CN108448568A - Power distribution network admixture method of estimation based on a variety of time cycle measurement data - Google Patents
Power distribution network admixture method of estimation based on a variety of time cycle measurement data Download PDFInfo
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- 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
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- 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
- H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
- H02J2203/20—Simulating, e g planning, reliability check, modelling or computer assisted design [CAD]
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
- Y02E40/00—Technologies for an efficient electrical power generation, transmission or distribution
- Y02E40/70—Smart grids as climate change mitigation technology in the energy generation sector
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
- Y02E60/00—Enabling technologies; Technologies with a potential or indirect contribution to GHG emissions mitigation
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y04—INFORMATION OR COMMUNICATION TECHNOLOGIES HAVING AN IMPACT ON OTHER TECHNOLOGY AREAS
- Y04S—SYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS
- Y04S10/00—Systems supporting electrical power generation, transmission or distribution
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y04—INFORMATION OR COMMUNICATION TECHNOLOGIES HAVING AN IMPACT ON OTHER TECHNOLOGY AREAS
- Y04S—SYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS
- Y04S10/00—Systems supporting electrical power generation, transmission or distribution
- Y04S10/22—Flexible AC transmission systems [FACTS] or power factor or reactive power compensating or correcting units
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- Power Engineering (AREA)
- Supply And Distribution Of Alternating Current (AREA)
- Remote Monitoring And Control Of Power-Distribution Networks (AREA)
Abstract
The invention discloses a kind of power distribution network admixture methods of estimation based on a variety of time cycle measurement data, present situation based on current power distribution network systematic survey and information system obtains the state estimation result of short duration high quality by the data fusion of AMI, PMU and RTU metrical information.The method proposed can be significantly reduced the period of estimation and to provide real time status information similar to the advanced control application of voltage control and system jams control etc.This method contributes to the catastrophe that detection does well.Further by the robustness of optimization algorithm and the influence that bad data and leverage points bring estimated result can be reduced.In addition, the details of DKF processes can advanced optimize.
Description
Technical field
The present invention relates to a kind of power distribution network admixture methods of estimation based on a variety of time cycle measurement data.
Background technology
The concept of Power system state estimation is proposed in the 1970s.From after that, state estimation is increasingly becoming
Indispensable unit in EMS system, it can provide system current real time status information for electric system operator, be
The control and exploitation of system high application are carried out on the basis of system state aware.What the use of these technologies utilized to greatest extent
Current system assets simultaneously provide guarantee for the safe and stable operation of system.In traditional research, power distribution network is taken as into one
The single passive network of kind, as just a link between power transmission network to user, the flowing of energy is also unidirectional.This
Outside, influence of traditional distribution network failure for bulk power grid stability is also extremely limited, therefore attention degree is relatively low.It is several recently
Year, with application of a large amount of renewable energy source currents in distribution network system, novel load increases and Demand Side Response technology
With the development of policy, the function more generalization of power distribution network, while the importance in energy resource system is higher and higher.Due to distribution
The promotion of net importance, the measurement in distribution network system are also improved with the communications infrastructure.
It is to carry out the basis of State Estimation Study to meet the observability of system and have certain redundancy, and observability is protected
The calculating of card can be solved in theory, and the redundancy of information can improve the quality of estimation while facilitate carry out umber of defectives
According to identification and detection.In distribution network system, in order to make up the deficiency of real measured data, added in past State Estimation Study
Many pseudo- measurement informations (Pseudo Measurements).In past decades, how a large amount of literature research carries out
The foundation of pseudo- measurement information and the determination of weight.The pseudo- measurement information of early stage is from the load investigation based on electricity metering system
Information is obtained in conjunction with influence of the factors to load such as the loads and season, period obtained in long term power system operation
The load characteristic curve of several characteristic features is determined further combined with the size of each distribution transformer capacity in a power distribution network
Set pseudo- measurement information in calculating.The pseudo- measurement information obtained in this way is uncertain big, often with actual information on load
There is larger discrepancy.For Modern power distribution net system, since user greatly enhances the controllability of load, the electronic vapour of synchronic typological
The removable dynamic load quantity of vehicle etc increases, and the operational management that traditional pseudo- measurement information can not adapt to Modern power distribution net needs
It asks.
Energy internet system, including primary energy system, electric system and information system, emphasize comprehensive energy efficiency
It maximizes.Angle analysis from energy internet, distribution network system become bulk power system and primary energy system and number
According to the interface of system.Energy internet has open connect as a kind of information system (Cyber-Physical System)
Mouthful, it is participated in jointly by electric power enterprise and various users, ensures that more information and dates are reliably handled.Electric system is transported
The variation of row condition centainly will produce certain influence to energy resource system optimized operation state.Novel measuring technique, including it is same
Step phasor measuring technique and advanced measuring system (AMI) provide prodigious possibility preferably to carry out State Estimation Study.
