CN116502024A - Power distribution network line parameter identification method based on model driving - Google Patents
Power distribution network line parameter identification method based on model driving Download PDFInfo
- Publication number
- CN116502024A CN116502024A CN202211477468.0A CN202211477468A CN116502024A CN 116502024 A CN116502024 A CN 116502024A CN 202211477468 A CN202211477468 A CN 202211477468A CN 116502024 A CN116502024 A CN 116502024A
- Authority
- CN
- China
- Prior art keywords
- distribution network
- power distribution
- power
- admittance matrix
- node
- 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 23
- 239000011159 matrix material Substances 0.000 claims abstract description 43
- 238000012937 correction Methods 0.000 claims abstract description 18
- 238000002347 injection Methods 0.000 claims abstract description 17
- 239000007924 injection Substances 0.000 claims abstract description 17
- 238000005259 measurement Methods 0.000 claims description 9
- 238000013459 approach Methods 0.000 claims description 2
- 238000012545 processing Methods 0.000 claims description 2
- 230000000694 effects Effects 0.000 description 8
- 230000005540 biological transmission Effects 0.000 description 4
- 230000005855 radiation Effects 0.000 description 4
- 238000004422 calculation algorithm Methods 0.000 description 3
- 238000010276 construction Methods 0.000 description 3
- 230000007547 defect Effects 0.000 description 3
- 238000011160 research Methods 0.000 description 3
- 238000004364 calculation method Methods 0.000 description 2
- 230000008859 change Effects 0.000 description 2
- 238000011161 development Methods 0.000 description 2
- 239000000243 solution Substances 0.000 description 2
- 238000007476 Maximum Likelihood Methods 0.000 description 1
- 238000004458 analytical method Methods 0.000 description 1
- 238000013461 design Methods 0.000 description 1
- 229910003460 diamond Inorganic materials 0.000 description 1
- 239000010432 diamond Substances 0.000 description 1
- 238000004146 energy storage Methods 0.000 description 1
- 238000011156 evaluation Methods 0.000 description 1
- 230000006872 improvement Effects 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000005457 optimization Methods 0.000 description 1
- 230000035699 permeability Effects 0.000 description 1
- 238000010248 power generation Methods 0.000 description 1
- 238000003672 processing method Methods 0.000 description 1
- 238000007670 refining Methods 0.000 description 1
- 238000004088 simulation Methods 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
- 238000012360 testing method Methods 0.000 description 1
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
- G06F17/11—Complex mathematical operations for solving equations, e.g. nonlinear equations, general mathematical optimization problems
-
- 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
- G06F30/00—Computer-aided design [CAD]
- G06F30/20—Design optimisation, verification or simulation
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F2113/00—Details relating to the application field
- G06F2113/04—Power grid distribution networks
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
- Y02E60/00—Enabling technologies; Technologies with a potential or indirect contribution to GHG emissions mitigation
Landscapes
- Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Mathematical Physics (AREA)
- Theoretical Computer Science (AREA)
- Data Mining & Analysis (AREA)
- Mathematical Optimization (AREA)
- Computational Mathematics (AREA)
- Pure & Applied Mathematics (AREA)
- Mathematical Analysis (AREA)
- General Engineering & Computer Science (AREA)
- Software Systems (AREA)
- Databases & Information Systems (AREA)
- Algebra (AREA)
- Operations Research (AREA)
- Computing Systems (AREA)
- Computer Hardware Design (AREA)
- Evolutionary Computation (AREA)
- Geometry (AREA)
- Supply And Distribution Of Alternating Current (AREA)
Abstract
The invention discloses a power distribution network line parameter identification method based on model driving, which comprises the following steps: s1: acquiring node voltage amplitude and node injection active power of a power distribution network; s2: solving a power flow equation by using the regression of the measured data to obtain a pseudo node admittance matrix; s3: correcting the pseudo node admittance matrix by combining the topological structure of the power distribution network; s4: iteratively solving a correction equation, and further correcting the pseudo node admittance matrix; s5: and outputting the final corrected node admittance matrix, namely the identified line parameters. The invention can reduce the data requirement and improve the identification accuracy.
Description
Technical Field
The invention relates to the field of power distribution network line parameter identification, in particular to a power distribution network line parameter identification method based on model driving.
