CN112230087A - Linear state estimation method and device, electronic equipment and storage medium - Google Patents

Linear state estimation method and device, electronic equipment and storage medium Download PDF

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CN112230087A
CN112230087A CN202011091496.XA CN202011091496A CN112230087A CN 112230087 A CN112230087 A CN 112230087A CN 202011091496 A CN202011091496 A CN 202011091496A CN 112230087 A CN112230087 A CN 112230087A
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measurement
residual
data
preset
state estimation
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CN112230087B (en
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顾颖中
张琦兵
樊海锋
於喆
史迪
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State Grid Corp of China SGCC
State Grid Jiangsu Electric Power Co Ltd
Global Energy Interconnection Research Institute
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State Grid Corp of China SGCC
State Grid Jiangsu Electric Power Co Ltd
Global Energy Interconnection Research Institute
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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
    • G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/10—Complex mathematical operations
    • G06F17/16—Matrix or vector computation, e.g. matrix-matrix or matrix-vector multiplication, matrix factorization
    • 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
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Abstract

The application provides a linear state estimation method, a linear state estimation device, an electronic device and a storage medium, wherein the method comprises the following steps: determining system state estimation data based on a preset system measurement matrix, a weight matrix and measurement data of each preset measurement node; determining a measurement residual error corresponding to each measurement data according to the system state estimation data; judging whether each measured data is abnormal data or not according to the measured residual error; measuring and compensating the measured residual error according to the measured residual error and a preset residual error sensitivity matrix, and returning measurement data based on a preset system measurement matrix, a preset weight matrix and each preset measurement node to determine system state estimation data; and when the measurement residual error corresponding to each measurement data is smaller than the residual error threshold value, performing state estimation according to each measurement data. By identifying and measuring and compensating abnormal data, the reliability of the measured data is improved, and the accuracy of a state estimation result is further improved.

Description

Linear state estimation method and device, electronic equipment and storage medium
Technical Field
The present disclosure relates to the field of power system management technologies, and in particular, to a method and an apparatus for estimating a linearity state, an electronic device, and a storage medium.
Background
State estimation is the basis and core of a grid energy management system. The state estimation can assist various applications of modern power grid such as operation, control and planning, and the like, including optimal power flow, low-frequency oscillation monitoring, voltage stability monitoring and the like. The precision of the state estimation method has important influence on operations such as electric energy distribution, scheduling and safety analysis of the power grid.
In the prior art, the state of the power system is usually estimated directly according to the collected measurement data of each power grid node, but in practical application, the requirement on the real-time performance of the state estimation technology is high, the number of the power grid nodes is large, and the measurement precision of each measurement device is uneven, so that the accuracy of the state estimation result is low. Therefore, a state estimation method with higher accuracy is urgently needed, and has important significance for improving the management efficiency of the power grid energy management system.
Disclosure of Invention
The application provides a linear state estimation method, a linear state estimation device, an electronic device and a storage medium, which are used for solving the defects that the accuracy of a state estimation result obtained by a state estimation method in the prior art is low and the like.
A first aspect of the present application provides a linearity state estimation method applied to a power system including a phasor measurement unit, the method including:
acquiring measurement data of each preset measurement node in the power system;
determining system state estimation data based on a preset system measurement matrix, a preset weight matrix and measurement data of each preset measurement node;
determining a measurement residual error corresponding to each measurement data according to the system state estimation data;
judging whether the measurement residual corresponding to each measurement data is smaller than a preset residual threshold value or not;
when the measurement residual corresponding to at least one piece of measurement data is not smaller than a preset residual threshold, determining the measurement data with the largest measurement residual in the at least one piece of measurement data as abnormal data;
measuring and compensating the abnormal data according to the measurement residual and a preset residual sensitivity matrix to obtain compensated measurement data, and returning the measurement data based on a preset system measurement matrix, a preset weight matrix and each preset measurement node to determine system state estimation data;
and when the measurement residual corresponding to each measurement data is smaller than a preset residual threshold, performing state estimation on the power system according to each measurement data, and generating a state estimation result.
Optionally, the performing measurement compensation on the abnormal data according to the measurement residual and a preset residual sensitivity matrix to obtain compensated measurement data includes:
constructing an abnormal data set according to the node serial number of the preset measuring node corresponding to the abnormal data, and generating a corresponding mask matrix;
determining a residual error compensation sensitivity matrix according to the mask matrix and the residual error sensitivity matrix;
determining the measurement compensation quantity of the abnormal data according to the residual compensation sensitivity matrix and the measurement residual;
and carrying out measurement compensation on the abnormal data by using the measurement compensation quantity to obtain compensated measurement data.
Optionally, the determining a residual compensation sensitivity matrix according to the mask matrix and the residual sensitivity matrix includes:
determining the residual compensation sensitivity matrix according to the following equation (1):
Figure BDA0002722273330000021
wherein S iscRepresenting said residual compensated sensitivity matrix, McRepresenting the mask matrix and S the residual sensitivity matrix.
Optionally, the determining a measurement compensation amount of the abnormal data according to the residual compensation sensitivity matrix and the measurement residual includes:
calculating a measurement compensation amount of the abnormal data according to the following formula (2):
δzc＝-(Sc)-1rc (2)
wherein, deltazcRepresenting the measured compensation quantity, ScRepresenting said residual compensated sensitivity matrix, rcAnd the measurement residual error corresponding to the abnormal data is represented.
Optionally, the determining system state estimation data based on a preset system measurement matrix, a preset weight matrix, and measurement data of each preset measurement node includes:
calculating the system state estimation data according to the following equation (3):
Figure BDA0002722273330000031
wherein the content of the first and second substances,
Figure BDA0002722273330000032
and representing the system state estimation data, H representing the system measurement matrix, W representing the weight matrix, and z representing the measurement data of each preset measurement node.
Optionally, the determining, according to the system state estimation data, a measurement residual corresponding to each measurement data includes:
calculating the measurement residual according to the following formula (4):
Figure BDA0002722273330000033
wherein r represents the measurement residual.
