CN114048819A - Power distribution network topology identification method based on attention mechanism and convolutional neural network - Google Patents
Power distribution network topology identification method based on attention mechanism and convolutional neural network Download PDFInfo
- Publication number
- CN114048819A CN114048819A CN202111360101.6A CN202111360101A CN114048819A CN 114048819 A CN114048819 A CN 114048819A CN 202111360101 A CN202111360101 A CN 202111360101A CN 114048819 A CN114048819 A CN 114048819A
- Authority
- CN
- China
- Prior art keywords
- power distribution
- distribution network
- topology
- convolutional neural
- neural network
- 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
- 238000013527 convolutional neural network Methods 0.000 title claims abstract description 31
- 238000000034 method Methods 0.000 title claims abstract description 30
- 238000005259 measurement Methods 0.000 claims abstract description 34
- 238000012549 training Methods 0.000 claims abstract description 10
- 238000007781 pre-processing Methods 0.000 claims abstract description 7
- 238000012216 screening Methods 0.000 claims abstract description 4
- 238000004422 calculation algorithm Methods 0.000 claims description 9
- 238000002347 injection Methods 0.000 claims description 7
- 239000007924 injection Substances 0.000 claims description 7
- 238000010586 diagram Methods 0.000 claims description 6
- 230000002159 abnormal effect Effects 0.000 claims description 4
- 238000007637 random forest analysis Methods 0.000 claims description 4
- 238000004140 cleaning Methods 0.000 claims description 3
- 238000012163 sequencing technique Methods 0.000 claims description 3
- 230000000694 effects Effects 0.000 description 7
- 239000011159 matrix material Substances 0.000 description 7
- 238000004364 calculation method Methods 0.000 description 5
- 238000012360 testing method Methods 0.000 description 5
- 230000007547 defect Effects 0.000 description 3
- 238000003066 decision tree Methods 0.000 description 2
- 238000011156 evaluation Methods 0.000 description 2
- 238000012544 monitoring process Methods 0.000 description 2
- 238000010248 power generation Methods 0.000 description 2
- 238000013528 artificial neural network Methods 0.000 description 1
- 230000005540 biological transmission Effects 0.000 description 1
- 238000004891 communication Methods 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 238000012417 linear regression Methods 0.000 description 1
- 238000013507 mapping Methods 0.000 description 1
- 238000010606 normalization Methods 0.000 description 1
- 238000005457 optimization Methods 0.000 description 1
- 238000012545 processing Methods 0.000 description 1
- 230000005855 radiation Effects 0.000 description 1
- 239000000243 solution Substances 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
- 230000001131 transforming effect Effects 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/24—Classification techniques
- G06F18/243—Classification techniques relating to the number of classes
- G06F18/24323—Tree-organised classifiers
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/04—Architecture, e.g. interconnection topology
- G06N3/045—Combinations of networks
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/08—Learning methods
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
- G06Q50/06—Energy or water supply
-
- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
-
- 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)
- Theoretical Computer Science (AREA)
- Physics & Mathematics (AREA)
- Data Mining & Analysis (AREA)
- Health & Medical Sciences (AREA)
- General Physics & Mathematics (AREA)
- Life Sciences & Earth Sciences (AREA)
- Artificial Intelligence (AREA)
- Business, Economics & Management (AREA)
- General Health & Medical Sciences (AREA)
- Evolutionary Computation (AREA)
- General Engineering & Computer Science (AREA)
- Computational Linguistics (AREA)
- Mathematical Physics (AREA)
- Biomedical Technology (AREA)
- Biophysics (AREA)
- Economics (AREA)
- Software Systems (AREA)
- Molecular Biology (AREA)
- Computing Systems (AREA)
- Bioinformatics & Cheminformatics (AREA)
- Human Resources & Organizations (AREA)
- Power Engineering (AREA)
- Evolutionary Biology (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Public Health (AREA)
- Water Supply & Treatment (AREA)
- Bioinformatics & Computational Biology (AREA)
- Marketing (AREA)
- Primary Health Care (AREA)
- Strategic Management (AREA)
- Tourism & Hospitality (AREA)
- General Business, Economics & Management (AREA)
- Supply And Distribution Of Alternating Current (AREA)
Abstract
The invention relates to a power distribution network topology identification method based on an attention mechanism and a convolutional neural network, which comprises the following steps of: s1: acquiring measurement data of the power distribution network and a corresponding topological structure, and constructing a database; s2: preprocessing the measured data; s3: screening the features according to the feature contribution degree, and constructing a feature set; s4: constructing a power distribution network topology identification model, and training the power distribution network topology identification model based on the feature set; s5: and sending the measurement data of the power distribution network to be identified into the power distribution network topology identification model to obtain the topology structure of the power distribution network to be identified. Compared with the prior art, the method has the advantages of only needing section measurement data, having high identification and classification accuracy, overcoming data noise and the like.