Background
With the continuous development of the times, the large-scale access of distributed power supplies, electric automobiles and user-side energy storage presents great challenges for the safety and reliability of power distribution network planning and operation. The importance of the distribution network as the last kilometer connecting the network with the subscribers is self-evident. In recent years, the construction scale of the power distribution network in China is continuously increased, the grid structure is continuously improved, the construction of the intelligent power distribution network is greatly improved, and a good foundation is laid for improving the power supply reliability.
Accurate grid model parameters are the basis for grid safety analysis and control effectiveness. At present, line parameters in a power grid are obtained only by means of original design data, but actual line parameters often change along with surrounding environment and service time. At present, transmission network parameters are mainly obtained through a large number of real-time measurement devices installed in the transmission network parameters, compared with the transmission network parameters, the number of the real-time measurement devices installed in the distribution network is much smaller, and therefore, a distribution network line parameter identification model with high applicability is needed to be provided.
A great deal of research is carried out on line parameter identification at home and abroad, but the current research objects are power transmission networks, and the parameter identification method for the power distribution network is mostly realized by means of a least square method. Luan Wenpeng et al solve objective function for calculating voltage and minimum optimization model of difference of measured voltage to realize line parameter identification by utilizing multi-time section measured data, foreign students establish a linearization tide model (LC-PF) based on intelligent ammeter data, and obtain line parameters by utilizing a recursive grouping algorithm, but the applicable object is only a radial network structure, and the requirements on power supply quality are continuously improved along with the continuous improvement of distributed renewable energy permeability in the future, so that the development and construction of novel power distribution networks such as 'diamond type', 'petal type', and the like become trends, and the operation of the power distribution network including the ring network becomes an important means for guaranteeing reliable power supply. Yu et al build a variable error model using micro synchrophasor measurement unit data, and obtain topology and line parameters based on maximum likelihood estimation; and then, introducing an expected maximization algorithm based on the previous step, so that the joint identification of the line parameters under different topological structures can be realized. However, the above researches depend on advanced measurement devices such as PMU, and are limited by expensive equipment cost, and it is difficult to meet the practical requirements in a short period of time. Ningjia Xin et al propose that line parameters can be identified independently of voltage angles, but threshold settings are more and purely empirical.
Disclosure of Invention
The invention aims to overcome the defects of the prior art, and provides a power distribution network line parameter identification method based on model driving, which can reduce data requirements and improve identification accuracy.
The technical scheme for achieving the purpose is as follows: a power distribution network line parameter identification method based on model driving comprises the following steps:
s1: acquiring node voltage amplitude and node injection active power of a power distribution network;
s2: solving a power flow equation by using the regression of the measured data to obtain a pseudo node admittance matrix;
s3: correcting the pseudo node admittance matrix by combining the topological structure of the power distribution network;
s4: iteratively solving a correction equation, and further correcting the pseudo node admittance matrix;
s5: and outputting the final corrected node admittance matrix, namely the identified line parameters.
Further, when the power distribution network normally operates, for a determined network topology structure, the node voltage amplitude and the node injection power are known to determine the line parameters of the power distribution network, the node injection power is active injection power and reactive injection power, and section measurement data are selected as node voltage data and node injection power data of the power distribution network.
Further, the specific step of solving the power flow equation through regression in S2 includes:
firstly, carrying out corresponding simplified rewriting on a classical power flow equation, and respectively defining pseudo node admittance matrixes;
then, the simplified equation is further simplified by utilizing the fact that the voltage angle is generally smaller or approaches zero in the actual power grid;
finally, solving the pseudo node admittance matrix by a regression method.
Further, the modified pseudo node admittance matrix of S3 specifically includes:
firstly, carrying out symmetry processing on a pseudo node admittance matrix by utilizing the symmetry of the node admittance matrix of the power distribution network, and finishing the first correction of the pseudo node admittance matrix;
and then, the connectivity among the nodes is obtained by utilizing the structure of the power distribution network, and the admittance of the nodes obtained above is subjected to second correction by utilizing the connectivity among the nodes.
Further, the step of iteratively solving the correction equation in S4 is as follows:
firstly, solving a power flow equation by using the obtained pseudo node admittance matrix to obtain a voltage angle;
then substituting the data into a correction equation to obtain the power unbalance amount, further obtaining the correction amount of the node admittance matrix, and correcting the node admittance matrix;
finally, the relation between the power unbalance amount and the set threshold value is judged, so that whether to output or iterate again is judged.
Further, a line parameter identification model is constructed based on the model driver.
