CN113297714A - Power distribution station terminal voltage analysis method and system - Google Patents

Power distribution station terminal voltage analysis method and system Download PDF

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CN113297714A
CN113297714A CN202110735471.7A CN202110735471A CN113297714A CN 113297714 A CN113297714 A CN 113297714A CN 202110735471 A CN202110735471 A CN 202110735471A CN 113297714 A CN113297714 A CN 113297714A
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CN113297714B (en
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杨金东
刘红文
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Electric Power Research Institute of Yunnan Power Grid Co Ltd
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Abstract

The application provides a power distribution station terminal voltage analysis method which comprises the steps of carrying out voltage distance equivalent transformation on a power distribution station and establishing a global simplified equivalent model; performing probability load flow calculation on the global simplified equivalent model by a probability load flow calculation method to obtain the probability distribution condition of the node voltage; according to the probability distribution condition of the node voltage, performing efficient probability load flow calculation to determine a high-low voltage out-of-limit area; restoring the equivalent model of the high-low voltage out-of-limit area and establishing a local accurate model; and analyzing the distribution areas of high voltage and low voltage on the local precise model through deterministic load flow calculation. The application also provides a distribution substation terminal voltage analysis system. According to the power distribution area line topological structure, voltage distance equivalent transformation is carried out on the power distribution area according to the topological structure of the power distribution area line, global probability power flow analysis and local certainty power flow analysis are combined, the analysis of the tail end voltage of the power distribution area is efficiently and accurately achieved, early warning voltage is out of limit, and voltage management and planning transformation of the support area are achieved.

Description

Power distribution station terminal voltage analysis method and system
Technical Field
The application relates to the technical field of analysis of tail end voltage of a power distribution station area, in particular to a method and a system for analyzing tail end voltage of the power distribution station area.
Background
At present, in the aspect of a power distribution station terminal voltage analysis method, a common method is a big data mining and model judgment method, the method searches the influence of load change of a power distribution station on the voltage of the power distribution station by collecting massive voltage data and load data of users of the power distribution station and utilizing a big data mining technology, establishes a mathematical model of the load change influencing the voltage change according to a load-voltage sensitivity index, and judges possible high and low voltage problems according to the load growth characteristic of the power distribution station and the current voltage condition of the power distribution station. However, the method needs a large amount of historical data and prediction data for support, the calculation speed is slow, and the voltage analysis real-time performance is low.
Disclosure of Invention
The application provides a power distribution station terminal voltage analysis method and system, which are used for solving the problems that in the prior art, a large amount of historical data and prediction data are required to support, the calculation speed is low, and the voltage analysis real-time performance is low.
In one aspect, the present application provides a power distribution station terminal voltage analysis method, which specifically includes the following steps:
performing voltage distance equivalent transformation on the power distribution area through a K-Means clustering algorithm, and establishing a global simplified equivalent model of the power distribution area, wherein the voltage distance equivalent transformation comprises load equivalence and topology equivalence;
performing probability load flow calculation on the global simplified equivalent model by using a probability load flow calculation method of an RBF neural network to obtain the probability distribution condition of the node voltage;
according to the probability distribution condition of the node voltage, performing efficient probability load flow calculation to determine a high-low voltage out-of-limit area;
restoring the equivalent model of the high-low voltage out-of-limit area, and establishing a local accurate model;
and analyzing to obtain a distribution region of high and low voltages on the local precise model through deterministic load flow calculation.
In a preferred embodiment of the present application, the load equivalence and topology equivalence specifically include the following steps:
presetting a load distribution distance value d according to the average distance data between actual towers;
calculating the number K of divided areas of the load sample set according to the load distribution distance value d and the distance L between the outlet of the transformer area and the tail end of the longest line;
performing heuristic iterative computation through a K-Means clustering algorithm, and clustering and dividing a load sample set into K regions;
calculating the voltage mathematical expectation E (U) and the voltage standard deviation sigma of K areas;
if the standard deviation sigma of the voltage is less than 4.4, carrying out load equivalence on the region;
calculating user load capacity of K areas, comparing the user load capacity with a line equivalent topology library, and determining a topology equivalent type;
and performing topology equivalence on corresponding regions respectively according to the topology equivalence types of the K regions.