Description
Technical Field
The invention relates to the field of power distribution network topology identification, in particular to a power distribution network topology identification method based on an attention mechanism and a convolutional neural network.
Background
With the development of the times, the living standard of people is gradually improved, and the demand on the quality of electric energy is also continuously improved. The power distribution network is used as an important energy basic measure in modern society. Because a large amount of new energy is accessed, the topology of the power distribution network changes frequently, and the monitoring equipment and the monitoring capability are far insufficient compared with the power transmission network, the topological structure of the power distribution network is often unknown, however, the accurate topological structure is the basis of analysis means such as load flow calculation, state estimation and setting calculation of a power system and is a prerequisite condition for planning, operating and controlling the power distribution network, and therefore a method capable of accurately identifying the topological structure of the power distribution network is urgently needed.
In the prior art, there are identification methods, such as searching for a bridge line by using a depth-first algorithm, performing mixed integer quadratic optimization to obtain a primary topology structure, transforming an undirected graph into trees by using a tree generation algorithm, traversing the trees, and finding a tree with the smallest evaluation index matching with a power flow, which is the final topology; the relation among the distribution area, the distribution transformer, the feeder line and the user electric meter is utilized to construct a knowledge graph for topology identification, so that the method is not applicable to the power distribution network with frequent change; and judging the attachment of the feeder line by adopting a correlation method, and determining the position of the load by using the voltage amplitude value which is gradually reduced along with the trend direction, wherein the correlation threshold is difficult to determine, and the trend of the power grid does not flow in a single direction along with the addition of the distributed energy.
In the prior art, a physical model is established, linear regression and a Jacobian matrix are introduced for iterative solution, accurate topology identification is obtained through measured data, but the measured data of each node is required to be complete; in the existing identification process, voltage phase angle equivalent measurement is needed, which needs high-grade measurement equipment such as mu PMU or PMU, and the equipment is expensive and cannot be equipped for each node in a power distribution network; still other topology identification methods require data to be continuously measured, which is difficult to achieve in real-world measurements due to communication and measurement devices.
Disclosure of Invention
The invention aims to overcome the defects of the prior art and provide a power distribution network topology identification method based on an attention mechanism and a convolutional neural network.
The purpose of the invention can be realized by the following technical scheme:
a power distribution network topology identification method based on an attention mechanism and a convolutional neural network comprises the following steps:
s1: acquiring historical measurement data of the power distribution network and a corresponding topological structure, and constructing a database;
s2: preprocessing the measured data;
s3: screening the features according to the feature contribution degree, and constructing a feature set;
s4: constructing a power distribution network topology identification model, and training the power distribution network topology identification model based on the feature set;
s5: and sending the measurement data of the power distribution network to be identified into the power distribution network topology identification model to obtain the topology structure of the power distribution network to be identified.
Preferably, the preprocessing comprises data cleaning, missing value filling and abnormal value removing.
Preferably, the specific step of step S3 includes:
s31: acquiring the characteristic contribution of different measurement data to the topology identification of the power distribution network;
s32: and sequencing the feature contribution degrees, selecting the measurement data with the highest feature contribution degree as a feature set, and taking the corresponding topological structure as a label of the feature set.
Preferably, in step S31, a random forest algorithm is used to calculate the feature contribution degree of the measurement data.
Preferably, the power distribution network topology identification model is a convolutional neural network based on an attention mechanism.
Preferably, the convolutional neural network comprises an attention module, and an input layer, a hidden layer and an output layer which are connected in sequence, wherein the attention module is arranged behind the first layer of the hidden layer.