Synchronized phasor measurement technology provides accurately time synchronization characteristic with GPS signal, can accurately measure power train
Amplitude and phase information in system and possess very high information upload frequencies.PMU (Power Management Unit, power
Management unit) terminal as synchronized phasor measurement technology, whole 500KV and higher voltage etc. are covered at present
The substation and most 220KV substations of grade.With the technological development of Micro-PMU and the continuous decline of cost, PMU
It is expected to undertake important role in for state of electric distribution network estimating.RTU (remote measurement and control terminal, Remote Terminal Unit) is used
In monitoring, the application of control and data acquisition.With telemetering, remote signalling, remote regulating, distant control function.AMI(Advanced Measurem
Ent Infrastructure) include intelligent electric meter, data communication network and data concentrator three parts, can obtain user or
Person's concentrated load information, and (15 minutes or 30 minutes) are uploaded at a certain time interval.The application of AMI technologies to save
The load information of point is no longer black box, also provides stronger support for pseudo- measurement information data.Intelligent electric meter currently exists
User side is known as overdetermination extensively using making distribution network system owe fixed (Underdetermined System) system from one
(Overdetermined System) system.
Invention content
The purpose of the present invention is exactly to solve the above-mentioned problems, to provide a kind of based on a variety of time cycle measurement data
Power distribution network admixture method of estimation, present situation of this method based on current power distribution network systematic survey and information system, research pass through
The data fusion of various information obtains the state estimation result of short duration high quality；On the other hand, by introducing rational technology,
The problem difficult in maintenance for avoiding the excess investment in infrastructure level and bringing.
To achieve the above object, the present invention uses following technical proposals, including：
The invention discloses a kind of power distribution network admixture methods of estimation based on a variety of time cycle measurement data, including
Following steps：
(1) determine the topology information of distribution network system and the installation situation of different measuring device, including type with
Quantity information；
(2) according to specific producer explanation and system state estimation it needs to be determined that measuring device PMU in distribution network system,
The frequency acquisition of the respective data of RTU and AMI；
(3) system state variables used in calculating are determined according to the topological structure of system and load investigation；
(4) Load flow calculation is carried out according to load investigation, using result of calculation as the initial value of system state variables；
(5) initial error prediction model matrix Q is determined；
(6) prediction of state, the state variable predicted are carried out using Holt two-parameter exponential smoothing processing methods
The predicted value of measurandAnd predicted state variableCovariance matrix
(7) when PMU measured datas update, according to the difference of the predicted value of measurand and measured value, determination is
The no operating status there are bad data or system changes；
The state variable after correction is calculated using Kalman filter method if there is no the above situationWith
And the covariance matrix of estimated state variable
(8) straight to RTU institutes using the result of a upper moment linear dynamic state estimation when RTU measured datas update
The information for connecing measurement carries out rectangular co-ordinate processing；
(9) electric according to the measured data of PMU and RTU and with AMI last equivalent node Injection Current or load
Stream, establishes the object function of state estimation, system state variables is calculated using linear least square methodAs next
The normal condition vector of moment dynamic state estimator, and to the pseudo- metric data at next RTU data updates moment into line
Property prediction；
(10) when AMI measurement data updates, the information of load power or node injecting power is converted to
The node Injection Current or load current of effect；
(11) according to the measured data of PMU, RTU and AMI, total system static linear state estimation is carried out, obtains system
State variable vector x.
Further, in the step (6), the state variable predictedSpecially：
Holt ' s two-parameter exponential smoothing processing methods are utilized, to the state variable under the k momentIt is calculated, wherein
Parameter alpha and β are between 0-1；
Fk=α (1+ β) I
bk=β (Sk-Sk-1)+(1-β)·bk-1
Predicted state variableCovariance matrixSpecially：
Wherein,Predicted value of the state variable at the k moment is represented,For the State variable information after correction, matrix Fk
It is the transition matrix from the k-1 moment to the state variable at k moment, gkIt is the control variable for calculating state variable predicted value；Sk、bk
Respectively intermediate quantity；
Represent state variable the predicted value at k moment covariance matrix,Estimated state variable after correction
Covariance matrix.
Further, in the step (6), the predicted value of measurand is obtainedSpecially：
Measurand includes：The real part and imaginary part of node voltage；The real part and imaginary part of branch current；Node Injection Current
Real part and imaginary part；
Call function h (x) calculates measurand, and under the conditions of linear, h (x)=H*x, H are Jacobian squares
Battle array：
A) for node voltage, corresponding h (x) function is as follows：
B) for branch current, corresponding h (x) function is as follows：
C) for node Injection Current, the form that several branch currents are mutually added and subtracted of being write as is defined according to KCL.
Wherein, Ibr、IbxThe respectively real and imaginary parts of branch current, Vi r、Vi xThe respectively voltage real part and void of node i
Portion,The respectively real and imaginary parts of root node voltage, rkFor the resistance of branch k, xkFor the reactance of branch k, liIndicate from
Root node is to the path of node i.
