracy of system model.Establish Robust filter again afterwards
Model is adjusted and improves to bad data existing for system, it is ensured that distribution network status estimation system model stable operation.
Specifically, establishing the dynamic model of system:
Wherein, x_{k}State vector, and x are tieed up for n_{k}=[U_{k},θ_{k}]∈R^{n}, wherein U_{k}For the voltage magnitude of each node of system, θ_{k}
For the phase angle of each node of system；y_{k}It is tieed up for m and measures vector, and y_{k}=[U_{k},P_{k},Q_{k},P_{i},Q_{i}]^{T}∈R^{m}, wherein P_{k}And Q_{k}Respectively it is
Each node trend active power and reactive power, P in uniting_{i}And Q_{i}Each Branch Power Flow active power and idle function respectively in system
Rate.F is state transition function；H is to measure function；q_{k}And v_{k}The systematic error of respectively n dimension and the error in measurement of m dimension, are obeyed equal
Value is zero, standard deviation is Q and the distribution of the white noise of R.
State equation uses two exponential smoothing of Holt, by the way that different smoothing parameters is arranged, carrys out the smooth original state time
Two kinds of factors in ordered series of numbers.State equation are as follows:
Wherein, x_{k}Indicate that the k moment is directed to the status predication value of power distribution network；Indicate k moment state estimation；α_{k}And β_{k}For
Smoothing parameter, between 0~1；(s_{k}Indicate the smoothed out value of current state；b_{k}Indicate current state it is smoothed out become
Gesture).
Symmetric sampling method is corrected using ratio to the sampling plan of distribution quantity of state, by measuring multiple sigma to state
It is added and is sampled after the different weights of point imparting, be the nonlinear system of higherdimension for power distribution network, can obtain more accurately
Quantity of state random distribution information generates higher order term error when avoiding sampling, and accurately captures high level matrix information:
λ=α^{2}(n+κ)n (5)
Wherein,It is the system state estimation value at k1 moment；P is status predication covariance；λ is a correction factor,
Error is generated in sampling process for reducing；Wherein α is ratio modifying factor, by the adjustment to α can control Sigma point with
The distance of mean value, makes high order effects become smaller；κ is positive semidefinite free parameter；β is a nonnegative influence coefficient, should by introducing
Its influence factor can be included by coefficient when containing higherorder function in systems；W_{i} ^{m}And W_{i} ^{c}Respectively Unscented transform when
Weighting weight needed for calculating mean value and covariance.
In the examples of implementation, for established state equation and sampling plan, Unscented kalman filtering mould is established
Type.
Flow chart is as shown in Figure 2.Specific steps include:
(1) according to the state equation of system, the prior state of Unscented kalman filtering model is obtained:
Wherein,It is Sigma sampling point set,It is that each point is calculated using Holt twoparameter exponential exponential smoothing
Prior state estimation,It is that the prior state about the k moment obtained by weight number combining is estimated,It is that system noise is added
The covariance of prior estimate error is calculated in sound error battle array.
(2) after obtaining system prior state, the measurement prediction of system is calculated:
Wherein,It is that resulting Sigma point set is calculated according to prior state stage the data obtained；It is by calculating
The measurement about each point arrived is predicted；y_{k}It is to be obtained measuring about the state at k moment by weight number combining；P_{y}It is to measure prediction
Covariance；P_{xy}It is to estimate about prior stateWith system measurements predicted value y_{k}Covariance.
(3) after obtaining metric data, system is filtered:
Wherein, K_{k}It is that kalman gain is calculated by covariance；Z_{k}It is realtime system measuring value；y_{k}It is system measurements
Predicted value；Pass through Z_{k}And y_{k}To the state estimation at k momentAnd covarianceIt is updated and corrects, obtain state variable
It updates.
In the examples of implementation, the Unscented kalman filtering algorithm based on Kalman's theory is improved, is added adaptive
Robust model is answered, to improve the filtering accuracy and system stability of algorithm, is used for the algorithm to solve Power system state estimation
Optimization problem.
