CN112327097A - Power failure line positioning method - Google Patents
Power failure line positioning method Download PDFInfo
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Abstract
The invention discloses a power fault line positioning method, which is characterized in that according to fault current information uploaded by a feeder terminal unit, a switch node for positioning the fault of a power distribution network is simplified by utilizing an active tree and passive tree simplification model, the switch fault current information and the line state information are coded, the line state information can be converted into corresponding switch fault current information by constructing a switch function, the solution of the switch function is converted into corresponding switch fault current information, the quality degree of each solution is evaluated by utilizing a target function, and the optimal solution under a multi-target fitness function is found by calculating the overall fitness based on a Pareto optimal concept, so that the positioning of a fault line is obtained. Thereby greatly improving accuracy.
Description
Technical Field
The invention relates to the field of power distribution network faults, in particular to a power fault line positioning method
Background
As a basic industry of national economy, electric power plays an important role in supporting and guaranteeing the development of economic society and the life of people, and electric power safety is a primary concern object in public safety no matter what emergency happens, so that the position of an electric power emergency plan in electric power safety is particularly important. With the continuous development of the internet of things technology, the transformer substations and the regional power distribution network become a correlated whole increasingly, the automation degree is also improved continuously, the fault types of the transformer substations and the power distribution network in operation are various, and higher requirements are provided for the practicability, the simplicity and the operability of the power fault line positioning method. When a fault occurs in a power distribution network, the existing emergency plan of a power enterprise continuously exposes the defects of complexity and low efficiency, the existing power emergency positioning method is mostly based on a static script, and the occurrence and development of a power accident are mostly random, so that the accuracy and timeliness of fault response cannot be well guaranteed, the fault area cannot be accurately and precisely segmented in a short time, troubles are caused for subsequent fault removal, and more economic losses are caused.
Disclosure of Invention
Aiming at the defects of the prior art, the invention provides the power fault line positioning method, when the line fails due to an emergency, the position of the fault line can be quickly and accurately positioned, so that the dynamic emergency response is prepared, and the method has higher accuracy and better timeliness.
A power-faulted line locating method, the method comprising:
when the power distribution network is identified to be in fault;
uploading current information of each switch node by a feeder terminal unit arranged on the switch node of the power distribution network;
simplifying switching nodes for positioning the faults of the power distribution network by using an active tree and passive tree simplification model;
encoding the current information of an open joint;
constructing a switching function and generating a solution of the switching function;
converting the solved line fault information into corresponding switch current information;
evaluating the quality degree of each solution by using a target function, and finding out the optimal solution under the multi-target fitness function by calculating the overall fitness based on the Pareto optimal concept;
and converting the optimal solution to obtain a fault position corresponding to the information uploaded by the feeder terminal unit.
Further, the simplification of the switch node for power distribution network fault location by using the active tree and passive tree simplified model specifically comprises:
the method comprises the steps that a power distribution network containing distributed power supplies is regarded as a directed graph, a network connection graph with the degree of 1 in the directed graph and power supply points including the distributed power supplies as vertexes is defined as an active tree, and a network path left after the active tree is removed from the power distribution network is called a passive tree;
when no fault current exists in a certain passive tree, no fault occurs in the passive tree, and the fault is not considered in positioning.
Preferably, the encoding of the switch fault current information and the line state information specifically includes: when the fault current direction detected by the feeder line terminal unit is the same as the defined positive direction, reporting information code as 1; when the fault current direction detected by the feeder line terminal unit is opposite to the defined positive direction, reporting information codes to be-1; if no fault current flows, reporting information to be coded as 0;
numbering switches and feeders in a power distribution network, the state information of which is in turn taken as xiAnd (i ═ 0,1,2, …, n) fills in the chromosome of length n.