Furthermore, the model drive builds a power grid line parameter identification model with high matching identification accuracy by means of a classical tide equation.
Compared with the prior art, the invention has the following advantages:
1) The method is high in identification precision aiming at the fact that the power distribution network line parameter identification model is built based on model driving in the power distribution network;
2) The invention can complete the identification of the line parameters by only relying on node voltage and injection power, and overcomes the defects of high data quality requirement and various requirements of a plurality of algorithms at present;
3) The method overcomes the defect that most of the current power distribution network line parameter identification methods are only applicable to radiation network structures.
Drawings
FIG. 1 is a schematic flow chart of the present invention;
FIG. 2 is a flow chart of an overall model for implementing the present invention;
FIG. 3 is a flow chart of the parameter refinement correction of the present invention
Fig. 4 is an exemplary IEEE33 node distribution network according to the present invention.
Detailed Description
In order to better understand the technical solution of the present invention, the following detailed description is given by way of specific examples:
as shown in fig. 1, the invention provides a method for identifying a line parameter of a power distribution network based on model driving, which realizes the identification of the line parameter of the power distribution network by using node voltage and injection power section measurement data, and comprises the following steps:
s1: acquiring node voltage amplitude and node injection active power of a power distribution network;
s2: solving a power flow equation by using the regression of the measured data to obtain a pseudo node admittance matrix;
s3: correcting the pseudo node admittance matrix by combining the topological structure of the power distribution network;
s4: iteratively solving a correction equation, and further correcting the pseudo node admittance matrix;
s5: and outputting the final corrected node admittance matrix, namely the identified line parameters.
In this embodiment, the specific implementation steps are as follows:
1) According to the invention, a topology identification frame of the distribution network is shown in fig. 2, an example IEEE33 node distribution network is shown in fig. 4, and the fact that distributed power generation access is possible in an actual distribution network is considered, so that nodes 7, 10, 14 and 33 are selected as distributed access, the line parameters adopt IEEE33 node standard parameters, and the measurement data of node voltage and injection power are obtained through simulation.
2) Solving the classical flow equation comprises simplified rewriting and regression solving of the classical flow equation:
the classical flow equation is as follows:
wherein: i and j are node numbers (i, j=1, 2, …, n), P i 、Q i And V i Respectively representing the injected active power, the injected reactive power and the voltage amplitude of the node i; θ ij Is the voltage phase angle difference between nodes i, j. G ij And B ij Representing the real and imaginary parts of the admittance matrix between node i and node j, respectively.
For the above equation, the node voltage, the injected active power and the injected reactive power are known, and there are three unknowns, the number of equations is only two, and the equations cannot be solved, so that the equations need to be deformed:
wherein [ p/v]＝[p 1 /v 1 …p n /v n ] T ,[v]＝[v 1 …v n ] T ,[q/v]＝[q 1 /v 1 …q n /v n ] T Pseudo admittance matrixAnd->The meaning of (2) is as follows:
considering that the voltage phase angle difference between adjacent nodes of the distribution network is small, theta ij Typically close to 0, in which case sin θ≡θ, cos θ≡1. The above can be simplified as follows:
from the above, it can be seen that due to θ ij The node admittance matrix of the line can be pseudo node admittance matrixAnd->To approximate a substitution. The calculation formula of the pseudo node admittance matrix is shown as follows;
3) In order to provide a relatively accurate iteration initial value for the second phase of line parameter identification as far as possible, preliminary correction is required to be carried out on the obtained pseudo node admittance matrix. The method comprises the steps of correcting the pseudo node admittance matrix for the first time by utilizing the symmetry of the node admittance matrix of the power distribution network, wherein the symmetry processing method comprises the following steps:
finally, since the topology of the distribution network is known, the connectivity of the nodes is also known, so that the pseudo admittance matrix can be further modified by the condition, and unconnected branches are deleted.
4) Inputting the measured data and the pseudo node admittance matrix obtained in the above to a parameter identification second stage for carrying out iterative solution of a correction equation, and further refining the correction node admittance matrix to obtain accurate line parameters. The iterative flow is shown in fig. 3.
5) The model herein uses Mean Absolute Percent Error (MAPE) as the evaluation index. The calculation is as follows:
wherein: n represents the predicted branch number, actual represents the actual line parameter, and predicted represents the line parameter obtained by the two-stage line parameter identification model.