By adopting the technical scheme, convenience can be provided for load flow calculation, the subsequent load flow calculation time can be greatly reduced by simplifying the equivalent topology, and the overall efficiency of a load flow calculation algorithm is improved.
In a preferred embodiment of the present application, calculating the mathematical expectation e (u) and standard deviation σ of the voltages for the K regions further comprises:
if the standard deviation sigma of the voltage is more than or equal to 4.4, calculating and screening out users with the voltage deviation delta U% of more than 2% in the region according to the mathematical expectation E (U) of the voltage in the region, wherein the voltage deviation is calculated according to the mathematical expectation of the voltage, and the specific counting formula is
Figure BDA0003141475290000031
Judging whether a user with voltage deviation delta U% larger than 2% can be divided into adjacent regions of the region;
and if the user with the voltage deviation delta U% larger than 2% can be divided into the adjacent regions, carrying out load equivalence on the adjacent regions.
In a preferred embodiment of the present application, determining whether a user with a voltage deviation Δ U% > 2% can be divided into adjacent regions of the region further includes:
if the user with the voltage deviation delta U% larger than 2% can not divide the adjacent region, correcting the load distribution distance value according to-5% to obtain a corrected load distribution distance value;
recalculating the number K of the divided regions of the load sample set according to the corrected load distribution distance value;
and performing heuristic iterative computation through a K-Means clustering algorithm, clustering the load sample set into K regions, and judging whether each region can perform load equivalence.
In a preferred embodiment of the present application, performing probability load flow calculation on the global simplified equivalent model by using a probability load flow calculation method of an RBF neural network to obtain a probability distribution condition of a node voltage, specifically including:
inputting system data, sampling times N and a probability density function of a load on the global simplified equivalent model, wherein the system data comprise system line parameters, loads, node voltages, currents, powers and the like, the probability density function of the load is given by a load probability model and described by active and reactive power of the load, specifically the probability density function of the load is given by the load probability model
Figure BDA0003141475290000032
According to the probability density function of the load, screening out samples by a Latin hypercube sampling method, and forming a sample matrix S ═ S (S)1,S2，...,SN)；
Inputting the sample into a trained RBF neural network to obtain a node voltage corresponding to the sample;
and carrying out probability statistics on the node voltages corresponding to all the samples in the sample matrix to obtain the probability distribution condition of the node voltages.
By adopting the technical scheme, the RBF neural network replaces a power flow equation, so that a repeated iteration process in the traditional power flow calculation is avoided, the calculation time is greatly reduced, and the calculation efficiency is improved.
In a preferred embodiment of the present application, the deterministic power flow calculation is calculated by a forward-backward substitution method, and the specific steps include:
acquiring network parameters and initializing network voltage in the network parameters;
calculating the injection current of the tip node according to the network parameters;
back-substituting the injection current of the peripheral node and calculating the injection current of the non-peripheral node;
forward the current injected into the end node, and calculating the node voltage;
respectively calculating the longitudinal component and the transverse component of the three-phase voltage of the node voltage according to the node voltage;
judging whether the variation of the node voltage between two iterations meets a preset convergence condition or not according to the longitudinal component and the transverse component;
and if the variable quantity of the node voltage between two iterations meets a preset convergence condition, obtaining a distribution area of high and low voltages.
In the preferred embodiment of the present application, the topology equivalence types mainly include uniform topology equivalence, incremental topology equivalence, decremental topology equivalence, incremental jagged topology equivalence, decremental jagged topology equivalence, and trapezoidal topology equivalence.
In another aspect, the present application further provides a power distribution station terminal voltage analysis system, including:
the system comprises a data storage module, a display module, a platform area topology equivalent calculation module, a load flow calculation module, a topology base construction module and a platform area load equivalent calculation module;
the data storage module is electrically connected with the display module, the display module is electrically connected with the platform area topology equivalent calculation module, the platform area topology equivalent calculation module is electrically connected with the power flow calculation module, the power flow calculation module is electrically connected with the topology base construction module, and the topology base construction module is electrically connected with the platform area load equivalent calculation module.
