CN112149273A - Fast-convergence alternating current power grid distributed state estimation method - Google Patents

Fast-convergence alternating current power grid distributed state estimation method Download PDF

Info

Publication number
CN112149273A
CN112149273A CN202010830082.8A CN202010830082A CN112149273A CN 112149273 A CN112149273 A CN 112149273A CN 202010830082 A CN202010830082 A CN 202010830082A CN 112149273 A CN112149273 A CN 112149273A
Authority
CN
China
Prior art keywords
power grid
distributed
bus
state
region
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Granted
Application number
CN202010830082.8A
Other languages
Chinese (zh)
Other versions
CN112149273B (en
Inventor
陈博
胡明南
石家宇
翁世清
俞立
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Zhejiang University of Technology ZJUT
Original Assignee
Zhejiang University of Technology ZJUT
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Zhejiang University of Technology ZJUT filed Critical Zhejiang University of Technology ZJUT
Priority to CN202010830082.8A priority Critical patent/CN112149273B/en
Publication of CN112149273A publication Critical patent/CN112149273A/en
Application granted granted Critical
Publication of CN112149273B publication Critical patent/CN112149273B/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Images

Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F30/00—Computer-aided design [CAD]
    • G06F30/20—Design optimisation, verification or simulation
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
    • G06Q50/06—Energy or water supply
    • 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
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F2113/00—Details relating to the application field
    • G06F2113/04—Power grid distribution networks
    • 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
    • H02J2203/10—Power transmission or distribution systems management focussing at grid-level, e.g. load flow analysis, node profile computation, meshed network optimisation, active network management or spinning reserve management
    • 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]
    • 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

Landscapes

  • Engineering & Computer Science (AREA)
  • Theoretical Computer Science (AREA)
  • Business, Economics & Management (AREA)
  • Physics & Mathematics (AREA)
  • Economics (AREA)
  • Health & Medical Sciences (AREA)
  • General Physics & Mathematics (AREA)
  • General Engineering & Computer Science (AREA)
  • General Health & Medical Sciences (AREA)
  • Geometry (AREA)
  • Evolutionary Computation (AREA)
  • Computer Hardware Design (AREA)
  • Public Health (AREA)
  • Water Supply & Treatment (AREA)
  • Power Engineering (AREA)
  • Human Resources & Organizations (AREA)
  • Marketing (AREA)
  • Primary Health Care (AREA)
  • Strategic Management (AREA)
  • Tourism & Hospitality (AREA)
  • General Business, Economics & Management (AREA)
  • Supply And Distribution Of Alternating Current (AREA)

Abstract

A fast convergence AC power grid distributed state estimation method comprises the following steps: 1) providing a mathematical model of the alternating current network system, and providing a measurement model under the mixed use condition of the SCADA measuring instrument and the PMU measuring instrument; 2) dividing a power grid system into a plurality of areas according to actual conditions, and providing a measurement model of each power grid area; 3) converting the nonlinear least square problem into an iterative linear least square problem by using a Gauss-Newton method to solve; 4) a nonlinear distributed state estimation method which is only communicated with neighbors and can be converged quickly is designed through a distributed linear least square method constructed based on the block matrix inversion idea. The invention can quickly calculate the state estimation value so as to find out the state abnormity in time, and greatly reduces the communication times while ensuring the estimation performance of the system; in addition, local original measurement information and a local state estimation value do not need to be exchanged, and the privacy of power grid users is protected to the greatest extent.