Preferably, the loss function of the convolutional neural network is:
wherein p ═ p1,…,pN]Is a probability distribution of each element piRepresenting the probability that the sample belongs to the topology i; y ═ y1,…,yN]Is a sample label, y when the sample belongs to the topology i i1, otherwise yi0; n is the total number of topology classes.
Preferably, the measured data includes node voltage amplitude and node injection power.
Preferably, the node injection power is active power.
Preferably, the topology structure is a topology structure diagram corresponding to the measurement data of each group of nodes.
Compared with the prior art, the invention has the following advantages:
1) according to the method, aiming at a large amount of redundant measurement data in the power distribution network, the feature set is screened by means of a random forest algorithm, the dimension of the data set is reduced, the calculation complexity and the space complexity of a subsequent model are reduced, and the model identification efficiency is improved;
2) the invention utilizes the convolutional neural network to re-divide and mine the incidence relation between the characteristic category and the topological structure, and learns the mapping rule thereof, realizes that the current topology can be identified only by section measurement data, and solves the defects that the current identification method is difficult in threshold setting and only suitable for a radiation network;
3) according to the method, an attention mechanism is added into the convolutional neural network, and attention is added to corresponding characteristics, so that the robustness of the model is greatly improved, the defect of high noise of measured data is overcome, the model has a good identification effect in data with high noise, and the method has a high practical application value.
Drawings
FIG. 1 is a flow chart of the present invention;
FIG. 2 is a flow chart of an embodiment of the present invention;
FIG. 3 is a diagram of an IEEE33 node distribution algorithm in accordance with an embodiment of the present invention;
FIG. 4 is a graph illustrating an ordering of the contribution of features of the present invention;
FIG. 5 is a schematic diagram of an implementation of the attention mechanism of the present invention;
FIG. 6 is a block diagram of a convolutional neural network of the present invention;
FIG. 7 is a graph of the confusion matrix of the test results of the present invention.
Detailed Description
The invention is described in detail below with reference to the figures and specific embodiments. Note that the following description of the embodiments is merely a substantial example, and the present invention is not intended to be limited to the application or the use thereof, and is not limited to the following embodiments.
Examples
A method for identifying a power distribution network topology based on an attention mechanism and a convolutional neural network is disclosed, as shown in FIG. 1, and comprises the following steps:
s1: acquiring historical measurement data of the power distribution network and a corresponding topological structure, and constructing a database;
in this embodiment, the measurement data includes a node voltage amplitude and a node injection power, where the obtained node injection power selects an active power that is easily obtained.
An IEEE33 node power distribution network of the calculation example in this embodiment is shown in fig. 3, and considering that there may be distributed power generation access in an actual power distribution network, nodes 7, 10, 14, and 33 are selected as distributed power generation access, and line parameters adopt IEEE33 node standard parameters, and line connection is changed on the basis of this topology, so that 28 topology structures are generated, of which 20 are radial networks and 8 ring-containing networks. And changing the working scene on the basis of each topology, and obtaining 3000 groups of sample data by means of MATLAB software simulation, wherein the total number is 84000. The data for each set of samples is the voltage amplitude and injected power for the 33 nodes.
S2: and preprocessing the measured data.
The preprocessing comprises data cleaning, missing value filling and abnormal value removing.
Specifically, firstly, the maximum voltage amplitude estimation is carried out on the data, and the value exceeding the specified variation range is deleted as an abnormal value;
then, the data is normalized:
in the formula: v and vnormThe voltage amplitude before and after normalization of the node, v, respectivelyminAnd vmaxThe minimum and maximum values of the voltage amplitude at the nodes in the training data set, respectively.
The normalized voltage data of a certain node is equal to the difference between the actual measurement value and the lowest measurement value of the node at all times, and the difference between the maximum measurement value and the minimum measurement value of the node at all times is compared;
and finally, performing leak repairing on the deleted value and the missing value:
wherein v isi,tRepresenting the voltage magnitude at time t of node i and n representing the total number of nodes on the same branch as node i.