Further, in the step (7), the status information vector after correction is calculatedAnd estimated state becomes
The covariance matrix of amountSpecially：
Wherein RzkFor the standard deviation matrix of real measured data, KkReferred to as Kalman gain matrixs,For the state after correction
Information variable,For the covariance matrix of the status information variable after correction；Represent state variable the k moment prediction
Value,Represent state variable the predicted value at k moment covariance matrix；
For metrical information zkEstimator, corresponding covariance matrix is：HkUnder the k moment
Jacobian matrixes.
Further, in the step (7), it is determined whether there are the operating statuses of bad data or system to become
Change, specially：
Calculate variable vkNormalized form：
Wherein,For the predicted value of measurand, zkFor the measured value of measurand；vk,iIndicate the measured value of measurand
And the difference of predicted value,Indicate the difference of the measured value and predicted value of the measurand after standardization, RkFor the association of measurement data
Variance matrix, H are Jacobian matrix sizes；
Given threshold t, comparison variableWith threshold value t；IfLess than threshold value t, then it is assumed that system is in stable operation
State does not consider the influence of bad data.
Further, in the step (8), rectangular co-ordinate processing is carried out to RTU institutes information measured directly, specifically
For：
Wherein,Real part for the voltage estimated value obtained after rectangular co-ordinateization processing,For rectangular co-ordinateization processing
The imaginary part of the voltage estimated value obtained afterwards；It is the node voltage real part estimated at a upper moment, imaginary part respectively,It is the real part and imaginary part of the branch current estimated at a upper moment respectively；Vm,iThe node voltage width measured for RTU
Value, Im,iThe branch current magnitudes directly measured for RTU.
Further, in the step (9), the object function of state estimation is established, specially：
J=(zpmu-hpmu)TWpmu(zpmu-hpmu)+(zrtu-hrtu)TWrtu(zrtu-hrtu)
+(zpseudo-hpseudo)TWpseudo(zpseudo-hpseudo)
Wherein, zpmuIt is the measurement data vector that PMU is provided, hpmuIt is the corresponding calculated value of PMU data vector, WpmuIt is PMU
The weight matrix of data；zrtuIt is the measurement data vector that RTU is provided, hrtuIt is the corresponding meter of measurement data vector that RTU is provided
Calculation value, WrtuIt is the corresponding weight matrix of RTU data；zpseudoIt is the pseudo- metric data vector that last time obtains, hpseudoIt is root
The pseudo- measurement vector being calculated according to current state variable, WpseudoPseudo- metric data weight matrix.
Further, in the step (9), the pseudo- metric data at next RTU data updates moment is carried out linear pre-
It surveys, concrete methods of realizing is：
Wherein, tkFrom tj-1Moment is to tjBetween moment, using RTU data updates each time as an interval；zp,kFor the moment
The corresponding RTU metric data of k, zp,jFor the corresponding RTU metric data of moment j, TpFor the RTU measurement data update cycles.
Further, it in the step (10), by the active and reactive load of AMI institutes user measured directly and concentrates negative
Lotus variables transformations are the form of node injecting power, specially：
Wherein,It is the real part of node injection equivalent current,It is the imaginary part of node injection equivalent current, Pinj,iIt is
The node that AMI is provided injects active power, Qinj,iIt is the node injection reactive power that AMI is provided, Vi rIt is to obtain last moment
Node voltage real part estimated data, Vi xIt is the node voltage imaginary part estimated data obtained last moment.
Further, in the step (11), according to the measured data of PMU, RTU and AMI, total system static line is carried out
Property state estimation, obtain system state variables vector x, specially：
Establish the object function of state estimation：
J=(zpmu-hpmu)TWpmu(zpmu-hpmu)+(zrtu-hrtu)TWrtu(zrtu-hrtu)
+(zpseudo-hpseudo)TWpseudo(zpseudo-hpseudo)
Wherein, zpmuIt is the measurement data vector that PMU is provided, hpmuIt is the corresponding calculated value of PMU data vector, WpmuIt is PMU
The weight matrix of data；zrtuIt is the measurement data vector that RTU is provided, hrtuIt is the corresponding meter of measurement data vector that RTU is provided
Calculation value, WrtuIt is the corresponding weight matrix of RTU data；zpseudoIt is the measurement data vector that AMI is provided, hpseudoIt is that AMI is provided
The corresponding calculated value of measurement data vector, WpseudoPseudo- metric data weight matrix.
Advantageous effect of the present invention：
The method of the present invention can be significantly reduced the period of estimation and to be controlled similar to voltage control and system jams
Etc advanced control application provide real time status information.This method contributes to the catastrophe that detection does well.The present invention can
Further by the robustness of optimization algorithm and to reduce the influence that bad data and leverage points bring estimated result.In addition,
The details of DKF (Discrete Kalman Filter) process can advanced optimize.
