CN113688488B - Power grid line planning method based on improved artificial fish swarm algorithm - Google Patents
Power grid line planning method based on improved artificial fish swarm algorithm Download PDFInfo
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Abstract
The application relates to a power grid line planning method based on an improved artificial fish swarm algorithm. The method comprises the following steps: acquiring power distribution network data, line parameters and related parameters of an improved artificial fish swarm algorithm; determining the number of artificial fish shoals according to the power grid lines to be optimized; according to the data of the power distribution network, the line parameters and the number of artificial fish shoals, each artificial fish represents one line, and whether the lines represented by the artificial fish are communicated or not is judged by utilizing a node association matrix, so that the artificial fish shoals are constructed; according to the number of artificial fish shoals, analyzing the center position of the artificial fish shoals, iteratively executing an improved artificial fish shoal algorithm according to the center position of the fish shoals, updating the state of the bulletin board until the current iteration number is less than or equal to the maximum iteration number, stopping iteration, and outputting a power grid line planning result according to the state of the current bulletin board. The improved artificial fish swarm algorithm is adopted to carry out power grid line planning, so that the convergence speed is improved, and the problems of easy sinking of local extremum and the like are solved.
Description
Technical Field
The application relates to the technical field of power grid line planning, in particular to a power grid line planning method based on an improved artificial fish swarm algorithm.
Background
The power grid line planning problem mathematically belongs to a complex multi-decision variable, multi-constraint optimization problem. The method is to properly build or newly build a circuit to adapt to the safe operation of the power system on the basis of meeting the economic development of the existing power supply area. How the safety and the economy are coordinated and consistent becomes the key of the study of the power grid line planning problem when a power grid is built or newly built.
At present, the power supply system in many areas with good economic development cannot well meet the safety and reliability of the operation of the power system, so that the operation failure of the power system can be caused. The existing solution is mainly to solve by using an intelligent optimization algorithm, such as a particle swarm algorithm, an ant colony algorithm, a genetic algorithm and the like. Although the algorithms achieve a certain effect in the aspect of power grid line planning, the problems of low convergence speed, easiness in sinking into local extremum and the like exist.
The artificial fish swarm algorithm has the advantages of low requirements on initial values and parameter setting, capability of parallel processing, high optimizing speed and the like, and can be applied to power grid line planning; however, when the traditional artificial fish swarm algorithm solves the problem of multi-constraint condition optimization, the problems of low optimizing efficiency, easiness in sinking into local extremum and the like exist.
Disclosure of Invention
In view of the foregoing, it is necessary to provide an improved artificial fish swarm algorithm-based power grid line planning method capable of solving the problems of low optimizing efficiency, easy sinking into local extremum and the like when optimizing multiple constraint conditions.
A power grid line planning method based on an improved artificial fish swarm algorithm, the method comprising:
acquiring power distribution network data, line parameters and related parameters of an improved artificial fish swarm algorithm;
determining the number of artificial fish shoals according to the power grid lines to be optimized;
according to the power distribution network data, the line parameters and the artificial fish swarm quantity, each artificial fish represents one line, and whether the lines represented by the artificial fish are communicated or not is judged by utilizing a node association matrix, so that an artificial fish swarm is constructed;
analyzing the central position of the artificial fish shoal according to the number of the artificial fish shoal, and executing the group gathering behavior and the rear-end collision behavior of the improved artificial fish shoal algorithm according to the central position of the artificial fish shoal to obtain a first fish shoal state;
comparing the first fish swarm state with the state of the current bulletin board, updating the state of the current bulletin board into the first fish swarm state and corresponding position information when the first fish swarm state is better than the state of the current bulletin board, and executing the foraging behavior of the improved artificial fish swarm algorithm when the state of the current bulletin board is better than the first fish swarm state to output a second fish swarm state;
comparing the second fish swarm state with the current state of the bulletin board, updating the current state of the bulletin board into the second fish swarm state and corresponding position information when the second fish swarm state is better than the current state of the bulletin board, executing the random behavior of the improved artificial fish swarm algorithm when the current state of the bulletin board is better than the second fish swarm state, outputting a third fish swarm state, and updating the current state of the bulletin board into the third fish swarm state and corresponding position information;
and when the current iteration number is smaller than the maximum iteration number, returning to the step of analyzing the central position of the artificial fish shoal according to the artificial fish shoal number, executing the group gathering behavior and the rear-end behavior of the improved artificial fish shoal algorithm according to the central position of the fish shoal, obtaining a first fish shoal state, stopping iteration until the current iteration number is smaller than or equal to the maximum iteration number, and outputting a power grid line planning result according to the state of a current bulletin board.
