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 PDF

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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
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CN114048819B (en
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田英杰
李凡
蒋家富
吴裔
赵莹莹
苏运
郭乃网
金妍斐
刘俊
杨帆
杜习周
陈琰
杨秀
刘方
傅广努
李承泽
张�浩
仇志鑫
刘欣雨
张倩倩
蒋倩
汤金璋
周从亨
陈浩然
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State Grid Shanghai Electric Power Co Ltd
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Abstract

本发明涉及一种基于注意力机制和卷积神经网络的配电网拓扑辨识方法，包括以下步骤：S1：获取配电网的量测数据以及对应的拓扑结构，构建数据库；S2：对量测数据进行预处理；S3：根据特征贡献度对特征筛选，构建特征集；S4：构建配电网拓扑辨识模型，基于特征集对配电网拓扑辨识模型进行训练；S5：将待辨识的配电网的量测数据送入配电网拓扑辨识模型，获取待辨识的配电网的拓扑结构。与现有技术相比，本发明具有仅需要断面量测数据，辨识分类准确性高，克服数据噪声等优点。

Figure 202111360101

The invention relates to a distribution network topology identification method based on an attention mechanism and a convolutional neural network. The data is preprocessed; S3: Screen features according to the feature contribution, and build a feature set; S4: Build a distribution network topology identification model, and train the distribution network topology identification model based on the feature set; S5: Use the distribution network to be identified. The measurement data of the network is sent to the distribution network topology identification model to obtain the topology structure of the distribution network to be identified. Compared with the prior art, the present invention has the advantages of only needing section measurement data, high identification and classification accuracy, and overcoming data noise.

Figure 202111360101

Description

Power distribution network topology identification method based on attention mechanism and convolutional neural network
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:
Figure BDA0003358886070000021
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:
Figure BDA0003358886070000041
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:
Figure BDA0003358886070000042
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:
Figure BDA0003358886070000051
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:
Figure BDA0003358886070000052
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:
Figure BDA0003358886070000061
Figure BDA0003358886070000062
Figure BDA0003358886070000063
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
Figure BDA0003358886070000064
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
Figure BDA0003358886070000071
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)

1.一种基于注意力机制和卷积神经网络的配电网拓扑辨识方法，其特征在于，包括以下步骤：1. a distribution network topology identification method based on attention mechanism and convolutional neural network, is characterized in that, comprises the following steps: S1：获取配电网的历史量测数据以及对应的拓扑结构，构建数据库；S1: Obtain the historical measurement data of the distribution network and the corresponding topology structure, and build a database; S2：对量测数据进行预处理；S2: Preprocess the measurement data; S3：根据特征贡献度对特征筛选，构建特征集；S3: Screen the features according to the feature contribution, and construct a feature set; S4：构建配电网拓扑辨识模型，基于特征集对配电网拓扑辨识模型进行训练；S4: Build a distribution network topology identification model, and train the distribution network topology identification model based on the feature set; S5：将待辨识的配电网的量测数据送入配电网拓扑辨识模型，获取待辨识的配电网的拓扑结构。S5: Send the measurement data of the distribution network to be identified into the distribution network topology identification model to obtain the topology structure of the distribution network to be identified. 2.根据权利要求1所述的一种基于注意力机制和卷积神经网络的配电网拓扑辨识方法，其特征在于，所述的预处理包括数据清洗、填补缺失值、去除异常值。2 . A distribution network topology identification method based on an attention mechanism and a convolutional neural network according to claim 1 , wherein the preprocessing includes data cleaning, filling in missing values, and removing outliers. 3 . 3.根据权利要求1所述的一种基于注意力机制和卷积神经网络的配电网拓扑辨识方法，其特征在于，所述的步骤S3的具体步骤包括：3. a kind of distribution network topology identification method based on attention mechanism and convolutional neural network according to claim 1, is characterized in that, the concrete steps of described step S3 comprise: S31：获取不同测量数据对配电网拓扑识别的特征贡献度；S31: Obtain the feature contribution degree of different measurement data to the distribution network topology identification; S32：对特征贡献度进行排序，选取特征贡献度最高测量数据作为特征集，并将对应的拓扑结构作为特征集的标签。S32: Sort the feature contribution degrees, select the measurement data with the highest feature contribution degree as the feature set, and use the corresponding topological structure as the label of the feature set. 4.根据权利要求1所述的一种基于注意力机制和卷积神经网络的配电网拓扑辨识方法，其特征在于，所述的步骤S31中采用随机森林算法计算测量数据的特征贡献度。4 . A distribution network topology identification method based on an attention mechanism and a convolutional neural network according to claim 1 , wherein, in the step S31 , a random forest algorithm is used to calculate the feature contribution of the measurement data. 5 . 5.根据权利要求1所述的一种基于注意力机制和卷积神经网络的配电网拓扑辨识方法，其特征在于，所述的配电网拓扑辨识模型为基于注意力机制的卷积神经网络。5. a kind of distribution network topology identification method based on attention mechanism and convolutional neural network according to claim 1, is characterized in that, described distribution network topology identification model is the convolutional neural network based on attention mechanism network. 6.根据权利要求5所述的一种基于注意力机制和卷积神经网络的配电网拓扑辨识方法，其特征在于，所述的卷积神经网络包括注意力模块和依次连接的输入层、隐藏层、输出层，所述注意力模块设于所述隐藏层的第一层后。6. a kind of distribution network topology identification method based on attention mechanism and convolutional neural network according to claim 5, is characterized in that, described convolutional neural network comprises attention module and input layer connected in sequence, Hidden layer, output layer, the attention module is arranged after the first layer of the hidden layer. 7.根据权利要求6所述的一种基于注意力机制和卷积神经网络的配电网拓扑辨识方法，其特征在于，所述的卷积神经网络的损失函数为：7. a kind of distribution network topology identification method based on attention mechanism and convolutional neural network according to claim 6, is characterized in that, the loss function of described convolutional neural network is:
Figure FDA0003358886060000011
Figure FDA0003358886060000011
 其中，p＝[p1,…,pN]是一个概率分布，每个元素pi表示样本属于拓扑结构i的概率；y＝[y1,…,yN]是样本标签，当样本属于拓扑结构i时yi＝1，否则yi＝0；N是拓扑结构类别总数。Among them, p=[p 1 ,...,p N ] is a probability distribution, each element p i represents the probability that the sample belongs to topology i; y=[y 1 ,...,y N ] is the sample label, when the sample belongs to yi = 1 for topology i, otherwise yi = 0; N is the total number of topology categories. 8.根据权利要求1所述的一种基于注意力机制和卷积神经网络的配电网拓扑辨识方法，其特征在于，所述的量测数据包括节点电压幅值和节点注入功率。8 . A distribution network topology identification method based on attention mechanism and convolutional neural network according to claim 1 , wherein the measurement data includes node voltage amplitude and node injection power. 9 . 9.根据权利要求8所述的一种基于注意力机制和卷积神经网络的配电网拓扑辨识方法，其特征在于，所述的节点注入功率为有功功率。9. A distribution network topology identification method based on an attention mechanism and a convolutional neural network according to claim 8, wherein the node injection power is active power. 10.根据权利要求1所述的一种基于注意力机制和卷积神经网络的配电网拓扑辨识方法，其特征在于，所述的拓扑结构为每一组节点的量测数据对应的拓扑结构图。10 . A distribution network topology identification method based on attention mechanism and convolutional neural network according to claim 1 , wherein the topology structure is the topology structure corresponding to the measurement data of each group of nodes. 11 . picture.
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