Optionally, before determining whether the measurement residual corresponding to each measurement data is smaller than a preset residual threshold, the method further includes:
calculating a normalized residual corresponding to the measured residual according to the following formula (5):
Figure BDA0002722273330000034
wherein, the
Figure BDA0002722273330000035
Representing said normalized residual, riRepresenting the measurement residual, ΩiAnd representing the distribution variance of the measurement residual errors, m representing the number of the preset measurement nodes, and i representing the serial numbers of the measurement nodes.
A second aspect of the present application provides a linearity state estimation apparatus applied to a power system including a phasor measurement unit, the apparatus including:
the acquisition module is used for acquiring the measurement data of each preset measurement node in the power system;
the first determining module is used for determining system state estimation data based on a preset system measurement matrix, a preset weight matrix and measurement data of each preset measurement node;
the second determining module is used for determining a measurement residual error corresponding to each measurement data according to the system state estimation data;
the judging module is used for judging whether the measurement residual error corresponding to each measurement data is smaller than a preset residual error threshold value;
a third determining module, configured to determine, when a measurement residual corresponding to at least one piece of measurement data is not smaller than a preset residual threshold, that the measurement data with the largest measurement residual among the at least one piece of measurement data is abnormal data;
a measurement compensation module for performing measurement compensation on the abnormal data according to the measurement residual and a preset residual sensitivity matrix to obtain compensated measurement data, and returning the measurement data based on the preset system measurement matrix, the preset weight matrix and each preset measurement node to determine system state estimation data;
and the state estimation module is used for performing state estimation on the power system according to the measurement data and generating a state estimation result when the measurement residual corresponding to the measurement data is smaller than a preset residual threshold value.
Optionally, the measurement compensation module is specifically configured to:
constructing an abnormal data set according to the node serial number of the preset measuring node corresponding to the abnormal data, and generating a corresponding mask matrix;
determining a residual error compensation sensitivity matrix according to the mask matrix and the residual error sensitivity matrix;
determining the measurement compensation quantity of the abnormal data according to the residual compensation sensitivity matrix and the measurement residual;
and carrying out measurement compensation on the abnormal data by using the measurement compensation quantity to obtain compensated measurement data.
Optionally, the measurement compensation module is specifically configured to:
determining the residual compensation sensitivity matrix according to the following equation (1):
Figure BDA0002722273330000041
wherein S iscRepresenting said residual compensated sensitivity matrix, McRepresenting the mask matrix and S the residual sensitivity matrix.
Optionally, the measurement compensation module is specifically configured to:
calculating a measurement compensation amount of the abnormal data according to the following formula (2):
δzc＝-(Sc)-1rc (2)
wherein, deltazcRepresenting the measured compensation quantity, ScRepresenting said residual compensated sensitivity matrix, rcAnd the measurement residual error corresponding to the abnormal data is represented.
Optionally, the first determining module is specifically configured to:
calculating the system state estimation data according to the following equation (3):
Figure BDA0002722273330000042
wherein the content of the first and second substances,
Figure BDA0002722273330000043
and representing the system state estimation data, H representing the system measurement matrix, W representing the weight matrix, and z representing the measurement data of each preset measurement node.
Optionally, the second determining module is specifically configured to:
calculating the measurement residual according to the following formula (4):
Figure BDA0002722273330000044
wherein r represents the measurement residual.
Optionally, the determining module is further configured to:
calculating a normalized residual corresponding to the measured residual according to the following formula (5):
Figure BDA0002722273330000051
wherein, the
Figure BDA0002722273330000052
Representing said normalized residual, riRepresenting the measurement residual, ΩiAnd representing the distribution variance of the measurement residual errors, m representing the number of the preset measurement nodes, and i representing the serial numbers of the measurement nodes.
A third aspect of the present application provides an electronic device, comprising: at least one processor and memory;
the memory stores computer-executable instructions;
the at least one processor executes computer-executable instructions stored by the memory to cause the at least one processor to perform the method as set forth in the first aspect above and in various possible designs of the first aspect.
A fourth aspect of the present application provides a computer-readable storage medium having stored thereon computer-executable instructions that, when executed by a processor, implement a method as set forth in the first aspect and various possible designs of the first aspect.
This application technical scheme has following advantage:
according to the linear state estimation method, the linear state estimation device, the electronic equipment and the storage medium, measurement data of each preset measurement node in the power system are obtained; determining system state estimation data based on a preset system measurement matrix, a preset weight matrix and measurement data of each preset measurement node; determining a measurement residual error corresponding to each measurement data according to the system state estimation data; judging whether the measurement residual corresponding to each measurement data is smaller than a preset residual threshold value or not; when the measurement residual corresponding to at least one measurement data is not smaller than a preset residual threshold, determining the measurement data with the maximum measurement residual in the at least one measurement data as abnormal data; measuring and compensating the abnormal data according to the measurement residual and a preset residual sensitivity matrix to obtain compensated measurement data, and returning the measurement data based on a preset system measurement matrix, a preset weight matrix and each preset measurement node to determine system state estimation data; and when the measurement residual corresponding to each measurement data is smaller than a preset residual threshold, performing state estimation on the power system according to each measurement data, and generating a state estimation result. According to the linear state estimation method provided by the scheme, the reliability of the measured data is improved by identifying and measuring the abnormal data, and the accuracy of the state estimation result is further improved. The method has high measurement compensation efficiency, ensures the convergence of measured data, can efficiently process abnormal data, and lays a foundation for improving the management efficiency of the power grid capacity management system.
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In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings needed to be used in the description of the embodiments or the prior art will be briefly described below, and it is obvious that the drawings in the following description are some embodiments of the present application, and other drawings can be obtained by those skilled in the art according to these drawings.
Fig. 1 is a schematic structural diagram of an electric power system on which an embodiment of the present application is based;
fig. 2 is a schematic flowchart of a linear state estimation method according to an embodiment of the present application;
fig. 3 is a schematic structural diagram of a two-port equivalent circuit according to an embodiment of the present application;
fig. 4 is a schematic overall flowchart of a linear state estimation method according to an embodiment of the present application;
fig. 5 is a schematic structural diagram of a linear state estimation device according to an embodiment of the present application;
fig. 6 is a schematic structural diagram of an electronic device according to an embodiment of the present application.