Compared with the prior art, the power distribution station terminal voltage analysis method and the power distribution station terminal voltage analysis system have the following beneficial effects:
according to the method and the system, load equivalence is carried out on the power distribution area according to the topological structure of the power distribution area line, global probability power flow analysis and local certainty power flow analysis are combined, algorithm analysis is carried out on a simplified equivalent model of the power distribution area by adopting the global probability power flow analysis, high and low voltage areas can be locked, calculation efficiency is improved, algorithm operation time is reduced, meanwhile, the area model is restored in the locked high and low voltage areas, local certainty power flow analysis is carried out, distribution of high and low voltages can be accurately obtained, therefore, analysis of terminal voltage of the power distribution area can be efficiently and accurately achieved by adopting the analysis method and the system, and the problem of voltage overrun can be timely pre-warned.
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In order to more clearly explain the technical solution of the present application, the drawings needed to be used in the embodiments will be briefly described below, and it is obvious to those skilled in the art that other drawings can be obtained according to the drawings without creative efforts.
FIG. 1 is a schematic diagram illustrating a method for analyzing an end voltage of a power distribution area according to the present invention;
FIG. 2 is a flow chart illustrating a method for analyzing a terminal voltage of a power distribution area according to the present disclosure;
FIG. 3 is a flowchart of equivalent transformation of pressure gap in embodiment 1 of the present application;
fig. 4 is a flowchart of efficient computation of probability load flow in embodiment 1 of the present application;
FIG. 5 is a block diagram of a power distribution terminal voltage analysis system according to the present application;
description of reference numerals:
10-a data storage module; 20-a display module; 30-a platform area topology equivalent calculation module; 40-a power flow calculation module; 50-topology base building module; and 60, a platform area load equivalent calculation module.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are some embodiments of the present application, but not all embodiments. The components of the embodiments of the present application, generally described and illustrated in the figures herein, can be arranged and designed in a wide variety of different configurations.
Thus, the following detailed description of the embodiments of the present application, presented in the accompanying drawings, is not intended to limit the scope of the claimed application, but is merely representative of selected embodiments of the application. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
It should be noted that: like reference numbers and letters refer to like items in the following figures, and thus, once an item is defined in one figure, it need not be further defined and explained in subsequent figures.
Furthermore, the terms "comprises" and "comprising," as well as any variations thereof, are intended to cover a non-exclusive inclusion, such that a product or device that comprises a list of elements is not necessarily limited to those elements explicitly listed, but may include other elements not expressly listed or inherent to such product or device.
In the prior art, a commonly used method for analyzing the voltage at the tail end of a power distribution station area further comprises a correlation analysis method, wherein the method obtains voltage, frequency, current, active power and reactive power data of a bus where a load is located through field tests and on-line capture of natural disturbance of a power system, then identifies a comprehensive load model structure and model parameters according to the data, screens out main factors influencing the voltage qualification rate, establishes a multiple linear regression equation, and predicts the distribution of high and low voltages by using a gray correlation analysis method based on the weighting improvement of an analytic hierarchy process. However, the accuracy of the method is greatly influenced by the associated parameters, and the accuracy of voltage analysis is difficult to ensure.
Forward-forward substitution: since there are unique paths from any given bus to the source node, voltage and current (or power) can be modified along these unique paths. In the back-substitution process, calculating the injection current (or power) of each load node, and calculating the sum of the branch current (or power) from the end node to obtain the initial end current (or power) of each branch, and possibly correcting the node voltage; in the forward process, the voltage drop and the tail end voltage of each branch are calculated by using the set source node voltage as a boundary condition, and the current (or power) of each branch can be corrected; and repeating the forward pushing step and the backward replacing step continuously until convergence.
Example 1
Referring to fig. 1, a schematic diagram of a power distribution station terminal voltage analysis method according to the present application is shown.
As shown in fig. 1 and fig. 2, the power distribution station terminal voltage analysis method provided by the present application specifically includes the following steps:
s101, performing voltage distance equivalent transformation on the power distribution area through a K-Means clustering algorithm, and establishing a global simplified equivalent model of the power distribution area, wherein the voltage distance equivalent transformation comprises load equivalence and topology equivalence;
s102, performing probability load flow calculation on the global simplified equivalent model by using a probability load flow calculation method of the RBF neural network to obtain the probability distribution condition of the node voltage;
s103, according to the probability distribution condition of the node voltage, performing efficient probability load flow calculation and determining a high-low voltage out-of-limit area;
s104, restoring the equivalent model of the high-low voltage out-of-limit area, and establishing a local accurate model;
and S105, analyzing and obtaining a distribution region of high and low voltages through deterministic load flow calculation on the local precise model.
