CN112559963A - Power distribution network dynamic parameter identification method and device - Google Patents

Power distribution network dynamic parameter identification method and device Download PDF

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CN112559963A
CN112559963A CN202011309392.1A CN202011309392A CN112559963A CN 112559963 A CN112559963 A CN 112559963A CN 202011309392 A CN202011309392 A CN 202011309392A CN 112559963 A CN112559963 A CN 112559963A
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陶鸿飞
范强
蒋玮
谢栋
罗刚
王健
祁炜雯
赵洲
沈勇
赵峰
金渊文
俞永军
章立宗
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Shaoxing Jianyuan Electric Power Group Co ltd
Southeast University
NR Engineering Co Ltd
Shaoxing Power Supply Co of State Grid Zhejiang Electric Power Co Ltd
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Abstract

本发明公开了一种配电网动态参数辨识方法及装置。方法包括：对采集的配电网运行数据、外部环境数据进行数据预处理，生成配电网动态参数辨识样本；将配电网动态参数辨识样本离散化，获取离散化样本；基于预先建立的概率图模型，根据离散化样本，获取概率图模型的参数；所述概率图模型为两时间片概率图模型；根据观测变量以及获取参数后的概率图模型，基于置信度传播算法，获取配电网动态参数。本发明可以在部分量测数据或外部环境数据缺失的情况下利用概率图模型推断出配电网动态参数，有助于提高部分配电区域运行方式突变或外部环境突变情况下的参数辨识精度。

Figure 202011309392

The invention discloses a method and a device for identifying dynamic parameters of a distribution network. The method includes: preprocessing the collected distribution network operation data and external environment data to generate a distribution network dynamic parameter identification sample; discretizing the distribution network dynamic parameter identification sample to obtain a discrete sample; based on a pre-established probability Graph model, according to the discretized samples, obtain the parameters of the probability graph model; the probability graph model is a two-time slice probability graph model; according to the observed variables and the probability graph model after obtaining the parameters, based on the confidence propagation algorithm, obtain the distribution network dynamic parameters. The invention can use the probability graph model to infer the dynamic parameters of the distribution network when part of the measurement data or external environment data is missing, which helps to improve the parameter identification accuracy in the case of sudden changes in the operation mode of some distribution areas or sudden changes in the external environment.

Figure 202011309392

Description

Power distribution network dynamic parameter identification method and device
Technical Field
The invention belongs to the field of data-driven power distribution network parameter identification, and particularly relates to a power distribution network dynamic parameter identification method and device.
Background
The key points of energy conservation and loss reduction are in a power distribution network, and physical parameters of a distribution line are the basis of power grid loss calculation. Due to the fact that the power distribution network is large in scale, power distribution automation levels of all areas are different, and accurate physical parameters of the power distribution lines are difficult to obtain in partial power supply areas. In actual operation, the physical parameters of the power distribution network line are closely related to factors such as ambient temperature and line current-carrying capacity, and dynamic characteristics are presented along with changes of an operating environment, so that the physical parameters of the static power distribution network line stored in a Production Management System (PMS) are inaccurate. The power distribution network dispatching part in part of regions cannot accurately master the operation state of the power distribution network, and the possibility of power failure accidents caused by improper manual operation is increased. The mode of manually checking the dynamic parameters is low in efficiency and high in cost, and the key for realizing situation awareness of the smart power grid is how to utilize advanced measurement system data to identify the dynamic parameters of the power distribution network driven by data.
Disclosure of Invention
The purpose of the invention is as follows: aiming at the problems in the prior art, the invention provides a method and a device for identifying dynamic parameters of a power distribution network based on a probability graph model, which are used for analyzing the influence of the running state of the power distribution network and external environmental factors on the dynamic characteristics of the parameters of the power distribution network, deducing the probability distribution of the dynamic parameters of the power distribution network by using the probability graph model and realizing the accurate identification of the dynamic parameters of the power distribution network.
The technical scheme is as follows: in order to achieve the above object, in one aspect, the present invention provides a method for identifying dynamic parameters of a power distribution network, including the following steps:
carrying out data preprocessing on the collected power distribution network operation data and the collected external environment data to generate a power distribution network dynamic parameter identification sample;
discretizing a dynamic parameter identification sample of the power distribution network to obtain a discretization sample;
acquiring parameters of a probability map model according to a discretization sample based on a pre-established probability map model, wherein the probability map model is a two-time slice probability map model;
and acquiring dynamic parameters of the power distribution network based on a confidence coefficient propagation algorithm according to the observation variables and the probability map model after the parameters are acquired.