Description

Fast-convergence alternating current power grid distributed state estimation method
Technical Field
The invention relates to the field of distributed alternating current power grid state estimation, in particular to a fast convergence method based on nonlinear least square, which is applied to an alternating current power grid and solves the state estimation problem of a large-scale distributed system.
Background
The grid system is a large complex network formed by connecting hundreds of power buses. A conventional centralized power grid system is usually configured with a central dispatching center, and a Data Acquisition and monitoring (SCADA) system and a Phasor Measurement Unit (PMU) manage Measurement and Control signals of the whole power grid system. In order to monitor whether the power grid operates stably, a state estimator is usually installed in a central dispatching center, the state estimator estimates the current state of the power grid system according to measured data of the power grid, and the dispatching center can judge whether the operation of the power grid system is abnormal according to the state, or can control the dispatching of the power by taking the state as the input quantity of a controller in the power grid system. However, with the increase of the demand of electricity, the scale of the power grid becomes larger and larger, and the access of new energy power generation leads the power plant to be dispersed more and more in space, which makes the defects of high calculation cost, high communication delay, poor fault tolerance and the like of the centralized power grid more and more prominent. Therefore, distributed power grid systems with lower calculation and communication costs and higher robustness are receiving more and more attention.
The distributed power grid system is generally divided into a plurality of areas according to geographical positions or by using a clustering algorithm, wherein the areas where line connection exists are neighbor areas. In the distributed system, each area is designed to converge on a distributed state estimator of a centralized state estimation result through local measurement information and key data provided by a neighbor area, and power scheduling of the area is controlled according to the state estimation result. However, in the ac power grid, the measurement and the state of the system have a non-linear relationship, so that it is difficult to perform distributed decomposition, which brings difficulty to the design of the distributed state estimator. On the other hand, because the power grid system is large in scale, the jacobian matrix constructed through topological information is usually ill-conditioned, so that the convergence rate of the traditional distributed state estimation method based on matrix inversion design is very slow, and the control difficulty of each link in the power grid system is increased. On the premise of considering network scale and computational efficiency, it is still challenging to design a fast-convergence distributed state estimation method for a large-scale alternating-current power grid system.
Disclosure of Invention
In order to solve the problem of slow state estimation convergence rate in a large-scale distributed alternating current power grid system, the invention provides a fast-convergence alternating current power grid distributed state estimation method.
The technical scheme adopted by the invention for solving the technical problems is as follows:
a fast convergence AC power grid distributed state estimation method comprises the following steps:
1) establishing an alternating current power grid measuring model;
defining the voltage of a bus i in a power grid as ViPhase angle of voltage is thetaiThe resistance on the line between bus i and bus j is rijReactance is xijConductance is gijSusceptance ofijThe shunt conductance between bus i and ground is
Figure RE-GDA0002749377430000021
Shunt susceptance of
Figure RE-GDA0002749377430000022
Shunt admittance of
Figure RE-GDA0002749377430000023
The shunt admittance on the transmission line between buses is
Figure RE-GDA0002749377430000024
Let the state x of the gridi＝[Vi θi]TIf the shunt conductance and shunt susceptance of each bus in the power grid and the resistance and reactance of the transmission line between the buses are known, the measurement model of the alternating current power grid at the time k is
Figure RE-GDA0002749377430000025