With the above figures, there are shown specific embodiments of the present application, which will be described in more detail below. These drawings and written description are not intended to limit the scope of the disclosed concepts in any way, but rather to illustrate the concepts of the disclosure to those skilled in the art by reference to specific embodiments.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are some embodiments of the present application, but not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
Furthermore, the terms "first", "second", etc. are used for descriptive purposes only and are not to be construed as indicating or implying relative importance or implicitly indicating the number of technical features indicated. In the description of the following examples, "plurality" means two or more unless specifically limited otherwise.
In the prior art, the state of the power system is usually estimated directly according to the collected measurement data of each power grid node, but in practical application, the requirement on the real-time performance of the state estimation technology is high, the number of the power grid nodes is large, and the measurement precision of each measurement device is uneven, so that the accuracy of the state estimation result is low.
In order to solve the above problems, in the linear state estimation method, the linear state estimation device, the electronic device, and the storage medium provided in the embodiments of the present application, measurement data of each preset measurement node in the power system is obtained; determining system state estimation data based on a preset system measurement matrix, a preset weight matrix and measurement data of each preset measurement node; determining a measurement residual error corresponding to each measurement data according to the system state estimation data; judging whether the measurement residual corresponding to each measurement data is smaller than a preset residual threshold value or not; when the measurement residual corresponding to at least one measurement data is not smaller than a preset residual threshold, determining the measurement data with the maximum measurement residual in the at least one measurement data as abnormal data; measuring and compensating the abnormal data according to the measurement residual and a preset residual sensitivity matrix to obtain compensated measurement data, and returning the measurement data based on a preset system measurement matrix, a preset weight matrix and each preset measurement node to determine system state estimation data; and when the measurement residual corresponding to each measurement data is smaller than a preset residual threshold, performing state estimation on the power system according to each measurement data, and generating a state estimation result. According to the linear state estimation method provided by the scheme, the reliability of the measured data is improved by identifying and measuring the abnormal data, and the accuracy of the state estimation result is further improved. The method has high measurement compensation efficiency, ensures the convergence of measured data, can efficiently process abnormal data, and lays a foundation for improving the management efficiency of the power grid capacity management system.
The following several specific embodiments may be combined with each other, and details of the same or similar concepts or processes may not be repeated in some embodiments. Embodiments of the present invention will be described below with reference to the accompanying drawings.
First, a configuration of a power system based on the present application will be described:
the linear state estimation method and device, the electronic device and the storage medium provided by the embodiment of the application are suitable for estimating the state of the power system. As shown in fig. 1, the schematic structural diagram of an electric power system based on the embodiment of the present application mainly includes a phasor measurement unit and a linear state estimation device for performing state estimation. Specifically, the phasor measurement unit is configured to collect measurement data of each preset measurement node in the power system, and send the collected measurement data to the linear state estimation device, and the linear state estimation device is configured to process and analyze the obtained measurement data, perform state estimation on the power system according to the processed measurement data, and generate a state estimation result.
The embodiment of the application provides a linear state estimation method, which is applied to a power system, wherein the power system comprises a phasor measurement unit, and the method is used for estimating the state of the power system. The execution subject of the embodiment of the present application is an electronic device, such as a server, a desktop computer, a notebook computer, a tablet computer, and other electronic devices that can be used for state estimation.
As shown in fig. 2, a schematic flow chart of a linear state estimation method provided in the embodiment of the present application is shown, where the method includes:
step 201, measurement data of each preset measurement node in the power system is acquired.
In the embodiment of the application, a Phasor Measurement Unit (PMU) is adopted to collect Measurement data, the collected Measurement data includes node voltage Phasor (three-phase voltage amplitude and voltage phase angle) and branch current Phasor (three-phase current amplitude and current phase angle), and a certain linear relationship exists between the node voltage Phasor and the branch current Phasor, so that the state estimation efficiency is improved, and the convergence rate of the Measurement data is improved.
Specifically, a phasor measurement unit is used for collecting measurement data of each preset measurement node in the power system and sending the collected measurement data to the electronic equipment for executing the method.
Step 202, determining system state estimation data based on a preset system measurement matrix, a preset weight matrix and measurement data of each preset measurement node.
It should be explained that the system measurement matrix and the weight matrix are set according to actual conditions.
For example, as shown in fig. 3, a schematic structural diagram of a two-port equivalent circuit provided in the embodiment of the present application is shown, where the measurement data includes a two-terminal node voltage phasor ViAnd VjAnd a two-terminal current phasor IijAnd IjiThe system state data comprises estimated voltage phasors of two-terminal nodes
Figure BDA0002722273330000081
And
Figure BDA0002722273330000082
then the system state equation can be expressed as equation (6):
Figure BDA0002722273330000083
wherein, Yij＝(rij+jxij)-1Admittance of the circuit, biAnd bjIs a two-terminal ground susceptance, a two-terminal ground electric conductor giOr gjAre generally ignored because their values are relatively small.
Figure BDA0002722273330000084
Is a composite transformation ratio of transformer-like elements, which comprises an amplitude transformation ratio gamma and a phase angle phase shift quantity thetas. Let z be [ V ]i,Vj,Iij,Iji]TAs measurement vectors (measurement data of each predetermined measurement node),
Figure BDA0002722273330000085
the 4 × 2 matrix is a predetermined system measurement matrix H, where e is [ e ═ e { [ for system state vector } is a predetermined system measurement matrix H1… e4]TAn error vector is represented. Since the system state vector x is unknown data, the error vector e is also unknown.
The above system state equation can be simplified to equation (7):
z＝Hx+e (7)
wherein, if the present power system includes n node voltage phasor measurement data, m-n branch current phasor measurement data (including positive and negative bi-directional), and m measurement data in total, the above equation (6) can be expanded to equation (8):
Figure BDA0002722273330000091
specifically, in one embodiment, the system state estimation data may be calculated according to equation (3) as follows:
Figure BDA0002722273330000092
wherein the content of the first and second substances,
Figure BDA0002722273330000093
representing system state estimation data, H representing a system measurement matrix, W representing a weight matrixAnd z represents the measurement data of each preset measurement node.
