CN112632456A - Power distribution network parameter calibration method and device based on forgetting factor - Google Patents
Power distribution network parameter calibration method and device based on forgetting factor Download PDFInfo
- Publication number
- CN112632456A CN112632456A CN202011427570.0A CN202011427570A CN112632456A CN 112632456 A CN112632456 A CN 112632456A CN 202011427570 A CN202011427570 A CN 202011427570A CN 112632456 A CN112632456 A CN 112632456A
- Authority
- CN
- China
- Prior art keywords
- value
- parameter matrix
- verified
- distribution network
- impedance
- 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.)
- Pending
Links
- 238000000034 method Methods 0.000 title claims abstract description 46
- 239000011159 matrix material Substances 0.000 claims abstract description 151
- 238000012795 verification Methods 0.000 claims abstract description 56
- 238000004364 calculation method Methods 0.000 claims abstract description 48
- 238000006243 chemical reaction Methods 0.000 claims abstract description 23
- 238000012544 monitoring process Methods 0.000 abstract description 4
- 238000005259 measurement Methods 0.000 description 6
- 230000006399 behavior Effects 0.000 description 2
- 238000010586 diagram Methods 0.000 description 2
- 230000006870 function Effects 0.000 description 2
- 238000004519 manufacturing process Methods 0.000 description 2
- WCUXLLCKKVVCTQ-UHFFFAOYSA-M Potassium chloride Chemical compound [Cl-].[K+] WCUXLLCKKVVCTQ-UHFFFAOYSA-M 0.000 description 1
- 230000032683 aging Effects 0.000 description 1
- 230000008859 change Effects 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000013488 ordinary least square regression Methods 0.000 description 1
- 230000008569 process Effects 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
Images
Classifications
-
- 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
- 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
- G06F17/11—Complex mathematical operations for solving equations, e.g. nonlinear equations, general mathematical optimization problems
- G06F17/12—Simultaneous equations, e.g. systems of linear equations
-
- 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
-
- 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]
Landscapes
- Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Mathematical Physics (AREA)
- Computational Mathematics (AREA)
- Mathematical Analysis (AREA)
- Mathematical Optimization (AREA)
- Pure & Applied Mathematics (AREA)
- Data Mining & Analysis (AREA)
- Theoretical Computer Science (AREA)
- Databases & Information Systems (AREA)
- Algebra (AREA)
- Software Systems (AREA)
- General Engineering & Computer Science (AREA)
- Computing Systems (AREA)
- Power Engineering (AREA)
- Operations Research (AREA)
- Supply And Distribution Of Alternating Current (AREA)
Abstract
The invention discloses a power distribution network parameter calibration method and device based on forgetting factors, wherein the method comprises the steps of obtaining a power distribution network parameter calibration instruction, wherein the power distribution network parameter calibration instruction carries parameters to be calibrated and node voltage values and load current values measured by nodes in a low-voltage power distribution network to be calibrated for n times; carrying out matrix conversion on the node voltage value and the load current value measured for n times to obtain a parameter matrix; acquiring a target equation based on the parameter matrix; calculating the node voltage value and the load current value through a target equation to obtain an impedance calculation value; and verifying the parameters to be verified based on the impedance calculated value, acquiring a verification result, reducing old data calculation, well reflecting the characteristics of new data, and improving the accuracy of the verification result, thereby improving the monitoring accuracy of the state of the power distribution network.
Description
Technical Field
The invention relates to the field of power grid state estimation, in particular to a power distribution network parameter calibration method and device based on forgetting factors.
Background
At present, the state monitoring of a low-voltage alternating-current power distribution network mainly adopts an engineering production management system, parameters of distribution network elements in the PMS (power production management system) are manually input, and errors cannot be avoided; in addition, under the influence of element aging and external environment, element parameters can also change, so that the real state of the power distribution network cannot be sensed due to inaccurate power distribution network parameters. Therefore, on the basis of the existing measurement data, the parameters of the distribution network elements are verified through the distribution network parameter identification, the parameter accuracy is improved, and the method has important significance for improving the accuracy of the distribution network state.
