CN117526320B - Method for reversely generating power distribution network by analyzing safety domain - Google Patents
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Abstract
The invention relates to a method for reversely generating a power distribution network in a safety domain analytic type, which comprises the steps of determining a plurality of network blocks by analyzing a DSSR0 analytic type, determining a load transfer situation according to multivariate constraint in the DSSR1 analytic type, further determining a connection situation between adjacent feeder lines, and finally determining a power distribution network structure. The invention realizes reverse generation of the power distribution network topology under the condition of unknown network initial topology or network node contact relation, and can generate reverse network topology so as to provide a new thought for optimizing the power distribution network structure.
Description
Technical Field
The invention belongs to the technical field of power distribution network planning, and particularly relates to a method for reversely generating a power distribution network by using a safety domain analysis type.
Background
The distribution system security domain (distribution system security region, DSSR) is defined as a closed set of all operating points in the state space that meet security, which is one of the bases of modern distribution network planning and operation. DSSR is formed from complete security data of the distribution network, implying rich information, and thus it is contemplated that these mathematical information can be used to reverse the generation of the distribution network.
The inverse generation network is a known mathematical model generation network. It is a "reverse" process, and general research is based on the network and the problem of interest to obtain a mathematical model, a "forward" process. The research on reverse network topology generation has important significance, and is helpful for analyzing network performance and optimizing network structure. The related research has solved the optimal topology selection of the active distribution network, the large calculation amount of the topology generation of the ship power system, the increase of satellite network time delay, the network elasticity assessment, the high-efficiency communication performance requirement of the network on chip and the massive generation of the query redundant information of the communication coverage network, and the method can obtain the optimal network topology, effectively reduce the system operation cost, improve the calculation efficiency of the generated network topology, enhance the network elasticity, improve the communication network performance and the like. However, the existing research is a network topology generation method researched under the condition of known network initial topology or network node contact relation, and the method essentially belongs to the optimization problem.
Network synthesis is to determine the network based on given frequency domain characteristics or time domain characteristics. It is an important branch of network theory, and the passive and active filter networks are widely used in various fields of modern electronic information technology. The network synthesis is researched, the analysis and optimization of the network are facilitated, the optimal network structure with functions is found, and the network survivability, elasticity and the like are improved. Therefore, the network synthesis method is used for researching the power distribution network, and a new theoretical tool can be brought to the optimization planning of the power distribution network. The existing comprehensive researches on a plurality of networks solve the problems in the aspects of solving the parameters of devices, realizing a power switching network and the like, and the comprehensive researches on the passive networks also obtain further results. However, no method for planning a power distribution network by using reverse generation network and network synthesis exists at present.
Disclosure of Invention
The invention aims to overcome the defects of the prior art, and provides a method for reversely generating a power distribution network by using a safety domain analysis type, which can generate reverse network topology and further provide a new thought for optimizing the structure of the power distribution network.
The invention solves the technical problems by adopting the following technical scheme:
a method for reversely generating a power distribution network by analyzing a safety domain comprises the following steps:
step 1, judging a feeder structure of a network;
step 2, determining main transformer constraint and feeder constraint of the network;
step 3, determining the serial or parallel structure of the branch in the feeder line constraint;
step 4, repeatedly executing the steps 2 to 3 on the multi-variable constraint which is not analyzed in the step 2, and determining all main transformer constraints, feeder constraints and corresponding feeder structures;
step 5, determining the number of main transformers according to the selected main transformer constraint;
step 6, determining the structure of a feeder line carried by the main transformer according to the selected main transformer constraint;
step 7, traversing all selected main transformer constraints to determine the structure of a feeder line carried by the main transformer, and initially constructing a network topology;
step 8, determining feeder line transfer constraint and main transformer transfer constraint;
step 9, repeatedly executing the step 8 on the N-1 multivariable constraint which is not analyzed in the step 8 so as to determine the main transformer band transfer constraint and the feeder line band transfer constraint;
step 10, when the preset constraint is a bivariate feeder line transfer constraint, further determining the connection condition between adjacent feeder lines;
and 11, constructing a final network topology according to the initial network topology and the connection condition.