Wherein u isii(k),uij(k) Measuring noise for zero mean gaussian; z is a radical ofii(k) Is a direct measurement of the bus voltage and the phase angle of the voltage, and when the measuring instrument is a SCADA instrument, only the bus voltage, namely C, can be measuredi＝[1 0]When the measuring instrument is a PMU instrument, it can measure voltage and voltage phase angle, i.e.
Figure RE-GDA0002749377430000026
zij(k) Is to the active power P of the transmission lineij(k) And reactive power Qij(k) Is measured by a factor of hij(xi(k),xj(k))T＝(Pij(k),Qij(k))TWherein, in the step (A),
Figure RE-GDA0002749377430000027
wherein
Figure RE-GDA0002749377430000031
By extending the measurement equations of all buses and transmission lines, a global measurement model z (k) ═ h (x (k)) + u (k) is obtained, assuming that there are m measurements in the power grid, some
Figure RE-GDA0002749377430000032
2) Dividing a power grid distributed region;
dividing the power grid into a plurality of areas according to the geographical position of the power grid; each power grid region only has local parameter information and measurement data, and parameter information and measurement data on a connection line with an adjacent region; if the grid is divided into n regions, then global measurements are taken
Figure RE-GDA0002749377430000033
Wherein the measurement model of the region i is
Figure RE-GDA0002749377430000034
Wherein z isii(k) Is the bus i in the region1,...,ilMeasured value of, zij(k) For the region and the bus j connected in the region j1...jcMeasured values on the transmission line, which are respectively indicated as
Figure RE-GDA0002749377430000035
Figure RE-GDA0002749377430000036
Measuring noise of a measuring instrument in the area i; in general, formula (5) and formula (6) are collectively written as zi(k)＝hi(x(k))+ui(k) (ii) a In addition, the area connected to the bus in the area i with the transmission line is defined as the neighborhood of the area i and is recorded as
Figure RE-GDA0002749377430000037
3) Designing a distributed state estimator;
estimating the state by taking the minimum mean square error of the residual error e (k) ═ z (k) — h (x (k)) as a criterion, namely solving the following minimization problem
Figure RE-GDA0002749377430000041
Wherein, the value of the state x (k) where the derivative of phi (x (k)) is zero is the estimation value to be evaluated, and let
Figure RE-GDA0002749377430000042
J (x (k)) is abbreviated as J (k); due to hi(k) The formula is a nonlinear equation, the above formula has no analytic solution, so that the iterative solution is carried out by using a Gauss-Newton method, and the formula of the p-th iteration is
Figure RE-GDA0002749377430000043
x(p+1)＝x(p)+Δx(p), (10)
Wherein phi-1To estimate the error covariance matrix, equation (9) has the same form as the linear least squares estimation; however, in the distributed power grid system, each region only has measurement information of the region, and therefore, each region needs to use a distributed least square estimator to perform distributed solution on equation (9) to obtain a local state update value Δ xi(p) then passing xi(p+1)＝xi(p)+Δxi(p) updating the local state;
4) designing a distributed nonlinear least square estimator;
the distributed estimator is divided into an inner loop part and an outer loop part according to the formula (9) and the formula (10), the inner loop part constructs a distributed linear least square method based on the block matrix inversion idea, and the outer loop part performs distributed iteration based on the Gauss-Newton method in the step 3).
Further, the process of the step 4) is as follows:
step 401, an outer loop initialization phase; setting an initial state estimation value of each node i
Figure RE-GDA0002749377430000044
Step 402, for an outer loop iteration round K1, 2i(k)＝zi(k)-hi(k) And executing an inner loop (4.3) - (4.6);
step 403, an inner loop initialization phase; setting an initial estimation error covariance matrix of each node i to
Figure RE-GDA0002749377430000045
The linear estimate is
Figure RE-GDA0002749377430000046
For all neighbors
Figure RE-GDA0002749377430000047
Initialization settings
Figure RE-GDA0002749377430000048
Mij(0)＝Mi(0)；
Step 404, for an inner loop iteration round P1, 2
Figure RE-GDA0002749377430000049
Computing
Figure RE-GDA00027493774300000410
And will calculate the result betaij(p) and Ψij(p) to node j;
step 405, each node i receives the information sent by the neighbor node and updates
Figure RE-GDA0002749377430000051
Step 406, calculating a linear least squares estimate after inner loop convergence