Specifically, for training samples with missing part of data, directly discarding and re-collecting the data; and for the test sample with the missing part of data, performing data filling on the sample to ensure that the topology identification can be performed normally. And by utilizing the similarity of the fluctuation of the voltages of the adjacent nodes, the data missing part is filled with the average value of the difference values of the voltage amplitudes of the adjacent nodes and the previous moment and the voltage amplitude of the previous moment of the missing value.
S3: and screening the features according to the feature contribution degree, and constructing a feature set.
The specific steps of step S3 include:
s31: acquiring the characteristic contribution of different measurement data to the topology identification of the power distribution network;
s32: and sequencing the feature contribution degrees, selecting the measurement data with the highest feature contribution degree as a feature set, and taking the node voltage amplitude and the corresponding distribution network topological structure as a feature data set and a corresponding label for subsequent model training.
In order to reduce the computational complexity and the spatial complexity of subsequent model training, a random forest intelligent algorithm is adopted to calculate the contribution degree of each characteristic category to the power distribution network topology identification, and the principle is as follows:
wherein, N represents a tree of decision trees in the forest, for each decision tree, selecting corresponding out of bag data (OOB) to calculate out of bag data error, and recording as errOOB1Randomly adding noise interference to the characteristic X of all samples of the data outside the bag (the value of the sample at the characteristic X can be randomly changed), and calculating the error of the data outside the bag again and recording the error as errOOB2. Feature contribution ranking is shown in FIG. 4Shown in the figure. Except for the root node, the voltage amplitudes of all other nodes have higher contribution degrees than the node injection power, so the node voltage amplitude is selected as a characteristic set.
S4: and constructing a power distribution network topology identification model, and training the power distribution network topology identification model based on the feature set.
As shown in fig. 2, the topology identification model of the power distribution network is a convolutional neural network based on an attention mechanism. The convolutional neural network comprises an attention module, an input layer, a hidden layer and an output layer, wherein the input layer, the hidden layer and the output layer are sequentially connected, and the attention module is arranged behind the first layer of the hidden layer.
The attention module adopts an attention mechanism to rapidly scan all the features to obtain the feature classes needing important attention, and then focuses attention on the feature classes needing important attention, so that the attention applied to other feature classes which are not important is reduced, and the working efficiency and the accuracy are greatly improved. The implementation principle of the method is shown in fig. 5, and the feature data set after attention mechanism processing is obtained by calculating the feature importance of all input feature data sets, obtaining the weight of each feature category and multiplying the rest input features. An attention mechanism and a convolutional neural network are combined to construct a power distribution network topology identification model, the basic structure of the power distribution network topology identification model is shown in fig. 6, the input layer is the voltage amplitude of 33 nodes, the output layer is the probability of belonging to a certain topology of 28 topologies, and the attention mechanism is put into the power distribution network topology identification model to serve as a hidden layer to improve the anti-noise capability of the model.
The loss function of the convolutional neural network is:
wherein p ═ p1,…,pN]Is a probability distribution of each element piRepresenting the probability that the sample belongs to the topology i; y ═ y1,…,yN]Is a sample label, y when the sample belongs to the topology i i1, otherwise yi0; n is the total number of topology classes.
S5: and sending the measurement data of the power distribution network to be identified into the power distribution network topology identification model to obtain the topology structure of the power distribution network to be identified.
In this embodiment, in order to facilitate the effectiveness and superiority of the present invention, a power distribution Network topology model is respectively constructed based on several common intelligent algorithms such as Convolutional Neural Networks (CNN), Deep Neural Networks (DNN), and eXtreme Gradient Boosting (XGboost), and experimental results are compared.
The evaluation indexes of the model adopt Accuracy (ACC), Precision (PRE) and Recall (REC). The calculation formula is as follows:
wherein: t is the number of correct samples in all classes, N is the total number of samples in all classes, TP represents the number of positive samples classified as positive, FP represents the number of negative samples classified as positive, and FN represents the number of positive samples classified as negative.
Meanwhile, considering that the model function of the invention is actually a multi-classification function, the confusion matrix can be adopted to display the classification effect of a single sample class of the model, and the confusion matrix can display the classification effect of the model very intuitively.