At present, a least square method is adopted to verify parameters of distribution network elements, the least square method is used as a statistical parameter estimation method, probability statistical information of measurement data is not required to be known in a random environment, and an obtained estimation result has good statistical properties, so that the method has good tolerance capability. However, the least square method has infinite length memory, and in the process of recursive operation, the recursive result cannot well reflect the characteristics of new data due to more and more old data, so that the identification result is influenced, and the state of the fed-back power distribution network is inaccurate.
Disclosure of Invention
The technical problem to be solved by the invention is that the state of the power distribution network fed back by the existing method for identifying the state of the power distribution network is inaccurate, so that the invention provides a power distribution network parameter calibration method based on a forgetting factor to improve the monitoring accuracy of the state of the power distribution network.
The invention is realized by the following technical scheme:
a power distribution network parameter calibration method based on forgetting factors comprises the following steps:
acquiring a power distribution network parameter verification instruction, wherein the power distribution network parameter verification instruction carries parameters to be verified and node voltage values and load current values measured by nodes in a low-voltage power distribution network to be verified for n times;
performing matrix conversion on the node voltage value and the load current value measured for n times to obtain an input parameter matrix, a parameter matrix to be estimated and an output parameter matrix;
acquiring a target equation based on the input parameter matrix, the parameter matrix to be estimated and the output parameter matrix;
calculating the node voltage value and the load current value through the target equation to obtain an impedance calculation value;
and verifying the parameter to be verified based on the impedance calculation value to obtain a verification result.
Further, the performing matrix conversion on the node voltage value and the load current value measured n times to obtain an input parameter matrix, a parameter matrix to be estimated, and an output parameter matrix includes:
establishing n equations for the node voltage value and the load current value measured for n times according to a kirchhoff voltage law and a kirchhoff current law to form an equation set;
and converting the equation set into a corresponding input parameter matrix, a parameter matrix to be estimated and an output parameter matrix.
Further, the obtaining a target equation based on the input parameter matrix, the parameter matrix to be estimated, and the output parameter matrix includes:
acquiring a state estimation equation based on the input parameter matrix, the parameter matrix to be estimated and the output parameter matrix;
starting a least square method model according to a least square method starting condition;
and inputting the input parameter matrix of the state estimation equation serving as the input parameter matrix of the least square method model into the least square method model to obtain a target estimation equation.
Further, the least square method model specifically comprises:
wherein k represents a time sequence, P (k) represents an error covariance matrix of a kth parameter matrix to be estimated, and lambda represents a forgetting factor
Representing the kth input parameter matrix.
Further, the state estimation equation specifically includes:
wherein,
representing an input parameter matrix, theta representing a parameter matrix to be estimated, and y representing an output parameter matrix.
Further, the verifying the parameter to be verified based on the impedance calculation value to obtain a verification result includes:
when the parameter to be verified is the impedance to be verified, comparing whether the calculated impedance value is consistent with the impedance to be verified, if so, determining that the verification is passed, and if not, determining that the verification is inconsistent;
and when the parameter to be verified is the length of the line to be verified, converting the impedance calculated value into a power grid line length calculated value, comparing whether the power grid line length calculated value is consistent with the length of the line to be verified, if so, determining that the verification is passed, and if not, determining that the verification is inconsistent.
Further, the verifying the parameter to be verified based on the impedance calculation value to obtain a verification result, further comprising:
when the to-be-verified parameters comprise impedance to be verified and the length of the to-be-verified line, converting the impedance calculation value into a power grid line length calculation value, and comparing whether the impedance calculation value is consistent with the impedance to be verified and whether the power grid line length calculation value is consistent with the length of the to-be-verified line;
if the calculated impedance value is consistent with the impedance to be verified, and the calculated power grid line length value is consistent with the length of the line to be verified, the verification result is that verification is passed;
and if the calculated impedance value is inconsistent with the impedance to be verified or the calculated power grid line length value is inconsistent with the length of the line to be verified, the verification result is that the verification is not passed.
Further, the converting the calculated impedance value into a calculated grid line length value includes:
converting the impedance calculation value into a power grid line length calculation value through a power grid line length calculation formula; the power grid line length calculation formula specifically comprises:
wherein Z represents the calculated value of impedance, Z0Representing the unit impedance and L representing the calculated grid line length.