Preferably, the switching function contains switch upstream feeder state information, downstream feeder state information, and switch downstream distributed power supply state information, the switching function being represented as follows:
in the formula I*(Sj) Is the switching function between the jth switching node and the feeder, m is the total number of the feeder lines downstream thereof, n is the total number of the feeder lines upstream thereof; x is the number ofmIs the status information of the mth feeder line downstream of the jth switch, xnThe state information of the nth feeder line at the upstream of the jth switch is that the information value in the fault state is 1, and the information value in the normal state is 0; k is the state information of the distributed power supply grid-connected switch at the downstream of the switch, the grid-connected state is 1, and the off-line state is 0.
As a preferred embodiment, the evaluating the quality of each solution by using the objective function specifically includes: constructing a single-target fitness function, and when the difference between the uploaded information and the optimal solution is minimum, namely the optimal solution is closest to the actual condition, achieving the optimal solution, wherein the smaller the single-target fitness function is, the closer the feasible dissociation optimal solution is, and the single-target fitness function is as follows:
in the formula I (S)j) Is representative of the feed at the jth switchReporting information 1 when fault current flows through the out-of-limit signal condition of the fault current uploaded by the line terminal unit, and reporting information 0 when no fault current flows; i is*(Sj) The expected value of each switching node is obtained by calculation of a switching function; n is the number of switches; w is a weight coefficient, X (S)j) The number of faulty feed lines.
As another preferred mode, the single-target fitness function utilizes a Pareto optimal concept, and can find an optimal solution under a multi-target fitness function, where the multi-target fitness function is:
in the formula I (S)j) The situation represents the fault current out-of-limit signal uploaded by the feeder terminal unit at the jth switch, and information 1 is reported when the fault current flows through the jth switch, and information 0 is reported when no fault current exists;
I*(Sj) The expected value of each switching node is obtained by calculation of a switching function; n is the number of switches; x (S)j) The number of faulty feed lines.
Further, the solution of the multi-objective fitness function further includes an individual adaptive distance operator, and the calculation is specifically as follows:
the adaptation distance of the individual i is the distance between the individuals i +1 and i-1 adjacent to i in the target space;
initializing individual distances of the same layer, and sequencing individuals of the same generation in an ascending order according to the size of each objective function value;
setting the total number of the objective functions as M;
calculating the relative distance difference of two side points of the individual i under the objective function, namely the adaptive distance:
in the formula: l [ i ]]mFor the value of the ith individual at the mth objective function,
and
respectively obtaining the maximum value and the minimum value of the mth objective function in all feasible solution sets;
and superposing the adaptive distances of the individual i under each objective function to obtain the overall fitness of the individual: l [ i ]]d＝L[i]1+L[i]2。
Preferably, individuals with larger adaptation distances in the same non-dominant condition are preferentially selected in the overall fitness solution, and solution results of the model are uniformly distributed on the Pareto frontier.
According to the power fault line positioning method, the complex power distribution network is simplified by using the active tree and passive tree simplification model, the passive tree network of the fault-free line is removed, the operation dimension can be greatly reduced, the operation speed is accelerated, and therefore the timeliness of the intelligent plan is improved; the improved genetic algorithm model and the multi-target fitness function are constructed, so that the algorithm speed is further improved, and meanwhile, the line fault model caused by an emergency event has good fault tolerance, and the accuracy of the model is greatly improved. In conclusion, the fault line positioning method provided by the invention has extremely strong timeliness and accuracy.
Drawings
Fig. 1 is a flowchart of a power failure line locating method provided by the present invention.
Fig. 2 is a schematic flow chart of a simplified model of an active tree and a passive tree provided by the present invention.
Figure 3 is a power distribution network topology diagram of one embodiment of the present invention.
FIG. 4 is a simplified network structure diagram of a small power distribution network according to the present invention
Fig. 5 is a graph of a single-target fitness function simulation iterative process of a small distribution network fault provided by the invention.
FIG. 6 is a graph of a multi-objective fitness function simulation iterative process of a small distribution network fault provided by the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only some embodiments, not all embodiments, of the present invention. 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 invention.