Description of the drawings
Fig. 1 is present invention mixing State Estimation for Distribution Network schematic diagram；
Fig. 2 is IEEE-69 node system line charts；
Fig. 3 is the active injection power of node 38；
Fig. 4 is the voltage magnitude of node 9；
Fig. 5 is the voltage magnitude of node 5.
Specific implementation mode：
The invention will be further described below in conjunction with the accompanying drawings.
Kalman Filter principles
The dynamic state estimator that Kalman filter is applied to linear system is studied, and whole flow process is divided into two parts：In advance
Survey link and filtering link.In prediction link, to numerical value to be predicted and corresponding covariance matrix carry out it is certain estimate,
Wherein Fk-1Matrix connects system state variables under two moment of k-1 and k, qkThe process for representing prediction link is missed
Difference.
Second link is filtering estimating part, is carried out more to status values using the data of newest obtained practical measurement
Newly.
Matrix K thereinkFor gain matrix (Gain Matrix).The estimation link of Kalman filter is substantially to pre-
A kind of tradeoff of measurement information and real measured data is compromised, and the judgement of the weight or precision of different information seems particularly significant herein.
The advantage of Kalman filter is that it can provide a kind of determining various due weights of information of rational explanation,
Another aspect Kalman filter also provides the information of more redundancy.
Holt’s Exponential Smoothing Method
It is different from traditional linear dynamic system, actual electric system is a nonlinear system, while being difficult picture
Operating system equally writes out DAE of the state variable (including node voltage vector, branch current vector) in front and back moment sequence
(differential and algebraic equations).Therefore prediction link is needed according to statistics and linear regression side
The research in face is supplemented.
ARIMA (0,1,0) process models are applied in the research of partial dynamic state estimation, and reason is the number of PMU
It is closely spaced according to renewal time.This can obtain relatively good application under quasi steady-state condition, in order to which preferably trap state becomes
The variation characteristic of amount uses holt ' the s linearized index smoothing techniques of two parameter in of the invention.The characteristics of this method is to examine simultaneously
Consider two parts of trend of the level and variation of current estimation,
Sk+1=α xk+(1-α)(Sk+bk) (7)
bk+1=β (Sk+1-Sk)+(1-β)bk (8)
Wherein parameter а and β can directly be set, and numerical value is between 0 to 1.Formula (9) gives the pre- of moment k+1
It surveys numerical value to be provided by two parts, that is, the trend of the estimation level and variation predicted.The horizontal S of k+1 moment estimations also divides in formula (7)
For two part form, one be a upper moment k final estimated result xk, the other is a upper moment provide it is estimated
Measure Sk+bk。
Mix state of electric distribution network estimation scheme
As first part is carried, there are three types of different measuring devices, PMU, RTU and AMI altogether in distribution network system.Such as figure
Shown in 1, AMI sets every 15 minutes and carries out a data update in the present invention, if whole newest metrical informations to be waited for go out
Existing, then to wait until that could carry out within every 15 minutes a static state estimation calculates, such case is unsatisfactory for modern power distribution systems
The requirement of state aware.The voltage magnitude and branch current magnitudes information that RTU is measured, renewal frequency are that update in every 20 seconds is primary
Data.PMU is as previously described, can upload 50 data each second.There are three parts for whole process：Only PMU data carries out
The newer period, at the time of PMU and RTU data updates, at the time of PMU, RTU and AMI data all exist.For first two feelings
Condition, system, there are prodigious problem, need to add pseudo- measurement information in observability and data redundancy.
State of electric distribution network estimation of the selection based on branch current, the state variable of system are selected as root node in the present invention
The real part and imaginary part of the voltage of (being connected with bulk power grid) and each branch current, are calculated using rectangular coordinate system, convenient entire
The linearisation of calculating process,
The relationship between information and state variable measured by PMU be it is linear and invariable, it is corresponding
Jacobian matrixes need not also modify.It is between 0 and 1 between state variable for current vector measurement itself
Relationship, corresponding parameter is completely by the topological structure of system and circuit or other ingredients between voltage measurement information and state variable
Parameter determined.
Information measured by RTU is the nonlinear function of state variable, in order to make the process of calculating effectively,
It needs to carry out rectangular co-ordinate processing to the information of measurement.A kind of more satisfactory method is to utilize preceding linear dynamic estimation knot
Fruit merges amplitude information, obtains the real and imaginary parts of electrical quantity.
In formula (11)WithBe respectively the node voltage real part estimated at a upper moment, imaginary part with
And the real part and imaginary part of branch current.Since variable weight derived from formula (11) is also required to be modified according to formula (11).
By the processing of rectangular co-ordinate, the corresponding Jacobian matrix elements of RTU metrical informations be also it is changeless, element it is interior
Hold only 0 or 1.