Further, the establishing step of the probability map model comprises:
selecting temperature, humidity, feeder line section voltage drop and feeder line section transmission power at a moment as observation variables in a probability map model, and selecting impedance of a line at the moment as an implicit variable of the probability map model;
adding each variable into a probability graph model one by one according to the causal relationship between each observation variable and each hidden variable under a single time slice to construct a static Bayesian network;
setting an initial time slice, and appointing prior probability distribution of each variable under the time slice;
and specifying the causal relationship of each state between adjacent time slices to construct a transfer model.
Further, the generating a distribution network dynamic parameter identification sample comprises:
performing secondary spline interpolation on the external environment data to enable the frequencies of the external environment data of different data sources to be identical;
merging data of different data sources, and eliminating redundant fields in the data;
and removing repeated data in the operating data of the power distribution network and performing data null removal.
Further, discretizing the power distribution network dynamic parameter identification sample, wherein a calculation formula is as follows:
Figure BDA0002789244850000031
wherein Z is an implicit variable, m is the number of divided discrete intervals, Ncount(Z ═ s) is the number of samples of hidden variables in the data at state s; n is a radical ofamount(Z) is the total number of samples.
Further, the obtaining of the parameters of the probability map model includes:
based on the probability mass function, obtaining an initial probability distribution table of each variable according to the discretization sample;
calculating a conditional probability distribution table among all variables according to the discretization sample based on a maximum expectation algorithm;
counting a transition probability distribution table of each variable from t moment to t +1 moment from continuous data samples on a time axis;
and determining the correctness of the conditional probability distribution table by checking whether the sum of the probability distributions of each variable is 1 or not and whether the conditional probability distribution is consistent with the causal relationship in the Bayesian network or not.
Further, the expression of the probability mass function is:
Figure BDA0002789244850000032
wherein
Figure BDA0002789244850000033
Probability of the hidden variable being initially in state s; n is a radical ofcount(Z ═ s) is the number of samples of hidden variables in the data at state s; n is a radical ofamount(Z) is the total number of samples.
Further, the calculating a conditional probability distribution table between variables according to the discretization sample based on the maximum expectation algorithm includes:
calculating the posterior probability of the hidden variable as the current expected value of the hidden variable according to the initial value of the conditional probability or the conditional probability obtained by the previous iteration, wherein the expression is as follows:
Pposterior(Z)＝P(Z|X；θcpt)
wherein Z is an implicit variable, Pposterior(Z) posterior probability of hidden variable, θcptA conditional probability distribution table in a probability graph model, wherein X is an observation variable;
and (3) updating the conditional probability distribution table by taking the likelihood function maximization as a target, wherein the expression is as follows:
Figure BDA0002789244850000041
where m is the number of hidden variable states, P (X, Z; theta)cpt) (ii) a desire for a hidden variable obtained from the sample;
and when the probability of the training data sample is maximum according to the condition probability distribution table in the probability graph model, the iteration of the maximum expectation algorithm is finished.
Further, the expression of the transition probability distribution table is:
Figure BDA0002789244850000042
wherein
Figure BDA0002789244850000043
Representing the probability of the hidden variable Z transitioning from state 1 to state 2 from t-1 to t; n is a radical ofcount(s1,s2) Representing the times of transferring the hidden variable Z from the state 1 to the state 2 from the t-1 to the t moment in the acquired historical data; n is a radical ofamount(s1) Representing the number of samples in the acquired historical data with the hidden variable Z in state 1.
Further, the obtaining of the dynamic parameters of the power distribution network based on the confidence propagation algorithm includes:
initializing the probability distribution of each variable according to the sample;
randomly selecting a certain state variable Y in the network, and replacing the confidence coefficient of the node with b (Y)t)：
Figure BDA0002789244850000051
Wherein phi (Y)t,Xt) Representing the joint compatibility of the node Y at the time t for a likelihood function between the corresponding state variable Y and the observation variable X at the time t, G being a first-order neighborhood of the node Y, mxY(Yt) A message passed to node Y for node x;
updating information between variables:
Figure BDA0002789244850000052
wherein psi (Y)t,Yt-1) Is a section ofThe potential energy between the nodes from the t-1 moment to the t moment at the point Y;
until the convergence condition is satisfied:
b(n)(Yt)-b(n-1)(Yt)＜10-5
the confidence b (Y) of the final hidden variablet) As the result of the estimation of the probability distribution of the hidden variables in each state interval.