In this embodiment, as shown in fig. 3, the steps of load equivalence and topology equivalence in the pressure distance equivalent transformation in step S101 are specifically as follows:
presetting a load distribution distance value d according to the average distance data between actual towers;
calculating the number K of divided areas of the load sample set according to the load distribution distance value d and the distance L between the outlet of the transformer area and the tail end of the longest line, wherein the calculation formula of K is
Figure BDA0003141475290000081
Performing heuristic iterative computation through a K-Means clustering algorithm, and clustering and dividing a load sample set into K regions;
calculating the voltage mathematical expectation E (U) and the voltage standard deviation sigma of K areas;
if the standard deviation sigma of the voltage is less than 4.4, carrying out load equivalence on the region;
calculating user load capacity of K areas, comparing the user load capacity with a line equivalent topology library, and determining a topology equivalent type;
and performing topology equivalence on corresponding regions respectively according to the topology equivalence types of the K regions.
By adopting the technical scheme, convenience can be provided for load flow calculation, the subsequent load flow calculation time can be greatly reduced by simplifying the equivalent topology, and the overall efficiency of a load flow calculation algorithm is improved.
It should be particularly noted that in this embodiment, when load equivalence is performed, loads on lines in a distribution substation area need to be distinguished, so that, with the use of the K-Means clustering algorithm, for a given load sample set, the load sample set is divided into K clusters, that is, K regions, according to the distance between the loads, and with the use of the K-Means clustering algorithm, points in each cluster can be connected together as closely as possible, that is, the distance between the loads needs to be as small as possible, and the distance between the clusters is as large as possible. In addition, the specific values of the load distribution distance value d and the distance L between the transformer outlet of the transformer area and the tail end of the longest line can be set according to actual conditions, and the load distribution distance value d and the distance L between the transformer outlet of the transformer area and the tail end of the longest line are not limited in the application.
Further, in this embodiment, the topology equivalent types mainly include uniform topology equivalence, incremental topology equivalence, decremental topology equivalence, incremental jagged topology equivalence, decremental jagged topology equivalence, and trapezoidal topology equivalence.
Further, as shown in fig. 3, calculating the mathematical expectation e (u) and the standard deviation σ of the voltages of the K regions further includes:
if the standard deviation sigma of the voltage is more than or equal to 4.4, calculating and screening out users with the voltage deviation delta U% of more than 2% in the region according to the mathematical expectation E (U) of the voltage in the region, wherein the voltage deviation is calculated according to the mathematical expectation of the voltage, and the specific counting formula is
Figure BDA0003141475290000091
Judging whether a user with voltage deviation delta U% larger than 2% can be divided into adjacent regions of the region;
and if the user with the voltage deviation delta U% larger than 2% can be divided into the adjacent regions, carrying out load equivalence on the adjacent regions.
Further, as shown in fig. 3, determining whether the user with the voltage deviation Δ U% > 2% can be divided into adjacent regions of the region further includes:
if the user with the voltage deviation delta U% larger than 2% can not divide the adjacent region, correcting the load distribution distance value according to-5% to obtain a corrected load distribution distance value;
recalculating the number K of the divided regions of the load sample set according to the corrected load distribution distance value;
heuristic iterative computation is performed through a K-Means clustering algorithm, a load sample set is clustered and divided into K regions, and whether each region can perform load equivalence is judged, namely, iteration in the figure 3 refers to an iterative loop which starts from recalculating the number K of the divided regions of the load sample set.