The utility model provides a distribution network parameter calibration equipment based on factor of forgetting, includes:
the data acquisition module is used for acquiring a power distribution network parameter calibration instruction, and the power distribution network parameter calibration instruction carries parameters to be calibrated and node voltage values and load current values measured by nodes in the low-voltage power distribution network to be calibrated for n times;
the matrix conversion module is used for carrying out matrix conversion on the node voltage value and the load current value which are measured for n times to obtain an input parameter matrix, a parameter matrix to be estimated and an output parameter matrix;
the target equation obtaining module is used for obtaining a target equation based on the input parameter matrix, the parameter matrix to be estimated and the output parameter matrix;
the target equation calculation module is used for calculating the node voltage value and the load current value through the target equation to obtain an impedance calculation value;
and the checking module is used for checking the parameter to be checked based on the impedance calculation value to obtain a checking result.
Further, the matrix conversion module includes:
the equation set establishing unit is used for establishing n equations for the node voltage value and the load current value measured for n times according to a kirchhoff voltage law and a kirchhoff current law to form an equation set;
and the equation set conversion unit is used for converting the equation set into a corresponding input parameter matrix, a parameter matrix to be estimated and an output parameter matrix.
According to the power distribution network parameter calibration method and device based on the forgetting factor, the power distribution network parameter calibration instruction is obtained, and carries the parameter to be calibrated and the node voltage value and the load current value measured by each node in the low-voltage power distribution network to be calibrated for n times; carrying out matrix conversion on the node voltage value and the load current value measured for n times to obtain a parameter matrix; acquiring a target equation based on the parameter matrix; calculating the node voltage value and the load current value through a target equation to obtain an impedance calculation value; and verifying the parameters to be verified based on the impedance calculated value, acquiring a verification result, reducing old data calculation, well reflecting the characteristics of new data, and improving the accuracy of the verification result, thereby improving the monitoring accuracy of the state of the power distribution network.
Drawings
The accompanying drawings, which are included to provide a further understanding of the embodiments of the invention and are incorporated in and constitute a part of this application, illustrate embodiment(s) of the invention and together with the description serve to explain the principles of the invention. In the drawings:
fig. 1 is a flowchart of a power distribution network parameter calibration method based on a forgetting factor according to the present invention.
Fig. 2 is a specific flowchart of step S20 in fig. 1.
Fig. 3 is a specific flowchart of step S50 in fig. 1.
FIG. 4 is a diagram of a topology structure of a low-voltage distribution network line to be verified in the embodiment of the present invention
Fig. 5 is a schematic block diagram of a power distribution network parameter calibration device based on forgetting factors.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is further described in detail below with reference to examples and accompanying drawings, and the exemplary embodiments and descriptions thereof are only used for explaining the present invention and are not meant to limit the present invention.
Example 1
As shown in fig. 1, the present invention provides a power distribution network parameter calibration method based on forgetting factor, which specifically includes the following steps:
s10: and acquiring a power distribution network parameter verification instruction, wherein the power distribution network parameter verification instruction carries parameters to be verified and node voltage values and load current values measured by each node in the low-voltage power distribution network to be verified for n times.
Specifically, the power distribution network parameter verification instruction refers to an instruction for verifying the power distribution network parameters. The power distribution network parameter verification instruction carries parameters to be verified and node voltage values and load current values measured by nodes in the low-voltage power distribution network to be verified for n times. The parameters to be verified refer to parameters of the power distribution network which need to be verified, and the parameters to be verified in the embodiment include impedance values which need to be verified and/or length values of lines which need to be verified. The low-voltage distribution network to be verified refers to a low-voltage distribution network line needing to be verified.
S20: and performing matrix conversion on the node voltage value and the load current value measured for n times to obtain an input parameter matrix, a parameter matrix to be estimated and an output parameter matrix.
The input parameter matrix refers to a matrix obtained by matrix conversion of the input load current value. The parameter matrix to be estimated refers to the parameter matrix to be estimated, and comprises the voltage of the node to be estimated and the line resistance corresponding to the node. The output parameter matrix parameter moment refers to a matrix obtained after matrix conversion is carried out on the output node voltage values.