AMI variable measured directly include the active and reactive load of user and the concentrated load of numerous users, in system
In showed in the form of node injecting power, node injecting power itself can express the linear function as subcircuits
(needing to update the information of Jacobian matrixes), can also be converted into the form of node injecting power, the latter is selected in the present invention
Method, handled according to formula (12), and handle conversion after uncertain feature,
In formula (12), variable Pinj,i,And Qinj,iBe not the information that can be obtained at each moment, AMI simultaneously
Under at the time of not carrying out data update, corresponding node Injection Current information is calculated by the SE of previous moment.By
After being handled according to formula (12), corresponding Jacobian matrix parts element is only related with node-branch incident matrix
System.
The reason of handling in this way is：Be first Jacobian matrixes element all from 0,1 and circuit itself
Parameter, matrix keeps constant constant；Reduce the conditional number of Jacobian matrix Hs.
The mark of an execution cycle is regarded in the update of AMI data as.When new AMI data informations are (from intelligent electric meter system
Derived power injects information) it is available when, system calculates according to stringent WLS and completes state estimation.
Below to the present invention is based on the specific realities of the power distribution network admixture method of estimation of a variety of time cycle measurement data
Existing process is described in detail, and the method for the present invention specifically includes following steps：
(1) determine the topology information of distribution network system and the installation situation of different measuring device, including type with
Quantity information；
(2) according to specific producer explanation and system state estimation it needs to be determined that measuring device PMU in distribution network system,
The frequency acquisition of the respective data of RTU and AMI；
(3) system state variables used in calculating are determined according to the topological structure of system and load investigation；
(4) Load flow calculation is carried out according to load investigation, using result of calculation as the initial value of system state variables；
(5) initial error prediction model matrix Q is determined；
Error prediction model matrix Q is diagonal matrix, and initial element is set as 1 × 10-6。
(6) prediction of state, the state variable predicted are carried out using Holt two-parameter exponential smoothing processing methods
The predicted value of measurandAnd predicted state variableCovariance matrix
Holt ' s two-parameter exponentials are utilized to handle (Holt ' s two parameter exponential smoothing)
Method, first to the state variable under the k momentIt is calculated, wherein parameter alpha and β is between 0-1.
Fk=α (1+ β) I
bk=β (Sk-Sk-1)+(1-β)·bk-1
Wherein,State variable is represented in the predicted value at k moment, matrix FkIt is the state variable from the k-1 moment to the k moment
Transition matrix, gkIt is the control variable of calculating state just predicted value.
For the covariance matrix of prediction
Measurand component part：The real part and imaginary part of node voltage；The real part and imaginary part of branch current；Node injection electricity
The real part and imaginary part of stream.
Call function h (x) calculates measurand, under the conditions of linear, h (x)=H*x, function h (x) and
The computational methods of Jacobian matrix Hs are described as follows.
For node voltage, h (x) functions are as follows
The part of corresponding Jacobian matrix Hs is as follows
For branch current, corresponding h (x) function is as follows
The part of corresponding Jacobian matrix Hs is as follows
For node Injection Current, whether h (x) expression formulas and the corresponding element of Jacobian matrixes can bases
KCL defines the form that several branch currents are mutually added and subtracted of being write as, repeats no more.
(7) when PMU measured datas update, according to the difference of the predicted value of measurand and measured value, determination is
The no operating status there are bad data or system changes；
Operating status variation if there is bad data or there are system, it is suitable to be selected according to data redundancy implementations
Method handled.Otherwise, using Kalman filter method, the state variable after correction is calculatedAnd estimation
The covariance matrix of state variable
The changed method of operating status with the presence or absence of bad data or system is：
It calculates
The normalized form of variable vk is calculated,
The variable comparedWith set threshold value t, threshold value t is set here by empirical value.If it is less than threshold
Value t then thinks that system is in (standard) steady operational status, does not also consider the influence of bad data.
The status information vector after correction is calculatedAnd the covariance matrix of estimated state variableSpecially：
Wherein RzkFor the standard deviation matrix of real measured data, KkReferred to as Kalman gain matrixs,For the state after correction
Variable information.
To inscribe the estimator of metrical information when k, corresponding covariance matrix is：
(8) straight to RTU institutes using the result of a upper moment linear dynamic state estimation when RTU measured datas update
The information for connecing measurement carries out rectangular co-ordinate processing, specially：
Wherein,Real part for the voltage estimated value obtained after rectangular co-ordinateization processing,For rectangular co-ordinateization processing
The imaginary part of the voltage estimated value obtained afterwards；It is the node voltage real part estimated at a upper moment, imaginary part respectively,It is the real part and imaginary part of the branch current estimated at a upper moment respectively；Vm,iThe node voltage width measured for RTU
Value, Im,iThe branch current magnitudes directly measured for RTU.