In one embodiment, the clustering behavior of the improved artificial fish swarm algorithm comprises:
the current position state of the artificial fish is recorded as S i The number of artificial fish in the field of search with the field of view as radius is N f And when N f Not equal to 0, search the center of the artificial fishS is arranged c Combining with a firefly optimization algorithm, mutually attracting and moving through the fluorescence intensity among fireflies, and moving the artificial fish to the center position according to a group moving step formula;
when N is f =0, then foraging behavior is performed.
In one embodiment, the group movement step formula is:
where mu is the step size of the movement, omega is the disturbance factor, rand is the random factor subject to uniform distribution,
In one embodiment, the rear-end collision behavior of the improved artificial fish swarm algorithm includes:
the current position state of the artificial fish is recorded as S i Searching for another position state S in the artificial fish visual field perception maxk Calculating the food concentration K at the other position k When the food concentration K at the other position k Food concentration K greater than the current position i And another position state S maxk Crowding factor delta, number of artificial fish N in search field f The total number n of artificial fish is as follows
The artificial fish moves to another position state S maxk The direction is moved by one step length, the step length is an adaptive step length, the self-adaptive step length formula of the rear-end collision movement is used for representing the self-adaptive step length formula of the rear-end collision movement, and the self-adaptive step length formula of the rear-end collision movement is as follows:
wherein S is inext For the next moment of artificial fish, omega is disturbance factor S i S is the current position state of the artificial fish maxk For finding another position state in the artificial fish visual field perception, rand is a random factor obeying even distribution, step is the step length of the previous movement.
In one embodiment, the random behavior of the modified artificial fish swarm algorithm is to randomly select a position in the field of view and move in that direction.
In one embodiment, the foraging behavior of the improved artificial fish swarm algorithm comprises:
the current position state of the artificial fish is recorded as S i Randomly selecting a position state S in the visual field perception range j Calculating the food concentration at the current position and the food concentration at a randomly selected position, respectively, and recording as K i And K j Comparing the current location with the food concentration of a randomly selected location, if K j >K i The artificial fish randomly selects a position state S by taking the step length of the previous movement as the step length of the current movement j The direction movement and foraging movement step formula is as follows:
wherein S is inext The position of the artificial fish at the next moment; rand () is a random number within 0 to 1 subject to uniform distribution;
if K j ≤K i A random action is performed and the search for new locations is continued.
According to the power grid line planning method based on the improved artificial fish swarm algorithm, the power distribution network data, the line parameters and the related parameters of the improved artificial fish swarm algorithm are obtained; determining the number of artificial fish shoals according to the power grid lines to be optimized; according to the data of the power distribution network, the line parameters and the number of artificial fish shoals, each artificial fish represents one line, and whether the lines represented by the artificial fish are communicated or not is judged by utilizing a node association matrix, so that the artificial fish shoals are constructed; according to the number of artificial fish shoals, analyzing the center position of the artificial fish shoals, iteratively executing an improved artificial fish shoal algorithm according to the center position of the fish shoals, updating the state of the bulletin board until the current iteration number is less than or equal to the maximum iteration number, stopping iteration, and outputting a power grid line planning result according to the state of the current bulletin board. The improved artificial fish swarm algorithm is adopted to carry out power grid line planning, so that the convergence speed is improved, and the problems of easy sinking of local extremum and the like are solved.