Figure RE-GDA0002749377430000052
And is provided with
Figure RE-GDA0002749377430000053
Finishing the internal circulation;
step 407, after the inner loop is finished, each node i updates the state
Figure RE-GDA0002749377430000054
The technical conception of the invention is as follows: firstly, a mathematical model of an alternating current power grid system is given, and a measurement model under the condition that an SCADA measuring instrument and a PMU measuring instrument are mixed for use is given. And then, dividing the power grid system into a plurality of areas according to the actual situation, and providing a measurement model of each area power grid. And finally, designing a fast-convergence alternating current power grid distributed state estimation method, converting the nonlinear least square problem into an iterative linear least square problem by using a Gauss-Newton method, and designing a fast-convergence nonlinear distributed state estimation method only communicating with neighbors by using a distributed linear least square method constructed based on a block matrix inversion idea.
The invention has the following beneficial effects: by using a fast-convergence distributed state estimation method, early warning can be immediately given under the condition that the state is abnormal, and the method greatly reduces the communication times while ensuring the system performance; in addition, the method does not need to exchange local original measurement information and a local state estimation value, and the privacy of the power grid user is protected to the greatest extent.
Drawings
FIG. 1 is a schematic diagram of an IEEE 118-bus power grid distributed partitioning system;
FIG. 2 is a block diagram of a distributed state estimator;
fig. 3 is an inner loop iteration simulation diagram and an outer loop iteration estimation error simulation diagram.
Detailed Description
The invention is further described below with reference to the accompanying drawings.
Referring to fig. 1 to 3, a fast converging ac power grid distributed state estimation method includes the following steps:
1) establishing an alternating current power grid measuring model;
defining the voltage of each bus i in the power grid as ViPhase angle of voltage is thetaiThe resistance on the line between bus i and bus j is rijReactance is xijConductance is gijSusceptance ofijThe shunt conductance between bus i and ground is
Figure RE-GDA0002749377430000061
Shunt susceptance of
Figure RE-GDA0002749377430000062
Shunt admittance of
Figure RE-GDA0002749377430000063
The shunt admittance on the transmission line between buses is
Figure RE-GDA0002749377430000064
Let the state x of the gridi＝[Vi θi]TIf the shunt conductance and shunt susceptance of each bus in the power grid and the resistance and reactance of the transmission line between the buses are known, the measurement model of the alternating current power grid at the time k is
Figure RE-GDA0002749377430000065
Wherein u isii(k),uij(k) Measuring noise for zero mean gaussian; z is a radical ofii(k) Is a direct measurement of the bus voltage and the phase angle of the voltage, and when the measuring instrument is a SCADA instrument, only the bus voltage, namely C, can be measuredi＝[1 0]When the measuring instrument is a PMU instrument, it can measure voltage and voltage phase angle, i.e.
Figure RE-GDA0002749377430000066
zij(k) Is to the active power P of the transmission lineij(k) And reactive power Qij(k) Is measured by a factor of hij(xi(k),xj(k))T＝(Pij(k),Qij(k))TWherein, in the step (A),
Figure RE-GDA0002749377430000067
wherein
Figure RE-GDA0002749377430000068
By extending the measurement equations of all buses and transmission lines, a global measurement model z (k) ═ h (x (k)) + u (k) is obtained, assuming that there are m measurements in the power grid, some
Figure RE-GDA0002749377430000069
2) Dividing a power grid distributed region;
dividing the power grid into a plurality of regions according to the geographical position of the power grid, wherein each power grid region only has local parameter information and measurement data, and parameter information and measurement data on a connection line with an adjacent region; if the grid is divided into n regions, then global measurements are taken
Figure RE-GDA0002749377430000071
Wherein the measurement model of the region i is
Figure RE-GDA0002749377430000072