Referring to fig. 1, it is a flowchart of a method for locating a power failure line provided in the present invention. As shown in fig. 1, the method includes S101 to S108:
when the power distribution network is identified to be in fault;
uploading current information of each switch node by a feeder terminal unit arranged on the switch node of the power distribution network;
simplifying switching nodes for positioning the faults of the power distribution network by using an active tree and passive tree simplification model;
encoding the current information of an open joint;
constructing a switching function and generating a solution of the switching function;
converting the solved line fault information into corresponding switch current information;
evaluating the quality degree of each solution by using a target function, and finding out the optimal solution under the multi-target fitness function by calculating the overall fitness based on the Pareto optimal concept;
and converting the optimal solution to obtain a fault position corresponding to the information uploaded by the feeder terminal unit.
S101, when the power distribution network is identified to have a fault;
s102, uploading current information of each switch node by a feeder terminal unit arranged on the switch node of the power distribution network;
s103, simplifying switching nodes for power distribution network fault positioning by using an active tree and passive tree simplification model;
s104, encoding the current information of the open joint;
s105, constructing a switching function and generating a solution of the switching function;
s106, converting the solved line fault information into corresponding switch current information;
s107, evaluating the quality degree of each solution by using a target function, and finding out the optimal solution under the multi-target fitness function by calculating the overall fitness based on the Pareto optimal concept;
and S108, converting the optimal solution to obtain a fault position corresponding to the information uploaded by the feeder terminal unit.
According to the power fault line positioning method, the complex power distribution network is simplified by using the active tree and passive tree simplification model, the operation dimension can be greatly reduced, the operation speed is accelerated, and the fault line positioning method in the intelligent power emergency plan mode is more accurate and timely by constructing the coding mode, the switching function and the fitness function of the genetic algorithm in the fault line positioning model.
When considering how the power emergency plan accurately positions a line with a fault under the condition of containing a distributed power supply network, the power distribution network containing the distributed power supply is regarded as a directed graph, a network connection graph with the directed graph middle degree being 1 and the power supply point being a vertex is defined as an active tree, and a network path left after the active tree is removed from the network is called as a passive tree, so that the whole power distribution network containing the distributed power supply can be divided into a directed network containing an active tree and a plurality of passive branches by utilizing a simplified model of the active tree and the passive tree.
Referring to fig. 2, it is a schematic flow chart of a simplified model of an active tree and a passive tree provided by the present invention, and as shown in fig. 2, the specific flow includes:
(1) firstly, initializing parameters, recording the initial distributed Power number as n, recording the Endnode as an end node and recording the Endnode as m, respectively storing a source tree and a Passive tree node in a Power tree library and a Passive branch library, wherein a represents a distributed Power node, b represents a Passive tree node, i represents a terminal node, and j represents an active tree node, and firstly, initializing a, b, i and j to 1;
(2) storing the distributed nodes into a Power tree library, and searching for an active tree and a passive tree from the distributed Power nodes;
(3) judging whether j is a main transformer power supply node, namely a power supply S node and a distributed power supply DG node; if yes, jumping to (7); if the result is no, executing (4);
(4) judging whether j is in a Power tree library, if not, executing the step (5); if yes, executing (6);
(5) store j in Power tree library
(6) J is self-adding 1; jumping to (3);
(7) judging whether i is smaller than n, and if so, jumping to (9); if the result is no, executing (8);
(8) i is added with 1, j is assigned with 1, and the step jumps to (2);
(9) storing the end node Endnode in a Pasive branch library, and carrying out deep search from the end node Endnode to the main transformer;
(10) judging whether the b is in the Passive branch library, and if so, jumping to (13); if the result is no, executing (11);
(11) storing b into a Passive branch library;
(12) b is added by 1 and jumps to (10);
(13) judging whether a is smaller than m, if not, jumping to (15); if yes, executing (14);
(14) adding 1 to a, assigning the value of b to be 1, and jumping to the step (10);
(15) outputting an active tree Power tree library and a Passive tree Passive branch library;
referring to fig. 3, it is a power distribution network topology diagram of an embodiment of the method for locating a power failure line provided in the present invention.
