CN113469432A - Distribution network transfer intelligent analysis auxiliary method - Google Patents
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Abstract
The invention discloses an intelligent analysis auxiliary method for distribution network transfer, which comprises the following steps: collecting three remote information of the power distribution network when a fault occurs; acquiring state information of distribution network equipment according to the three remote information of the distribution network, mapping the state information of the distribution network equipment to a distribution network topological graph to construct a transfer analysis model, and visualizing the transfer analysis model; constructing an expert database, and inputting a visual transfer analysis model into a neural network after training the neural network to obtain a subjective optimal transfer path; carrying out switching control on various distribution network equipment combinations in the power supply analysis model to obtain a plurality of initial power supply switching paths; optimizing the initial transfer path by adopting a wolf algorithm, and obtaining an objective optimal transfer path; and obtaining a final optimal transfer path according to the subjective optimal transfer path and the objective optimal transfer path, and controlling a distribution network switch and a transformer according to the final optimal transfer path to realize the rapid adjustment of abnormal states such as overload and low voltage of a distribution network and the rapid isolation and transfer adjustment of a short-circuit fault state.
Description
Technical Field
The invention relates to the technical field of electric power, in particular to an intelligent analysis auxiliary method for distribution network switching.
Background
Along with the continuous development of intellectualization, the heavy investment construction is considerable, measurable and controllable intelligent power distribution network engineering is also carried out rapidly, but the automation application of the power distribution network is relatively slow, part of the power distribution network is weak in operation foundation, the power distribution automation system is difficult to directly enter a centralized control mode, and is difficult to completely realize the on-site self-healing contact function, after a circuit is tripped, the circuit is usually repaired by means of reclosing and power distribution network automation logic, the load behind a fault section cannot be recovered rapidly, the load in a non-fault area can be recovered after a dispatcher judges through manually collecting information, the power recovery is relatively slow, and the requirement on the power supply reliability of important sensitive loads cannot be met.
For a power distribution network, a plurality of power transmission lines exist, when one of the power transmission lines fails, the load on the line needs to be powered in a power supply transferring mode to ensure normal power utilization of the load, currently, for power supply transferring, most of workers open and close switches at different positions according to self experience to realize power supply transferring, and a power supply transferring path selected according to the experience has large subjectivity, so that the selected power supply transferring path cannot be guaranteed to be an optimal path, fluctuation of power transmission on other power transmission lines is easily caused, and even power failure faults are caused.
Disclosure of Invention
Therefore, the invention provides an intelligent analysis auxiliary method for distribution network power transfer, which can quickly recover power transmission and ensure the stability of other power transmission lines by acquiring an optimal power transfer path through a Husky algorithm.
The technical scheme of the invention is realized as follows:
the intelligent analysis auxiliary method for the network distribution transfer comprises the following steps:
s1, collecting the three remote information of the power distribution network when a fault occurs;
step S2, acquiring distribution network equipment state information according to the distribution network three-remote information, mapping the distribution network equipment state information to a distribution network topological graph to construct a transfer analysis model, and visualizing the transfer analysis model;
step S3, constructing an expert database, and inputting a visual transfer analysis model into the neural network after training the neural network to obtain a subjective optimal transfer path;
s4, performing opening and closing control on various distribution network equipment combinations in the power supply analysis model to obtain a plurality of initial power supply switching paths;
step S5, optimizing the initial transfer path by adopting a wolf algorithm, and obtaining an objective optimal transfer path;
and step S6, obtaining a final optimal transfer path according to the subjective optimal transfer path and the objective optimal transfer path.
Preferably, the distribution network device status information in step S2 includes distribution network switch status information and transformer status information.
Preferably, after the transshipment analysis model is visualized in step S2, the non-closed distribution network switch and the non-opened transformer in the transshipment analysis model are in a gray state, and the closed distribution network switch and the opened transformer are in a bright colored state.
Preferably, the expert database in step S3 includes power distribution network topology diagrams and corresponding transfer path diagrams in various fault states, the input data of the neural network is a transfer analysis model picture when a fault occurs, the output data is a transfer path, the expert database is divided into a training set and a test set, and the neural network is trained through the training set and tested for accuracy through the test set.
Preferably, the specific step of step S5 includes:
step S51, initializing a wolf population, and setting iteration times and population scale;
step S52, an initial transfer path is arbitrarily selected, a quickness evaluation index of the initial transfer path is obtained, and a fitness value of the wolf is calculated and obtained according to the quickness evaluation index;
step S53, another initial transfer path is selected, after a corresponding fitness value is obtained, the fitness value is compared with the fitness value calculated last time, and an initial transfer path with a larger fitness value is reserved;
and step S54, performing iterative computation, traversing all initial transfer paths, obtaining an initial transfer path with the maximum fitness value, and outputting the initial transfer path as an objective optimal transfer path.