S30: and acquiring a target equation based on the input parameter matrix, the parameter matrix to be estimated and the output parameter matrix.
Wherein, the target equation refers to a state estimation equation based on a least square method.
Specifically, a state estimation equation is obtained based on an input parameter matrix, a parameter matrix to be estimated and an output parameter matrix, a least square model is started according to a least square starting condition, and then the input parameter matrix of the state estimation equation is input into the least square model as an input parameter matrix of the least square model to obtain a target estimation equation.
The starting conditions of the least square method are as follows:
the method is used for obtaining an estimated value of a parameter matrix to be estimated of theta according to an extreme value principle, wherein the theta represents the parameter matrix to be estimated. P0α I is the largest amount according to the actual situation, and α in this embodiment is 103And I is an identity matrix.
Further, the first behavior of the parameter matrix θ to be estimated is the voltage of the node to be evaluated, and the remaining behaviors are the line resistances of the nodes to be evaluated.
Further, the least square method model is specifically as follows:
wherein k represents a time sequence, P (k) represents an error covariance matrix of a kth parameter matrix to be estimated, and lambda represents a forgetting factor
Representing the kth input parameter matrix. The ordinary least square method is used when λ is 1, the smaller λ is, the stronger the tracking ability is, but the larger the fluctuation is, and generally 0.95 is taken<λ<1。
The state estimation equation is specifically:
wherein,
representing an input parameter matrix, theta representing a parameter matrix to be estimated, and y representing an output parameter matrix.
S40: and calculating the node voltage value and the load current value through a target equation to obtain an impedance calculation value.
Specifically, the node voltage value and the load current value are calculated by adopting the target equation, so that the calculation of old data can be reduced, the characteristics of new data can be well reflected, and the calculation accuracy of a subsequent verification result is improved.
S50: and verifying the parameters to be verified based on the calculated impedance value to obtain a verification result.
Further, as shown in fig. 2, step S20 is to perform matrix conversion on the node voltage values and the load current values measured n times to obtain a parameter matrix, and specifically includes the following steps:
s21: and establishing n equations according to the kirchhoff voltage law and the kirchhoff current law and the node voltage values and the load current values measured for n times to form an equation set.
S22: and converting the equation set into a corresponding input parameter matrix, a parameter matrix to be estimated and an output parameter matrix.
Specifically, the low-voltage distribution network line 1 to be verified is taken as an example for explanation, and the low-voltage distribution network line 1 to be verified includes 6 nodes as shown in fig. 4, and the number of each node is 1 to 6. Taking node 5 as an example, modeling is performed according to Kirchhoff Current Law (KCL) and Kirchhoff Voltage Law (KVL):
Um＝X12(i2+i3+i5+i6)+X23(i3+i5+i6)+X34(i5+i6)+X45i5+U5
the above formula is rewritten as follows:
Um-X12(i2+i3+i5+i6)-X23(i3+i5+i6)-X34(i5+i6)-X45i5＝U5
n equation sets can be obtained through n measurements:
Um-X12(i2，1+i3，1+i5，1+i6，1)-X23(i3，1+i5，1+i6，1)-X34(i5，1+i6，1)-X45i5，1＝U5，1
Um-X12(i2，2+i3，2+i5，2+i6，2)-X23(i3，2+i5，2+i6，2)-X34(i5，2+i6，2)-X45i5，2＝U5，2
...
Um-X12(i2,n+i3,n+i5,n+i6,n)-X23(i3,n+i5,n+i6,n)-X34(i5,n+i6,n)-X45i5,n＝U5,n
writing the above formula into matrix form
Namely, it is
Wherein
And y are both measured values. From left to right, the first matrix on the left of the equal sign is an input parameter matrix, the second matrix is a parameter matrix to be estimated, and the matrix on the right of the equal sign is an output parameter matrix.
Wherein, UmRepresenting the secondary side voltage of the distribution transformer, X represents the lineRoad resistance, U5，nRepresenting the value of the node voltage, i, of the nth measurement of the 5 th node2nRepresenting the load current value, i, of the nth measurement of the 2 nd node3nRepresenting the load current value, i, of the nth measurement of the 3 rd node5nRepresenting the value of the load current measured n-th at node 5, i6nRepresents the load current value measured the nth time at the 6 th node.