Specifically, the essence of the linear state estimation solution is to solve the following optimization problem (9):
min J(x)＝(z-Hx)TW(z-Hx) (9)
wherein min J (x) represents an objective function, specifically, a sum of minimized full-system weighted errors, and the decision variable is a system state vector x, since it can be determined that the problem is a quadratic programming problem, a global optimal solution can be determined using a KKT condition, that is, equation (3) is obtained, thereby determining system state estimation data
Figure BDA0002722273330000094
Step 203, determining a measurement residual corresponding to each measurement data according to the system state estimation data.
Specifically, in one embodiment, the measurement residual may be calculated according to the following formula (4):
Figure BDA0002722273330000095
wherein r represents the measurement residual.
Specifically, the gain matrix G and the hat matrix K may be defined according to the system measurement matrix and the weight matrix:
G＝HTWH (10)
K＝HG-1HTW (11)
substituting equation (10) into equation (3) can result in the corresponding equation (12):
Figure BDA0002722273330000096
wherein the measured estimated data
Figure BDA0002722273330000097
The calculation can be made according to equation (13):
Figure BDA0002722273330000098
substituting equation (13) into equation (4) can result in equation (14):
r＝z-Kz＝(I-K)z＝(I-K)(Hx+e) (14)
wherein, according to the characteristics of the hat matrix: (I-K) · H ═ 0, equation (14) can be converted to equation (15):
r＝(I-K)e＝Se (15)
wherein S is a residual sensitivity matrix, and if the system measurement matrix H represents the mapping relation between the measurement data z and the system state vector x, the hat matrix K represents the measurement data z and the measurement estimation data
Figure BDA0002722273330000101
The residual sensitivity matrix S represents the mapping between the measurement residual r and the measurement true error e, and the physical significance of the residual sensitivity matrix S indicates how different measurement errors affect the measurement residual of a certain measurement estimation data.
Step 204, determining whether the measurement residual corresponding to each measurement data is smaller than a preset residual threshold.
It should be explained that the residual threshold may be set according to actual conditions, and in the embodiment of the present application, in order to ensure objectivity and accuracy of the residual threshold, a rule 3 may be applied, and three times of standard deviation may be set as the residual threshold.
Step 205, when the measurement residual corresponding to at least one measurement data is not less than the preset residual threshold, determining the measurement data with the largest measurement residual among the at least one measurement data as abnormal data.
The abnormal data is also called bad data, and specifically refers to bad data in the measured data.
And step 206, performing measurement compensation on the abnormal data according to the measurement residual and a preset residual sensitivity matrix to obtain compensated measurement data, and returning measurement data based on a preset system measurement matrix, a preset weight matrix and each preset measurement node to determine system state estimation data (step 202).
It should be explained that the residual sensitivity matrix may be set according to the system measurement matrix, and specifically, may be obtained by performing correlation calculation on the hat matrix and the gain matrix.
Specifically, in an embodiment, an abnormal data set may be constructed according to a node serial number of a preset measurement node corresponding to the abnormal data, and a corresponding mask matrix may be generated; determining a residual compensation sensitivity matrix according to the mask matrix and the residual sensitivity matrix; determining the measurement compensation quantity of the abnormal data according to the residual compensation sensitivity matrix and the measurement residual; and measuring and compensating the abnormal data by using the measurement compensation amount to obtain compensated measurement data.
Specifically, when m measurement data are acquired and the residual threshold is three times the standard deviation, the abnormal data set C can be represented by equation (16):
C＝{C′∪i||ri|≥3.0,i∈[1,m]} (16)
wherein, C 'represents the abnormal data set determined before the current time, and if the abnormal data set is an empty set before that, C' is an empty set.
Exemplarily, when it is determined that the node numbers corresponding to the measurement data with the measurement residual greater than the residual threshold are 1, 2, 3, 4, 5, and 6, respectively, where the measurement residual corresponding to the number 1 is the largest, then the current abnormal data set C is {1 }; after the measurement compensation is performed on the measurement data corresponding to the sequence number 1, if the node sequence numbers corresponding to the measurement data whose current measurement residual is greater than the residual threshold are 3, 4, 5, and 6, respectively, where the measurement residual corresponding to the sequence number 3 is the largest, the current abnormal data set C is {1,3}, at this time, the measurement compensation is performed on the measurement data corresponding to the sequence numbers 1 and 3, and so on, until the measurement residual corresponding to each measurement data is less than the residual threshold.
Further, according to the obtained abnormal data set C, a mask matrix M is generatedcThe size of the matrix is m × m, so that the ith element in each row is 1, and the rest are 0. For example, when C ═ {1,3,5,6}, the corresponding mask matrix is:
Figure BDA0002722273330000111
wherein, when the existing measurement data z is superimposed with any measurement compensation quantity deltazAnd obtaining compensated measurement data z':
z′＝z+δz＝Hx+e+δzc (18)
when the measured data is changed, the corresponding measured residual error is also changed; let the changed measurement residual be r', which can be determined according to equation (14):
Figure BDA0002722273330000112
wherein the mask matrix M is multiplied on both sides of equation (18)cEquation (20) can be derived:
Mcz+Mcδz＝McHx+Mc(e+δz) (20)
further, in the process of performing measurement compensation on the abnormal data, it can be determined that:
zc+δzc＝Hcx+ec+δzc (21)
wherein z isc、δzc、HcAnd ecAnd respectively representing abnormal data, measurement compensation quantity, system measurement matrix and measurement error vector selected by the mask matrix.
By substituting equation (21) into equation (19), it can be determined that:
rc′＝Sc(ec+δzc) (22)
wherein r isc' represents the measurement residual corresponding to the compensated measurement data,
Figure BDA0002722273330000113
representing the residual sensitivity matrix multiplied by a mask matrix, having a matrix size mc*mc。
Left-multiplying the mask matrix in equation (15) yields equation (23):
rc＝Scec (23)
wherein if r is to be reducedc' Compensation to 0, equation (24) can be obtained:
Figure BDA0002722273330000121
further, the compensated measurement data z 'are measured'cCan be determined according to the following equation (25):
Figure BDA0002722273330000122
specifically, in the case of the abnormal data zcAfter the measurement compensation is carried out, compensated measurement data z 'can be obtained'c。
Specifically, in one embodiment, in order to improve the measurement compensation efficiency, and thus further improve the state estimation efficiency, the residual compensation sensitivity matrix may be determined according to the following formula (1):
Figure BDA0002722273330000123
wherein S iscRepresenting a residual compensated sensitivity matrix, McDenotes a mask matrix and S denotes a residual sensitivity matrix.