Preferably, the number of iterations in step S51 is the same as the number of initial handover paths.
Preferably, the specific step of step S6 includes:
step S61, obtaining evaluation parameters of the subjective optimal transfer path and the objective optimal transfer path;
step S62, subjectively weighting the evaluation parameters of the subjectively optimal transfer path, obtaining a transfer subjective value, objectively weighting the evaluation parameters of the objectively optimal transfer path, and obtaining a transfer objective value;
and step S63, comparing the converted subjective value with the converted objective value, and selecting the optimal converted path corresponding to the party with larger value as the final optimal converted path.
Preferably, the evaluation parameters in step S61 include rapidity obtained by the time for resuming power transmission, economy obtained by the total length of the power transfer path, and stability obtained by the voltage-current sudden change of a certain electric device at the time of resuming power transmission.
Preferably, the expression of the transferred subjective value in step S62 is:
Fsubjective method and apparatus＝αFRapidity of operation+βFEconomy of use+δFStability of；
The expression for the objective value is:
Fobjective＝λFRapidity of operation+ρFEconomy of use+ωFStability of；
Wherein FRapidity of operation、FEconomy of use、FStability ofRespectively as evaluation parameters, alpha, beta and delta are main parametersThe observation weighting indexes, lambda, rho and omega are objective weighting indexes, and the subjective weighting index and the objective weighting index are constants set according to actual conditions.
Compared with the prior art, the invention has the beneficial effects that:
the invention provides an intelligent analysis auxiliary method for distribution network transfer, which is characterized in that distribution network equipment state information is obtained by collecting three remote information of a distribution network when a fault occurs, then the distribution network equipment state information is mapped into a distribution topological graph, a transfer analysis model is constructed, the transfer analysis model can respectively calculate a subjective and objective optimal transfer path through a neural network and a gray wolf algorithm, and finally a final optimal transfer path can be obtained according to the subjective optimal transfer path and the objective optimal transfer path, so that the distribution network can be transferred according to the final optimal transfer path, the rapid adjustment of abnormal states such as overload and low voltage of the distribution network, the rapid isolation of short-circuit fault states and the like can be realized, and the normal transmission of electric energy can be ensured.
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In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings needed to be used in the description of the embodiments will be briefly introduced below, and it is obvious that the drawings in the following description are only preferred embodiments of the present invention, and it is obvious for those skilled in the art that other drawings can be obtained according to these drawings without inventive exercise.
Fig. 1 is a flowchart of an auxiliary method for intelligent analysis of distribution network switching according to the present invention.
Detailed Description
For a better understanding of the technical content of the present invention, a specific embodiment is provided below, and the present invention is further described with reference to the accompanying drawings.
Referring to fig. 1, the distribution network transfer intelligent analysis auxiliary method provided by the invention comprises the following steps:
and S1, collecting the three remote information of the power distribution network when a fault occurs, and realizing the collection of the three remote information when the fault occurs by utilizing the three remote centralized monitoring function of the power distribution automation main station, wherein the three remote information comprises remote measurement, remote signaling and remote control information, and mainly comprises an alarm state or a switch position, a transformer switch state and the like, and the load monitoring of each key node is realized by utilizing the DTU and the FTU of the power distribution network while collecting the three remote information.
Step S2, acquiring distribution network equipment state information according to the distribution network three-remote information, wherein the distribution network equipment state information comprises distribution network switch state information and transformer state information, mapping the distribution network equipment state information to a distribution network topological graph to construct a transfer analysis model, visualizing the transfer analysis model, enabling the unclosed distribution network switches and the unopened transformers in the transfer analysis model to be in a grey state, and enabling the closed distribution network switches and the opened transformers to be in a bright colored state.
After the distribution network three remote information is processed, distribution network switch state information and transformer state information contained in the distribution network three remote information can be acquired, and the state information comprises whether the distribution network switch state information and the transformer state information are started or not, so that each switch and each transformer can be mapped into a distribution network topological graph in different states, the switches in the closed state and the transformers in the opened state are in bright colored states in the distribution network topological graph, the switches in the opened state and the transformers in the closed state are in gray states, and the switches in the opened state and the transformers in the closed state are visualized after forming a transfer analysis model and displayed in a picture form for processing of a subsequent neural network.