Further, as shown in fig. 3, in step S50, verifying the parameter to be verified based on the calculated impedance value to obtain a verification result, the method specifically includes the following steps:
s51: and when the parameter to be verified is the impedance to be verified, comparing whether the calculated impedance value is consistent with the impedance to be verified, if so, determining that the verification result is pass, and if not, determining that the verification result is inconsistent.
S52: and when the parameter to be verified is the length of the line to be verified, converting the impedance calculated value into a power grid line length calculated value, comparing whether the power grid line length calculated value is consistent with the length of the line to be verified, if so, determining that the verification is passed, and if not, determining that the verification is inconsistent.
S53: when the parameters to be verified comprise the impedance to be verified and the length of the line to be verified, the calculated impedance value is converted into a calculated power grid line length value, whether the calculated impedance value is consistent with the impedance to be verified or not is compared, and whether the calculated power grid line length value is consistent with the length of the line to be verified or not is compared.
S54: and if the calculated value of the impedance is consistent with the impedance to be verified and the calculated value of the length of the power grid line is consistent with the length of the line to be verified, the verification result is that the verification is passed.
S55: and if the calculated value of the impedance is inconsistent with the impedance to be verified or the calculated value of the length of the power grid line is inconsistent with the length of the line to be verified, the verification result is that the verification is not passed.
Further, converting the calculated impedance value into a calculated grid line length value, comprising:
through the electric wire netting line length computational formula, convert the impedance calculated value into electric wire netting line length calculated value, electric wire netting line length computational formula specifically is:
wherein Z represents the calculated value of impedance, Z0Representing the unit impedance and L representing the calculated grid line length.
Further, the parameter to be verified in this embodiment is also empty, and when the parameter to be verified is empty, the impedance calculation value and the corresponding power grid line length calculation value are used as the power distribution network parameter to be verified subsequently in the low-voltage power distribution network.
Example 2
As shown in fig. 5, the present embodiment is different from embodiment 1 in that a power distribution network parameter calibration apparatus based on a forgetting factor is provided, and includes:
the data acquisition module 10 is configured to acquire a power distribution network parameter verification instruction, where the power distribution network parameter verification instruction carries a parameter to be verified and a node voltage value and a load current value measured by each node in the low-voltage power distribution network to be verified n times.
And the matrix conversion module 20 is configured to perform matrix conversion on the node voltage values and the load current values measured n times to obtain an input parameter matrix, a parameter matrix to be estimated, and an output parameter matrix.
And the target equation obtaining module 30 is configured to obtain a target equation based on the input parameter matrix, the parameter matrix to be estimated, and the output parameter matrix.
And the target equation calculation module 40 is configured to calculate the node voltage value and the load current value through a target equation to obtain an impedance calculation value.
And the verification module 50 is configured to verify the parameter to be verified based on the impedance calculation value, and obtain a verification result.
Further, the matrix conversion module 20 includes an equation set establishing unit and an equation set conversion unit.
And the equation set establishing unit is used for establishing n equations according to the kirchhoff voltage law and the kirchhoff current law and the node voltage value and the load current value measured for n times to form an equation set.
And the equation set conversion unit is used for converting the equation set into the corresponding input parameter matrix, the parameter matrix to be estimated and the output parameter matrix.
Further, the target equation acquisition module 30 includes a state estimation equation acquisition unit, a least squares model starting unit, and a target estimation equation acquisition unit.
And the state estimation equation obtaining unit is used for obtaining the state estimation equation based on the input parameter matrix, the parameter matrix to be estimated and the output parameter matrix.
And the least square method model starting unit is used for starting the least square method model according to the least square method starting condition.
And the target estimation equation obtaining unit is used for inputting the input parameter matrix of the state estimation equation into the least square model as the input parameter matrix of the least square model to obtain the target estimation equation.
Further, the least square method model is specifically as follows:
wherein k represents a time sequence, P (k) represents an error covariance matrix of a kth parameter matrix to be estimated, and lambda represents a forgetting factor
Representing the kth input parameter matrix.