As shown in fig. 4, in this embodiment, in step S102, performing probability load flow calculation on the global simplified equivalent model by using a probability load flow calculation method of an RBF neural network, to obtain a probability distribution situation of a node voltage, specifically includes:
inputting system data, sampling times N and a probability density function of a load on the global simplified equivalent model, wherein the system data comprise system line parameters, loads, node voltages, currents, powers and the like, the probability density function of the load is given by a load probability model and described by active and reactive power of the load, specifically the probability density function of the load is given by the load probability model
Figure BDA0003141475290000101
According to the probability density function of the load, screening out samples by a Latin hypercube sampling method, and forming a sample matrix S ═ S (S)1,S2，...,SN)；
Inputting the sample into the trained RBF neural network to obtain the node voltage corresponding to the sample, i.e. obtaining the sample S by using the trained RBF neural network in fig. 4kLower corresponding tidal current resulting node voltage Uk；
If m is equal to N, carrying out probability statistics on node voltages corresponding to all samples in the sample matrix to obtain the probability distribution condition of the node voltages, wherein m is a sample data label subjected to the RBF neural network algorithm;
and if m is not equal to N, enabling m to be m +1, returning to calculate the node voltage, and repeating the subsequent steps until the calculation of the node voltages corresponding to all samples in the sample matrix is completed, wherein m is the sample data label after the RBF neural network algorithm is performed.
By adopting the technical scheme, the RBF neural network replaces a power flow equation, so that a repeated iteration process in the traditional power flow calculation is avoided, the calculation time is greatly reduced, and the calculation efficiency is improved.
In this embodiment, the deterministic power flow calculation adopts a forward-backward substitution method to calculate, and the specific steps include:
acquiring network parameters and initializing network voltage in the network parameters, wherein the network parameters mainly comprise branch impedance, load power and the like;
calculating the tip node injection current according to the network parameters,
Figure BDA0003141475290000111
wherein the content of the first and second substances,
Figure BDA0003141475290000112
injecting a current into node j; sja，Sjb，SjcThe injected power for node j;
Figure BDA0003141475290000113
the voltage of a node j is shown, a, B and C in the formula refer to three-phase data of the node, namely, an A phase, a B phase and a C phase of the three-phase voltage, k is the kth iteration, and k is more than or equal to 1;
the method includes the following steps of substituting the injection current of the end node, calculating the injection current of the non-end node, namely calculating the branch current taking the end node as a tail node, and specifically including:
and sequentially selecting nodes from the last node of the hierarchical node array to calculate:
Figure BDA0003141475290000114
wherein the content of the first and second substances,
Figure BDA0003141475290000115
injecting a current into node j; sja，Sjb，SjcThe injected power for node j;
Figure BDA0003141475290000116
injecting current into the lower branch directly connected with the node j; m is equal to the node jConnecting all the connected lower-layer branch circuits, wherein k represents the iteration times, is more than or equal to 1, and subscripts a, B and C respectively represent the phase A, the phase B and the phase C of the three-phase voltage;
for the transformer branch, the head node and the tail node of the branch are respectively set as p and q, the processing method of the tail node q is the same as that of a common node, and the current of the tail node q of the transformer branch is obtained by back substitution:
Figure BDA0003141475290000121
wherein the content of the first and second substances,
Figure BDA0003141475290000122
injecting a current into node q; sqa，Sqb，SqcThe injected power for node q; m is a set of all lower layer branches directly connected with the node q;
the current at the head node p is then:
Figure BDA0003141475290000123
the parameter a in the coefficient matrix is an operator of a symmetric component method, and the value of the parameter a is equal to ej120°；a2＝ej240°K is iteration times, and k is more than or equal to 1;
forward the current injected into the distal node, and calculating the node voltage, specifically comprising:
sequentially selecting nodes from back to front in the hierarchical node array for calculation:
Figure BDA0003141475290000124
wherein the content of the first and second substances,
Figure BDA0003141475290000125
is the node voltage at the node j,
Figure BDA0003141475290000126
the node voltage of the node i that is the last of the node j,
Figure BDA0003141475290000127
is the current of the branch with node j as tail node, Z is the line impedance, Zaa,j,Zbb,j,Zcc,jIs the self-impedance of the j node, Zab,j,Zbc,j,Zac,jJ nodes ab, bc and ac are mutual impedance among phases, k represents iteration times, k is more than or equal to 1, and subscripts a, B and C respectively represent an A phase, a B phase and a C phase of three-phase voltage;