The specific implementation method of the step 1 is as follows: selecting all univariate constraints, checking whether the repeatedly-occurring univariate constraints exist, if so, enabling the distribution network to comprise a main transformer with only one feeder structure without segmentation and branching, wherein the feeder corresponds to the variables, and the two univariate constraints with large capacity are main transformer constraints and the feeder constraint with small capacity are feeder constraints; if not, the distribution network does not contain a feeder structure with only a main transformer without segments and branches.
The specific implementation method of the step 2 is as follows: checking whether the constraint contains multiple variables or not, and if yes, selecting one constraint with the least variable number as a prepositive constraint; if the constraint with the least variable number exists, taking one constraint as a preposed constraint;
if the selected constraint is a feeder constraint, a series or parallel connection relationship exists in a feeder/feeder group corresponding to a variable in the constraint; if the selected constraint is a main transformer constraint, connecting a feeder line or a feeder line group corresponding to a variable in the constraint to the same main transformer; all variables in the prepositive constraint are prepositive variables;
checking whether other constraints containing all the prepositions exist or not, and if not, the prepositions are main-variant constraints; if the variable number exists, further judging according to the variable number: when there are other constraints including only all the pre-variables, the pre-constraint may be either a feeder constraint or a main-variable constraint, and the determination is based on the capacity value: the constraint with large capacity value is main variable constraint, and the constraint with small capacity value is feeder constraint; when the other constraint contains not only all the pre-variables but also other variables, then the pre-constraint is a feeder constraint.
The specific implementation method of the step 3 is as follows: selecting univariate constraints corresponding to all the prepositive variables for analysis, and if all the prepositive variables have the corresponding univariate constraints, enabling the prepositive constraints to be parallel constraints; if a certain prepositive variable does not have corresponding univariate constraint, the prepositive constraint is serial constraint, and the position of a branch corresponding to the prepositive variable which does not have corresponding univariate constraint is close to a bus.
The specific implementation method of the step 6 is as follows: selecting the main transformer constraint with the least variable number in the step 5, wherein all variables in the constraint are loads carried by the main transformer, further analyzing the structure of the load corresponding to the feeder line connected with the main transformer, and at the moment, two conditions exist: if part of variables in the main transformer constraint appear in the feeder constraint of the step 2 to the step 3, the feeders corresponding to the variables comprise series or parallel structures and are then connected to the main transformer; and (3) part of variables in the constraint do not appear in the feeder constraint of the steps 2-4, which indicates that the feeder corresponding to the variables does not have a series-parallel structure, and the feeder is directly connected to the main transformer.
The specific implementation method of the step 8 is as follows: for the multivariable constraint in the N-1 constraint, selecting one constraint with the least variable number as a prepositive constraint; if the constraint with the least variable number exists, taking one constraint as a preposed constraint, wherein all variables in the preposed constraint are preposed variables;
when other constraints only containing all the prepositions exist and the capacity values are all minimum values, the prepositions are feeder line transfer constraints; when other constraints only containing all the prepositions exist and the capacity values are all maximum values, the prepositions are main transformation rotation band constraints; when other constraints only comprising all the prepositions exist and the capacity value is large or small, the constraint with large capacity value is the main transformation band-transferring constraint, and the constraint with small capacity value is the feeder line band-transferring constraint; when other constraints include not only all the prepositive variables but also other variables, the prepositive constraints are feeder line transfer constraints; when there is no constraint containing a pre-variable, then the pre-constraint is a main-variable rotation-band constraint.
The invention has the advantages and positive effects that:
the invention determines a plurality of network blocks by analyzing the DSSR0 analytic expression, then determines the load transfer situation according to the multivariate constraint in the DSSR1 analytic expression, further determines the connection situation between adjacent feeder lines, and finally determines the distribution network structure. The invention realizes reverse generation of the power distribution network topology under the condition of unknown network initial topology or network node contact relation, and can generate reverse network topology so as to provide a new thought for optimizing the power distribution network structure.