Further, the state estimation equation is specifically:
wherein,
representing an input parameter matrix, theta representing a parameter matrix to be estimated, and y representing an output parameter matrix.
Further, the verification module 50 includes a first verification unit, a second verification unit, and a third verification unit.
The first checking unit is used for comparing whether the calculated value of the impedance is consistent with the impedance to be checked or not when the parameter to be checked is the impedance to be checked, if so, the checking result is checking pass, and if not, the checking result is checking inconsistency.
And the second checking unit is used for converting the impedance calculation value into a power grid line length calculation value when the parameter to be checked is the length of the line to be checked, comparing whether the power grid line length calculation value is consistent with the length of the line to be checked, if so, checking the result that the line passes through, and if not, checking the result that the line is inconsistent.
The third verification unit is used for converting the calculated value of the impedance into a calculated value of the length of the power grid line when the parameters to be verified comprise the impedance to be verified and the length of the line to be verified, and comparing whether the calculated value of the impedance is consistent with the impedance to be verified and whether the calculated value of the length of the power grid line is consistent with the length of the line to be verified; if the calculated value of the impedance is consistent with the impedance to be verified and the calculated value of the length of the power grid line is consistent with the length of the line to be verified, the verification result is that the verification is passed; and if the calculated value of the impedance is inconsistent with the impedance to be verified or the calculated value of the length of the power grid line is inconsistent with the length of the line to be verified, the verification result is that the verification is not passed.
It will be apparent to those skilled in the art that, for convenience and brevity of description, only the above-mentioned division of the functional units and modules is illustrated, and in practical applications, the above-mentioned function distribution may be performed by different functional units and modules according to needs, that is, the internal structure of the apparatus is divided into different functional units or modules to perform all or part of the above-mentioned functions.
The above-mentioned embodiments are intended to illustrate the objects, technical solutions and advantages of the present invention in further detail, and it should be understood that the above-mentioned embodiments are merely exemplary embodiments of the present invention, and are not intended to limit the scope of the present invention, and any modifications, equivalent substitutions, improvements and the like made within the spirit and principle of the present invention should be included in the scope of the present invention.
Claims (10)
1. A power distribution network parameter calibration method based on forgetting factors is characterized by comprising the following steps:
acquiring a power distribution network parameter verification instruction, wherein the power distribution network parameter verification instruction carries parameters to be verified and node voltage values and load current values measured by nodes in a low-voltage power distribution network to be verified for n times;
performing matrix conversion on the node voltage value and the load current value measured for n times to obtain an input parameter matrix, a parameter matrix to be estimated and an output parameter matrix;
acquiring a target equation based on the input parameter matrix, the parameter matrix to be estimated and the output parameter matrix;
calculating the node voltage value and the load current value through the target equation to obtain an impedance calculation value;
and verifying the parameter to be verified based on the impedance calculation value to obtain a verification result.
2. The power distribution network parameter calibration method based on forgetting factor according to claim 1, wherein the performing matrix conversion on the node voltage value and the load current value measured n times to obtain an input parameter matrix, a parameter matrix to be estimated, and an output parameter matrix comprises:
establishing n equations for the node voltage value and the load current value measured for n times according to a kirchhoff voltage law and a kirchhoff current law to form an equation set;
and converting the equation set into a corresponding input parameter matrix, a parameter matrix to be estimated and an output parameter matrix.
3. The power distribution network parameter calibration method based on forgetting factor according to claim 1, wherein the obtaining of the target equation based on the input parameter matrix, the parameter matrix to be estimated, and the output parameter matrix comprises:
acquiring a state estimation equation based on the input parameter matrix, the parameter matrix to be estimated and the output parameter matrix;
starting a least square method model according to a least square method starting condition;
and inputting the input parameter matrix of the state estimation equation serving as the input parameter matrix of the least square method model into the least square method model to obtain a target estimation equation.
4. The power distribution network parameter calibration method based on forgetting factor according to claim 3, wherein the least square method model specifically comprises:
wherein k represents a time sequence, P (k) represents an error covariance matrix of a kth parameter matrix to be estimated, and lambda represents a forgetting factor
Representing the kth input parameter matrix.