As shown in fig. 3, a power distribution network topology of an embodiment includes a power source S, a transformer T, and each of switch nodes S1 to S33, each of lines L1 to L33, a distributed power source DG1, a distributed power source DG2, a grid-connected switch K1, and a grid-connected switch K2.
In the specific implementation of this embodiment, the process of simplifying the distribution network topology diagram by using the active tree and passive tree simplification model includes:
in the figure, a section from a power supply S to a switch node S3, a section from a switch node S3 to a distributed power supply DG1 and a section from a switch node S4 to a distributed power supply DG2 are active trees, and a section from a switch S30 to a line L33 and a section from a line L6 to a line L17 are passive trees
In specific implementation, assuming that a fault is caused by an emergency event on the line L30, the intelligent power emergency plan needs to find the position of the faulty line at the fastest speed, and under normal conditions, the operating states of 33 lines in the system need to be judged one by one, so as to find the position of the line where the fault actually occurs, and if the system is divided into an active tree and a passive tree, no fault current flows through all of the passive trees L6 to L17, so that the fault current can be directly removed and not considered, the calculation dimensionality of the algorithm is reduced from 33 to 21, and the calculation amount of the model is greatly reduced.
The genetic algorithm adopted by the invention is essentially a searching method, and can efficiently and globally acquire and accumulate knowledge about a searching space in the searching process, and adjust the knowledge in the searching process to finally obtain the optimal solution.
The most important two parts in the genetic algorithm are variation and crossover respectively, wherein the variation part directly influences whether a population can generate a new gene, and the crossover capability can reflect the optimal solution searching capability of the algorithm, and the construction reasonability of the variation probability and the crossover probability has great influence on the evolution of the population.
Introduction of individual similarity coefficient in traditional genetic algorithm
The similarity degree of the ith generation population can be reflected:
in the formula: e is an expected value of population fitness; and D is a variance value of the fitness and reflects the individual fitness discrete condition. With the continuous evolution of the population, the corresponding expectation value is larger and larger, the variance is smaller and smaller, and the similarity is larger, which represents that the population tends to converge continuously.
The similarity function is limited in that the similarity function is not global, the similarity degree of population individuals is greatly influenced only in the late evolution stage, and the similarity of individuals in the population is small in the early evolution stage, so that if the similarity degree of individuals is expressed by using the traditional genetic algorithm, the difference between the individuals in the early stage population is completely ignored, and the accuracy of the algorithm is influenced.
The invention constructs a global similarity coefficient, and overcomes the defects of the traditional similarity coefficient in the prior stage by introducing an adjusting factor into a similarity function, which is specifically expressed as follows, wherein u (i, j) represents the ith generation jth individual adjusting factor.
In the formula: f. ofmax(i)、fmin(i) Respectively representing the maximum and minimum fitness of the ith generation of individuals in the population; f (i, j) represents the fitness of the j th individual of the ith generation subjected to mutation and crossover operations in the population.
The new similarity coefficient can be obtained according to equation (2):
compared with the traditional genetic algorithm, the improvement of the similarity coefficient in the invention can dynamically adjust the similarity coefficient in the algorithm in the evolution process of the population, so that the population individuals can be related to other population individuals according to the characteristics of the population individuals and the relationship between the population individuals and other population individuals in the whole evolution processAnd (6) adjusting the rows. Wherein u (i, j) has a major effect in the early stage of evolution, and the population individuals in the early stage have large differences, so that the similarity among individuals is reflected
Smaller, inter-response-individual variability u (i, j) is
The effect of (c) is greater, and the opposite is true in the later stage.
Known from genetic algorithm, when the difference between individuals in a population is large, the gene types of the whole population are very rich, and a larger cross probability and a smaller variation probability are given to the current generation of population during algorithm; conversely, if the difference between individuals in the current population is small, then it should be given a large mutation probability and a small crossover probability.
The coding mode of the genetic algorithm constructed in the invention in the fault line positioning model is that the line direction from a system power supply to a distributed power supply is defined as a positive direction, and if the distributed power supply does not exist in a certain direction, the line direction from the system power supply to a load side is defined as the positive direction.