And randomly segmenting the feature data set and the label set, wherein 70% of feature data set is used as a training set, and 30% of feature data set is used as a testing set. The model training results are shown in the following table:
TABLE 1 comparison of Performance of four algorithms
As can be seen from the above table, the classification problem focuses on accuracy, the accuracy of the convolutional neural network (ACNN) and CNN in combination with the attention mechanism is high, the accuracy and recall rate of ACNN are good, and the topology identification effect is good.
From theoretical analysis, ACNN should be able to show superiority of classification effect under the condition of noise in the data, and the superiority shows more obviously the greater the noise, considering that the PMU device and the micro PMU device with good performance and better measurement error have measurement errors of 0.05% and 0.01% respectively, and the other common measurement devices have larger errors, so that the noise of 0.01%, 0.05%, 0.5% and 1% respectively added in the data simulates real measurement data.
After adding noise to the test data, the recognition results are shown in the following table, where the Total Vector Error (TVE) is used to measure the noise level:
table 2 IEEE33 node test set accuracy with measurement noise taken into account
As can be seen from the above table results, within a certain range, the higher the noise level in the data is, the more excellent the classification effect of ACNN can be reflected.
To further verify the validity of the model, a normalized confusion matrix is calculated for the model, the result of which is shown in fig. 7 for a confusion matrix thermodynamic diagram. The meaning of the element in the ith row and the jth column of the confusion matrix is: the true topology label is i and the probability that the predicted topology label is j. The results show that the diagonal elements are almost all 1, and the off-diagonal elements are substantially 0, so the model has better effect. Therefore, the method can accurately and effectively realize the topology identification of the power distribution network.
The above embodiments are merely examples and do not limit the scope of the present invention. These embodiments may be implemented in other various manners, and various omissions, substitutions, and changes may be made without departing from the technical spirit of the present invention.
Claims (10)
1. A power distribution network topology identification method based on an attention mechanism and a convolutional neural network is characterized by comprising the following steps:
s1: acquiring historical measurement data of the power distribution network and a corresponding topological structure, and constructing a database;
s2: preprocessing the measured data;
s3: screening the features according to the feature contribution degree, and constructing a feature set;
s4: constructing a power distribution network topology identification model, and training the power distribution network topology identification model based on the feature set;
s5: and sending the measurement data of the power distribution network to be identified into the power distribution network topology identification model to obtain the topology structure of the power distribution network to be identified.
2. The method for identifying the topology of the power distribution network based on the attention mechanism and the convolutional neural network as claimed in claim 1, wherein the preprocessing comprises data cleaning, missing value filling and abnormal value removing.
3. The method for identifying the topology of the power distribution network based on the attention mechanism and the convolutional neural network as claimed in claim 1, wherein the specific step of step S3 includes:
s31: acquiring the characteristic contribution of different measurement data to the topology identification of the power distribution network;
s32: and sequencing the feature contribution degrees, selecting the measurement data with the highest feature contribution degree as a feature set, and taking the corresponding topological structure as a label of the feature set.
4. The method for identifying the topology of the power distribution network based on the attention mechanism and the convolutional neural network as claimed in claim 1, wherein a random forest algorithm is used to calculate the feature contribution degree of the measured data in step S31.
5. The method for identifying the topology of the power distribution network based on the attention mechanism and the convolutional neural network as claimed in claim 1, wherein the model for identifying the topology of the power distribution network is a convolutional neural network based on the attention mechanism.
6. The method for identifying the topology of the power distribution network based on the attention mechanism and the convolutional neural network as claimed in claim 5, wherein the convolutional neural network comprises an attention module and an input layer, a hidden layer and an output layer which are connected in sequence, and the attention module is disposed behind the first layer of the hidden layer.
7. The method for identifying the topology of the power distribution network based on the attention mechanism and the convolutional neural network as claimed in claim 6, wherein the loss function of the convolutional neural network is:
wherein p ═ p1,…,pN]Is a probability distribution of each element piRepresenting the probability that the sample belongs to the topology i; y ═ y1,…,yN]Is a sample label, y when the sample belongs to the topology ii1, otherwise yi0; n is the total number of topology classes.