Drawings
FIG. 1 is a preliminary build network architecture of an embodiment of the present invention;
fig. 2 is a final network architecture constructed in accordance with an embodiment of the present invention.
Detailed Description
The present invention will be described in further detail with reference to the accompanying drawings.
A method for reversely generating a power distribution network by analyzing a safety domain comprises the following steps:
and 1, judging the feeder structure of the network.
And selecting all univariate constraints, and checking whether the repeatedly-appearing univariate constraints exist, namely, the univariate constraints of the same variable appearing twice. If the two single variable constraints exist, the distribution network comprises a main transformer with only one feeder structure without segmentation and branching, the feeder corresponds to the variable, and the two single variable constraints are main transformer constraints with large capacity and feeder constraints with small capacity; if not, the distribution network does not contain a feeder structure with only a main transformer without segments and branches.
And 2, determining main transformer constraint and feeder constraint of the network.
Checking whether the constraint contains multiple variables or not, and if yes, selecting one constraint with the minimum variable number; if there are multiple, one of the constraints is taken. There are two cases at this time: the selected constraint is a feeder constraint, namely, a series/parallel connection relationship exists between feeder lines/feeder line groups corresponding to variables in the constraint; the selected constraint is a main transformer constraint, namely, the feeder lines/feeder line groups corresponding to the variables in the constraint are connected to the same main transformer. The constraint selected in step 2 is called a pre-constraint, and all variables in the pre-constraint are called pre-variables.
To determine feeder constraints and main transformer constraints, the two cases above need to be discussed further: checking if there are other constraints that contain all the pre-variables, there are two cases: if not, the prepositive constraint is a main transformer constraint; if the variable number exists, further judging according to the variable number: when there are other constraints that only contain all the pre-variables, the pre-constraints may be either feeder constraints or main-variable constraints, and the determination can be based on the capacity value: the constraint with large capacity value is main variable constraint, and the constraint with small capacity value is feeder constraint. When the other constraint contains not only all the pre-variables but also other variables, then the pre-constraint is a feeder constraint.
And 3, determining the serial or parallel structure of the branches in the feeder line constraint.
Selecting univariate constraints corresponding to all the prepositive variables for analysis, and if all the prepositive variables have the corresponding univariate constraints, enabling the prepositive constraints to be parallel constraints, namely enabling the branches corresponding to the variables to meet a parallel structure; if a certain pre-variable does not have corresponding univariate constraint, the pre-constraint is series constraint, namely, the branch corresponding to the variable meets the series structure, and the position of the branch corresponding to the pre-variable which does not have corresponding univariate constraint is closer to the bus.
It should be noted that the algorithm of the present invention can also identify that the feeder branch is a structure of first-string and then-parallel or first-parallel and then-string, because the algorithm starts to analyze from the constraint with a small number of variables, can identify part of branch structures of first-string and then-string, and then identify the structure of first-string and then-parallel or first-parallel and then-string by combining the constraint with a large number of variables.
And 4, repeatedly executing the steps 2 to 3 on the multi-variable constraint which is not analyzed in the step 2 by combining the known result, and determining all main transformer constraints, feeder constraints and corresponding feeder structures.
And 5, selecting all main transformer constraints according to the analysis results of the steps 1 to 4. The number of the main transformer constraints is the number of the main transformers in the network.
And 6, determining the structure of the feeder line carried by the main transformer according to the selected main transformer constraint.
Selecting the main transformer constraint with the least variable number in the step 5, wherein all variables in the constraint are the load carried by the main transformer, further analyzing the structure of connecting the load corresponding to the feeder line with the main transformer, and at the moment, two conditions exist: if part of variables in the main transformer constraint appear in the feeder constraint of the step 2 to the step 4, the feeders corresponding to the variables comprise structures such as series connection or parallel connection and the like and are then connected to the main transformer; part of variables in the constraint are not appeared in the feeder constraint of the step 2 to the step 4, which indicates that the feeder corresponding to the variables has no serial-parallel structure and is directly connected to the main transformer.