5. The power distribution network parameter calibration method based on forgetting factor according to claim 3, wherein the state estimation equation specifically comprises:
wherein,
representing an input parameter matrix, theta representing a parameter matrix to be estimated, and y representing an output parameter matrix.
6. The power distribution network parameter calibration method based on forgetting factor according to claim 1, wherein the calibrating the parameter to be calibrated based on the impedance calculation value to obtain a calibration result comprises:
when the parameter to be verified is the impedance to be verified, comparing whether the calculated impedance value is consistent with the impedance to be verified, if so, determining that the verification is passed, and if not, determining that the verification is inconsistent;
and when the parameter to be verified is the length of the line to be verified, converting the impedance calculated value into a power grid line length calculated value, comparing whether the power grid line length calculated value is consistent with the length of the line to be verified, if so, determining that the verification is passed, and if not, determining that the verification is inconsistent.
7. The power distribution network parameter calibration method based on forgetting factor according to claim 1, wherein the calibrating the parameter to be calibrated based on the impedance calculation value to obtain a calibration result, further comprises:
when the to-be-verified parameters comprise impedance to be verified and the length of the to-be-verified line, converting the impedance calculation value into a power grid line length calculation value, and comparing whether the impedance calculation value is consistent with the impedance to be verified and whether the power grid line length calculation value is consistent with the length of the to-be-verified line;
if the calculated impedance value is consistent with the impedance to be verified, and the calculated power grid line length value is consistent with the length of the line to be verified, the verification result is that verification is passed;
and if the calculated impedance value is inconsistent with the impedance to be verified or the calculated power grid line length value is inconsistent with the length of the line to be verified, the verification result is that the verification is not passed.
8. The power distribution network parameter calibration method based on the forgetting factor according to claim 6 or 7, wherein the converting the impedance calculation value into a power grid line length calculation value comprises:
converting the impedance calculation value into a power grid line length calculation value through a power grid line length calculation formula; the power grid line length calculation formula specifically comprises:
wherein Z represents the calculated value of impedance, Z0Represents a unit impedanceAnd L represents a grid line length calculation.
9. The utility model provides a distribution network parameter calibration equipment based on factor of forgetting which characterized in that includes:
the data acquisition module is used for acquiring a power distribution network parameter calibration instruction, and the power distribution network parameter calibration instruction carries parameters to be calibrated and node voltage values and load current values measured by nodes in the low-voltage power distribution network to be calibrated for n times;
the matrix conversion module is used for carrying out matrix conversion on the node voltage value and the load current value which are measured for n times to obtain an input parameter matrix, a parameter matrix to be estimated and an output parameter matrix;
the target equation obtaining module is used for obtaining a target equation based on the input parameter matrix, the parameter matrix to be estimated and the output parameter matrix;
the target equation calculation module is used for calculating the node voltage value and the load current value through the target equation to obtain an impedance calculation value;
and the checking module is used for checking the parameter to be checked based on the impedance calculation value to obtain a checking result.
