CN114169118A - Power distribution network topological structure identification method considering distributed power supply output correlation - Google Patents
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Abstract
A power distribution network topological structure identification method considering the output correlation of a distributed power supply includes the steps that preprocessed power grid data serve as a training set to train a neural network in an off-line stage, a test set is used for inputting the trained neural network to obtain a synthesized node voltage amplitude sample in an on-line stage, then a maximum spanning tree algorithm based on a maximum information coefficient is adopted to conduct power distribution network topological structure identification, all node pairs of a power distribution network are obtained, and each node pair represents one edge in the power distribution network topological structure and serves as a power distribution network topological structure identification result. The invention adopts a neural network to establish the relation between the node voltage amplitude and the node injection power, and provides a maximum spanning tree algorithm based on the maximum information coefficient to identify the topological structure of the power distribution network.
Description
Technical Field
The invention relates to a technology for analyzing the running state of a power distribution network, in particular to a power distribution network topological structure identification method considering the output correlation of a distributed power supply.
Background
The power system topology abstracts the grid into a graph containing nodes and edges. The nodes of the graph represent nodes in a power grid, and are connected with devices such as a generator, a distributed power supply, a load and the like; the edges of the graph represent lines in the grid. The topological structure of the power system describes the connection relation among nodes in the power grid, and is an important basis for analysis and application of power system optimization operation, fault location, power failure management and the like. Generally, the operator of the power system can accurately solve the topology of the power transmission network, and only needs to identify the error of the topology according to the relatively complete measurement information. However, due to numerous nodes and branches of the power distribution network, measurement information is incomplete, and the topology structure changes frequently, and particularly, under the background that the distributed power supply is accessed to the power distribution network in a large scale, the topology structure of the power distribution network is difficult to accurately know.
With the large-scale deployment of an Advanced Measurement Infrastructure (AMI) And a data acquisition And monitoring Control System (SCADA), node voltage amplitude And injection power measurement information can be acquired, And the information has important value for identifying a topological structure. The existing power distribution network topological structure method depends on certain assumed conditions, such as known power distribution network line parameters, independent node injection power, node voltage obeying normal distribution and the like.
Disclosure of Invention
Aiming at the defects of the existing method, the invention provides a power distribution network topological structure identification method considering the output correlation of a distributed power supply, a neural network is adopted to establish the relation between the node voltage amplitude and the node injection power, and a maximum spanning tree algorithm based on the maximum information coefficient is provided for identifying the power distribution network topological structure.
The invention is realized by the following technical scheme:
the invention relates to a power distribution network topological structure identification method considering the output correlation of a distributed power supply.
The power grid data refer to node injection power and node voltage amplitude measurement data of all nodes of the power distribution network.
The pretreatment is as follows: measure p the injection power of node iiConversion to a random variable p obeying a standard normal distributionn,iThen, P is calculatedn＝{pn,1,pn,2,…,pn,NAnd (4) obtaining a lower triangular matrix L by using Cholesky decomposition to obtain an input sample P of the neural network training sett＝L-1Pn TWherein: i is 1,2, …, N.
The training set is a training set input sample PtAnd training set output samples, i.e. node voltage amplitude measurement Ut＝{u1,u2,…,uNThe set of (c).
The test set is obtained by the following steps:
1) calculating a standard deviation vector
Wherein:
is a matrix L-1Row i, column k.
2) The mean value is 0 and the standard deviation is sigmaiIs subject to an independent normal distributionc,i。
3) Obtaining a test set input sample Ps＝L-1Pc TIn which P isc＝{pc,1,pc,2,…,pc,N}。
The neural network comprises: preprocessing module, input layer, hidden layer and output layer, wherein: the preprocessing module converts the node voltage amplitude corresponding to the node injection power with the correlation into the node voltage amplitude corresponding to the independent node injection power.
The number of neurons of the input layer is the number of nodes minus 1, the number of layers of the hidden layer is 2, the number of neurons is 1000, and the number of neurons of the output layer is the number of nodes minus 1.
The maximum spanning tree algorithm based on the maximum information coefficient specifically includes:
i) a graph T is established containing N nodes.
ii) calculating the maximum information coefficient of the voltage amplitude of all the nodes.
iii) arranging all node pairs in the order of maximum voltage amplitude information coefficient from large to small, and using e for the ordered node pairskAnd k is 1,2, …, N (N-1)/2.
iv) mixing e1And e2Add panel T.
v) setting k to 3.
vi) when the number of edges in the graph T is less than N-1, performing steps vii) to ix), otherwise go to step x):
vii) when e is equal tokAfter adding graph T, if no ring is formed, e is addedkAdd panel T.
viii)k＝k+1。
ix) when k is larger than N (N-1)/2, go to step x), otherwise go to step vi).
x) obtaining a graph T which is the result of the topology identification.