6) Firstly, the identification effect of the line parameter identification model in the radiation network is tested, and the result is used for displaying the identification effect of the conductance and susceptance of each branch. As a result, when the error level is zero, the node line parameters and the actual parameters are not far apart after the first stage identification, and the line parameters and the actual parameters almost coincide after the second stage identification, so that the effectiveness of the model is seen.
7) By showing the change condition of the average absolute error percentage of the conductance g and the susceptance b along with the iteration times, the result shows that after four iterations, the average absolute error percentage of the two parameters is reduced to zero, so that the model identification speed is high, and the real-time application prospect of the model is greatly improved.
8) However, in the actual power grid, there is a certain error in the measured data due to various factors, in order to verify the application effect of the model in the actual scene, 0.01%, 0.05%, 0.2% and 1% of errors are added to the measured data, the identification effect is tested in the error environment, and in order to more clearly show the line parameter identification effect under 5 data error levels, the identification effect is listed in the following table, including the average absolute percentage error, time cost and iteration number of the identification parameters under each error level.
Table 1IEEE33 node radiation type distribution network line parameter identification result
As can be seen from the above table, in this example, the model has good effect of parameter identification at 5 error levels, and the identification speed is high. When the equivalent measurement error is zero, the result error of parameter identification is zero, when the error grade is added to 1%, after 8 iterations, the average absolute percentage error of g is not more than 1.573139%, the average absolute percentage error of b is not more than 2.118335%, and the time is not more than 23 seconds, so that the parameter identification model has good robustness in the IEEE33 node radiation network.
9) In order to verify the applicability of the parameter identification model in the ring network, the same test is carried out on the proposed model in the IEEE33 node ring network, and the ring network selected here is the state when the standard IEEE33 node distribution network connection lines are fully connected.
10 In order to more clearly show the identification effect of the two-stage parameter identification model in the looped network scene, the identification result is given in the following table.
Table 2IEEE33 node ring-containing distribution network parameter identification results
As can be seen from the results shown in the table, the parameter identification method provided herein is also applicable in the ring network, and the error of the identification result is zero when the error is zero. When the error grade reaches 1%, the identification is completed after 8 iterations, the average absolute percentage error of the conductance g is not more than 3.6%, the average absolute percentage error of the susceptance b is not more than 3.2802%, and the time is only 22.569924s, so that the proposed model is also applicable to a looped network scene.
It will be appreciated by persons skilled in the art that the above embodiments are provided for illustration only and not for limitation of the invention, and that variations and modifications of the above described embodiments are intended to fall within the scope of the claims of the invention as long as they fall within the true spirit of the invention.
Claims (7)
1. The power distribution network line parameter identification method based on model driving is characterized by comprising the following steps of:
s1: acquiring node voltage amplitude and node injection active power of a power distribution network;
s2: solving a power flow equation by using the regression of the measured data to obtain a pseudo node admittance matrix;
s3: correcting the pseudo node admittance matrix by combining the topological structure of the power distribution network;
s4: iteratively solving a correction equation, and further correcting the pseudo node admittance matrix;
s5: and outputting the final corrected node admittance matrix, namely the identified line parameters.
2. The method for identifying the circuit parameters of the power distribution network based on model driving according to claim 1, wherein when the power distribution network operates normally, for a determined network topology structure, the circuit parameters of the power distribution network are determined by knowing node voltage amplitude and node injection power, the node injection power is active injection power and reactive injection power, and section measurement data are selected as node voltage data and node injection power data of the power distribution network.
3. The model-driven power distribution network line parameter identification method according to claim 1, wherein the specific step of solving the power flow equation by regression in S2 comprises the following steps:
firstly, carrying out corresponding simplified rewriting on a classical power flow equation, and respectively defining pseudo node admittance matrixes;
then, the simplified equation is further simplified by utilizing the fact that the voltage angle is generally smaller or approaches zero in the actual power grid;
finally, solving the pseudo node admittance matrix by a regression method.
4. The method for identifying the power distribution network line parameters based on model driving according to claim 1, wherein the modified pseudo node admittance matrix of S3 specifically comprises:
firstly, carrying out symmetry processing on a pseudo node admittance matrix by utilizing the symmetry of the node admittance matrix of the power distribution network, and finishing the first correction of the pseudo node admittance matrix;
and then, the connectivity among the nodes is obtained by utilizing the structure of the power distribution network, and the admittance of the nodes obtained above is subjected to second correction by utilizing the connectivity among the nodes.