In another aspect, the present invention provides a device for identifying dynamic parameters of a power distribution network, including:
the data preprocessing module is used for preprocessing the acquired running data and external environment data of the power distribution network to generate a dynamic parameter identification sample of the power distribution network;
the discretization processing module is used for discretizing the dynamic parameter identification sample of the power distribution network to obtain a discretization sample;
the model parameter determining module is used for acquiring parameters of the probability map model according to the discretization sample based on the pre-established probability map model; the probability map model is a two-time slice probability map model;
and the power distribution network dynamic parameter generation module is used for acquiring power distribution network dynamic parameters based on a confidence coefficient propagation algorithm according to the observation variables and the probability map model after the parameters are acquired.
Has the advantages that:
1. according to the method, the influence and the factor of the dynamic parameters of the power distribution network are analyzed according to data statistics and priori knowledge, the problem that the dynamic parameters cannot be obtained in a part of power distribution areas is solved, the parameter identification precision under the condition that the operation modes of the part of power distribution areas suddenly change or the external environment suddenly changes is improved, the operation state of the power distribution network is mastered and analyzed by power distribution network scheduling personnel, and the final identification result can provide a good basis for the upper-layer application of a power distribution automation system;
2. the invention carries out scientific analysis on the collected operation data and meteorological data of the power distribution network, provides a basis for power distribution network situation perception, is beneficial to realizing a panoramic visible and controllable power distribution network, and is beneficial to providing more reliable, safe and economic electric energy for power grid companies.
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Fig. 1 is a flowchart of a method for identifying dynamic parameters of a power distribution network according to an embodiment of the present invention;
FIG. 2 is a diagram of a dynamic Bayesian model for identifying physical parameters of a line according to an embodiment of the present invention;
FIG. 3 is a flow diagram of raw data pre-processing according to an embodiment of the invention;
FIG. 4 is a schematic diagram of reasoning dynamic parameters of a power distribution network based on a belief propagation algorithm, according to an embodiment of the invention;
fig. 5 is a simplified topology model of a medium voltage distribution network used in a simulation experiment according to an embodiment of the present invention.
Detailed Description
The invention is further described with reference to specific examples. The following examples are only for illustrating the technical solutions of the present invention more clearly, and the protection scope of the present invention is not limited thereby.
Fig. 1 is a flowchart of a method for identifying dynamic parameters of a power distribution network according to an embodiment of the present invention, as shown in fig. 1, the method includes the following steps:
step 1, carrying out data preprocessing on collected power distribution network operation data and external environment data to generate a power distribution network dynamic parameter identification sample.
According to one embodiment, the raw data may be preprocessed in DataFrame form using Pandas.
The data required for identifying the data-driven line physical parameters comprises power distribution network operation data and external environment data: the operation data of the power distribution network is acquired by the intelligent electric meter, and comprises node voltage, current, active power and reactive power which are acquired every 15 min; com, including regional temperature and humidity collected every 3 hours, external environmental data was from 58238 weather station data (provided by meteomanz. The method mainly has three problems that the original data come from different data sources, the data in the different data sources need to be merged and integrated in a data frame; secondly, the dimensionality of the data needs stipulation, and the original data has too many attributes, so that the data modeling is not facilitated; and thirdly, the data has missing values and outliers, and data cleaning is needed.
To address the above issues, in one embodiment, the following operations may be taken with respect to the raw data:
performing secondary spline interpolation on the external environment data to enable the frequencies of the external environment data of different data sources to be identical;
merging data of different data sources, and eliminating redundant fields in the data;
and removing repeated data in the operating data of the power distribution network and performing data null removal.
In a specific example, as shown in fig. 3, it can further adopt:
considering the mismatching of the acquisition frequency of two types of data from different data sources, inserting data point complete data into every two pieces of external environment data by using a spline interpolation method, specifically, interpolating a time sequence of temperature and humidity by using an interp1d function in a python script module package, and completing meteorological historical data to match the frequency of the two data sources;
merging data from different data sources through a merge function in a pandas module, and removing redundant fields in original data by using a drop function;
processing data vacancy and data repeatedly; removing repeated data in the running data by using a drop _ duplicates function; detecting the loss proportion of variables by using pandas, isnull and sum (), and using a dropna function to perform data emptying under the conditions of low loss rate (less than 95%) and low importance, thereby finally obtaining a cleaned dynamic parameter identification data sample of the power distribution network. The structure of the sample is shown in table 1 below.
TABLE 1 distribution line parameter identification data sample
Figure BDA0002789244850000081
Figure BDA0002789244850000091
And 2, discretizing the dynamic parameter identification sample of the power distribution network to obtain a discretization sample.