Wherein z isii(k) Is the bus i in the region1,...,ilMeasured value of, zij(k) For the region and the bus j connected in the region j1...jcMeasured values on the transmission line, which are respectively indicated as
Figure RE-GDA0002749377430000073
Figure RE-GDA0002749377430000074
Measuring noise of a measuring instrument in the area i; in general, formula (5) and formula (6) are collectively written as zi(k)＝hi(x(k))+ui(k) (ii) a In addition, the area connected to the bus in the area i with the transmission line is defined as the neighborhood of the area i and is recorded as
Figure RE-GDA0002749377430000075
3) Designing a distributed state estimator;
estimating the state by taking the minimum mean square error of the residual error e (k) ═ z (k) — h (x (k)) as a criterion, namely solving the following minimization problem
Figure RE-GDA0002749377430000076
Wherein, the value of the state x (k) where the derivative of phi (x (k)) is zero is the estimation value to be evaluated, and let
Figure RE-GDA0002749377430000077
For convenience of description, J (x (k)) is abbreviated as J (k); due to hi(k) The formula is a nonlinear equation, the above formula has no analytic solution, so that the iterative solution is carried out by using a Gauss-Newton method, and the formula of the p-th iteration is
Figure RE-GDA0002749377430000081
x(p+1)＝x(p)+Δx(p), (24)
Wherein phi-1To estimate the error covariance matrix, equation (9) has the same form as the linear least squares estimation; however, in the distributed power grid system, each region only has measurement information of the region, and therefore, each region needs to use a distributed least square estimator to perform distributed solution on equation (9) to obtain a local state update value Δ xi(p) then passing xi(p+1)＝xi(p)+Δxi(p) updating the local state;
4) designing a distributed least square estimator;
dividing the distributed estimator into an inner loop part and an outer loop part according to the formula (9) and the formula (10), wherein the inner loop part constructs a distributed linear least square method based on the block matrix inversion idea, and the outer loop part performs distributed iteration based on the Gauss-Newton method in the step 3);
the process of the step 4) is as follows:
step 401, an outer loop initialization phase; setting an initial state estimation value of each node i
Figure RE-GDA0002749377430000082
Step 402, for an outer loop iteration round K1, 2i(k)＝zi(k)-hi(k) And executing an inner loop (4.3) - (4.6);
step 403, an inner loop initialization phase; setting an initial estimation error covariance matrix of each node i to
Figure RE-GDA0002749377430000083
The linear estimate is
Figure RE-GDA0002749377430000084
For all neighbors
Figure RE-GDA0002749377430000085
Initialization settings
Figure RE-GDA0002749377430000086
Mij(0)＝Mi(0)；
Step 404, for an inner loop iteration round P1, 2
Figure RE-GDA0002749377430000087
Computing
Figure RE-GDA0002749377430000088
And will calculate the result betaij(p) and Ψij(p) to node j;
step 405, each node i receives the information sent by the neighbor node and updates
Figure RE-GDA0002749377430000089
Step 406, calculating a linear least squares estimate after inner loop convergence
Figure RE-GDA00027493774300000810
And is provided with
Figure RE-GDA0002749377430000091
Finishing the internal circulation;
step 407, after the inner loop is finished, each node i updates the state
Figure RE-GDA0002749377430000092
With reference to fig. 3, firstly, the IEEE 118-bus grid system is divided into 8 areas according to fig. 1, and grid parameters are selected as CDF files provided by http:// labs.ece.uw.edu/pstca/pf118/pg _ tca118bus.htm; in addition, the voltage measurement error of the PMU measuring instrument is 0.002, the voltage angle measurement error is 0.01, the voltage measurement error and the power measurement error of the SCADA measuring instrument are 0.3, the circle in FIG. 1 represents a power bus, the 3 rd, 5 th, 9 th, 12 th, 15 th, 17 th, 21 th, 25 th, 28 th, 34 th, 37 th, 40 th, 45 th, 53 th, 56 th, 62 th, 64 th, 68 th, 76 th, 79 th, 85 th, 86 th, 89 th, 92 th, 96 th, 105 th, 110 th and 114 th buses are buses with PMU measurement, other buses are buses with the SCADA measuring instrument, and the connecting line between the circles is a transmission line between the buses; as shown in fig. 3, the simulation experiment starts with a certain initial deviation, each external iteration only needs 4 internal iterations to be basically stable, and the global estimation can be converged through 3 external iterations, so that the convergence rate is very high.