8. The method of claim 1, wherein the measurement data includes node voltage amplitude and node injection power.
9. The method according to claim 8, wherein the node injection power is active power.
10. The method according to claim 1, wherein the topology structure is a topology structure diagram corresponding to the measured data of each group of nodes.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111360101.6A CN114048819A (en) | 2021-11-17 | 2021-11-17 | Power distribution network topology identification method based on attention mechanism and convolutional neural network |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111360101.6A CN114048819A (en) | 2021-11-17 | 2021-11-17 | Power distribution network topology identification method based on attention mechanism and convolutional neural network |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN114048819A true CN114048819A (en) | 2022-02-15 |
Family
ID=80209637
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202111360101.6A Pending CN114048819A (en) | 2021-11-17 | 2021-11-17 | Power distribution network topology identification method based on attention mechanism and convolutional neural network |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN114048819A (en) |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114912338A (en) * | 2022-03-07 | 2022-08-16 | 天津大学 | Topology identification method based on two-stage feature selection and Gelam angular field |
| CN115935262A (en) * | 2023-01-05 | 2023-04-07 | 合肥工业大学 | Graph data driven power distribution network topology identification method, electronic equipment and storage medium |
| CN117913895A (en) * | 2024-01-16 | 2024-04-19 | 重庆大学 | LightGBM-LSTM-based power distribution network topology identification method and device |
Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US5371834A (en) * | 1992-08-28 | 1994-12-06 | The United States Of America As Represented By The Administrator Of The National Aeronautics And Space Administration | Adaptive neuron model--an architecture for the rapid learning of nonlinear topological transformations |
| WO2020256517A2 (en) * | 2019-06-21 | 2020-12-24 | 주식회사 쓰리아이 | Automatic phase mapping processing method and system based on omnidirectional image information |
| CN112381667A (en) * | 2020-11-12 | 2021-02-19 | 国网四川省电力公司电力科学研究院 | Distribution network electrical topology identification method based on deep learning |
| US20210141969A1 (en) * | 2019-11-08 | 2021-05-13 | X Development Llc | Scalably generating distribution grid topology |
| CN113343427A (en) * | 2021-05-11 | 2021-09-03 | 华中科技大学 | Structural topology configuration prediction method based on convolutional neural network |
| CN113657171A (en) * | 2021-07-20 | 2021-11-16 | 国网上海市电力公司 | Low-voltage distribution network platform region topology identification method based on graph wavelet neural network |
| CN117421571A (en) * | 2023-10-16 | 2024-01-19 | 国网江苏省电力有限公司镇江供电分公司 | Topology real-time identification method and system based on power distribution network |
-
2021
- 2021-11-17 CN CN202111360101.6A patent/CN114048819A/en active Pending
Patent Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US5371834A (en) * | 1992-08-28 | 1994-12-06 | The United States Of America As Represented By The Administrator Of The National Aeronautics And Space Administration | Adaptive neuron model--an architecture for the rapid learning of nonlinear topological transformations |
| WO2020256517A2 (en) * | 2019-06-21 | 2020-12-24 | 주식회사 쓰리아이 | Automatic phase mapping processing method and system based on omnidirectional image information |
| US20210141969A1 (en) * | 2019-11-08 | 2021-05-13 | X Development Llc | Scalably generating distribution grid topology |
| CN112381667A (en) * | 2020-11-12 | 2021-02-19 | 国网四川省电力公司电力科学研究院 | Distribution network electrical topology identification method based on deep learning |
| CN113343427A (en) * | 2021-05-11 | 2021-09-03 | 华中科技大学 | Structural topology configuration prediction method based on convolutional neural network |
| CN113657171A (en) * | 2021-07-20 | 2021-11-16 | 国网上海市电力公司 | Low-voltage distribution network platform region topology identification method based on graph wavelet neural network |
| CN117421571A (en) * | 2023-10-16 | 2024-01-19 | 国网江苏省电力有限公司镇江供电分公司 | Topology real-time identification method and system based on power distribution network |
Non-Patent Citations (5)
| Title |
|---|
| SHI SU等: "Intelligent power equipment identification model based on grid topology analysis", 2021 INTERNATIONAL CONFERENCE ON ADVANCED ENERGY, POWER AND ELECTRICAL ENGINEERING (AEPEE2021), vol. 260, 16 July 2021 (2021-07-16), pages 02001 * |