In one embodiment, the data sample may be discretized using a maximum entropy algorithm, which is calculated as follows:
Figure BDA0002789244850000092
wherein Z is an implicit variable, m is the number of divided discrete intervals, Ncount(Z ═ s) is the number of samples of variables in the data at state s; n is a radical ofamount(Z) is the total number of samples. Each variable in the network is distributed to a respective state space according to the condition that the mutual information entropy is maximum, and under the condition that no priori knowledge exists, the mutual information entropy between the states is maximum when the number of samples contained in each discrete interval is the same. The discretization granularity was chosen to be 10% of the number of samples, and the results of discretization for each variable are shown in table 2 below.
TABLE 2 discretization results for the variable at 10% discrete particle size
Figure BDA0002789244850000093
And 3, acquiring parameters of the probability map model according to the discretization sample based on the pre-established probability map model.
Wherein, the probability graph model is a two-time slice probability graph model.
The probabilistic graphical model is pre-established, and according to one embodiment, the step of establishing the probabilistic graphical model may include:
selecting the temperature T at time TtHumidity HtVoltage drop Δ V of feeder sectiontAnd feeder section transmission power StAs an observation variable in the probabilistic graphical model, the impedance Z of the line at that moment is selectedtAs hidden variables of the probabilistic graphical model;
adding each variable into a probability graph model one by one according to the causal relationship between each observation variable and each hidden variable under a single time slice to construct a static Bayesian network;
setting an initial time slice, and appointing prior probability distribution of each variable under the time slice;
and specifying the causal relationship of each state between adjacent time slices to construct a transfer model.
The model building process is further described below.
Specifically, considering that the dynamic parameters of the power distribution network are related to the external environment and the running state of the power distribution network, the temperature T at the moment T is selected according to the relation between the variables and the line impedance parameterstHumidity HtVoltage drop Δ V of feeder sectiontAnd feeder section transmission power StThe expression of an observed variable as a probabilistic graphical model, namely an observed variable X of the probabilistic graphical model at the time t is as follows:
Xt＝{Tt,Ht,ΔVt,St}
considering that the power distribution network line is relatively short, the ground capacitance of the line is ignored when the probability graph model is constructed. Therefore, the hidden variable Y of the probability map model at the time ttFor the impedance of the line at that moment, using ZtAnd (4) showing.
After the random variables of the model are determined, the order of the variables is selected according to the causal relationship. T representing an external environmenttAnd HtAre all factors affecting the line impedance, and thus the line impedance ZtAnd (4) a parent node. The expression for the line node voltage drop is as follows:
Figure BDA0002789244850000101
wherein V1，V2Representing the difference in voltage amplitudes at node 1 and node 2, P, Q being the active and reactive power respectively flowing between node 1 and node 2, R, X being the resistance and reactance of the line connecting nodes 1 and 2, respectively, IR，IXRespectively representing the corresponding active and reactive currents. In addition, StCharacterizing the apparent power; ztIs the line impedance. From the above equation, apparent power and line impedance are the factors that affect the voltage drop, and thus are the line voltage drop Δ VtThe parent node of (2). And finally, starting from a null graph, adding the variables into the probability graph model one by one according to the dependency relationship among the variables to form a directed acyclic graph.
And selecting an initial time slice on the basis, designating prior probability distribution of each variable under the time slice, designating causal relationship of each state between adjacent time slices, constructing a transfer model, and completing construction of a power distribution network dynamic parameter identification model based on a two-time slice probability graph model. And obtaining a two-time slice probability map model for identifying the dynamic parameters of the power distribution network in the figure 2.
According to one embodiment, the parameters of the probabilistic graphical model may be obtained by:
based on the probability mass function, obtaining an initial probability distribution table of each variable according to the discretization sample;
calculating a conditional probability distribution table among all variables according to the discretization sample based on a maximum expectation algorithm;
counting a transition probability distribution table of each variable from t moment to t +1 moment from continuous data samples on a time axis;
and determining the correctness of the conditional probability distribution table by checking whether the sum of the probability distributions of each variable is 1 or not and whether the conditional probability distribution is consistent with the causal relationship in the Bayesian network or not.