Step S3, constructing an expert database, and inputting a visual transfer analysis model into the neural network after training the neural network to obtain a subjective optimal transfer path;
the expert database in the step S3 includes power distribution network topology diagrams and corresponding transfer path diagrams in various fault states, the input data of the neural network is a transfer analysis model picture when a fault occurs, the output data is a transfer path, the expert database is divided into a training set and a test set, and the neural network is trained through the training set and tested for accuracy through the test set.
The invention adopts the neural network as the subjective basis of the selection of the transfer path, wherein the training of the neural network uses an expert database, a plurality of power distribution network topological graphs in different fault states and corresponding transfer paths are stored in the expert database, after the training of the neural network, the neural network can be detected and identified according to the power distribution network topological graphs in the fault states, and the corresponding transfer paths are output, and because the data for training and testing the neural network are the transfer paths selected by the experts according to the power distribution network topological graphs of the faults through self experience, the transfer paths obtained by the neural network are the subjective optimal transfer paths.
S4, performing opening and closing control on various distribution network equipment combinations in the power supply analysis model to obtain a plurality of initial power supply switching paths;
in the step S3, a subjective optimal handover path is obtained through neural network calculation, in order to ensure that the handover path is an optimal path, the objective optimal handover path is calculated, after different combinations are performed on a plurality of distribution network devices in the handover analysis model, the distribution network devices are closed, so that a plurality of different initial handover paths can be formed, at this time, the initial handover paths are further required to be screened to find out an optimal handover path, for this reason, the initial handover path is optimized by using a sirius algorithm, and the specific process is shown in step S5.
Step S5, optimizing the initial transfer path by using a Grey wolf algorithm, and obtaining an objective optimal transfer path, the specific steps include:
step S51, initializing a wolf population, and setting iteration times and population scale, wherein the iteration times are the same as the number of initial transfer paths;
step S52, an initial transfer path is arbitrarily selected, a quickness evaluation index of the initial transfer path is obtained, and a fitness value of the wolf is calculated and obtained according to the quickness evaluation index;
step S53, another initial transfer path is selected, after a corresponding fitness value is obtained, the fitness value is compared with the fitness value calculated last time, and an initial transfer path with a larger fitness value is reserved;
and step S54, performing iterative computation, traversing all initial transfer paths, obtaining an initial transfer path with the maximum fitness value, and outputting the initial transfer path as an objective optimal transfer path.
Within the set iteration times, each head of wolf carries out calculation of the fitness value by using the rapidity evaluation index of the initial transfer path, and is used for comparing with the previous fitness value, then the initial transfer path corresponding to the larger fitness value is reserved, and after all the initial transfer paths are compared, the final remained fitness value is the largest objective optimal transfer path.
Step S6, obtaining a final optimal transfer path according to the subjective optimal transfer path and the objective optimal transfer path, the concrete steps include:
and step S61, obtaining evaluation parameters of the subjective optimal transfer path and the objective optimal transfer path, wherein the evaluation parameters comprise rapidity, economy and stability, the rapidity is obtained through the time for recovering power transmission, the economy is obtained through the total length of the transfer path, and the stability is obtained through the voltage and current abrupt change of certain electric equipment at the time of recovering power transmission.
The method comprises the steps of selecting three indexes of rapidity, economy and stability to carry out weighted calculation, wherein the rapidity corresponds to the speed of the power restoration time, the economy corresponds to the consumption of electric energy in power transfer, the stability refers to the influence condition on other lines, and whether a power transfer path is the optimal path or not is comprehensively evaluated through the three indexes.
Step S62, subjectively weighting the evaluation parameters of the subjectively optimal switch path, and obtaining a switch subjective value, wherein the expression of the switch subjective value is as follows:
Fsubjective method and apparatus＝αFRapidity of operation+βFEconomy of use+δFStability of；
Objectively weighting the evaluation parameters of the objective optimal transfer path, and obtaining a transfer objective value, wherein the expression of the transfer objective value is as follows:
Fobjective＝λFRapidity of operation+ρFEconomy of use+ωFStability of；
Wherein FRapidity of operation、FEconomy of use、FStability ofThe evaluation parameters are respectively, alpha, beta and delta are subjective weighting indexes, lambda, rho and omega are objective weighting indexes, the subjective weighting indexes and the objective weighting indexes are constants set according to actual conditions, and for a subjective optimal transfer path, the value of the selected subjective weighting indexes is smaller than that of the objective weighting indexes due to strong subjectivity.
And step S63, comparing the converted subjective value with the converted objective value, and selecting the optimal converted path corresponding to the party with larger value as the final optimal converted path.