Drawings
FIG. 1 is a flow chart of a power grid line planning method based on an improved artificial fish swarm algorithm in one embodiment;
FIG. 2 is an initial schematic diagram of a path of an 18-node power distribution system;
FIG. 3 is a schematic diagram of an improved artificial fish swarm algorithm optimized path;
FIG. 4 is a graph of a FA-AFSA, AFSA, GA, PSO algorithm convergence analysis.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more apparent, the present application will be further described in detail with reference to the accompanying drawings and examples. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the present application.
In one embodiment, as shown in fig. 1, there is provided a power grid line planning method based on an improved artificial fish swarm algorithm, comprising the steps of:
and acquiring power distribution network data, line parameters and related parameters of the improved artificial fish swarm algorithm.
Wherein, the improved artificial fish swarm algorithm is expressed as FA-AFSA. The related parameters of the improved artificial fish swarm algorithm are initialized and set, and comprise the related parameters of maximum iteration times, maximum foraging times, bulletin boards, maximum try times, visual fields and the like.
And determining the number of artificial fish shoals according to the power grid lines to be optimized.
And according to the data of the power distribution network, the line parameters and the number of artificial fish shoals, each artificial fish represents one line, and whether the lines represented by the artificial fish are communicated or not is judged by utilizing the node association matrix, so that the artificial fish shoals are constructed.
And constructing artificial fish shoals according to the power distribution network data, the line parameters and the artificial fish shoals, so as to solve the power grid line planning problem based on an improved artificial fish shoal algorithm (FA-AFSA).
And analyzing the central position of the artificial fish shoal according to the number of the artificial fish shoal, and executing the group gathering behavior and the rear-end collision behavior of the improved artificial fish shoal algorithm according to the central position of the artificial fish shoal to obtain a first fish shoal state.
Comparing the first fish swarm state with the state of the current bulletin board, updating the state of the current bulletin board into the first fish swarm state and corresponding position information when the first fish swarm state is better than the state of the current bulletin board, executing the foraging behavior of the improved artificial fish swarm algorithm when the state of the current bulletin board is better than the first fish swarm state, and outputting a second fish swarm state; comparing the second fish swarm state with the state of the current bulletin board, when the second fish swarm state is better than the state of the current bulletin board, updating the state of the current bulletin board into the second fish swarm state and corresponding position information, and when the state of the current bulletin board is better than the second fish swarm state, executing the random behavior of the improved artificial fish swarm algorithm, outputting a third fish swarm state, and updating the state of the current bulletin board into the third fish swarm state and corresponding position information; and when the current iteration number is smaller than the maximum iteration number, returning to analyze the center position of the artificial fish shoal according to the number of the artificial fish shoal, executing the clustering behavior and the rear-end collision behavior of the improved artificial fish shoal algorithm according to the center position of the artificial fish shoal, obtaining a first fish shoal state, stopping iteration until the current iteration number is equal to the maximum iteration number, and outputting a power grid line planning result according to the state of the current bulletin board.
The bulletin board is used for recording the state and corresponding position information of the optimal artificial fish after each iteration, namely, the state of the current artificial fish after each iteration is compared with the state recorded by the bulletin board, and if the state of the current artificial fish is better, the bulletin board is updated and changed into the state of the current artificial fish. When iteration is stopped, the state and the corresponding position information of the artificial fish recorded on the current bulletin board are optimal, so that the artificial fish corresponds to a power grid line to be optimized according to the state and the corresponding position information of the artificial fish recorded on the current bulletin board, a power grid line planning result is obtained, and according to the power grid line planning result, an objective function of a power transmission line expansion planning mathematical model of investment operation, load cost and network loss of the power system is solved, so that the objective function value can be optimized.
The objective function is:
wherein, minG is the minimum objective function value, n is the number of lines allowing wire laying, C i For the line cost of the ith unit length, B i For the length of the ith line to be selected, x i The number of the lines of the line upper frame coil is A is the electricity price of the line loss of the power grid, n 0 For the number of originally existing lines in the power grid e i For the number of lines already present on line I, I i R is the magnitude of the current passing in the line i T is the effective run time in the system, which is the magnitude of the resistance in the line.