for the transformer branch, the processing method of the first node p is the same as that of the common node, and the voltage of the first node p of the transformer branch is obtained by forward pushing (the last node of p is s):
Figure BDA0003141475290000131
wherein the content of the first and second substances,
Figure BDA0003141475290000132
is the node voltage of the node p,
Figure BDA0003141475290000133
the node voltage of the node s which is the previous node p,
Figure BDA0003141475290000134
for a branch with node p as tail node, Zaa,p,Zbb,p,Zcc,pIs the self-impedance of node p, Zab,p,Zbc,p,Zac,pThe mutual impedance of a node p between ab, bc and ac phases is represented, k represents the iteration number, k is more than or equal to 1, and subscripts a, B and C respectively represent an A phase, a B phase and a C phase of three-phase voltage;
the voltage at the end node q is then:
Figure BDA0003141475290000135
respectively calculating the longitudinal component and the transverse component of the three-phase voltage of the node voltage according to the node voltage, wherein the formula is as follows:
calculating the unbalance amount of the phase voltage of each node ABC as a criterion for judging convergence:
Figure BDA0003141475290000141
wherein, is Δ Vja，ΔVjb，ΔVjcRepresenting the longitudinal component, δ V, of each phase voltageja，δVjb，δVjcRepresenting the transverse component of each phase voltage, wherein the superscript k represents the kth iteration, k-1 represents the kth-1 iteration, and k is more than or equal to 1; in addition, since the node voltage is the sum of the longitudinal and lateral components, i.e., Vja'＝ΔVja'+jδVja' therefore, through the calculation of the matrix, the variation of the node voltage between two iterations can be obtained;
judging whether the variation of the node voltage between two iterations meets a preset convergence condition or not according to the longitudinal component and the transverse component;
if the variable quantity of the node voltage between two iterations meets a preset convergence condition, obtaining a distribution area of high and low voltages;
and if the variable quantity of the node voltage between two iterations does not meet the preset convergence condition, returning to recalculate the injection current of the tip node and the subsequent steps, and performing the next iteration.
It should be noted that, in the embodiments of the present application, the repeated appearance of the same letter represents a plurality of meanings (e.g. a, k, etc.), but different explanations are made according to specific scenarios or formulas, and in the technical solution of the present application, the meaning of each letter is subject to the relevant description in the specific associated formula and content. In addition, the formulas and the corresponding character meanings presented in the present application without explanation are all formulas and characters whose specific meanings can be obtained by those skilled in the art according to the common general knowledge. Therefore, the above-described related formulas and characters do not obscure the technical solution of the present application, and should not be considered as limitations to the technical solution of the present application.
Example 2
As shown in fig. 5, the present application also provides a power distribution substation end voltage analysis system, including:
the system comprises a data storage module 10, a display module 20, a platform region topology equivalence calculation module 30, a load flow calculation module 40, a topology base construction module 50 and a platform region load equivalence calculation module 60;
the data storage module 10 is electrically connected to the display module 20, the display module 20 is electrically connected to the platform topology equivalence calculation module 30, the platform topology equivalence calculation module 30 is electrically connected to the power flow calculation module 40, the power flow calculation module 40 is electrically connected to the topology base construction module 50, and the topology base construction module 50 is electrically connected to the platform load equivalence calculation module 60.
The embodiments provided in the present application are only a few examples of the general concept of the present application, and do not limit the scope of the present application. Any other embodiments extended according to the scheme of the present application without inventive efforts will be within the scope of protection of the present application for a person skilled in the art.

Claims (8)

1. A power distribution station terminal voltage analysis method is characterized by comprising the following steps:
performing voltage distance equivalent transformation on the power distribution area through a K-Means clustering algorithm, and establishing a global simplified equivalent model of the power distribution area, wherein the voltage distance equivalent transformation comprises load equivalence and topology equivalence;
performing probability load flow calculation on the global simplified equivalent model by using a probability load flow calculation method of an RBF neural network to obtain the probability distribution condition of the node voltage;
according to the probability distribution condition of the node voltage, performing efficient probability load flow calculation to determine a high-low voltage out-of-limit area;
restoring the equivalent model of the high-low voltage out-of-limit area, and establishing a local accurate model;
and analyzing to obtain a distribution region of high and low voltages on the local precise model through deterministic load flow calculation.