Referring to fig. 4, the simplified network structure diagram of the small power distribution network provided by the present invention, as shown in fig. 4, simulates a fault condition.
As shown in fig. 4, S denotes a power source, T denotes a transformer, DG denotes a distributed power source, L denotes a distribution line, S denotes a switch node, and K denotes a grid-connected switch of the distributed power source. In this embodiment there is a feeder termination device at each switch.
In this embodiment, the current information uploaded by the feeder terminal unit is used as a main basis for inputting codes, and when the fault current direction detected by the feeder terminal unit is the same as the defined positive direction, the reported information code is 1; if the fault current direction detected by the feeder line terminal unit is opposite to the defined positive direction, reporting information codes to be-1; if no fault current flows, the reported information is coded as 0.
In the improved genetic algorithm, forSwitches and feeders in the network are numbered, and the state information of the switches and feeders is sequentially used as xiAnd (i ═ 0,1,2, …, n) fills in the chromosome of length n.
For the improved genetic algorithm and the coding mode definition in the previous subsection in the present invention, the switching function of the given algorithm is as follows:
in the formula I*(Sj) Is the switching function between the jth switching node and the feeder, m is the total number of the feeder lines downstream thereof, n is the total number of the feeder lines upstream thereof; x is the number ofmIs the status information of the mth feeder line downstream of the jth switch, xnThe state information of the nth feeder line at the upstream of the jth switch is that the information value in the fault state is 1, and the information value in the normal state is 0; k is the state information of the grid-connected switch of the distributed power supply at the downstream of the switch, the grid-connected state is 1, and the off-grid state is 0.
In fault location, the principle of constructing the single-target fitness function is that when the difference between the uploaded information and the optimal solution is minimum, namely the optimal solution is closest to the actual condition, the optimal solution can be considered to be reached. Therefore, the smaller the single-target fitness function, the closer the feasible dissociation optimal solution. Taking a single target fitness function:
in the formula I (S)j) The situation represents the fault current out-of-limit signal uploaded by feeder terminal equipment at the jth switch, and information 1 is reported when the fault current flows through the jth switch, and information 0 is reported when no fault current exists; i is*(Sj) The expected value of each switching node is obtained by calculation of a switching function; n is the number of switches; w is a weight coefficient, X (S)j) The number of faulty feed lines.
The fitness function is used for evaluating the quality of chromosomes or particles, and in the genetic algorithm part, the chromosomes are determined to be copied and directly reserved, the probability of the chromosomes participating in the cross operation is higher, and the fitness function can evaluate whether the current solution is the optimal solution or the distance from the optimal solution.
For analyzing the line fault situation of a single line caused by an emergency, the present embodiment simulates three different line fault scenarios, and the simulation result of the single-target fitness function is shown in table 1 below.
TABLE 1 Single Fault type simulation results
Tab.1 Simulation results of single fault type
In the above table, [ K1, K2] indicates the access situation of the DG, 1 indicates the DG access system, 0 indicates the no access system, and the simulation result curve is shown in fig. 5.
As can be seen from fig. 5, the objective function value is minimized when the iteration is about 22 generations, and it can be seen from table 1 that, in the single line fault location problem caused by an emergency, the model of the present invention has the advantages of reduced iteration number, higher efficiency, and better fault line location accuracy.
Based on the analysis, the method can comprehensively and globally find the optimal solution under the multi-target fitness function by utilizing the Pareto optimal concept without considering the influence of the weight. The difference index of the fault section state and the actual state and the fault line minimum index are a pair of mutually constrained objective functions. There is a contradiction between the above two objectives, i.e. the optimal solution cannot be achieved at the same time, so a reasonable decision method needs to be proposed for selection. The multi-target fitness function in the invention is as follows:
in order to ensure that the solved optimal solution set is uniformly distributed on the Pareto frontier, thereby ensuring the diversity of the solution. The invention designs an individual fitness distance operator. The adaptive distance of the individual i is the distance between the individual i +1 and the individual i-1 which are adjacent to the individual i in the target space, the individual distance of the same layer is initialized, the individuals of the same generation are sorted in an ascending order according to the size of each objective function value, and the total number of the objective functions is set as M; and finally, calculating the relative distance difference of two side points of the individual i under the objective function, namely the adaptive distance:
in the formula: l [ i ]]mFor the value of the ith individual at the mth objective function,
and
respectively the maximum value and the minimum value of the mth objective function in all the feasible solutions.