(1) adaptive SageHusa timevarying noise statistics Valuation Modelling is as shown in figure 3, be added timevarying after filtering
Noise statistics Valuation Modelling, the system noise data of discontinuity surface recently when receiving each forget the outmoded number for influencing system accuracy
According to.Concrete model is as follows:
d_{k1}=(1b)/(1b^{k}) (16)
Wherein, b is forgetting factor, V_{k}Newly to cease update equation and V_{k}=Z_{k}y_{k}。
(2) Robust filter model flow figure is as shown in figure 4, measuring covariance P_{y}A judgment basis is added before to determine
The presence or absence of bad data, if it does not exist bad data, then μ_{k}=1, otherwise introduce robust model:
Wherein, μ_{k}It is robust enhancement factor, by reducing kalman gain, the amendment of system in Lai Youhua renewal process
Amount is minimized measurement rough error to systematic influence；Tr () is the mark of matrix；P_{y}' for there are measurement association sides when bad data
Difference.
Substitute into new measurement covariance P_{y}' after, by formula (12)~(14) obtain have robust effect status predication value and
Covariance, to carry out the state estimation at next moment.
Examples of implementation two
This specification embodiment provides the distribution network status estimation system based on adaptive robust Unscented kalman filtering,
It is achieved through the following technical solutions:
Include:
Model building module determines the dynamic model and sampling plan of distribution network system, establishes Unscented kalman filtering mould
Type determines and measures mixed media；
Optimization processing module introduces the Optimized model of adaptive robust, utilizes timevarying SageHusa noise statistics valuation
Device, discontinuity surface optimizes processing to distribution network system noise when each, receives most recent data；
State estimation module is established Robust filter model and is handled bad data existing for system, obtains with anti
Poor effect status predication value and covariance, to carry out the state estimation at next moment.
The specific implementation of module in the examples of implementation is referring to the specific computation model in examples of implementation one, herein not
It is described in detail again.
Examples of implementation three
This specification embodiment provides a kind of computer equipment, including memory, processor and storage are on a memory
And the computer program that can be run on a processor, which is characterized in that the processor realizes embodiment when executing described program
Son one in the State Estimation for Distribution Network based on adaptive robust Unscented kalman filtering the step of.
Examples of implementation four
This specification embodiment provides a kind of computer readable storage medium, is stored thereon with computer program, special
Sign is, the matching based on adaptive robust Unscented kalman filtering in examples of implementation one is realized when which is executed by processor
The step of Power Network Status Estimation method.
It is understood that in the description of this specification, reference term " embodiment ", " another embodiment ", " other
The description of embodiment " or " first embodiment~N embodiment " etc. means specific spy described in conjunction with this embodiment or example
Sign, structure, material or feature are included at least one embodiment or example of the invention.In the present specification, to abovementioned
The schematic representation of term may not refer to the same embodiment or example.Moreover, the specific features of description, structure, material
Person's feature can be combined in any suitable manner in any one or more of the embodiments or examples.
The foregoing is merely preferred embodiment of the present disclosure, are not limited to the disclosure, for the skill of this field
For art personnel, the disclosure can have various modifications and variations.It is all within the spirit and principle of the disclosure, it is made any to repair
Change, equivalent replacement, improvement etc., should be included within the protection scope of the disclosure.
Claims (10)
1. the State Estimation for Distribution Network based on adaptive robust Unscented kalman filtering, characterized in that
Include:
The dynamic model and sampling plan for determining distribution network system establish Unscented kalman filtering model, determine and measure mixing hand
Section；
The Optimized model for introducing adaptive robust, using timevarying SageHusa Noise statistics extimators, discontinuity surface pair when each
Distribution network system noise optimizes processing, receives most recent data；
Robust filter model is established to handle bad data existing for system, obtain have robust effect status predication value and
Covariance, to carry out the state estimation at next moment.