In one embodiment, the clustering behavior of the improved artificial fish swarm algorithm comprises:
the current position state of the artificial fish is recorded as S i The number of artificial fish in the field of search with the field of view as radius is N f And when N f Not equal to 0, search for the center position S of the artificial fish c Combining with a firefly optimization algorithm, mutually attracting and moving through the fluorescence intensity among fireflies, and moving the artificial fish to the center position according to a group moving step formula; when N is f =0, then foraging behavior is performed.
The formula of the cluster moving step length is as follows:
where mu is the step size of the movement, omega is the disturbance factor, rand is the random factor subject to uniform distribution,
as disturbance term, S i S is the current position state of the artificial fish j Randomly selecting a position state in the visual field perception range of the artificial fish, S inext Is the next time position of the artificial fish. The group moving step formula is combined with a firefly optimization algorithm, and firefly individuals are utilized to move according to the attraction of fluorescence, so that the aim of reducing the group sinking into a local optimal solution is achieved.
In one embodiment, the rear-end collision behavior of the improved artificial fish swarm algorithm comprises: the current position state of the artificial fish is recorded as S i Searching for another position state S in the artificial fish visual field perception maxk Calculating the food concentration K at the other position k When the food concentration K at another position k Food concentration K greater than the current position i And another position state S maxk Crowding factor delta, number of artificial fish N in search field f The total number n of artificial fish is as follows
The artificial fish moves to another position state S maxk The direction is moved by one step length, the step length is an adaptive step length, the self-adaptive step length formula of the rear-end collision movement is used for representing the self-adaptive step length formula of the rear-end collision movement, and the self-adaptive step length formula of the rear-end collision movement is as follows:
wherein S is inext For the next moment of artificial fish, omega is disturbance factor S i S is the current position state of the artificial fish maxk Searching for artificial fish in visual field perceptionAnother position state, rand, is a random factor subject to uniform distribution, step is the step size of the previous move.
Wherein another position state S maxk Crowding factor delta, number of artificial fish N in search field f The total number n of artificial fish is as follows
Indicating that the degree of congestion is low.
In one embodiment, the random behavior of the modified artificial fish swarm algorithm is to randomly select a location within the field of view and move in that direction.
Wherein the random behavior is a default to the foraging behavior.
In one embodiment, the foraging behavior of the improved artificial fish swarm algorithm comprises:
the current position state of the artificial fish is recorded as S i Randomly selecting a position state S in the visual field perception range j Calculating the food concentration at the current position and the food concentration at a randomly selected position, respectively, and recording as K i And K j Comparing the current location with the food concentration of a randomly selected location, if K j >K i The artificial fish randomly selects a position state S by taking the step length of the previous movement as the step length of the current movement j The direction movement and foraging movement step formula is as follows:
wherein S is inext The position of the artificial fish at the next moment; rand () is a random number within 0 to 1 subject to uniform distribution;
if K j ≤K i A random action is performed and the search for new locations is continued.
According to the power grid line planning method based on the improved artificial fish swarm algorithm, the power distribution network data and the related parameters of the improved artificial fish swarm algorithm are obtained; determining the number of artificial fish shoals according to the power grid lines to be optimized; according to the data of the power distribution network, the line parameters and the number of artificial fish shoals, each artificial fish represents one line, and whether the lines represented by the artificial fish are communicated or not is judged by utilizing a node association matrix, so that the artificial fish shoals are constructed; according to the number of artificial fish shoals, analyzing the center position of the artificial fish shoals, iteratively executing an improved artificial fish shoal algorithm according to the center position of the fish shoals, updating the state of the bulletin board until the current iteration number is less than or equal to the maximum iteration number, stopping iteration, and outputting a power grid line planning result according to the state of the current bulletin board. The improved artificial fish swarm algorithm is adopted to carry out power grid line planning, so that the convergence speed is improved, and the problems of easy sinking of local extremum and the like are improved; and the optimization of multiple constraint conditions is also satisfied, and the purposes of consistent safety and economical coordination are also satisfied.