2. The distribution substation terminal voltage analysis method according to claim 1, wherein the load equivalence and topology equivalence specifically comprises the following steps:
presetting a load distribution distance value d according to the average distance data between actual towers;
calculating the number K of divided areas of the load sample set according to the load distribution distance value d and the distance L between the outlet of the transformer area and the tail end of the longest line;
performing heuristic iterative computation through a K-Means clustering algorithm, and clustering and dividing a load sample set into K regions;
calculating the voltage mathematical expectation E (U) and the voltage standard deviation sigma of K areas;
if the standard deviation sigma of the voltage is less than 4.4, carrying out load equivalence on the region;
calculating user load capacity of K areas, comparing the user load capacity with a line equivalent topology library, and determining a topology equivalent type;
and performing topology equivalence on corresponding regions respectively according to the topology equivalence types of the K regions.
3. The method of claim 2, wherein calculating the mathematical expectations e (u) and standard deviations σ of the voltages for the K zones further comprises:
if the standard deviation sigma of the voltage is more than or equal to 4.4, calculating and screening out users with the voltage deviation delta U% of the region being more than 2% according to the mathematical expectation E (U) of the voltage of the region;
judging whether a user with voltage deviation delta U% larger than 2% can be divided into adjacent regions of the region;
and if the user with the voltage deviation delta U% larger than 2% can be divided into the adjacent regions, carrying out load equivalence on the adjacent regions.
4. The method for analyzing the voltage at the end of the power distribution station area according to claim 3, wherein the step of judging whether the user with the voltage deviation Δ U% > 2% can be divided into the adjacent areas of the area further comprises the following steps:
if the user with the voltage deviation delta U% larger than 2% can not divide the adjacent region, correcting the load distribution distance value according to-5% to obtain a corrected load distribution distance value;
recalculating the number K of the divided regions of the load sample set according to the corrected load distribution distance value;
and performing heuristic iterative computation through a K-Means clustering algorithm, clustering the load sample set into K regions, and judging whether each region can perform load equivalence.
5. The method for analyzing the terminal voltage of the power distribution substation according to claim 1, wherein the probability load flow calculation is performed on the global simplified equivalent model through a probability load flow calculation method of an RBF neural network to obtain a probability distribution condition of the node voltage, and specifically comprises:
inputting system data, sampling times N and a probability density function of a load on the global simplified equivalent model;
according to the probability density function of the load, screening out samples by a Latin hypercube sampling method, and forming a sample matrix S ═ S (S)1,S2，...,SN)；
Inputting the sample into a trained RBF neural network to obtain a node voltage corresponding to the sample;
and carrying out probability statistics on the node voltages corresponding to all the samples in the sample matrix to obtain the probability distribution condition of the node voltages.
6. The method for analyzing the voltage at the tail end of the power distribution station area according to claim 1, wherein the deterministic power flow calculation is calculated by adopting a forward-backward substitution method, and the method comprises the following specific steps:
acquiring network parameters and initializing network voltage in the network parameters;
calculating the injection current of the tip node according to the network parameters;
back-substituting the injection current of the peripheral node and calculating the injection current of the non-peripheral node;
forward the current injected into the end node, and calculating the node voltage;
respectively calculating the longitudinal component and the transverse component of the three-phase voltage of the node voltage according to the node voltage;
judging whether the variation of the node voltage between two iterations meets a preset convergence condition or not according to the longitudinal component and the transverse component;
and if the variable quantity of the node voltage between two iterations meets a preset convergence condition, obtaining a distribution area of high and low voltages.
7. The method according to claim 2, wherein the topology equivalence types mainly include uniform topology equivalence, incremental topology equivalence, decremental topology equivalence, incremental sawtooth topology equivalence, decremental sawtooth topology equivalence, and ladder topology equivalence.
8. A power distribution terminal voltage analysis system, wherein a power distribution terminal voltage analysis method according to any one of claims 1 to 7 is adopted, comprising:
the power distribution network load equivalence calculation method comprises a data storage module (10), a display module (20), a distribution room topology equivalence calculation module (30), a load flow calculation module (40), a topology base construction module (50) and a distribution room load equivalence calculation module (60);
the data storage module (10) is electrically connected with the display module (20), the display module (20) is electrically connected with the platform region topology equivalence calculation module (30), the platform region topology equivalence calculation module (30) is electrically connected with the power flow calculation module (40), the power flow calculation module (40) is electrically connected with the topology base construction module (50), and the topology base construction module (50) is electrically connected with the platform region load equivalence calculation module (60).
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