(9) electric according to the measured data of PMU and RTU and with AMI last equivalent node Injection Current or load
Stream, establishes the object function of state estimation, system state variables is calculated using linear least square methodAs next
The normal condition vector of moment dynamic state estimator, and to the pseudo- metric data at next RTU data updates moment, (node is noted
Enter electric current) carry out linear prediction；
The object function of state estimation is established, specially：
J=(zpmu-hpmu)TWpmu(zpmu-hpmu)+(zrtu-hrtu)TWrtu(zrtu-hrtu)
+(zpseudo-hpseudo)TWpseudo(zpseudo-hpseudo)
Wherein, zpmuIt is the measurement data vector that PMU is provided, hpmuIt is the corresponding calculated value of PMU data vector, WpmuIt is PMU
The weight matrix of data；zrtuIt is the measurement data vector that RTU is provided, hrtuIt is the corresponding meter of measurement data vector that RTU is provided
Calculation value, WrtuIt is the corresponding weight matrix of RTU data；zpseudoIt is the pseudo- metric data vector that last time obtains, hpseudoIt is root
The pseudo- measurement vector being calculated according to current state variable, WpseudoPseudo- metric data weight matrix.
Linear prediction is carried out to the pseudo- metric data at next RTU data updates moment, concrete methods of realizing is：
Wherein, tkIt is from tj-1Moment is to tjA moment between moment, using RTU data updates each time as between one
Every.
(10) when AMI measurement data updates, the information of load power or node injecting power is converted to
The node Injection Current or load current of effect；Specially：
Wherein,It is the real part of node injection equivalent current,It is the imaginary part of node injection equivalent current, Pinj,iIt is
The node that AMI is provided injects active power, Qinj,iIt is the node injection reactive power that AMI is provided, Vi rIt is to obtain last moment
Node voltage real part estimated data, Vi xIt is the node voltage imaginary part estimated data obtained last moment.
(11) according to the measured data of PMU, RTU and AMI, total system static linear state estimation is carried out, obtains system
State variable vector x.
Concrete methods of realizing is：
It is as follows to establish object function：
Min J=(zpmu-hpmu)TWpmu(zpmu-hpmu)+(zrtu-hrtu)TWrtu(zrtu-hrtu)
+(zpseudo-hpseudo)TWpseudo(zpseudo-hpseudo)
It utilizes WLS (weighted least-squares method is calculated)；
Δzk=zmk-h(xk)
zmk=[zpmu,zrtu,zpseudo]T
h(xk)=[hpmu,hrtu,hpseudo]T
hpmuCalculating referring to step (6)；
hrtuProcessing (voltage magnitude and branch current magnitudes) it is as follows：
Indicate node i voltage magnitude, δiFor the voltage-phase of node i, liIndicate the path from root node to node i.
hpseudoCalculating referring to step (6)；
H=[Hpmu；Hrtu；Hpseudo]。
It should be noted that (9) step and (11) step all use least square method, but corresponding scene is different,
The sources metrical information z used are also not exactly the same.(9) step corresponds to the update of only PMU and RTU real time datas, node
Injecting power or node Injection Current are estimated by estimated result before；In (11) step, PMU, RTU and
AMI data are obtained for update, are directly calculated using real time data.
Emulate analysis of cases
In this part, two simulation analysis have been carried out using 69 node power distribution net systems of IEEE.First emulation case
Example is used for illustrating ability of the method for estimating state based on branch current and DKF to tracking system state.In order to better illustrate
Effect, one is also compared together based on the obtained result of WLS methods.The case of second emulation gives front institute
The power distribution network synthesis state estimation Policy Result stated.
A. about the test of ability of tracking
Fig. 2 gives the line chart of 69 node systems.69 node systems of IEEE are the three-phases of a complete radial distribution
Balance system.Relevant parameter and basic Power Flow Information can be consulted in the prior art.
In order to test in the case where limited quantity PMU is participated in, DKF methods are to the ability of tracking of system state variables, and two
The result of kind method compares：The result obtained using DKF methods and the result obtained using traditional WLS methods.Two kinds
Method compares (including the case where load) at the same conditions.
The measurement error of system interior joint injecting power variable is considered obeying the normal distribution law that mean value is 0.Distribution
Standard deviation be no more than 20% being set for measured value according to worst error.Fig. 3 gives node 38 active injection power
As a result, Fig. 4 gives the voltage magnitude of node 9.
Emulation is at the time of give 50 differences, the results showed that the method based on DKF can regular hour range it
Inside obtain the no worse than method of WLS.It should be noted that in true scene, the plant engineer of system can not possibly be every
One moment obtains the node injecting power information of system.The purpose of this emulation, which is only in that, proves having based on DKF methods
Effect property with can application prospect.
A. the analysis of cases under hybrid measurement information
In this part, emulation is related to three kinds of measuring techniques with the different time period.As shown in Figure 1, being based on
The state estimation of DKF is calculated every two seconds and is carried out once.Every 20 seconds of linear WLS (Weighted Least Square) state estimation
It carries out once, the information used includes the pseudo- amount of real-time measurement information and node injecting power presentation from PMU and RTU
Measurement information.The WLS once based on all practical metrical informations is carried out within every 15 minutes to calculate.