| YANG X等: "Hybrid data and model-driven joint identification of distribution-network topology and parameters", IET GENERATION TRANSMISSION & DISTRIBUTION, vol. 16, no. 23, 18 October 2022 (2022-10-18), pages 4846 - 4866 * |
| ZHANG XJ等: "Topology Analysis of Distribution Network Based on μPMU and SCADA", INTERNATIONAL CONFERENCE ON POWER SYSTEM TECHNOLOGY (POWERCON), 31 December 2018 (2018-12-31), pages 3427 - 3433 * |
| 李婷婷等: "基于图注意力神经网络与拓扑势的关键节点识别", 中国自动化学会.2021中国自动化大会论文集, 22 October 2021 (2021-10-22) * |
| 杨秀等: "基于注意力机制和卷积神经网络的配电网拓扑辨识", 电网技术, vol. 46, no. 05, 31 March 2022 (2022-03-31), pages 1672 - 1682 * |
Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114912338A (en) * | 2022-03-07 | 2022-08-16 | 天津大学 | Topology identification method based on two-stage feature selection and Gelam angular field |
| CN115935262A (en) * | 2023-01-05 | 2023-04-07 | 合肥工业大学 | Graph data driven power distribution network topology identification method, electronic equipment and storage medium |
| CN115935262B (en) * | 2023-01-05 | 2023-05-12 | 合肥工业大学 | Graph data driven power distribution network topology identification method, electronic equipment and storage medium |
| CN117913895A (en) * | 2024-01-16 | 2024-04-19 | 重庆大学 | LightGBM-LSTM-based power distribution network topology identification method and device |
| CN117913895B (en) * | 2024-01-16 | 2024-10-01 | 重庆大学 | LightGBM-LSTM-based power distribution network topology identification method and device |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN112699913B (en) | Method and device for diagnosing abnormal relationship of household transformer in transformer area | |
| CN108038300B (en) | Optical fiber state evaluation method based on improved membership function combined with neural network | |
| CN114048819A (en) | Power distribution network topology identification method based on attention mechanism and convolutional neural network | |
| CN103077402B (en) | Partial discharge of transformer mode identification method based on singular value decomposition algorithm | |
| CN111860982A (en) | Wind power plant short-term wind power prediction method based on VMD-FCM-GRU | |
| CN110596492A (en) | Transformer fault diagnosis method based on particle swarm optimization random forest model | |
| CN109783879B (en) | Radar radiation source signal identification efficiency evaluation method and system | |
| CN106055918A (en) | Power system load data identification and recovery method | |
| CN110232434A (en) | A kind of neural network framework appraisal procedure based on attributed graph optimization | |
| Chen et al. | Data-driven robust state estimation through off-line learning and on-line matching | |
| CN114578087B (en) | Wind speed uncertainty measurement method based on non-dominant sorting and stochastic simulation algorithm | |
| CN105572572A (en) | WKNN-LSSVM-based analog circuit fault diagnosis method | |
| CN114330486A (en) | Power system bad data identification method based on improved Wasserstein GAN | |
| CN110321493A (en) | A kind of abnormality detection of social networks and optimization method, system and computer equipment | |
| CN112596016A (en) | Transformer fault diagnosis method based on integration of multiple one-dimensional convolutional neural networks | |
| CN113484813A (en) | Intelligent ammeter fault rate estimation method and system under multi-environment stress | |
| CN115201563B (en) | Multi-harmonic source positioning method and system based on joint entropy | |
| CN116010884A (en) | Fault diagnosis method of SSA-LightGBM oil-immersed transformer based on principal component analysis | |
| CN115409317A (en) | Transformer area line loss detection method and device based on feature selection and machine learning | |
| CN110544047A (en) | Bad data identification method | |
| CN107204616B (en) | Power system random state estimation method based on self-adaptive sparse pseudo-spectral method | |
| CN114266396A (en) | Transient stability discrimination method based on intelligent screening of power grid characteristics | |
| CN117745096A (en) | Evaluation method and system for control screen cloth measurement scheme | |
| CN115031794B (en) | Novel gas-solid two-phase flow measuring method based on multi-feature graph convolution | |
| CN113496255B (en) | Power distribution network mixed observation point distribution method based on deep learning and decision tree driving |
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 |