The improved artificial fish swarm algorithm is utilized to carry out simulation calculation on the 18-node power grid line planning cost-effective example, the feasibility and the effectiveness of the improved artificial fish swarm algorithm in solving the power grid line planning are verified, and the result is as follows:
the simulation calculation is carried out by using the 18-node power distribution system shown in fig. 2, wherein the power distribution system has 10 nodes and 9 lines, and the power distribution system has 18 nodes and 27 optional lines along with the increase of the power load. And (3) planning a power grid line of the power distribution system by adopting an improved artificial fish swarm algorithm to obtain an optimized path of the power distribution system shown in fig. 3.
In order to verify that the artificial fish swarm algorithm based on improvement provided by the application is suitable for solving the current power grid line planning problem, has better stability, and performs comparative analysis by taking the running results of the FA-AFSA, AFSA, GA, PSO algorithm running 20 times, wherein the convergence characteristic of the algorithm is shown in figure 4. Comparison analysis of the operation results of the FA-AFSA, AFSA, GA, PSO algorithm performed 20 times each shows in Table 1 that the improved artificial fish swarm algorithm (FA-AFSA) has the highest optimizing rate and is relatively short in time consumption.
Table 1 comparison of FA-AFSA planning method and AFSA, GA, PSO planning method
It should be understood that, although the steps in the flowchart of fig. 1 are shown in sequence as indicated by the arrows, the steps are not necessarily performed in sequence as indicated by the arrows. The steps are not strictly limited to the order of execution unless explicitly recited herein, and the steps may be executed in other orders. Moreover, at least some of the steps in fig. 1 may include multiple sub-steps or stages that are not necessarily performed at the same time, but may be performed at different times, nor do the order in which the sub-steps or stages are performed necessarily performed in sequence, but may be performed alternately or alternately with at least a portion of other steps or sub-steps of other steps.
The technical features of the above embodiments may be arbitrarily combined, and all possible combinations of the technical features in the above embodiments are not described for brevity of description, however, as long as there is no contradiction between the combinations of the technical features, they should be considered as the scope of the description.
The above examples merely represent a few embodiments of the present application, which are described in more detail and are not to be construed as limiting the scope of the invention. It should be noted that it would be apparent to those skilled in the art that various modifications and improvements could be made without departing from the spirit of the present application, which would be within the scope of the present application. Accordingly, the scope of protection of the present application is to be determined by the claims appended hereto.
Claims (4)
1. A power grid line planning method based on an improved artificial fish swarm algorithm, the method comprising:
acquiring power distribution network data, line parameters and related parameters of an improved artificial fish swarm algorithm;
determining the number of artificial fish shoals according to the power grid lines to be optimized;
according to the power distribution network data, the line parameters and the artificial fish swarm quantity, each artificial fish represents one line, and whether the lines represented by the artificial fish are communicated or not is judged by utilizing a node association matrix, so that an artificial fish swarm is constructed;
analyzing the central position of the artificial fish shoal according to the number of the artificial fish shoal, and executing the group gathering behavior and the rear-end collision behavior of the improved artificial fish shoal algorithm according to the central position of the artificial fish shoal to obtain a first fish shoal state;
comparing the first fish swarm state with the state of the current bulletin board, updating the state of the current bulletin board into the first fish swarm state and corresponding position information when the first fish swarm state is better than the state of the current bulletin board, and executing the foraging behavior of the improved artificial fish swarm algorithm when the state of the current bulletin board is better than the first fish swarm state to output a second fish swarm state;
comparing the second fish swarm state with the current state of the bulletin board, updating the current state of the bulletin board into the second fish swarm state and corresponding position information when the second fish swarm state is better than the current state of the bulletin board, executing the random behavior of the improved artificial fish swarm algorithm when the current state of the bulletin board is better than the second fish swarm state, outputting a third fish swarm state, and updating the current state of the bulletin board into the third fish swarm state and corresponding position information;