Claims (2)

1. A fast converging AC power grid distributed state estimation method is characterized by comprising the following steps;
1) establishing an alternating current power grid measuring model;
defining the voltage of a bus i in a power grid as ViPhase angle of voltage is thetaiThe resistance on the line between bus i and bus j is rijReactance is xijConductance is gijSusceptance ofijThe shunt conductance between bus i and ground is
Figure RE-FDA0002749377420000011
Shunt susceptance of
Figure RE-FDA0002749377420000012
Shunt admittance of
Figure RE-FDA0002749377420000013
The shunt admittance on the transmission line between buses is
Figure RE-FDA0002749377420000014
Let the state x of the gridi＝[Vi θi]TIf the shunt conductance and shunt susceptance of each bus in the power grid and the resistance and reactance of the transmission line between the buses are known, the measurement model of the alternating current power grid at the time k is
Figure RE-FDA0002749377420000015
Wherein u isii(k),uij(k) Measuring noise for zero mean gaussian; z is a radical ofii(k) The voltage and the phase angle of the voltage on the bus are directly measured, and the measuring instrument is usedWhen the meter is a SCADA meter, only the bus voltage, i.e. C, can be measuredi＝[1 0]When the measuring instrument is a PMU instrument, it can measure voltage and voltage phase angle, i.e.
Figure RE-FDA0002749377420000016
zij(k) Is to the active power P of the transmission lineij(k) And reactive power Qij(k) Is measured by a factor of hij(xi(k),xj(k))T＝(Pij(k),Qij(k))TWherein, in the step (A),
Figure RE-FDA0002749377420000017
wherein
Figure RE-FDA0002749377420000018
By extending the measurement equations of all buses and transmission lines, a global measurement model z (k) ═ h (x (k)) + u (k) is obtained, assuming that there are m measurements in the power grid, some
Figure RE-FDA0002749377420000019
2) Dividing a power grid distributed region;
dividing the power grid into a plurality of areas according to the geographical position of the power grid; each power grid region only has local parameter information and measurement data, and parameter information and measurement data on a connection line with an adjacent region; if the grid is divided into n regions, then global measurements are taken
Figure RE-FDA00027493774200000110
Wherein the measurement model of the region i is
Figure RE-FDA00027493774200000111
Wherein z isii(k) Is the bus i in the region1,...,ilMeasured value of, zij(k) For the region and the bus j connected in the region j1...jcMeasured values on the transmission line, which are respectively indicated as
Figure RE-FDA0002749377420000021
Figure RE-FDA0002749377420000022
Figure RE-FDA0002749377420000023
Measuring noise of a measuring instrument in the area i; in general, formula (5) and formula (6) are collectively written as zi(k)＝hi(x(k))+ui(k) (ii) a In addition, the area connected to the bus in the area i with the transmission line is defined as the neighborhood of the area i and is recorded as
Figure RE-FDA0002749377420000024
3) Designing a distributed state estimator;
estimating the state by taking the minimum mean square error of the residual error e (k) ═ z (k) — h (x (k)) as a criterion, namely solving the following minimization problem
argminφ(x(k))
Figure RE-FDA0002749377420000025
Wherein, the value of the state x (k) where the derivative of phi (x (k)) is zero is the estimation value to be evaluated, and let
Figure RE-FDA0002749377420000026
J (x (k)) is abbreviated as J (k); due to hi(k) The formula is a nonlinear equation, the above formula has no analytic solution, so that the iterative solution is carried out by using a Gauss-Newton method, and the formula of the p-th iteration is
Figure RE-FDA0002749377420000027
x(p+1)＝x(p)+Δx(p), (10)
Wherein phi-1To estimate the error covariance matrix, equation (9) has the same form as the linear least squares estimation; however, in the distributed power grid system, each area only possesses the measurement information of the area, and therefore, each area needs to use the distributed least square estimator to perform distributed solution on equation (9) to obtain the local state update value Δ xi(p) then passing xi(p+1)＝xi(p)+Δxi(p) updating the local state;
4) designing a distributed least square estimator;
the distributed estimator is divided into an inner loop part and an outer loop part according to the formula (9) and the formula (10), the inner loop part constructs a distributed linear least square method based on the block matrix inversion idea, and the outer loop part performs distributed iteration based on the Gauss-Newton method in the step 3).
2. The distributed state estimation method for an alternating current grid according to claim 1, characterized in that the procedure of step 4) is as follows:
4.1) an external circulation initialization stage; setting an initial state estimation value of each node i
Figure RE-FDA0002749377420000031
4.2) for the outer loop iteration round K1, 2i(k)＝zi(k)-hi(k) And executing an inner loop (4.3) - (4.6);
4.3) an internal circulation initialization stage; is arranged at each timeThe initial estimation error covariance matrix of each node i is
Figure RE-FDA00027493774200000311
The linear estimate is
Figure RE-FDA0002749377420000032
For all neighbors
Figure RE-FDA0002749377420000033
Initialization settings
Figure RE-FDA0002749377420000034
Mij(0)＝Mi(0)；
4.4) for the inner loop iteration round P1, 2
Figure RE-FDA0002749377420000035
Computing
Figure RE-FDA0002749377420000036
And will calculate the result betaij(p) and Ψij(p) to node j;
4.5) each node i receives the information sent by the neighbor nodes and updates
Figure RE-FDA0002749377420000037
4.6) calculating the Linear least squares estimate after inner loop convergence
Figure RE-FDA0002749377420000038
And is provided with
Figure RE-FDA0002749377420000039
Finishing the internal circulation;
4.7) after the inner loop is finished, each node i updates the state
Figure RE-FDA00027493774200000310
CN202010830082.8A 2020-08-18 2020-08-18 Fast-convergence alternating-current power grid distributed state estimation method Active CN112149273B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202010830082.8A CN112149273B (en) 2020-08-18 2020-08-18 Fast-convergence alternating-current power grid distributed state estimation method