And 7, traversing all selected main transformer constraints to determine the structure of a feeder line carried by the main transformer, and initially constructing a network topology.
For the obtained preliminary network blocks, the connection condition between the network blocks cannot be uniquely determined only by the N-0 constraint, so that the N-1 constraint needs to be analyzed to determine the connection condition of the feeder lines between different network blocks to determine the final network topology.
And 8, determining feeder line transfer constraint and main transformer transfer constraint.
Selecting one constraint with the least variable number from the N-1 constraint; if there are multiple, one of the constraints is taken. The constraint selected in step 8 is also referred to as a pre-constraint, and all variables in the pre-constraint are referred to as pre-variables.
To determine feeder turn-around constraints and main transformer turn-around constraints, further discussion is provided: when other constraints only including all the prepositions exist and the capacity values are smaller values, the prepositions are feeder line transfer constraints; when other constraints only containing all the prepositions exist and the capacity values are larger values, the prepositions are main transformation rotation band constraints; when other constraints only comprising all the prepositions exist and the capacity value is large or small, the constraint with large capacity value is the main transformation band-transferring constraint, and the constraint with small capacity value is the feeder line band-transferring constraint; when other constraints include not only all the prepositive variables but also other variables, the prepositive constraints are feeder line transfer constraints; when there is no constraint containing a pre-variable, then the pre-constraint is a main-variable rotation-band constraint.
And 9, repeatedly executing the step 8 on the N-1 multivariable constraint which is not analyzed in the step 8 by combining the known result to determine the main transformer band transfer constraint and the feeder line band transfer constraint.
And step 10, when the preset constraint is a bivariate feeder line transfer constraint, further determining the connection condition between adjacent feeder lines.
And selecting variables corresponding to all the prepositive constraints for analysis, wherein the feeder lines corresponding to the variables are connected through a feeder line interconnection switch.
And 11, constructing a final network topology according to the initial network topology and the connection condition.
Step 10 is repeatedly executed on the unanalyzed bivariate feeder line transfer constraint until all constraints are traversed, so that the final network topology is determined. For the determined network, the determined network can be verified to meet other feeder line transfer constraint and main transformer transfer constraint, and the rationality and the correctness of the network can be further described.
The N-1 safety refers to that when a certain main transformer or a certain line of a power grid fails or overhauls, a non-failure area cannot be powered off. The mathematical constraint model of the distribution network N-1 security Domain (DSSR) is as follows:
wherein,the load value transferred to the feeder j when the feeder i generates N-1; />The load value transferred to the main transformer t when the main transformer s generates N-1; />The total load value of the t-th main transformer is indicated; />Refers to the load on feeder i; />Refers to the rated capacity of the feeder j; />Refers to main transformer->Is used for the control of the capacity of the fuel cell,T S is numbered assIs used for the main transformer of the (a),T t is numbered astIs used for the main transformer of the (a),Wis an n-dimensional vector of feeder loads representing operating points, < >>Is the security domain of the power distribution network N-1.
According to the method for reversely generating the power distribution network by the analysis type safety domain, as shown in the table 1, the effect of the invention is verified by calculating the model.
TABLE 1 DSSR expression for larger example
The right data of the inequality in table 1 represents the feeder or main transformer capacity, and in practical application, the size of the data is related to the voltage class and the cable cross-sectional area.
First, N-0 constraint is analyzed:
1) According to step 1, there is a recurring univariate constraint (L 2 ≤5、L 2 20) so that there is a constraint (L) that the main transformer has a feeder structure without segments and branches and has a large capacity 2 And less than or equal to 20) is the main transformer constraint.
2) Of the N-0 constraints, 3 were multi-variable constraints (L 11 +L 12 ≤7、L 11 +L 12 +L 4 ≤25、L 3 +L 5 And is less than or equal to 23). Selecting the N-0 constraint (L 11 +L 12 Less than or equal to 7) is a prepositioned constraint, there are other N-0 constraints containing all prepositioned variables and also other variables (L) 11 +L 12 +L 4 Less than or equal to 25), according to the step 2, determining a constraint (L 11 +L 12 And less than or equal to 7) is feeder constraint.