Less than the 1% of actual value, the measurement error of phase can be believed according to time synchronization for the amplitude measurement error setting of PMU
Number worst error set, according to current technical standard, the time error of GPS is within 1 microsecond.The survey of RTU information
Measure 2% of error setting less than actual value.Practical 5% of error setting less than substantial amount measured of AMI.Above-mentioned error is recognized
For all Normal Distributions.
Fig. 5 gives the information of the 5th node voltage amplitude in system.The line of big rise and fall therein represents estimation and obtains
Numerical value, substantially smooth straight line represents true numerical value.Obtained result is estimated compared with actual value, and error exists
Within 0.2%, meet the requirement of state of electric distribution network estimation.
Above-mentioned, although the foregoing specific embodiments of the present invention is described with reference to the accompanying drawings, not protects model to the present invention
The limitation enclosed, those skilled in the art should understand that, based on the technical solutions of the present invention, those skilled in the art are not
Need to make the creative labor the various modifications or changes that can be made still within protection scope of the present invention.
Claims (10)
1. the power distribution network admixture method of estimation based on a variety of time cycle measurement data, which is characterized in that including following step
Suddenly：
(1) topology information of distribution network system and the installation situation of different measuring devices, including type and quantity are determined
Information；
(2) according to specific producer explanation and system state estimation it needs to be determined that in distribution network system measuring device PMU, RTU and
The frequency acquisition of the respective data of AMI；
(3) system state variables used in calculating are determined according to the topological structure of system and load investigation；
(4) Load flow calculation is carried out according to load investigation, using result of calculation as the initial value of system state variables；
(5) initial error prediction model matrix Q is determined；
(6) prediction of state, the state variable predicted are carried out using Holt two-parameter exponential smoothing processing methodsIt measures
The predicted value of variableAnd predicted state variableCovariance matrix
(7) when PMU measured datas update, according to the difference of the predicted value of measurand and measured value, it is determined whether deposit
It changes in the operating status of bad data or system；
The state variable after correction is calculated using Kalman filter method if there is no the above situationAnd estimate
Count the covariance matrix of state variable
(8) when RTU measured datas update, RTU is directly surveyed using the result of a upper moment linear dynamic state estimation
The information of amount carries out rectangular co-ordinate processing；
(9) it according to the measured data of PMU and RTU and the node Injection Current or load current equivalent with the AMI last times, builds
The object function of vertical state estimation, system state variables are calculated using linear least square method It is dynamic as subsequent time
The normal condition vector of state state estimation, and linear prediction is carried out to the pseudo- metric data at next RTU data updates moment；
(10) when AMI measurement data updates, the information of load power or node injecting power is converted to equivalent
Node Injection Current or load current；
(11) according to the measured data of PMU, RTU and AMI, total system static linear state estimation is carried out, obtains system mode
Variable vector x.
2. the power distribution network admixture method of estimation based on a variety of time cycle measurement data as described in claim 1, special
Sign is, in the step (6), the state variable predictedSpecially：
Holt ' s two-parameter exponential smoothing processing methods are utilized, to the state variable under the k momentIt is calculated, wherein parameter alpha
And β is between 0-1；
Fk=α (1+ β) I
bk=β (Sk-Sk-1)+(1-β)·bk-1
Predicted state variableCovariance matrixSpecially：
Wherein,Predicted value of the state variable at the k moment is represented,For the State variable information after correction, matrix FkIt is from k-
1 moment is to the transition matrix of the state variable at k moment, gkIt is the control variable for calculating state variable predicted value；Sk、bkRespectively
Intermediate quantity；
Represent state variable the predicted value at k moment covariance matrix,The association side of estimated state variable after correction
Poor matrix.
3. the power distribution network admixture method of estimation based on a variety of time cycle measurement data as described in claim 1, special
Sign is, in the step (6), obtains the predicted value of measurandSpecially：
Measurand includes：The real part and imaginary part of node voltage；The real part and imaginary part of branch current；The real part of node Injection Current
With imaginary part；
Call function h (x) calculates measurand, and under the conditions of linear, h (x)=H*x, H are Jacobian matrixes：
For node voltage, corresponding h (x) function is as follows：
For branch current, corresponding h (x) function is as follows：
For node Injection Current, the form that several branch currents are mutually added and subtracted of being write as is defined according to KCL；
Wherein, Ibr、IbxThe respectively real and imaginary parts of branch current, Vi r、Vi xThe respectively voltage real and imaginary parts of node i,
Vs r、Vs xThe respectively real and imaginary parts of root node voltage, rkFor the resistance of branch k, xkFor the reactance of branch k, liIt indicates from root
The path of node-to-node i.