In one embodiment, the parameters of the probability map model, i.e., the initial probability distribution table, the conditional probability distribution table, and the transition probability distribution table, may be obtained as follows:
obtaining an initial probability distribution table of each variable from the discretized sample by calculating a probability mass function, wherein the structure of the initial probability distribution table is shown in the following table 3;
TABLE 3 initial probability distribution Table
Figure BDA0002789244850000121
The sum of all elements in the initial probability vector is 1, each element PiObtained by computing the Probability Mass Function (PMF):
Figure BDA0002789244850000122
wherein
Figure BDA0002789244850000123
Probability of the hidden variable being initially in state s; n is a radical ofcount(Z ═ s) is the number of samples of hidden variables in the data at state s; n is a radical ofamount(Z) is the total number of samples.
The conditional probability distribution table for calculating the values of the variables from the discretized sample by the max-expectation algorithm is a matrix of m × (n × k × v × h) in the case where the number of states of the hidden variables is m and the number of states of the observed variables is n, k, v, and h, respectively, and has a structure as shown in table 4 below.
TABLE 4 conditional probability distribution Table
Figure BDA0002789244850000124
Figure BDA0002789244850000131
The conditional probability distribution may be obtained by an EM algorithm. The EM algorithm initializes the probability distribution first and then iterates in two steps until convergence. The two-step iteration process is as follows:
1) step E calculation (Expectation Step): calculating the posterior probability of the hidden variable according to the initial value of the conditional probability or the conditional probability obtained by the previous iteration, and taking the posterior probability as the current expected value of the hidden variable:
Pposterior(Z)＝P(Z|X；θcpt)
wherein P isposterior(Z) posterior probability of hidden variable, θcptAs DBN (depth confidence)Network, Deep Belief Network) is a parameter of the conditional probability distribution table.
2) M Step calculation (Maximization Step): and (3) updating a conditional probability distribution table by taking the likelihood function maximization as a target:
Figure BDA0002789244850000132
where m is the number of hidden variable states, P (X, Z; theta)cpt) Is the expectation of the hidden variable obtained from the sample. And when the probability of the training data sample is maximum according to the condition probability distribution table in the probability graph model, the iteration of the maximum expectation algorithm is finished. The maximum expectation algorithm can carry out maximum likelihood estimation on parameters from the incomplete data set and is suitable for probability map model conditional probability distribution calculation under the condition of power distribution network data acquisition loss.
And counting a transition probability distribution table of each variable from t time to t +1 time from continuous data samples on a time axis. The transition probability distribution is a parameter for expressing variable timing transitions in the DBN and can be calculated by the following formula:
Figure BDA0002789244850000141
wherein
Figure BDA0002789244850000142
Representing the probability of the hidden variable Z transitioning from state 1 to state 2 from t-1 to t; n is a radical ofcount(s1,s2) Representing the times of transferring the hidden variable Z from the state 1 to the state 2 from the t-1 to the t moment in the acquired historical data; n is a radical ofamount(s1) Representing the number of samples in the acquired historical data with the hidden variable Z in state 1.
Finally, the correctness of the obtained parameters can be checked by checking whether the sum of the probability distributions of each variable is 1 or not, and whether the conditional probability distribution is consistent with the causal relationship in the bayesian network or not.
And 4, acquiring dynamic parameters of the power distribution network based on a confidence coefficient propagation algorithm according to the observation variables and the probability map model after the parameters are acquired.
According to one embodiment, the belief propagation algorithm is used to infer power distribution network dynamic parameters when the observed variables are known, as shown in fig. 4, which includes:
1) initializing the probability distribution of each variable according to the sample;
2) randomly selecting a certain state variable Y in the network, the confidence of the node can be represented as b (Y)t) And the confidence level, the adjacent node and all the information m transmitted to the node through the adjacent edgexY(Yt) In direct proportion, the confidence of a node can be replaced by a probability:
Figure BDA0002789244850000143
wherein phi (Y)t,Xt) And G is a first-order neighborhood of the node, namely a set of all nodes adjacent to the node. m isxY(Yt) The message passed to node Y for node x indicates the effect of node x on node Y at time t.
3) Updating information between variables:
Figure BDA0002789244850000151
wherein psi (Y)t,Yt-1) And reflecting the compatibility between hidden variables for the potential energy between the nodes from the t-1 moment to the t moment at the node Y.
4) Continuously repeating the steps 2) and 3) to continuously iterate message propagation and confidence coefficient updating until a convergence condition is met:
b(n)(Yt)-b(n-1)(Yt)＜10-5
5) and taking the confidence coefficient of the final hidden variable as an inference result of probability distribution of the hidden variable in each state interval. DBN Final reasoningThe result is a probability distribution that, compared to single point parameter identification, the DBN model can provide all the cases that may occur at that moment and their probabilities. For comparison with the conventional single-point line parameter identification model, the final line impedance parameter single-point identification result is obtained from the samples { Z ] in the state interval in the historical data1,Z2,…,ZNAnd (4) calculating, wherein the root mean square can be used as a point identification result of the line parameters.