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202010830082.8A CN112149273B (en) 2020-08-18 2020-08-18 Fast-convergence alternating-current power grid distributed state estimation method

Publications (2)

Publication Number Publication Date
CN112149273A true CN112149273A (en) 2020-12-29
CN112149273B CN112149273B (en) 2024-06-18

Family

ID=73888495

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202010830082.8A Active CN112149273B (en) 2020-08-18 2020-08-18 Fast-convergence alternating-current power grid distributed state estimation method

Country Status (1)

Country Link
CN (1) CN112149273B (en)

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN113315118A (en) * 2021-04-26 2021-08-27 中国南方电网有限责任公司 Power system state estimation method based on parallel computing and particle swarm optimization

Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN104092212A (en) * 2014-07-24 2014-10-08 河海大学 Electric system multi-domain distributed state estimation method based on PMU measurement
CN108075480A (en) * 2016-11-17 2018-05-25 中国电力科学研究院 The method for estimating state and system of a kind of ac and dc systems
CN109888773A (en) * 2019-02-25 2019-06-14 武汉大学 A kind of electric system multizone distributions appraisal procedure
US20190293699A1 (en) * 2018-03-22 2019-09-26 Northeastern University Electric grid state estimation system and method based on boundary fusion

Patent Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN104092212A (en) * 2014-07-24 2014-10-08 河海大学 Electric system multi-domain distributed state estimation method based on PMU measurement
CN108075480A (en) * 2016-11-17 2018-05-25 中国电力科学研究院 The method for estimating state and system of a kind of ac and dc systems
US20190293699A1 (en) * 2018-03-22 2019-09-26 Northeastern University Electric grid state estimation system and method based on boundary fusion
CN109888773A (en) * 2019-02-25 2019-06-14 武汉大学 A kind of electric system multizone distributions appraisal procedure

Non-Patent Citations (3)

* Cited by examiner, † Cited by third party
Title
MD MASUD RANA 等: "Distributed State Estimation Using RSC Coded Smart Grid Communications", IEEEACCESS, 25 August 2015 (2015-08-25) *
乐健 等: "电力系统多区域分布式状态估计方法", 电力自动化设备, no. 05, 13 May 2020 (2020-05-13) *
胡明南 等: "分布式最小二乘估计中隐匿FDI攻击策略的设计", 控制与决策, vol. 36, no. 8, 6 May 2020 (2020-05-06) *

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN113315118A (en) * 2021-04-26 2021-08-27 中国南方电网有限责任公司 Power system state estimation method based on parallel computing and particle swarm optimization

Also Published As

Publication number Publication date
CN112149273B (en) 2024-06-18

Similar Documents

Publication Publication Date Title
CN103248043B (en) Power system multi-zone distributed state estimation method based on synchronous phase angle measurement device
CN110299762B (en) PMU (phasor measurement Unit) quasi-real-time data-based active power distribution network robust estimation method
WO2016022603A1 (en) Distributed gradient descent for solving optimal power flow in radial networks
Feng et al. A practical multi-phase distribution state estimation solution incorporating smart meter and sensor data
Tai et al. A new distributed state estimation technique for power networks
CN108767844B (en) Self-adaptive state estimation method of networked multi-region power system under data injection attack
CN112234598B (en) Electromagnetic transient simulation initialization method
Wang et al. Augmented state estimation of line parameters in active power distribution systems with phasor measurement units
CN112149273A (en) Fast-convergence alternating current power grid distributed state estimation method
CN115453193A (en) Power distribution network harmonic state estimation method based on cooperation of PQM, TTU and SM measurement data
CN106058898B (en) A kind of power distribution network asymmetrical three-phase DC power flow computational methods
CN109494711B (en) Multi-region parallel full-distributed state estimation method
CN110768285A (en) Method for obtaining strength of alternating current-direct current hybrid direct current multi-feed-in receiving-end power grid
CN110707693A (en) Ensemble Kalman filtering dynamic state estimation method based on AMI full-scale measuring point partition
Cheng et al. A new state estimation using synchronized phasor measurements
CN107910881B (en) ADMM control method based on power grid load emergency management
CN113078670A (en) Method for evaluating resonance stability of receiving-end power grid under effect of hybrid cascade direct-current transmission
CN105977963B (en) Main and distribution network integrated distributed state estimation method based on two-stage method
CN115906353B (en) Power distribution network PMU (Power management unit) optimal configuration method based on node evaluation
Haji et al. Practical considerations in the design of distribution state estimation techniques
CN116257973A (en) Particle swarm optimization-based low-voltage power network line impedance and loss calculation method and system
CN116488234A (en) Power system node inertia calculation method and system considering power grid structure parameters
CN102136105A (en) Phase measurement unit-based power grid information graph parameter estimation method
Ji et al. An equivalent modeling method for multi-port area load based on the extended generalized ZIP load model
Liang et al. Hierarchical Optimal Power Flow with Improved Gradient Evaluation

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication
SE01 Entry into force of request for substantive examination
SE01 Entry into force of request for substantive examination
GR01 Patent grant
GR01 Patent grant