5. The method for identifying the parameters of the power distribution network based on the model driving according to claim 1, wherein the step of iteratively solving the correction equation in S4 is as follows:
firstly, solving a power flow equation by using the obtained pseudo node admittance matrix to obtain a voltage angle;
then substituting the data into a correction equation to obtain the power unbalance amount, further obtaining the correction amount of the node admittance matrix, and correcting the node admittance matrix;
finally, the relation between the power unbalance amount and the set threshold value is judged, so that whether to output or iterate again is judged.
6. The model-driven power distribution network line parameter identification method according to claim 1, wherein a line parameter identification model is built based on model driving.
7. The method for identifying the power distribution network line parameters based on the model driving according to claim 6, wherein the model driving builds a power distribution network line parameter identification model with high identification accuracy by means of a classical power flow equation.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202211477468.0A CN116502024A (en) | 2022-11-23 | 2022-11-23 | Power distribution network line parameter identification method based on model driving |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202211477468.0A CN116502024A (en) | 2022-11-23 | 2022-11-23 | Power distribution network line parameter identification method based on model driving |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN116502024A true CN116502024A (en) | 2023-07-28 |
Family
ID=87323659
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202211477468.0A Pending CN116502024A (en) | 2022-11-23 | 2022-11-23 | Power distribution network line parameter identification method based on model driving |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN116502024A (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117559454A (en) * | 2023-11-10 | 2024-02-13 | 哈尔滨工业大学 | Information-physical model combined driving power grid topological structure identification method |
-
2022
- 2022-11-23 CN CN202211477468.0A patent/CN116502024A/en active Pending
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117559454A (en) * | 2023-11-10 | 2024-02-13 | 哈尔滨工业大学 | Information-physical model combined driving power grid topological structure identification method |
| CN117559454B (en) * | 2023-11-10 | 2024-05-31 | 哈尔滨工业大学 | Information-physical model combined driving power grid topological structure identification method |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN107453357B (en) | Power distribution network state estimation method based on layered solution | |
| CN107104442B (en) | Method for calculating probability load flow of power system including wind power plant by considering parameter ambiguity | |
| CN110299762B (en) | PMU (phasor measurement Unit) quasi-real-time data-based active power distribution network robust estimation method | |
| CN103795057B (en) | Based on the power distribution network topology numbering generation method of search in distribution power system load flow calculation | |
| CN112488874B (en) | Data-driven distribution network topology estimation and line parameter identification method | |
| CN107591807B (en) | Optimization method for power transmission network planning under new energy access | |
| CN112288326B (en) | Fault scene set reduction method suitable for toughness evaluation of power transmission system | |
| CN109167387A (en) | Wind field wind power forecasting method | |
| CN108336739A (en) | A kind of Probabilistic Load Flow on-line calculation method based on RBF neural | |
| CN110350535A (en) | A kind of meter and large-scale wind power and the grid-connected distribution network voltage probability of stability appraisal procedure of electric car | |
| CN116502024A (en) | Power distribution network line parameter identification method based on model driving | |
| CN103324858A (en) | Three-phase load flow state estimation method of power distribution network | |
| CN110412417B (en) | Micro-grid data fault diagnosis method based on intelligent power monitoring instrument | |
| CN106503861A (en) | Wind power forecasting method based on many meteorological sources wind speed fusions of probability statistics and particle group optimizing | |
| Chang et al. | Data-driven estimation of voltage-to-power sensitivities considering their mutual dependency in medium voltage distribution networks | |
| CN109858061B (en) | Power distribution network equivalence and simplification method for voltage power sensitivity estimation | |
| CN109146336B (en) | Robust state estimation method for power system based on t distribution | |
| CN114204560A (en) | Medium voltage distribution network line parameter identification method | |
| CN112803402B (en) | Radiation network forward-push back substitution robust state estimation method containing bad data preprocessing | |
| CN107204616B (en) | Power system random state estimation method based on self-adaptive sparse pseudo-spectral method | |
| Wei et al. | Online distribution system topology monitoring with limited smart meter communication | |
| CN116451505A (en) | Power distribution network line parameter checking method, system, equipment and medium | |
| CN116257973A (en) | Particle swarm optimization-based low-voltage power network line impedance and loss calculation method and system | |
| CN107069710B (en) | Power system state estimation method considering new energy space-time correlation | |
| CN113177717B (en) | Quick evaluation method for toughness of power transmission system based on influence increment sensitivity |
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 |