when the current iteration number is smaller than the maximum iteration number, returning to the step of analyzing the central position of the artificial fish shoal according to the artificial fish shoal number, executing the group gathering behavior and the rear-end behavior of the improved artificial fish shoal algorithm according to the central position of the fish shoal to obtain a first fish shoal state, stopping iteration until the current iteration number is smaller than or equal to the maximum iteration number, and outputting a power grid line planning result according to the state of a current bulletin board;
the clustering behavior of the improved artificial fish swarm algorithm comprises the following steps:
the current position state of the artificial fish is recorded as S i The number of artificial fish in the field of search with the field of view as radius is N f And when N f Not equal to 0, search for the center position S of the artificial fish c Combining with a firefly optimization algorithm, mutually attracting and moving through the fluorescence intensity among fireflies, and moving the artificial fish to the center position according to a group moving step formula;
when N is f =0, then perform foraging behavior;
the group moving step formula is as follows:
where mu is the step size of the movement, omega is the disturbance factor, rand is the random factor subject to uniform distribution,
as disturbance term, S i S is the current position state of the artificial fish j Randomly selecting a position state in the visual field perception range of the artificial fish, S inext The position of the artificial fish at the next moment;
and outputting a power grid line planning result according to the state of the current bulletin board, wherein the power grid line planning result comprises the following steps:
according to the state and corresponding position information of the artificial fish recorded on the current bulletin board, the artificial fish is correspondingly arranged on a power grid line to be optimized, and a power grid line planning result is obtained;
according to the power grid line planning result, solving an objective function of a power grid line expansion planning mathematical model of the investment operation, the load cost and the network loss of the power system, so that the objective function value reaches the optimum;
the objective function is:
wherein, minG is the minimum objective function value, n is the number of lines allowing wire laying, C i For the line cost of the ith unit length, B i Length of the ith line to be selected，x i The number of the lines of the line upper frame coil is A is the electricity price of the line loss of the power grid, n 0 For the number of originally existing lines in the power grid e i For the number of lines already present on line I, I i R is the magnitude of the current passing in the line i T is the effective run time in the system, which is the magnitude of the resistance in the line.
2. The method of claim 1, wherein the rear-end collision behavior of the modified artificial fish swarm algorithm comprises:
the current position state of the artificial fish is recorded as S i Searching for another position state S in the artificial fish visual field perception maxk Calculating the food concentration K at the other position k When the food concentration K at the other position k Food concentration K greater than the current position i And another position state S maxk Crowding factor delta, number of artificial fish N in search field f The total number m of artificial fish is as follows
The artificial fish moves to another position state S maxk The direction is moved by one step length, the step length is an adaptive step length, the self-adaptive step length formula of the rear-end collision movement is used for representing the self-adaptive step length formula of the rear-end collision movement, and the self-adaptive step length formula of the rear-end collision movement is as follows:
wherein S is inext For the next moment of artificial fish, omega is disturbance factor S i S is the current position state of the artificial fish maxk For finding another position state in the artificial fish visual field perception, rand is a random factor obeying even distribution, step is the step length of the previous movement.
3. The method of claim 1, wherein the random behavior of the modified artificial fish swarm algorithm is to randomly select a location within the field of view and move in that direction.
4. The method of claim 1, wherein the foraging behavior of the modified artificial fish swarm algorithm comprises:
the current position state of the artificial fish is recorded as S i Randomly selecting a position state S in the visual field perception range j Calculating the food concentration at the current position and the food concentration at a randomly selected position, respectively, and recording as K i And K j Comparing the current location with the food concentration of a randomly selected location, if K j >K i The artificial fish randomly selects a position state S by taking the step length of the previous movement as the step length of the current movement j The direction movement and foraging movement step formula is as follows:
wherein S is inext The position of the artificial fish at the next moment; rand () is a random number within 0 to 1 subject to uniform distribution;
if K j ≤K i A random action is performed and the search for new locations is continued.
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| CN114124783B (en) * | 2022-01-26 | 2022-05-10 | 深圳市永达电子信息股份有限公司 | Path selection method based on improved fish swarm algorithm and computer storage medium |
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