3) According to step 3, a feeder constraint (L 11 +L 12 7) and the variable L 12 There is a corresponding univariate constraint (L 12 5) and variable L 11 Corresponding univariate constraint does not exist, and the prepositive constraint is judged to be serial constraint, namely L 11 、L 12 The corresponding branch circuit satisfies the serial structure, and L 11 Near the main change.
4) According to step 4, a constraint (L) with the least number of variables is selected for the remaining N-0 multi-variable constraints 3 +L 5 23) as a pre-constraint, there are no other N-0 constraints including all pre-variables, and determining a constraint (L according to step 2 3 +L 5 And less than or equal to 23) is the main transformer constraint.
5) According to step 4, the remaining 1N-0 multivariate constraints (L 11 +L 12 +L 4 25 or less), taking it as a pre-constraint, there is no other N-0 constraint including all pre-variables, and judging the constraint (L according to step 2 11 +L 12 +L 4 Less than or equal to 25) is the main transformer constraint.
6) According to step 4, there is no unanalyzed multivariate constraint, and principal transformation constraints, feeder constraints and corresponding feeder structures have been determined.
7) From the above analysis, it can be seen that there are 3N-0 main transformer constraints (L 11 +L 12 +L 4 ≤25、L 2 ≤20、L 3 +L 5 And 23) and combining the known analysis results, and according to the step 5, knowing that 3 main transformers T1, T2 and T3 exist in the network respectively.
8) According to step 6, the main transformer T1 (L 11 +L 12 +L 4 Variable L in 25) or less 11 、L 12 N-0 feeder constraint (L in steps 2-4 11 +L 12 And less than or equal to 7) and the variable L 4 Not shown, indicate L 11 、L 12 The corresponding feeder lines are connected to the main transformer T1 in a series structure; l (L) 4 The corresponding feeder line does not have a serial-parallel structure and is directly connected to the main transformer T1.
9) According to step 6, the main transformer T2 (L 2 Variable L in 20) or less 2 The corresponding feeder line does not have a series-parallel structure and is directly connected to the main transformer T2.
10 According to step 6, the main transformer T3 (L 3 +L 5 Variable L in.ltoreq.23) 3 、L 5 N-0 feeder constraint (L in steps 2-4 11 +L 12 No occurrence of 7), indicating L 3 、L 5 The corresponding feeder line does not have a series-parallel structure and is directly connected to the main transformer T3.
11 According to step 7, no N-0 is present which is not analyzed.
The primary constraint, the primary network topology for the larger example is shown in fig. 1. It can be seen that through analysis of the N-0 constraint, 3 network blocks are obtained, but in particular how the 3 parts are connected is not yet determinable, requiring further analysis of the N-1 constraint. The N-1 constraint is analyzed as follows:
12 Selecting the constraint (L) with the least number of variables 12 +L 2 Less than or equal to 7) is a prepositioned constraint, other constraints only including all prepositioned variables exist and the capacity values are not the same (L 12 +L 2 20% or less), according to step 8, determining a constraint (L) having a smaller capacity value 12 +L 2 Less than or equal to 7) is a feeder line transfer constraint, and a constraint (L) with a larger capacity value 12 +L 2 Less than or equal to 20) is the main transformation rotation band constraint.
13 According to step 9, for the remaining multi-variable constraints, a constraint (L) with the least number of variables is selected 11 +L 3 Less than or equal to 7) as a front constraint, constraint (L) 3 +L 11 +L 12 Not more than 8) contains not only all the pre-variables but also other variables, and according to step 8, a constraint (L 11 +L 3 Less than or equal to 7) is the feeder line rotation band constraint.
14) According to step 9, for the remaining multi-variable constraints, a constraint (L 4 +L 5 Less than or equal to 7) making a pre-constraint, there are other constraints including only all pre-variables and the capacity values are all smaller values (L 5 +L 4 7% or less), according to step 8, determining a constraint (L 4 +L 5 Less than or equal to 7) is the feeder line rotation band constraint. And the same principle can be used for constraint (L 5 +L 4 Less than or equal to 7) is the feeder line rotation band constraint.