The invention relates to a system for realizing the method, which comprises the following steps: the data reading module, the preprocessing module, the synthesized node voltage amplitude generation module, the maximum spanning tree algorithm module and the result output module are connected in sequence, wherein: the data reading module reads measurement data of the node injection power and the node voltage amplitude of the power distribution network; the preprocessing module generates a neural network training set and a test set sample according to the node injection power and the node voltage amplitude measurement data; the synthesized node voltage amplitude generation module obtains a synthesized node voltage amplitude by adopting a neural network; the maximum spanning tree algorithm module obtains a maximum spanning tree according to the maximum information coefficient between the synthesized node voltage amplitudes; and the result output module outputs the maximum spanning tree as a topology identification result.
Technical effects
According to the invention, the normal distribution property is utilized, the neural network input sets which obey different distributions are converted into the input sets which obey the same distribution through cumulative distribution function transformation, the coverage range of the training sample input set is ensured to contain the test sample input set, the training precision of the neural network is improved, and meanwhile, the maximum information coefficient is adopted to accurately calculate the measurement correlation of the node voltage amplitude by the maximum spanning tree algorithm based on the maximum information coefficient, so that the precision of the topological structure identification algorithm is improved. Compared with the prior art, the topological structure identification method effectively accounts for the influence of the output correlation of the distributed power supply, and obtains an accurate distribution network topological structure identification result on the premise of not needing to know the line parameters of the distribution network, not needing to assume that the node injection powers are mutually independent and not needing to assume node voltage amplitude probability distribution.
Drawings
FIG. 1 is a flow chart of the method of the present invention;
FIG. 2 is a system wiring diagram of an embodiment IEEE 69 node;
FIG. 3 is a schematic diagram illustrating the effects of the embodiment.
Detailed Description
As shown in fig. 2, in this embodiment, an IEEE 69 node standard system is used as an example, node injection power measurement data disclosed by an actual power distribution network is used, a sampling scale is set to 8000, a neural network used in this embodiment is a fully-connected neural network, and includes 2 hidden layers, the number of neurons in the hidden layers is 1000, an Adam optimizer and a root-mean-square loss function are used, and a learning rate is set to 5 e-4.
The embodiment specifically comprises the following steps:
step 1) acquiring power grid data and acquiring a node injection power measurement sample pi(i-1, 2, …, 69) and a node voltage magnitude measurement sample ui(i＝1,2,…，69)。piAnd uiAre vectors with dimensions 8000.
Step 2) adding piConversion to a random variable p obeying a standard normal distributionn,iThe method specifically comprises the following steps:
i) estimate piCumulative distribution function value c of each sample point ink(k＝1,2,…，8000)。
ii) estimating the standard normal distribution at ckThe inverse cumulative distribution function value p ofn,i。
Step 3) calculating Pn＝{pn,1,pn,2,…,pn,69And (4) decomposing the covariance matrix sigma by Cholesky to obtain a lower triangular matrix L.
Step 4) calculating input sample P of neural network training sett＝L-1Pn T。
Step 5) generating input sample P of neural network test setsThe method specifically comprises the following steps:
i) calculating a standard deviation vector
Wherein
Is a matrix L-1Row i, column k.
ii) mean 0 and standard deviation σ are generatediIs subject to an independent normal distributionc,i。
iii) obtaining a test set input sample Ps＝L-1Pc TIn which P isc＝{pc,1,pc,2,…,pc,69}。
And 6) training the neural network by adopting the input samples of the training set and the node voltage amplitude samples.
Step 7) inputting the test set input sample into a neural network to obtain a synthesized node voltage amplitude sample Us。
Step 8) calculating the maximum information coefficient among all node pairs, which specifically comprises the following steps:
i) and drawing a scatter diagram according to the node voltage amplitude samples of the two nodes i and j.
ii) the scatter diagram is divided by grids of different row and column numbers.
iii) for the grid G with the resolution ratio of k rows and l columns, changing the division position of the grid, and calculating the maximum mutual information I between the voltage amplitude samples of the two nodes based on the grid with the resolution ratiok,l((Ui，Uj)，k,l)＝maxI((Ui，Uj)|G)。
iv) calculating based onNormalized mutual information I of grid G with resolution of k rows and l columns* k,l＝Ik,l((Ui，Uj)，k,l)/logmin{k,l}。
v) normalized mutual information I of the grid based on all resolutions* k,lForming a matrix M with the k row and l column elements of I* k,l。
vi) the largest information coefficient is the largest element value in the matrix M.