10. The power distribution network parameter calibration device based on forgetting factor according to claim 9, wherein the matrix conversion module comprises:
the equation set establishing unit is used for establishing n equations for the node voltage value and the load current value measured for n times according to a kirchhoff voltage law and a kirchhoff current law to form an equation set;
and the equation set conversion unit is used for converting the equation set into a corresponding input parameter matrix, a parameter matrix to be estimated and an output parameter matrix.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202011427570.0A CN112632456A (en) | 2020-12-09 | 2020-12-09 | Power distribution network parameter calibration method and device based on forgetting factor |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202011427570.0A CN112632456A (en) | 2020-12-09 | 2020-12-09 | Power distribution network parameter calibration method and device based on forgetting factor |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN112632456A true CN112632456A (en) | 2021-04-09 |
Family
ID=75308856
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202011427570.0A Pending CN112632456A (en) | 2020-12-09 | 2020-12-09 | Power distribution network parameter calibration method and device based on forgetting factor |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN112632456A (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115017660A (en) * | 2022-06-23 | 2022-09-06 | 国网四川省电力公司电力科学研究院 | Power distribution network topology and parameter calibration method |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104600657A (en) * | 2015-01-30 | 2015-05-06 | 国网北京经济技术研究院 | Power system fault calculation result calibration method |
| EP3361272A1 (en) * | 2017-02-09 | 2018-08-15 | ABB Schweiz AG | Grid impedance estimation based on total least squares |
| CN110932755A (en) * | 2020-02-15 | 2020-03-27 | 光一科技股份有限公司 | Distributed low-voltage distribution network line parameter estimation method based on recursive least square method |
| CN111221811A (en) * | 2020-02-15 | 2020-06-02 | 光一科技股份有限公司 | Low-voltage distribution network line parameter estimation method based on centralized meter reading system |
-
2020
- 2020-12-09 CN CN202011427570.0A patent/CN112632456A/en active Pending
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104600657A (en) * | 2015-01-30 | 2015-05-06 | 国网北京经济技术研究院 | Power system fault calculation result calibration method |
| EP3361272A1 (en) * | 2017-02-09 | 2018-08-15 | ABB Schweiz AG | Grid impedance estimation based on total least squares |
| CN110932755A (en) * | 2020-02-15 | 2020-03-27 | 光一科技股份有限公司 | Distributed low-voltage distribution network line parameter estimation method based on recursive least square method |
| CN111221811A (en) * | 2020-02-15 | 2020-06-02 | 光一科技股份有限公司 | Low-voltage distribution network line parameter estimation method based on centralized meter reading system |
Non-Patent Citations (1)
| Title |
|---|
| 丁锋 等: "时变系统遗忘因子最小二乘法的有界收敛性", 《控制理论与应用》 * |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115017660A (en) * | 2022-06-23 | 2022-09-06 | 国网四川省电力公司电力科学研究院 | Power distribution network topology and parameter calibration method |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN108593260B (en) | Optical cable line fault positioning and detecting method and terminal equipment | |
| CN108896853B (en) | Power grid voltage sag frequency evaluation method and device with distributed power supply | |
| CN110289613B (en) | Sensitivity matrix-based power distribution network topology identification and line parameter identification method | |
| CN106291381B (en) | A kind of method of Combined estimator electrokinetic cell system state-of-charge and health status | |
| CN106505557B (en) | Remote measurement error identification method and device | |
| WO2017016021A1 (en) | State enumeration reliability evaluation method based on influence increment and device therefor | |
| WO2021208079A1 (en) | Method and apparatus for obtaining power battery life data, computer device, and medium | |
| CN113985339B (en) | Error diagnosis method and system for intelligent ammeter, equipment and storage medium | |
| TW202246793A (en) | Battery state determination method, and battery state determination apparatus | |
| CN112803402B (en) | Radiation network forward-push back substitution robust state estimation method containing bad data preprocessing | |
| CN116520068B (en) | Diagnostic method, device, equipment and storage medium for electric power data | |
| CN102915514B (en) | Power system state estimation credibility evaluation method based on Cumulants method | |
| CN115158076A (en) | Metering error evaluation method, device and computer readable storage medium | |
| CN114048819A (en) | Power distribution network topology identification method based on attention mechanism and convolutional neural network | |
| Wang et al. | Augmented state estimation of line parameters in active power distribution systems with phasor measurement units | |
| CN116662771A (en) | Method and system for self-adaptive PCA error quantitative evaluation of capacitive voltage transformer | |
| CN112632456A (en) | Power distribution network parameter calibration method and device based on forgetting factor | |
| CN115201563A (en) | Multi-harmonic source positioning method and system based on joint entropy | |
| CN117972460A (en) | Operation fault discrimination method for high-voltage current transformer | |
| CN117113833A (en) | Verification method and system of verification device | |
| CN113189513A (en) | Ripple-based redundant power supply current sharing state identification method | |
| CN116561638B (en) | Protective pressing plate non-correspondence checking method based on neural network learning and state evaluation | |
| CN109193639B (en) | Robust estimation method for power system | |
| CN107132500A (en) | A kind of synchronous phasor measurement unit on-line calibration method and apparatus | |
| CN105843736A (en) | Test method for power system state estimation software |
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 | ||
| RJ01 | Rejection of invention patent application after publication | ||
| RJ01 | Rejection of invention patent application after publication |
Application publication date: 20210409 |