Figure BDA0002789244850000152
Wherein N is the number of samples in the history data in the same state as the identification result, ZiIs the value of the impedance of the sample,
Figure BDA0002789244850000153
the root mean square of these sample impedance values. The two 10kV feeder lines connected through the interconnection switch in fig. 5 are used as parameter identification objects, and the parameter identification results of 14 lines are shown in table 5 below.
TABLE 5 dynamic parameter identification results for distribution networks
Figure BDA0002789244850000154
Figure BDA0002789244850000161
Because the lengths of all lines are different, the average error rate of parameter identification of each line is taken as an evaluation standard, the average error rate of impedance parameter identification of the proposed probabilistic graphical model is 3.80%, and the average error rate of reactance parameter identification is 9.05%.
In another embodiment, the present invention provides a device for identifying dynamic parameters of a power distribution network, including:
the data preprocessing module is used for preprocessing the acquired running data and external environment data of the power distribution network to generate a dynamic parameter identification sample of the power distribution network;
the discretization processing module is used for discretizing the dynamic parameter identification sample of the power distribution network to obtain a discretization sample;
the model parameter determining module is used for acquiring parameters of the probability map model according to the discretization sample based on the pre-established probability map model; the probability map model is a two-time slice probability map model;
and the power distribution network dynamic parameter generation module is used for acquiring power distribution network dynamic parameters based on a confidence coefficient propagation algorithm according to the observation variables and the probability map model after the parameters are acquired.
In conclusion, the invention constructs a novel power distribution network dynamic parameter identification probability graph model to solve the problem of power distribution network parameter identification errors caused by operation condition changes and data errors. The problem of uncertainty in the process of identifying the parameters of the power distribution network is solved by using knowledge in the field of probability theory, and accurate line physical parameters are provided for power distribution network situation perception and line loss calculation. The model provided by the invention can improve the accuracy and robustness of distribution line impedance parameter identification, improves the intelligent degree of distribution network analysis and management, and provides a parameter basis for distribution network scheduling personnel to master, analyze and control the operation mode of the distribution network.
As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
The present invention has been disclosed in terms of the preferred embodiment, but is not intended to be limited to the embodiment, and all technical solutions obtained by substituting or converting equivalents thereof fall within the scope of the present invention.

Claims (10)

1.一种配电网动态参数辨识方法，其特征在于，包括以下步骤：1. a method for identifying dynamic parameters of power distribution network, is characterized in that, comprises the following steps: 对采集的配电网运行数据、外部环境数据进行数据预处理，生成配电网动态参数辨识样本；Data preprocessing is performed on the collected distribution network operation data and external environment data to generate a sample of distribution network dynamic parameter identification; 将配电网动态参数辨识样本离散化，获取离散化样本；Discretize the identification samples of the dynamic parameters of the distribution network to obtain the discrete samples; 基于预先建立的概率图模型，根据离散化样本，获取概率图模型的参数，其中所述概率图模型为两时间片概率图模型；Based on the pre-established probabilistic graphical model, according to the discretized samples, the parameters of the probabilistic graphical model are obtained, wherein the probabilistic graphical model is a two-time slice probability graphical model; 根据观测变量以及获取参数后的概率图模型，基于置信度传播算法，获取配电网动态参数。According to the observed variables and the probabilistic graphical model after obtaining the parameters, based on the confidence propagation algorithm, the dynamic parameters of the distribution network are obtained. 2.