2. the State Estimation for Distribution Network as described in claim 1 based on adaptive robust Unscented kalman filtering, special
Sign is the dynamic model of distribution network system are as follows:
Wherein, x_{k}State vector, and x are tieed up for n_{k}=[U_{k},θ_{k}]∈R^{n}, wherein U_{k}For the voltage magnitude of each node of system, θ_{k}For system
The phase angle of each node；y_{k}It is tieed up for m and measures vector, and y_{k}=[U_{k},P_{k},Q_{k},P_{i},Q_{i}]^{T}∈R^{m}, wherein P_{k}And Q_{k}It is each respectively in system
Node trend active power and reactive power, P_{i}And Q_{i}Each Branch Power Flow active power and reactive power, f are respectively in system
State transition function；H is to measure function；q_{k}And v_{k}The systematic error of respectively n dimension and the error in measurement of m dimension, obeying mean value is
Zero, the white noise that standard deviation is Q and R is distributed.
3. the State Estimation for Distribution Network as claimed in claim 2 based on adaptive robust Unscented kalman filtering, special
Sign is that the state equation of distribution network system uses two exponential smoothing of Holt, by the way that different smoothing parameters is arranged, is come smooth former
Two kinds of factors in state for time ordered series of numbers, state equation are as follows:
Wherein, x_{k}Indicate that the k moment is directed to the status predication value of power distribution network；Indicate k moment state estimation；α_{k}And β_{k}It is smooth
Parameter, between 0~1；s_{k}Indicate the smoothed out value of current state；b_{k}Indicate the smoothed out trend of current state.
4. the State Estimation for Distribution Network as claimed in claim 3 based on adaptive robust Unscented kalman filtering, special
Sign is symmetric sampling method to be corrected using ratio to the sampling plan of state of electric distribution network amount, by measuring multiple sigma points to state
It assigns being added after different weights and be sampled, be the nonlinear system of higherdimension for power distribution network, obtain more accurate quantity of state
Random distribution information.
5. the State Estimation for Distribution Network as described in claim 1 based on adaptive robust Unscented kalman filtering, special
Sign is, for established state equation and sampling plan, to establish Unscented kalman filtering model:
According to the state equation of system, the prior state of Unscented kalman filtering model is obtained；
After acquisition system prior state, the measurement prediction of system is calculated；
After obtaining metric data, system is filtered.
6. the State Estimation for Distribution Network as claimed in claim 5 based on adaptive robust Unscented kalman filtering, special
Sign is that adaptive robust model is added to the Unscented kalman filtering algorithm based on Kalman's theory, solves the shape of electric system
State estimation.
7. the State Estimation for Distribution Network as claimed in claim 5 based on adaptive robust Unscented kalman filtering, special
Sign is that timevarying noise statistics Valuation Modelling, discontinuity surface when receiving each are added after Unscented kalman filtering model filtering
Recently system noise data forget the stale data for influencing system accuracy；
The measurement covariance P of Unscented kalman filtering model_{y}Be added before a judgment basis determine the presence of bad data with
It is no, bad data if it does not exist, then μ_{k}=1, otherwise introduce robust model；
After substituting into new measurement covariance, being obtained using Unscented kalman filtering model has robust effect status predication value and association
Variance, to carry out the state estimation at next moment.
8. the distribution network status estimation system based on adaptive robust Unscented kalman filtering, characterized in that include:
Model building module determines the dynamic model and sampling plan of distribution network system, establishes Unscented kalman filtering model, really
Quantitatively survey mixed media；
Optimization processing module introduces the Optimized model of adaptive robust, using timevarying SageHusa Noise statistics extimators,
Discontinuity surface optimizes processing to distribution network system noise when each, receives most recent data；
State estimation module is established Robust filter model and is handled bad data existing for system, and obtaining has robust effect
Fruit status predication value and covariance, to carry out the state estimation at next moment.
9. a kind of computer equipment including memory, processor and stores the meter that can be run on a memory and on a processor
Calculation machine program, which is characterized in that the processor is realized as claimed in claim 1 to 7 based on adaptive when executing described program
The step of answering the State Estimation for Distribution Network of robust Unscented kalman filtering.
10. a kind of computer readable storage medium, is stored thereon with computer program, which is characterized in that the program is by processor
The distribution network status estimation side as claimed in claim 1 to 7 based on adaptive robust Unscented kalman filtering is realized when execution
The step of method.
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CN101615794A (en) *  20090805  20091230  河海大学  Electrical Power System Dynamic method for estimating state based on no mark transformation Kalman filter 
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