Further, the measurement compensation amount of the abnormal data may be calculated according to the following formula (2):
δzc＝-(Sc)-1rc (2)
wherein, deltazcIndicating the measured compensation quantity, ScRepresenting a residual compensated sensitivity matrix, rcIndicating the measurement residual corresponding to the abnormal data.
Step 207, when the measurement residual corresponding to each measurement data is smaller than a preset residual threshold, performing state estimation on the power system according to each measurement data, and generating a state estimation result.
Specifically, a weighted least square method may be adopted to perform state estimation on the power system, and compensated measurement data z 'may be obtained after measurement compensation is performed on the measurement'cIn the process of performing state estimation, the following state characteristics can be obtained:
Figure BDA0002722273330000124
wherein, KcRepresentation system measurement matrix HcA corresponding hat matrix.
It should be explained that the residual compensation sensitivity matrix does not compensate the measurement error e to zero, since e is not known per se. Which in effect compensates the residual r to zero. Compensating the residual error to zero does not eliminate the error, but can eliminate the influence of the measured data containing abnormal data on the system state estimation, and does not change the dimensionality and the size of all calculation matrixes, so that millisecond linear state estimation can be realized, and the accuracy of the state estimation result is higher.
On the basis of the above embodiment, since the weighted least square method is based on the assumption that the measurement error e follows a normal distribution, the distribution variance corresponding to the measurement residual can be obtained:
Ω＝E(rrT)＝S·E(eeT)·ST＝SW-1 (27)
in order to further improve the calculation efficiency and accuracy of the subsequent measurement compensation quantity, before determining whether the measurement residual corresponding to each measurement data is smaller than a preset residual threshold, the method further includes: calculating a normalized residual corresponding to the measured residual according to the following formula (5):
Figure BDA0002722273330000131
wherein the content of the first and second substances,
Figure BDA0002722273330000132
denotes the normalized residual, riDenotes the measurement residual, ΩiThe distribution variance of the measurement residual is represented, m represents the number of preset measurement nodes, and i represents the serial number of the measurement nodes.
Furthermore, the abnormal data can be determined according to the magnitude relation between the standardized residual error corresponding to each measured data and a preset residual error threshold value.
It should be explained that, in the prior art, usually, in an iterative manner, the measured data with the maximum normalized residual error of each iteration and larger than a preset residual error threshold is taken as the abnormal data, and the abnormal data is removed, the method is called as the maximum normalized residual error test, and the three preconditions for effective application of the maximum normalized residual error test are as follows: 1) the abnormal data is not key measurement data, 2) removing the abnormal data does not cause the rest measurement data to become key measurement data, and 3) the abnormal data does not influence other abnormal data, so that the abnormal data in the measurement data can be effectively identified. However, even if the above conditions are met, the high iteration time cost is still a huge disadvantage for state estimation. The maximum normalized residual test has a relatively high iteration time cost, first because only one outlier can be identified and culled per iteration. If the 3 sigma rule is simply applied to a plurality of measured data in the same iteration to remove, there will be a large probability of causing false recognition (taking normal measured data as abnormal data) and missing recognition (taking abnormal data as normal measured data), because the removal of one abnormal data will affect the measurement residual error of other measured data. Secondly, each iteration removes one measurement data, which causes the dimension of the system measurement matrix and the measurement vector z (measurement data) to change, so all the matrices (such as S, G and K) and phasors (such as r and Ω) related to the measurement matrix and the measurement vector z need to be recalculated, and the calculation efficiency is low, thereby affecting the state estimation efficiency.
For example, in an embodiment, an iterative algorithm based on a sensitivity matrix may be used to perform abnormal data processing, and only a part of measured data needs to be compensated without reconstructing a measurement vector and a measurement matrix, so that the speed of each iterative calculation is increased. The specific iteration method is as follows, and the parenthesis superscript (t) in the following process indicates the t-th iteration:
1) obtaining a current measurement vector z(t):
2) Calculating current system state estimation data
Figure BDA0002722273330000141
Figure BDA0002722273330000142
3) Calculating current system metrology estimation data
Figure BDA0002722273330000143
Figure BDA0002722273330000144
4) Calculating the current system measurement residual r(t)：
Figure BDA0002722273330000145
5) Calculating a normalized residual
Figure BDA0002722273330000146
Figure BDA0002722273330000147
6) And if the maximum normalized residual value is less than 3.0 (three times of standard deviation), judging that the calculation of the current round is converged and ending the iteration. Otherwise, adding the measurement serial number i with the maximum standardized residual error into the original abnormal data set C(t-1)To obtain a current abnormal data set C(t)：
Figure BDA0002722273330000148
7) Generating a mask matrix
Figure BDA0002722273330000149
8) Computing residual compensation sensitivity matrix by mask matrix
Figure BDA00027222733300001410
Figure BDA00027222733300001411
9) Calculating the measurement compensation amount of the iteration
Figure BDA00027222733300001412
Figure BDA00027222733300001413
10) Updating the iteration measurement data by using the newly calculated measurement compensation quantity:
Figure BDA00027222733300001414
returning to the step 1), and carrying out the next iteration.
According to the embodiment of the application, the traditional iterative reconstruction measurement matrix is replaced by the residual sensitivity matrix compensation, so that the calculation efficiency is effectively improved, and the linear state estimation and abnormal data processing millisecond-level response speed is achieved in the application of a large-scale power grid. And moreover, iterative solution of a nonlinear power flow equation is effectively avoided, so that calculation convergence can be ensured, all calculations are linear matrix operations, and the calculations can be converged no matter what working condition the power system is under, so that the real-time availability of state estimation results under the condition of a power grid event or fault is ensured.
In the practical application process, since the measurement residual after each iteration or compensation is usually very small, and the value is close to 0(<1e-8), but since the core idea of the embodiment of the present application is to ensure that all system matrices are unchanged, the residual of the measurement value gradually deviates from 0 with the compensation of other measurement values in the last two rounds of calculation, the formula (32) must be executed, and the previously compensated measurement data must be added to the compensation calculation of (34).