根据权利要求1所述的方法，其特征在于，所述概率图模型的建立步骤包括：2. The method according to claim 1, wherein the step of establishing the probabilistic graphical model comprises: 选择一个时刻下的温度、湿度、馈线段电压降以及馈线段传输功率作为概率图模型中的观测变量，选择线路在该时刻的阻抗作为概率图模型的隐变量；Select the temperature, humidity, voltage drop of the feeder segment and transmission power of the feeder segment at a moment as the observed variables in the probability graph model, and select the impedance of the line at that moment as the hidden variable of the probability graph model; 依据单个时间片下各观测变量与隐变量间的因果关系，逐个将各个变量加入到概率图模型中，构建静态贝叶斯网络；According to the causal relationship between the observed variables and the latent variables under a single time slice, each variable is added to the probability graph model one by one to construct a static Bayesian network; 设定一个初始时间片，指定该时间片下各变量的先验概率分布；Set an initial time slice and specify the prior probability distribution of each variable under this time slice; 指定相邻时间片之间各状态的因果关系，构建转移模型。Specify the causal relationship between states between adjacent time slices to build a transition model. 3.根据权利要求1所述的方法，其特征在于，所述生成配电网动态参数辨识样本，包括：3. The method according to claim 1, wherein the generating a dynamic parameter identification sample of the distribution network comprises: 对外部环境数据进行二次样条插值，使得不同数据来源的外部环境数据的频率齐同；Perform quadratic spline interpolation on external environmental data, so that the frequency of external environmental data from different data sources is the same; 合并不同数据源的数据，剔除其中的冗余字段；Merge data from different data sources and eliminate redundant fields; 剔除配电网运行数据中的重复数据，以及进行数据去空。Eliminate duplicate data in distribution network operation data, and perform data de-blanking. 4.根据权利要求1所述的方法，其特征在于，所述将配电网动态参数辨识样本离散化，其计算公式如下：4. The method according to claim 1, characterized in that, said discretizing the identification samples of the dynamic parameters of the distribution network, its calculation formula is as follows:
Figure FDA0002789244840000021
Figure FDA0002789244840000021
 其中Z为隐变量，m为划分的离散区间的个数，Ncount(Z＝s)为数据中隐变量在状态s的样本数量；Namount(Z)为样本总数。Among them, Z is the hidden variable, m is the number of divided discrete intervals, N count (Z=s) is the number of samples of the hidden variable in the data in state s; N amount (Z) is the total number of samples. 5.根据权利要求1所述的方法，其特征在于，所述获取概率图模型的参数，包括：5. The method according to claim 1, wherein the obtaining parameters of the probabilistic graphical model comprises: 基于概率质量函数，根据离散化样本中获得各变量的初始概率分布表；Based on the probability mass function, the initial probability distribution table of each variable is obtained according to the discretized sample; 基于最大期望算法，根据离散化样本中计算各变量之间的条件概率分布表；Based on the maximum expectation algorithm, the conditional probability distribution table between variables is calculated according to the discretized samples; 从时间轴上连续的数据样本中统计出各变量从t时刻到t+1时刻的转移概率分布表；Calculate the transition probability distribution table of each variable from time t to time t+1 from the continuous data samples on the time axis; 通过检查每个变量的概率分布之和是否为1，检查条件概率分布是否与贝叶斯网络中的因果关系一致，确定所述条件概率分布表的正确性。The correctness of the conditional probability distribution table is determined by checking whether the sum of the probability distributions of each variable is 1, and whether the conditional probability distribution is consistent with the causal relationship in the Bayesian network. 6.根据权利要求5所述的方法，其特征在于，所述概率质量函数的表达式为：6. The method according to claim 5, wherein the expression of the probability mass function is:
Figure FDA0002789244840000031
Figure FDA0002789244840000031
 其中
Figure FDA0002789244840000032
为隐变量初始在状态s的概率；Ncount(Z＝s)为数据中隐变量在状态s的样本数量；Namount(Z)为样本总数。in
Figure FDA0002789244840000032
is the initial probability of the hidden variable in state s; N count (Z=s) is the number of samples of the hidden variable in the data in state s; N amount (Z) is the total number of samples. 7.根据权利要求5所述的方法，其特征在于，所述基于最大期望算法，根据离散化样本中计算各变量之间的条件概率分布表，包括：7. The method according to claim 5, wherein, based on the maximum expectation algorithm, the conditional probability distribution table between the variables is calculated according to the discretized samples, comprising: 依据条件概率初始值或上一步迭代所得的条件概率来计算隐变量的后验概率，作为隐变量的现期望值，其表达式为：The posterior probability of the hidden variable is calculated according to the initial value of the conditional probability or the conditional probability obtained from the previous iteration, as the current expected value of the hidden variable, and its expression is: Pposterior(Z)＝P(Z|X；θcpt)P posterior (Z)=P(Z|X; θ cpt ) 其中Z为隐变量，Pposterior(Z)为隐变量的后验概率，θcpt为概率图模型中条件概率分布表，X为观测变量；where Z is the hidden variable, P posterior (Z) is the posterior probability of the hidden variable, θ cpt is the conditional probability distribution table in the probability graph model, and X is the observed variable; 以似然函数最大化为目标更新条件概率分布表，其表达式为：The conditional probability distribution table is updated with the goal of maximizing the likelihood function, and its expression is:
Figure FDA0002789244840000033
Figure FDA0002789244840000033
 其中m为隐变量状态个数，P(X,Z；θcpt)为从样本中获得的隐变量的期望；where m is the number of hidden variable states, and P(X, Z; θ cpt ) is the expectation of the hidden variable obtained from the sample; 当依据概率图模型中条件概率分布表抽到训练数据样本的概率最大时，最大期望算法迭代结束。When the probability of drawing a training data sample according to the conditional probability distribution table in the probabilistic graphical model is the largest, the iteration of the maximum expectation algorithm ends. 8.根据权利要求5所述的方法，其特征在于，所述转移概率分布表的表达式为：8. The method according to claim 5, wherein the expression of the transition probability distribution table is:
Figure FDA0002789244840000034
Figure FDA0002789244840000034
 其中
Figure FDA0002789244840000035
表示隐变量Z从t-1到t时刻从状态1转移为状态2的概率；Ncount(s1,s2)代表获取的历史数据中隐变量Z从t-1到t时刻从状态1转移为状态2的次数；Namount(s1)代表获取的历史数据中隐变量Z处于状态1的样本数量。in
Figure FDA0002789244840000035
Represents the probability that the latent variable Z transfers from state 1 to state 2 from time t-1 to time t; N count (s 1 , s 2 ) represents the transfer of hidden variable Z from time t-1 to time t from state 1 in the acquired historical data is the number of times of state 2; N amount (s 1 ) represents the number of samples of the hidden variable Z in state 1 in the acquired historical data. 9.根据权利要求1所述的方法，其特征在于，所述基于置信度传播算法，获取配电网动态参数，包括：9. The method according to claim 1, wherein the obtaining of the dynamic parameters of the distribution network based on a confidence propagation algorithm comprises: 依据样本初始化每个变量的概率分布；Initialize the probability distribution of each variable according to the sample; 随机选择网络中某一状态变量Y，将该节点的置信度替换为b(Yt)：Randomly select a state variable Y in the network, and replace the confidence of the node with b(Y t ):
Figure FDA0002789244840000041
Figure FDA0002789244840000041
 其中φ(Yt,Xt)为t时刻相应状态变量Y与观测变量X之间的似然函数，表示节点Y在t时刻的联合相容度，G为节点Y的一阶邻域，mxY(Yt)为节点x传递给节点Y的消息；where φ(Y t , X t ) is the likelihood function between the corresponding state variable Y and the observed variable X at time t, representing the joint compatibility of node Y at time t, G is the first-order neighborhood of node Y, m xY (Y t ) is the message passed by node x to node Y; 更新变量间的信息：Update information between variables:
Figure FDA0002789244840000042
Figure FDA0002789244840000042
 其中ψ(Yt,Yt-1)为节点Y处t-1时刻到t时刻的节点间的势能量；where ψ(Y t , Y t-1 ) is the potential energy between nodes at node Y from time t-1 to time t; 直至满足收敛条件：until the convergence conditions are met: b(n)(Yt)-b(n-1)(Yt)＜10-5 b (n) (Y t )-b (n-1) (Y t )<10 -5 将最终隐变量的置信度b(Yt)作为隐变量在各状态区间概率分布的推断结果。The confidence level b(Y t ) of the final hidden variable is taken as the inference result of the probability distribution of the hidden variable in each state interval. 10.一种配电网动态参数辨识装置，其特征在于，包括：10. A device for identifying dynamic parameters of a power distribution network, comprising: 数据预处理模块，用于对采集的配电网运行数据、外部环境数据进行数据预处理，生成配电网动态参数辨识样本；The data preprocessing module is used to perform data preprocessing on the collected distribution network operation data and external environment data, and generate a distribution network dynamic parameter identification sample; 离散化处理模块，用于将配电网动态参数辨识样本离散化，获取离散化样本；The discretization processing module is used to discretize the dynamic parameter identification samples of the distribution network and obtain the discretized samples; 模型参数确定模块，用于基于预先建立的概率图模型，根据离散化样本，获取概率图模型的参数；所述概率图模型为两时间片概率图模型；The model parameter determination module is used to obtain parameters of the probability graphical model based on the pre-established probability graphical model and according to the discretized samples; the probability graphical model is a two-time-slice probability graphical model; 配电网动态参数生成模块，用于根据观测变量以及获取参数后的概率图模型，基于置信度传播算法，获取配电网动态参数。The distribution network dynamic parameter generation module is used to obtain the distribution network dynamic parameters based on the confidence propagation algorithm according to the observed variables and the probability graph model after obtaining the parameters.
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