15 According to step 9, for the remaining multi-variable constraints, a constraint (L) with the least number of variables is selected 2 +L 12 +L 11 Less than or equal to 8) as a front constraint, constraint (L) 2 +L 12 +L 11 +L 4 Not more than 30) not only all the pre-variables but also other variables, and determining the constraint (L) according to step 8 2 +L 12 +L 11 Less than or equal to 8) is the feeder line rotation band constraint.
16 According to step 9, for the remaining multi-variable constraints, a constraint (L) with the least number of variables is selected 3 +L 11 +L 12 Less than or equal to 8) as a front constraint, constraint (L) 3 +L 11 +L 12 +L 5 +L 4 Not more than 35) not only all the pre-variables but also other variables, and determining the constraint (L) according to step 8 3 +L 11 +L 12 Less than or equal to 8) is the feeder line rotation band constraint.
17 According to step 9, for the remaining multi-variable constraints, a constraint (L) with the least number of variables is selected 2 +L 12 +L 11 +L 4 Less than or equal to 30) making a pre-constraint, wherein no constraint containing a pre-variable exists, and determining a constraint (L 2 +L 12 +L 11 +L 4 Less than or equal to 30) is the main transformation rotation band constraint.
18 According to step 9, for the remaining multi-variable constraints, a constraint (L) with the least number of variables is selected 3 +L 11 +L 12 +L 5 +L 4 35) making a pre-constraint, wherein no constraint containing a pre-variable exists, and determining a constraint (L 3 +L 11 +L 12 +L 5 +L 4 Less than or equal to 35) is the main transformation rotation band constraint.
19 According to step 9, no unanalyzed multivariable constraints exist, and main transformer and feeder transfer constraints have been determined.
20 According to step 10, for feeder-line carryover constraints (L 4 +L 5 ≤7、L 5 +L 4 ≤7)，L 4 、L 5 The corresponding feeder lines are connected through feeder line interconnection switches; for feeder turn-around constraint (L 12 +L 2 ≤7)，L 12 、L 2 The corresponding feeder lines are connected through feeder line interconnection switches; for feeder turn-around constraint (L 11 +L 3 ≤7)，L 11 、L 3 The corresponding feeder lines are connected through feeder line tie switches.
21 According to step 11, there are no unanalyzed bivariate feeder carryover constraints. The final network topology for the larger example is shown in fig. 2. And the network meets other N-1 constraints, indicating that the network meets the required security domain requirements.
It should be emphasized that the examples described herein are illustrative rather than limiting, and therefore the invention includes, but is not limited to, the examples described in the detailed description, as other embodiments derived from the technical solutions of the invention by a person skilled in the art are equally within the scope of the invention.
Claims (6)
1. A method for reversely generating a power distribution network by analyzing a safety domain is characterized by comprising the following steps of: the method comprises the following steps:
step 1, judging a feeder structure of a network;
step 2, determining main transformer constraint and feeder constraint of the network;
step 3, determining the serial or parallel structure of the branch in the feeder line constraint;
step 4, repeatedly executing the steps 2 to 3 on the multi-variable constraint which is not analyzed in the step 2, and determining all main transformer constraints, feeder constraints and corresponding feeder structures;
step 5, determining the number of main transformers according to the selected main transformer constraint;
step 6, determining the structure of a feeder line carried by the main transformer according to the selected main transformer constraint;
step 7, traversing all selected main transformer constraints to determine the structure of a feeder line carried by the main transformer, and initially constructing a network topology;
step 8, determining feeder line transfer constraint and main transformer transfer constraint;
step 9, repeatedly executing the step 8 on the N-1 multivariable constraint which is not analyzed in the step 8 so as to determine the main transformer band transfer constraint and the feeder line band transfer constraint;
step 10, when the preset constraint is a bivariate feeder line transfer constraint, further determining the connection condition between adjacent feeder lines;
and 11, constructing a final network topology according to the initial network topology and the connection condition.