On the basis of the above embodiments, the specific calculation processes provided by the embodiments of the present application are all based on phasor. If a large complex matrix is directly calculated, the calculation speed is very slow, and the requirement of online real-time state estimation on the power system cannot be met.
Therefore, in order to solve the above problem, embodiments of the present application provide a method for normalizing a phasor, which specifically adopts a rectangular coordinate expansion method to decompose the phasor into a real part and an imaginary part.
Specifically, let z ═ zR,zI]T，x＝[xR,xI]T，H＝HR+jHIRepresenting the real and imaginary parts of the metrology vector and the system state vector, respectively. Then (7) can be written as (36). Wherein HAAIs the corresponding augmented system measurement matrix, which is a real matrix.
Figure BDA0002722273330000151
Further, formula (12) can be written as formula (37) in rectangular coordinates, where GAAIs an augmented gain matrix. Accordingly, (10) can be written as equation (38), where KAAIs an augmented matrix of hat matrices:
Figure BDA0002722273330000152
Figure BDA0002722273330000153
finally, the metrology vector and the system state vector may be calculated according to equations (39) and (40):
Figure BDA0002722273330000154
Figure BDA0002722273330000155
fig. 4 is a schematic overall flow chart of the linear state estimation method according to the embodiment of the present application. First, various initializations are performed, including reading the yaml parameter file or obtaining the control parameters from the database. And then starting the three data acquisition modules in parallel through the multithreading technology. The first module is a QS file reading module, and various system network parameters (a transformer substation, a generator, a transformer, a node, a breaker, a disconnector, a power transmission line, a series compensation device, a parallel compensation device and the like) and a latest state estimation result can be transmitted through a QS file (a text format power grid data file) in a power grid energy management system (such as a D5000 system). The system will automatically match the QS file closest to the current target calculation time and read its associated data into the grid energy management system. The power grid energy management system uses a Node-breaker Model (Node-breaker Model), so that a power system Node analysis with a depth-first algorithm as a core is required to be performed, and a Bus-Branch Model (Bus-Branch Model) of the current system, a power grid electric island Model, equivalent Model parameters of all branches, a system incidence matrix and a system Node admittance matrix are calculated.
Correspondingly, the second module is mainly used for reading the system WFES file. The WFES file defines all PMU plant information and channel information. By which each PMU data record may be parsed and each PMU device mapped to the system topology in the QS file. And the third module is connected with a PMU database to obtain the amplitude and the phase angle of the three-phase voltage and the current measured by each PMU device, and the three modules are performed in parallel. When PMU data is read, the first round of bad data preprocessing and data self-checking can be carried out on the data by combining basic information of the WFES. And after calculating a system bus branch model through QS files, performing a second round of abnormal data preprocessing, namely cross-node and cross-station data mutual check. The power system is then observably analyzed. And performing third-round abnormal data preprocessing-network model mutual checking according to the observability analysis result of the power system and simultaneously generating corresponding virtual measurement (also called pseudo measurement) to solve the problem of insufficient observability of the local power system.
Further, the computing system measures a matrix, a gain matrix, a cap matrix, and a residual sensitivity matrix. These matrices will be stored in memory in the form of sparse matrices in subsequent calculations and will not be recalculated until there is a new QS or WFES file to be reparsed. After the matrixes are generated, state estimation calculation is started, the maximum normalized residual is distinguished in the calculation process, residual sensitivity matrix compensation correction is carried out if the maximum normalized residual is larger than or equal to a threshold, data summarization calculation and state updating are carried out if the maximum normalized residual is smaller than the threshold, the result is output to other high-level applications and is stored in a database to provide data for a front-end user interaction interface. And when all data is finished, returning to the highest PMU data reading module, and circularly waiting for the next batch of data.
According to the linear state estimation method provided by the embodiment of the application, measurement data of each preset measurement node in an electric power system are obtained; determining system state estimation data based on a preset system measurement matrix, a preset weight matrix and measurement data of each preset measurement node; determining a measurement residual error corresponding to each measurement data according to the system state estimation data; judging whether the measurement residual corresponding to each measurement data is smaller than a preset residual threshold value or not; when the measurement residual corresponding to at least one measurement data is not smaller than a preset residual threshold, determining the measurement data with the maximum measurement residual in the at least one measurement data as abnormal data; measuring and compensating the abnormal data according to the measurement residual and a preset residual sensitivity matrix to obtain compensated measurement data, and returning the measurement data based on a preset system measurement matrix, a preset weight matrix and each preset measurement node to determine system state estimation data; and when the measurement residual corresponding to each measurement data is smaller than a preset residual threshold, performing state estimation on the power system according to each measurement data, and generating a state estimation result. According to the linear state estimation method provided by the scheme, the reliability of the measured data is improved by identifying and measuring the abnormal data, and the accuracy of the state estimation result is further improved. The method has high measurement compensation efficiency, ensures the convergence of measured data, can efficiently process abnormal data, and lays a foundation for improving the management efficiency of the power grid capacity management system.
The embodiment of the present application provides a linear state estimation apparatus, which is used for executing the linear state estimation method provided by the foregoing embodiment.
Fig. 5 is a schematic structural diagram of a linear state estimation device according to an embodiment of the present application. The linear state estimation device 50 includes an obtaining module 501, a first determining module 502, a second determining module 503, a judging module 504, a third determining module 505, a measurement compensating module 506, and a state estimating module 507.
The acquiring module 501 is configured to acquire measurement data of each preset measurement node in the power system; a first determining module 502, configured to determine system state estimation data based on a preset system measurement matrix, a preset weight matrix, and measurement data of each preset measurement node; a second determining module 503, configured to determine, according to the system state estimation data, a measurement residual corresponding to each measurement data; a determining module 504, configured to determine whether a measurement residual corresponding to each measurement data is smaller than a preset residual threshold; a third determining module 505, configured to determine, when a measurement residual corresponding to at least one piece of measurement data is not smaller than a preset residual threshold, the measurement data with the largest measurement residual among the at least one piece of measurement data is abnormal data; a measurement compensation module 506, configured to perform measurement compensation on the abnormal data according to the measurement residual and a preset residual sensitivity matrix to obtain compensated measurement data, and return measurement data based on a preset system measurement matrix, a preset weight matrix, and each preset measurement node to determine system state estimation data; the state estimation module 507 is configured to perform state estimation on the power system according to each measurement data when the measurement residual corresponding to each measurement data is smaller than a preset residual threshold, and generate a state estimation result.