And superposing the adaptive distances of the individual i under each objective function to obtain the overall fitness of the individual:
L[i]d＝L[i]1+L[i]2
by preferentially selecting the individuals with larger adaptation distance in the same non-dominant layer, the solution results are uniformly distributed on the Pareto frontier, and the diversity of the solution set is ensured.
For the simulation analysis of multiple fault scenarios, the present embodiment respectively simulates three different line fault scenarios, and the simulation result using the multi-objective fitness function is shown in table 2 below.
TABLE 2 multiple fault type simulation results
As shown in fig. 6, the simulation result curve is shown in fig. 6, and it can be seen from fig. 6 that the objective function value is the smallest when the iteration is about 30 generations, and at the same time, as can be seen from table 2, the multi-objective fitness function simulation model in this embodiment has better fault line location accuracy in the problem of multi-line fault location caused by an emergency, and the improved genetic algorithm has a strong advantage in increasing the algorithm speed.
In this embodiment, a gain effect of the Pareto-based optimal multi-target fitness function on the condition that misjudgment or missed judgment is reduced by the algorithm is established, and the result is shown in table 3 below, where each iteration is performed 100 times:
TABLE 5 gain of Pareto optimal pair model
As can be seen from table 3, the convergence times of the two methods are approximately equivalent, the accuracy of the Pareto-based optimal multi-objective fitness function on the line fault positioning method caused by the emergency is high, and the single-objective optimal power distribution network fault positioning method cannot be completely and accurately positioned. Therefore, the algorithm of the invention can still accurately position the fault section under the condition of single or multiple types of information false alarm, and has high reliability. With the continuous increase of the scale of the model, the fault problem is continuously complicated, the reliability requirement of fault positioning is correspondingly higher, and the Pareto-based optimal multi-objective fitness function has obvious reliability gain on the line fault positioning problem caused by an emergency.
According to the power fault line positioning method, the complex power distribution network is simplified by using the active tree and passive tree simplification model, the passive tree network of the fault-free line is removed, the operation dimension can be greatly reduced, the operation speed is accelerated, and the positioning timeliness is improved; the construction of the improved genetic algorithm model and the multi-target fitness function can reduce the iteration times in the operation process, further improve the algorithm speed, and simultaneously enable the line fault caused by an emergency to have good fault tolerance, thereby greatly improving the accuracy of the model. In conclusion, the fault line positioning method adopted by the invention has strong timeliness and accuracy.
While the foregoing is directed to the preferred embodiment of the present invention, it will be understood by those skilled in the art that various changes and modifications may be made without departing from the spirit and scope of the invention.
Claims (8)
1. A method of locating a power-faulted line, the method comprising:
when the power distribution network is identified to be in fault;
uploading current information of each switch node by a feeder terminal unit arranged on the switch node of the power distribution network;
simplifying switching nodes for positioning the faults of the power distribution network by using an active tree and passive tree simplification model;
encoding the current information of an open joint;
constructing a switching function and generating a solution of the switching function;
converting the solved line fault information into corresponding switch current information;
evaluating the quality degree of each solution by using a target function, and finding out the optimal solution under the multi-target fitness function by calculating the overall fitness based on the Pareto optimal concept;
and converting the optimal solution to obtain a fault position corresponding to the information uploaded by the feeder terminal unit.