Step 9) obtaining a topological structure by adopting a maximum spanning tree algorithm based on a maximum information coefficient, and specifically comprises the following steps:
i) a graph T is established containing N nodes.
ii) arranging all node pairs in the order of maximum voltage amplitude information coefficient from large to small, and using e for the ordered node pairskAnd k is 1,2, …, N (N-1)/2.
iii) reacting e1And e2Add panel T.
iv) let k equal 3.
v) when the number of edges in the graph T is less than N-1, executing steps vi) to viii), otherwise, turning to step ix).
vi) when e is equal tokAfter adding graph T, if no ring is formed, e is addedkAdd panel T.
vii)k＝k+1。
viii) when k > N (N-1)/2, go to step ix), otherwise go to step v).
ix) graph T is the result of topology identification.
Compared with the prior art, the topological structure identification method effectively accounts for the influence of the output correlation of the distributed power supply, and obtains an accurate distribution network topological structure identification result on the premise of not needing to know the line parameters of the distribution network, not needing to assume that the node injection powers are mutually independent and not needing to assume node voltage amplitude probability distribution.
The foregoing embodiments may be modified in many different ways by those skilled in the art without departing from the spirit and scope of the invention, which is defined by the appended claims and all changes that come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein.
Claims (5)
1. A power distribution network topological structure identification method considering the output correlation of a distributed power supply is characterized in that preprocessed power grid data are used as a training set to train a neural network in an off-line stage, a test set is used to input the trained neural network to obtain a synthesized node voltage amplitude sample in an on-line stage, then a maximum spanning tree algorithm based on a maximum information coefficient is adopted to carry out power distribution network topological structure identification to obtain all node pairs of the power distribution network, each node pair represents one edge in the power distribution network topological structure and serves as a power distribution network topological structure identification result;
the power grid data refer to node injection power and node voltage amplitude measurement data of all nodes of the power distribution network;
the pretreatment is as follows: measure p the injection power of node iiConversion to a random variable p obeying a standard normal distributionn,iThen, P is calculatedn＝{pn,1,pn,2,…,pn,NAnd (4) obtaining a lower triangular matrix L by using Cholesky decomposition to obtain an input sample P of the neural network training sett＝L-1Pn TWherein: i is 1,2, …, N.
2. The method of claim 1, wherein the training set is a training set input sample PtAnd training set output samples, i.e. node voltage amplitude measurement Ut＝{u1,u2,…,uNA set of { fraction };
the test set is obtained by the following steps:
1) calculating a standard deviation vector
Wherein:
is a matrix L-1Row i, column k elements;
2) the mean value is 0 and the standard deviation is sigmaiIs subject to an independent normal distributionc,i；
3) Obtaining a test set input sample Ps＝L-1Pc TIn which P isc＝{pc,1,pc,2,…,pc,N}。
3. The method of claim 1, wherein the neural network comprises: preprocessing module, input layer, hidden layer and output layer, wherein: the preprocessing module converts the node voltage amplitude corresponding to the node injection power with the correlation into the node voltage amplitude corresponding to the independent node injection power;
the number of neurons of the input layer is the number of nodes minus 1, the number of layers of the hidden layer is 2, the number of neurons is 1000, and the number of neurons of the output layer is the number of nodes minus 1.
4. The method for identifying a power distribution network topology structure considering distributed power output correlations as claimed in claim 1, wherein the maximum spanning tree algorithm based on the maximum information coefficient specifically comprises:
i) establishing a graph T containing N nodes;
ii) calculating the maximum information coefficient of the voltage amplitude of all the nodes;
iii) arranging all node pairs in the order of maximum voltage amplitude information coefficient from large to small, and using e for the ordered node pairskK is 1,2, …, N (N-1)/2;
iv) mixing e1And e2Adding a graph T;
v) setting k to 3;
vi) when the number of edges in the graph T is less than N-1, executing steps vii) to ix), otherwise, turning to step x);
vii) when e is equal tokAfter adding graph T, if no ring is formed, e is addedkAdding a graph T;
viii)k＝k+1；
ix) when k is larger than N (N-1)/2, go to step x), otherwise go to step vi);
x) obtaining a graph T which is the result of the topology identification.
5. A system for implementing the method of any one of claims 1 to 4, comprising: the data reading module, the preprocessing module, the synthesized node voltage amplitude generation module, the maximum spanning tree algorithm module and the result output module are connected in sequence, wherein: the data reading module reads measurement data of the node injection power and the node voltage amplitude of the power distribution network; the preprocessing module generates a neural network training set and a test set sample according to the node injection power and the node voltage amplitude measurement data; the synthesized node voltage amplitude generation module obtains a synthesized node voltage amplitude by adopting a neural network; the maximum spanning tree algorithm module obtains a maximum spanning tree according to the maximum information coefficient between the synthesized node voltage amplitudes; and the result output module outputs the maximum spanning tree as a topology identification result.
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