2. The method for reversely generating a power distribution network by analyzing a safety domain according to claim 1, wherein the method comprises the following steps: the specific implementation method of the step 1 is as follows: selecting all univariate constraints, checking whether the repeatedly-occurring univariate constraints exist, if so, enabling the distribution network to comprise a main transformer with only one feeder structure without segmentation and branching, wherein the feeder corresponds to the variables, and the two univariate constraints with large capacity are main transformer constraints and the feeder constraint with small capacity are feeder constraints; if not, the distribution network does not contain a feeder structure with only a main transformer without segments and branches.
3. The method for reversely generating the power distribution network by using the security domain analysis method according to claim 2, wherein the method comprises the following steps: the specific implementation method of the step 2 is as follows: checking whether the constraint contains multiple variables or not, and if yes, selecting one constraint with the least variable number as a prepositive constraint; if the constraint with the least variable number exists, taking one constraint as a preposed constraint;
if the selected constraint is a feeder constraint, a series or parallel connection relationship exists in a feeder/feeder group corresponding to a variable in the constraint; if the selected constraint is a main transformer constraint, connecting a feeder line or a feeder line group corresponding to a variable in the constraint to the same main transformer; all variables in the prepositive constraint are prepositive variables;
checking whether other constraints containing all the prepositions exist or not, and if not, the prepositions are main-variant constraints; if the variable number exists, further judging according to the variable number: when there are other constraints including only all the pre-variables, the pre-constraint may be either a feeder constraint or a main-variable constraint, and the determination is based on the capacity value: the constraint with large capacity value is main variable constraint, and the constraint with small capacity value is feeder constraint; when the other constraint contains not only all the pre-variables but also other variables, then the pre-constraint is a feeder constraint.
4. A method for reverse generation of a power distribution network by a security domain according to claim 3, wherein: the specific implementation method of the step 3 is as follows: selecting univariate constraints corresponding to all the prepositive variables for analysis, and if all the prepositive variables have the corresponding univariate constraints, enabling the prepositive constraints to be parallel constraints; if a certain prepositive variable does not have corresponding univariate constraint, the prepositive constraint is serial constraint, and the position of a branch corresponding to the prepositive variable which does not have corresponding univariate constraint is close to a bus.
5. The method for reversely generating the power distribution network by analyzing the security domain according to claim 4, wherein the method comprises the following steps: the specific implementation method of the step 6 is as follows: selecting the main transformer constraint with the least variable number in the step 5, wherein all variables in the constraint are loads carried by the main transformer, further analyzing the structure of the load corresponding to the feeder line connected with the main transformer, and at the moment, two conditions exist: if part of variables in the main transformer constraint appear in the feeder constraint of the step 2 to the step 3, the feeders corresponding to the variables comprise series or parallel structures and are then connected to the main transformer; and (3) part of variables in the constraint do not appear in the feeder constraint of the steps 2-4, which indicates that the feeder corresponding to the variables does not have a series-parallel structure, and the feeder is directly connected to the main transformer.
6. The method for reversely generating the power distribution network by using the security domain analysis method according to claim 5, wherein the method comprises the following steps: the specific implementation method of the step 8 is as follows: for the multivariable constraint in the N-1 constraint, selecting one constraint with the least variable number as a prepositive constraint; if the constraint with the least variable number exists, taking one constraint as a preposed constraint, wherein all variables in the preposed constraint are preposed variables;
when other constraints only containing all the prepositions exist and the capacity values are all minimum values, the prepositions are feeder line transfer constraints; when other constraints only containing all the prepositions exist and the capacity values are all maximum values, the prepositions are main transformation rotation band constraints; when other constraints only comprising all the prepositions exist and the capacity value is large or small, the constraint with large capacity value is the main transformation band-transferring constraint, and the constraint with small capacity value is the feeder line band-transferring constraint; when other constraints include not only all the prepositive variables but also other variables, the prepositive constraints are feeder line transfer constraints; when there is no constraint containing a pre-variable, then the pre-constraint is a main-variable rotation-band constraint.
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