The invention adopts a weight assignment method to respectively assign weights to the subjective optimal transfer path and the objective optimal transfer path, respectively calculate to obtain a transfer objective value and a transfer subjective value, then compare the numerical values of the transfer objective value and the transfer subjective value, select one with a larger numerical value as a final optimal transfer path, and finally transfer the load to the power distribution network according to the final optimal transfer path, thereby realizing rapid power restoration and ensuring normal transmission of electric energy.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents, improvements and the like that fall within the spirit and principle of the present invention are intended to be included therein.
Claims (9)
1. The distribution network transfer intelligent analysis auxiliary method is characterized by comprising the following steps:
s1, collecting the three remote information of the power distribution network when a fault occurs;
step S2, acquiring distribution network equipment state information according to the distribution network three-remote information, mapping the distribution network equipment state information to a distribution network topological graph to construct a transfer analysis model, and visualizing the transfer analysis model;
step S3, constructing an expert database, and inputting a visual transfer analysis model into the neural network after training the neural network to obtain a subjective optimal transfer path;
s4, performing opening and closing control on various distribution network equipment combinations in the power supply analysis model to obtain a plurality of initial power supply switching paths;
step S5, optimizing the initial transfer path by adopting a wolf algorithm, and obtaining an objective optimal transfer path;
and step S6, obtaining a final optimal transfer path according to the subjective optimal transfer path and the objective optimal transfer path.
2. The method as claimed in claim 1, wherein the distribution network transfer intelligent analysis auxiliary method in step S2 includes distribution network switch status information and transformer status information.
3. The method for assisting in intelligent analysis of distribution network switching according to claim 2, wherein after the switching analysis model is visualized in step S2, the non-closed distribution network switches and the non-opened transformers in the switching analysis model are in a gray state, and the closed distribution network switches and the opened transformers are in a bright colored state.
4. The distribution network switching intelligent analysis auxiliary method according to claim 1, wherein the expert database in step S3 includes distribution network topology maps and corresponding switching path maps in various fault states, the input data of the neural network is a switching analysis model picture when a fault occurs, the output data is a switching path, the expert database is divided into a training set and a testing set, and the neural network is trained through the training set and tested for accuracy through the testing set.
5. The method for assisting distribution network transfer intelligent analysis according to claim 1, wherein the specific steps of the step S5 include:
step S51, initializing a wolf population, and setting iteration times and population scale;
step S52, an initial transfer path is arbitrarily selected, a quickness evaluation index of the initial transfer path is obtained, and a fitness value of the wolf is calculated and obtained according to the quickness evaluation index;
step S53, another initial transfer path is selected, after a corresponding fitness value is obtained, the fitness value is compared with the fitness value calculated last time, and an initial transfer path with a larger fitness value is reserved;
and step S54, performing iterative computation, traversing all initial transfer paths, obtaining an initial transfer path with the maximum fitness value, and outputting the initial transfer path as an objective optimal transfer path.
6. The method as claimed in claim 5, wherein the number of iterations in step S51 is the same as the number of initial handover paths.
7. The method for assisting distribution network transfer intelligent analysis according to claim 1, wherein the specific steps of the step S6 include:
step S61, obtaining evaluation parameters of the subjective optimal transfer path and the objective optimal transfer path;
step S62, subjectively weighting the evaluation parameters of the subjectively optimal transfer path, obtaining a transfer subjective value, objectively weighting the evaluation parameters of the objectively optimal transfer path, and obtaining a transfer objective value;
and step S63, comparing the converted subjective value with the converted objective value, and selecting the optimal converted path corresponding to the party with larger value as the final optimal converted path.
8. The method for assisting in the intelligent analysis of distribution network power transfer according to claim 7, wherein the evaluation parameters in step S61 include rapidity, economy and stability, the rapidity is obtained by the time for resuming power transmission, the economy is obtained by the total length of the power transfer path, and the stability is obtained by the voltage and current abrupt change of a certain electric device at the time of resuming power transmission.
9. The method as claimed in claim 8, wherein the expression of the main handover value in step S62 is as follows:
Fsubjective method and apparatus＝αFRapidity of operation+βFEconomy of use+δFStability of；
The expression for the objective value is:
Fobjective＝λFRapidity of operation+ρFEconomy of use+ωFStability of；
Wherein FRapidity of operation、FEconomy of use、FStability ofThe evaluation parameters are respectively, alpha, beta and delta are subjective weighting indexes, lambda, rho and omega are objective weighting indexes, and the subjective weighting indexes and the objective weighting indexes are constants set according to actual conditions.
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