4. the power distribution network admixture method of estimation based on a variety of time cycle measurement data as described in claim 1, special
Sign is, in the step (7), the status information vector after correction is calculatedAnd the covariance of estimated state variable
MatrixSpecially：
Wherein RzkFor the standard deviation matrix of real measured data, KkReferred to as Kalman gain matrixs,Become for the status information after correction
Amount,For the covariance matrix of the status information variable after correction；Predicted value of the state variable at the k moment is represented,Generation
Covariance matrix of the table-like state variable in the predicted value at k moment；
For metrical information zkEstimator, corresponding covariance matrix is：HkUnder the k moment
Jacobian matrixes.
5. the power distribution network admixture method of estimation based on a variety of time cycle measurement data as described in claim 1, special
Sign is, in the step (7), it is determined whether there are the operating statuses of bad data or system to change, specially：
Calculate variable vkNormalized form：
Wherein,For the predicted value of measurand, zkFor the measured value of measurand；vk,iIndicate the measured value of measurand with it is pre-
The difference of measured value,Indicate the difference of the measured value and predicted value of the measurand after standardization, RkFor the covariance of measurement data
Matrix, H are Jacobian matrix sizes；
Given threshold t, comparison variableWith threshold value t；IfLess than threshold value t, then it is assumed that system is in steady operational status,
The influence of bad data is not considered.
6. the power distribution network admixture method of estimation based on a variety of time cycle measurement data as described in claim 1, special
Sign is, in the step (8), carries out rectangular co-ordinate processing to RTU institutes information measured directly, specially：
Wherein,Real part for the voltage estimated value obtained after rectangular co-ordinateization processing,To be obtained after rectangular co-ordinateization processing
Voltage estimated value imaginary part；It is the node voltage real part estimated at a upper moment, imaginary part respectively,Point
It is not the real part and imaginary part of the branch current estimated at a upper moment；Vm,iFor the node voltage amplitude that RTU is measured, Im,iFor
RTU directly measures obtained branch current magnitudes.
7. the power distribution network admixture method of estimation based on a variety of time cycle measurement data as described in claim 1, special
Sign is, in the step (9), establishes the object function of state estimation, specially：
J=(zpmu-hpmu)TWpmu(zpmu-hpmu)+(zrtu-hrtu)TWrtu(zrtu-hrtu)
+(zpseudo-hpseudo)TWpseudo(zpseudo-hpseudo)
Wherein, zpmuIt is the measurement data vector that PMU is provided, hpmuIt is the corresponding calculated value of PMU data vector, WpmuIt is PMU data
Weight matrix；zrtuIt is the measurement data vector that RTU is provided, hrtuIt is the corresponding calculated value of measurement data vector that RTU is provided,
WrtuIt is the corresponding weight matrix of RTU data；zpseudoIt is the pseudo- metric data vector that last time obtains, hpseudoIt is that basis is worked as
The pseudo- measurement vector that preceding state variable is calculated, WpseudoPseudo- metric data weight matrix.
8. the power distribution network admixture method of estimation based on a variety of time cycle measurement data as described in claim 1, special
Sign is, in the step (9), linear prediction, specific implementation are carried out to the pseudo- metric data at next RTU data updates moment
Method is：
Wherein, tkFrom tj-1Moment is to tjBetween moment, using RTU data updates each time as an interval；zp,kFor k pairs of moment
The RTU metric data answered, zp,jFor the corresponding RTU metric data of moment j, TpFor the RTU measurement data update cycles.
9. the power distribution network admixture method of estimation based on a variety of time cycle measurement data as described in claim 1, special
Sign is, in the step (10), by the active and reactive load of AMI institutes user measured directly and concentrated load variables transformations
For the form of node injecting power, specially：
Wherein,It is the real part of node injection equivalent current,It is the imaginary part of node injection equivalent current, Pinj,iIt is that AMI is provided
Node inject active power, Qinj,iIt is the node injection reactive power that AMI is provided, Vi rIt is the node electricity obtained last moment
Compacting portion estimated data, Vi xIt is the node voltage imaginary part estimated data obtained last moment.
10. the power distribution network admixture method of estimation based on a variety of time cycle measurement data as described in claim 1, special
Sign is, in the step (11), according to the measured data of PMU, RTU and AMI, carries out total system static linear state and estimates
Meter obtains system state variables vector x, specially：
Establish the object function of state estimation：
J=(zpmu-hpmu)TWpmu(zpmu-hpmu)+(zrtu-hrtu)TWrtu(zrtu-hrtu)
+(zpseudo-hpseudo)TWpseudo(zpseudo-hpseudo)
Wherein, zpmuIt is the measurement data vector that PMU is provided, hpmuIt is the corresponding calculated value of PMU data vector, WpmuIt is PMU data
Weight matrix；zrtuIt is the measurement data vector that RTU is provided, hrtuIt is the corresponding calculated value of measurement data vector that RTU is provided,
WrtuIt is the corresponding weight matrix of RTU data；zpseudoIt is the measurement data vector that AMI is provided, hpseudoIt is the measurement that AMI is provided
The corresponding calculated value of data vector, WpseudoPseudo- metric data weight matrix.
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