Specifically, in one embodiment, the metrology compensation module 506 is specifically configured to:
constructing an abnormal data set according to the node serial number of the preset measuring node corresponding to the abnormal data, and generating a corresponding mask matrix;
determining a residual compensation sensitivity matrix according to the mask matrix and the residual sensitivity matrix;
determining the measurement compensation quantity of the abnormal data according to the residual compensation sensitivity matrix and the measurement residual;
and measuring and compensating the abnormal data by using the measurement compensation amount to obtain compensated measurement data.
Specifically, in one embodiment, the metrology compensation module 506 is specifically configured to:
determining a residual compensation sensitivity matrix according to the following equation (1):
Figure BDA0002722273330000171
wherein S iscRepresenting a residual compensated sensitivity matrix, McDenotes a mask matrix and S denotes a residual sensitivity matrix.
Specifically, in one embodiment, the metrology compensation module 506 is specifically configured to:
calculating a measurement compensation amount of the abnormal data according to the following formula (2):
δzc＝-(Sc)-1rc (2)
wherein, deltazcIndicating the measured compensation quantity, ScRepresenting a residual compensated sensitivity matrix, rcIndicating the measurement residual corresponding to the abnormal data.
Specifically, in an embodiment, the first determining module 502 is specifically configured to:
calculating system state estimation data according to the following equation (3):
Figure BDA0002722273330000181
wherein the content of the first and second substances,
Figure BDA0002722273330000182
and the system state estimation data is represented, H represents a system measurement matrix, W represents a weight matrix, and z represents the measurement data of each preset measurement node.
Specifically, in an embodiment, the second determining module 503 is specifically configured to:
calculating the measurement residual according to the following formula (4):
Figure BDA0002722273330000183
wherein r represents the measurement residual.
Specifically, in an embodiment, the determining module 504 is further configured to:
calculating a normalized residual corresponding to the measured residual according to the following formula (5):
Figure BDA0002722273330000184
wherein the content of the first and second substances,
Figure BDA0002722273330000185
denotes the normalized residual, riDenotes the measurement residual, ΩiThe distribution variance of the measurement residual is represented, m represents the number of preset measurement nodes, and i represents the serial number of the measurement nodes.
With regard to the linear state estimating apparatus in the present embodiment, the specific manner in which each module performs the operation has been described in detail in the embodiment related to the method, and will not be elaborated here.
The linear state estimation device provided in the embodiment of the present application is configured to execute the linear state estimation method provided in the foregoing embodiment, and an implementation manner and a principle thereof are the same and are not described again.
The embodiment of the present application provides an electronic device, configured to execute the linear state estimation method provided in the foregoing embodiment.
Fig. 6 is a schematic structural diagram of an electronic device according to an embodiment of the present application. The electronic device 60 includes: at least one processor 61 and memory 62;
the memory stores computer-executable instructions; the at least one processor executes computer-executable instructions stored by the memory to cause the at least one processor to perform a method as provided by any of the embodiments above.
The electronic device provided in the embodiment of the present application is configured to execute the linear state estimation method provided in the above embodiment, and an implementation manner and a principle of the electronic device are the same and are not described again.
The embodiment of the present application provides a computer-readable storage medium, in which computer-executable instructions are stored, and when a processor executes the computer-executable instructions, the linear state estimation method provided in any one of the above embodiments is implemented.
The storage medium containing the computer-executable instructions of the embodiment of the present application may be used to store the computer-executable instructions of the linear state estimation method provided in the foregoing embodiment, and the implementation manner and the principle thereof are the same and are not described again.
Finally, it should be noted that: the above embodiments are only used for illustrating the technical solutions of the present application, and not for limiting the same; although the present application has been described in detail with reference to the foregoing embodiments, it should be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some or all of the technical features may be equivalently replaced; and the modifications or the substitutions do not make the essence of the corresponding technical solutions depart from the scope of the technical solutions of the embodiments of the present application.

Claims (10)

1. A linearity state estimation method applied to a power system including a phasor measurement unit, the method comprising:
acquiring measurement data of each preset measurement node in the power system;
determining system state estimation data based on a preset system measurement matrix, a preset weight matrix and measurement data of each preset measurement node;
determining a measurement residual error corresponding to each measurement data according to the system state estimation data;
judging whether the measurement residual corresponding to each measurement data is smaller than a preset residual threshold value or not;
when the measurement residual corresponding to at least one piece of measurement data is not smaller than a preset residual threshold, determining the measurement data with the largest measurement residual in the at least one piece of measurement data as abnormal data;
measuring and compensating the abnormal data according to the measurement residual and a preset residual sensitivity matrix to obtain compensated measurement data, and returning the measurement data based on a preset system measurement matrix, a preset weight matrix and each preset measurement node to determine system state estimation data;
and when the measurement residual corresponding to each measurement data is smaller than a preset residual threshold, performing state estimation on the power system according to each measurement data, and generating a state estimation result.
2. The linear state estimation method of claim 1, wherein the performing measurement compensation on the abnormal data according to the measurement residual and a preset residual sensitivity matrix to obtain compensated measurement data comprises:
constructing an abnormal data set according to the node serial number of the preset measuring node corresponding to the abnormal data, and generating a corresponding mask matrix;
determining a residual error compensation sensitivity matrix according to the mask matrix and the residual error sensitivity matrix;
determining the measurement compensation quantity of the abnormal data according to the residual compensation sensitivity matrix and the measurement residual;
and carrying out measurement compensation on the abnormal data by using the measurement compensation quantity to obtain compensated measurement data.