2. The method for positioning the power fault line according to claim 1, wherein the simplification of the switch node for positioning the power distribution network fault by using the active tree and passive tree simplified model is specifically as follows:
the method comprises the steps that a power distribution network containing distributed power supplies is regarded as a directed graph, a network connection graph with the degree of 1 in the directed graph and power supply points including the distributed power supplies as vertexes is defined as an active tree, and a network path left after the active tree is removed from the power distribution network is called a passive tree;
when no fault current exists in a certain passive tree, no fault occurs in the passive tree, and the fault is not considered in positioning.
3. The method according to claim 1, wherein the encoding of the switch fault current information and the line state information specifically comprises: when the fault current direction detected by the feeder line terminal unit is the same as the defined positive direction, reporting information code as 1; when the fault current direction detected by the feeder line terminal unit is opposite to the defined positive direction, reporting information codes to be-1; if no fault current flows, reporting information to be coded as 0;
numbering switches and feeders in a power distribution network, the state information of which is in turn taken as xi，(i＝0,1,2,…,n)。
4. A power-fail line location method according to claim 1, wherein the switching function contains switch upstream feeder state information, downstream feeder state information, and switch downstream distributed power supply state information, and the switching function is expressed as follows:
in the formula I*(Sj) Is the switching function between the jth switching node and the feeder, m is the total number of the feeder lines downstream thereof, n is the total number of the feeder lines upstream thereof; x is the number ofmIs the status information of the mth feeder line downstream of the jth switch, xnThe state information of the nth feeder line at the upstream of the jth switch is that the information value in the fault state is 1, and the information value in the normal state is 0; k is the state information of the distributed power supply grid-connected switch at the downstream of the switch, the grid-connected state is 1, and the off-line state is 0.
5. The method according to claim 1, wherein the evaluating the quality of each solution by using the objective function specifically comprises: constructing a single-target fitness function, and when the difference between the uploaded information and the optimal solution is minimum, namely the optimal solution is closest to the actual condition, achieving the optimal solution, wherein the smaller the single-target fitness function is, the closer the feasible dissociation optimal solution is, and the single-target fitness function is as follows:
min:
in the formula I (S)j) The situation represents the out-of-limit signal condition of the fault current uploaded by the feeder line terminal unit at the jth switch, and the information 1 is reported when the fault current flows through the out-of-limit signal condition, and the information 0 is reported when no fault current exists; i is*(Sj) The expected value of each switching node is obtained by calculation of a switching function; n is the number of switches; w is a weight coefficient, X (S)j) The number of faulty feed lines.
6. The method according to claim 5, wherein the single-target fitness function utilizes a Pareto optimal concept to find an optimal solution under a multi-target fitness function, wherein the multi-target fitness function is:
min:
min:
in the formula I (S)j) The situation represents the fault current out-of-limit signal uploaded by the feeder terminal unit at the jth switch, and information 1 is reported when the fault current flows through the jth switch, and information 0 is reported when no fault current exists;
I*(Sj) The expected value of each switching node is obtained by calculation of a switching function; n is the number of switches; x (S)j) The number of faulty feed lines.
7. The method according to claim 6, wherein the solution of the multi-objective fitness function further comprises an individual adaptive distance operator, and the specific calculation is as follows:
the adaptation distance of the individual i is the distance between the individuals i +1 and i-1 adjacent to i in the target space;
initializing individual distances of the same layer, and sequencing individuals of the same generation in an ascending order according to the size of each objective function value;
setting the total number of the objective functions as M;
calculating the relative distance difference of two side points of the individual i under the objective function, namely the adaptive distance:
in the formula: l [ i ]]mFor the value of the ith individual at the mth objective function,
and
respectively obtaining the maximum value and the minimum value of the mth objective function in all feasible solution sets;
and superposing the adaptive distances of the individual i under each objective function to obtain the overall fitness of the individual: l [ i ]]d＝L[i]1+L[i]2。
8. The method according to claim 7, wherein individuals with larger adaptation distance in the same non-dominant phase are preferentially selected in the overall fitness solution, so that solution results of the model are uniformly distributed on a Pareto frontier.
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