3. The linear state estimation method of claim 2, wherein determining a residual compensated sensitivity matrix from the mask matrix and the residual sensitivity matrix comprises:
determining the residual compensation sensitivity matrix according to the following equation (1):
Figure FDA0002722273320000011
wherein S iscRepresenting said residual compensated sensitivity matrix, McRepresenting the mask matrix and S the residual sensitivity matrix.
4. The linear state estimation method of claim 3, wherein the determining a measurement compensation amount of the abnormal data according to the residual compensation sensitivity matrix and a measurement residual comprises:
calculating a measurement compensation amount of the abnormal data according to the following formula (2):
δzc＝-(Sc)-1rc (2)
wherein, deltazcRepresenting the measured compensation quantity, ScRepresenting said residual compensated sensitivity matrix, rcAnd the measurement residual error corresponding to the abnormal data is represented.
5. The linear state estimation method of claim 1, wherein the determining system state estimation data based on the predetermined system measurement matrix, the predetermined weight matrix and the measurement data of each predetermined measurement node comprises:
calculating the system state estimation data according to the following equation (3):
Figure FDA0002722273320000021
wherein the content of the first and second substances,
Figure FDA0002722273320000022
and representing the system state estimation data, H representing the system measurement matrix, W representing the weight matrix, and z representing the measurement data of each preset measurement node.
6. The linear state estimation method of claim 5, wherein determining a measurement residual corresponding to each measurement data according to the system state estimation data comprises:
calculating the measurement residual according to the following formula (4):
Figure FDA0002722273320000023
wherein r represents the measurement residual.
7. The linear state estimation method of claim 1, wherein before determining whether the measured residual corresponding to each measured data is smaller than a preset residual threshold, the method further comprises:
calculating a normalized residual corresponding to the measured residual according to the following formula (5):
Figure FDA0002722273320000024
wherein, the
Figure FDA0002722273320000025
Representing said normalized residual, riRepresenting the measurement residual, ΩiRepresenting the measurementsAnd the distribution variance of the residual errors, m represents the number of the preset measuring nodes, and i represents the serial number of the measuring nodes.
8. A linearity state estimation apparatus applied to a power system including a phasor measurement unit, the apparatus comprising:
the acquisition module is used for acquiring the measurement data of each preset measurement node in the power system;
the first determining module is used for determining system state estimation data based on a preset system measurement matrix, a preset weight matrix and measurement data of each preset measurement node;
the second determining module is used for determining a measurement residual error corresponding to each measurement data according to the system state estimation data;
the judging module is used for judging whether the measurement residual error corresponding to each measurement data is smaller than a preset residual error threshold value;
a third determining module, configured to determine, when a measurement residual corresponding to at least one piece of measurement data is not smaller than a preset residual threshold, that the measurement data with the largest measurement residual among the at least one piece of measurement data is abnormal data;
a measurement compensation module for performing measurement compensation on the abnormal data according to the measurement residual and a preset residual sensitivity matrix to obtain compensated measurement data, and returning the measurement data based on the preset system measurement matrix, the preset weight matrix and each preset measurement node to determine system state estimation data;
and the state estimation module is used for performing state estimation on the power system according to the measurement data and generating a state estimation result when the measurement residual corresponding to the measurement data is smaller than a preset residual threshold value.
9. An electronic device, comprising: at least one processor and memory;
the memory stores computer-executable instructions;
the at least one processor executing the computer-executable instructions stored by the memory causes the at least one processor to perform the method of any of claims 1-7.
10. A computer-readable storage medium having computer-executable instructions stored thereon which, when executed by a processor, implement the method of any one of claims 1 to 7.
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Citations (8)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2014182018A (en) * 2013-03-19 2014-09-29 Fujitsu Ltd Correction method of measurement data, measurement data monitoring system, and measurement data monitoring program
CN104166718A (en) * 2014-08-18 2014-11-26 国家电网公司 Bad data detection and recognition method suitable for large power grid
CN104901425A (en) * 2015-06-26 2015-09-09 国家电网公司 Wide area measurement system adaptive time-delay compensation method
CN106208050A (en) * 2016-08-17 2016-12-07 华北电力大学 A kind of grid branch static parameter detection and identification method based on PMU
CN106655170A (en) * 2016-12-26 2017-05-10 国电南瑞科技股份有限公司 Power system adverse data identification method
CN107370150A (en) * 2017-09-06 2017-11-21 清华大学 The Power system state estimation Bad data processing method measured based on synchronized phasor
CN107742885A (en) * 2017-11-13 2018-02-27 中国南方电网有限责任公司电网技术研究中心 Distribution network voltage power sensitivity method of estimation based on canonical match tracing
CN110750575A (en) * 2019-09-24 2020-02-04 河海大学 Method and device for identifying bad data of electric-thermal interconnection comprehensive energy system based on Lagrange state estimator

Patent Citations (8)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2014182018A (en) * 2013-03-19 2014-09-29 Fujitsu Ltd Correction method of measurement data, measurement data monitoring system, and measurement data monitoring program
CN104166718A (en) * 2014-08-18 2014-11-26 国家电网公司 Bad data detection and recognition method suitable for large power grid
CN104901425A (en) * 2015-06-26 2015-09-09 国家电网公司 Wide area measurement system adaptive time-delay compensation method
CN106208050A (en) * 2016-08-17 2016-12-07 华北电力大学 A kind of grid branch static parameter detection and identification method based on PMU
CN106655170A (en) * 2016-12-26 2017-05-10 国电南瑞科技股份有限公司 Power system adverse data identification method
CN107370150A (en) * 2017-09-06 2017-11-21 清华大学 The Power system state estimation Bad data processing method measured based on synchronized phasor
CN107742885A (en) * 2017-11-13 2018-02-27 中国南方电网有限责任公司电网技术研究中心 Distribution network voltage power sensitivity method of estimation based on canonical match tracing
CN110750575A (en) * 2019-09-24 2020-02-04 河海大学 Method and device for identifying bad data of electric-thermal interconnection comprehensive energy system based on Lagrange state estimator

Non-Patent Citations (2)

* Cited by examiner, † Cited by third party
Title
李碧君等: "基于快速分解正交变换状态估计算法的坏数据检测与辨识", 《电力系统自动化》 *
陈礼义等: "多不良数据补偿辨识法用于电力系统状态估计", 《天津大学学报》 *

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