CN113688488A - 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 data of a power distribution network, line parameters and related parameters of an improved artificial fish swarm algorithm; determining the number of artificial fish schools according to the power grid line to be optimized; according to the power distribution network data, the line parameters and the number of the artificial fish schools, each artificial fish represents one line, whether the lines represented by the artificial fish are communicated or not is judged by using the node incidence matrix, and the artificial fish schools are constructed; and analyzing the central position of the artificial fish school according to the number of the artificial fish school, iteratively executing an improved artificial fish school algorithm according to the central position of the fish school, 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 plan the power grid line, so that the convergence speed is increased, and the problems that the power grid line is easy to fall into local extreme values 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 belongs to a complex optimization problem with multiple decision variables and multiple constraint conditions mathematically. The method is characterized in that on the basis of meeting the economic development of the existing power supply area, a line is properly expanded or newly built to adapt to the safe operation of a power system. When a power grid is expanded or newly built, how to coordinate safety and economy becomes the key of the research on the problem of power grid line planning.
At present, power supply systems in 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 current solution is mainly solved 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 have certain effects on the aspect of power grid line planning, the algorithms also have the related problems of low convergence speed, easy falling into local extremum and the like.
The artificial fish swarm algorithm has many advantages, such as low requirements on initial values and parameter setting, parallel processing capability, high optimization speed and the like, and can also be applied to power grid line planning; however, when the traditional artificial fish swarm algorithm is used for solving the optimization problem of multiple constraint conditions, the problems of low optimization efficiency, easy falling into local extrema and the like exist.
Disclosure of Invention
Therefore, it is necessary to provide a power grid line planning method based on an improved artificial fish swarm algorithm, which can solve the problems of low optimization efficiency, easy falling into local extremum and the like when the optimization problem of multiple constraint conditions is solved.
A power grid line planning method based on an improved artificial fish swarm algorithm 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 schools according to the power grid line to be optimized;
according to the power distribution network data, the line parameters and the number of the artificial fish schools, each artificial fish represents one line, whether the lines represented by the artificial fish are communicated or not is judged by using a node incidence matrix, and the artificial fish schools are constructed;
analyzing the central position of the artificial fish school according to the number of the artificial fish school, and executing the clustering behavior and the rear-end collision behavior of the improved artificial fish school algorithm according to the central position of the fish school to obtain a first fish school state;
comparing the first fish swarm state with the state of the current bulletin board, updating the state of the current bulletin board to a first fish swarm state and corresponding position information when the first fish swarm state is more optimal than the state of the current bulletin board, executing foraging behavior of the improved artificial fish swarm algorithm when the state of the current bulletin board is more optimal 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, updating the state of the current bulletin board to the second fish swarm state and corresponding position information when the second fish swarm state is more optimal than the state of the current bulletin board, executing the random behavior of the improved artificial fish swarm algorithm when the state of the current bulletin board is more optimal than the second fish swarm state, outputting a third fish swarm state, and updating the state of the current bulletin board to the third fish swarm state and corresponding position information;
and when the current iteration times are less than the maximum iteration times, returning to the step of analyzing the central position of the artificial fish school according to the number of the artificial fish school, executing the clustering behavior and the rear-end collision behavior of the improved artificial fish school algorithm according to the central position of the fish school, obtaining a first fish school state, stopping iteration until the current iteration times are less than or equal to the maximum iteration times, and outputting a power grid line planning result according to the state of the 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 SiThe number of the artificial fishes in the search field by taking the visual field as the radius is NfWhen N isfNot equal to 0, searching the artificial fishCentral position S ofcCombining a firefly optimization algorithm, and moving the artificial fish to the central position according to a swarm moving step length formula through mutual attraction and movement of fluorescence degrees among the fireflies;
when N is presentfWhen the value is 0, foraging is performed.
In one embodiment, the cluster moving step size formula is:
wherein mu is the step length 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 comprises the following steps:
the current position state of the artificial fish is recorded as SiFinding another position state S in the artificial fish visual field perceptionmaxkCalculating the food concentration K of the other positionkWhen the food concentration K at said another locationkFood concentration K greater than current locationiAnd another position state SmaxkCongestion factor delta, number of artificial fish in search field NfAnd the total number n of artificial fish satisfies
The artificial fish moves to another position state SmaxkMoving a step length in the direction, wherein the step length is a self-adaptive step length and is expressed by a rear-end moving self-adaptive step length formula, and the rear-end moving self-adaptive step length formula is as follows:
wherein S isinextThe position of the artificial fish at the next moment, omega is a disturbance factor, SiFor the current position state of the artificial fish, SmaxkFor finding another position state in the artificial fish visual field perception, rand is a random factor subject to uniform distribution, and step is the step length of the previous movement.
In one embodiment, the random behavior of the improved artificial fish school algorithm is to randomly select a position in the visual field and move the position in the direction.
In one embodiment, the foraging behavior of the modified artificial fish swarm algorithm comprises:
the current position state of the artificial fish is recorded as SiRandomly selecting a position state S within the visual field perception rangejRespectively calculating the food concentration of the current position and the food concentration of a randomly selected position, and recording the calculated food concentration as KiAnd KjComparing the food concentration of the current location with a randomly selected one of the locations, if Kj>KiAnd randomly selecting a position state S by taking the previous moving step length of the artificial fish as the current moving step lengthjThe direction movement and the foraging movement step length formula are as follows:
wherein S isinextThe position of the artificial fish at the next moment is obtained; rand () is a random number within 0 to 1 subject to uniform distribution;
if K isj≤KiAnd executing random behavior and continuously searching for a new position.
According to the power grid line planning method based on the improved artificial fish swarm algorithm, power distribution network data, line parameters and related parameters of the improved artificial fish swarm algorithm are obtained; determining the number of artificial fish schools according to the power grid line to be optimized; according to the power distribution network data, the line parameters and the number of the artificial fish schools, each artificial fish represents one line, whether the lines represented by the artificial fish are communicated or not is judged by using the node incidence matrix, and the artificial fish schools are constructed; and analyzing the central position of the artificial fish school according to the number of the artificial fish school, iteratively executing an improved artificial fish school algorithm according to the central position of the fish school, 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 plan the power grid line, so that the convergence speed is increased, and the problems that the power grid line is easy to fall into local extreme values and the like are solved.
Drawings
FIG. 1 is a schematic 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 of a path of an 18-node power distribution system;
FIG. 3 is a schematic diagram of a path after an improved artificial fish swarm algorithm is optimized;
FIG. 4 is a graph of the convergence analysis of FA-AFSA, GA, and PSO algorithms.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more apparent, the present application is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the present application 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, including the following steps:
and acquiring the data of the power distribution network, the line parameters and the related parameters of the improved artificial fish swarm algorithm.
Wherein the improved artificial fish swarm algorithm is expressed as FA-AFSA. The improved artificial fish swarm algorithm has the relevant parameters of initialization setting, including the relevant parameters of maximum iteration number, maximum foraging number, bulletin board, maximum trial number, visual field and the like.
And determining the number of the artificial fish schools according to the power grid line to be optimized.
And according to the data of the power distribution network, the line parameters and the number of the artificial fish schools, representing a line by each artificial fish, judging whether the lines represented by the artificial fish are communicated or not by using the node incidence matrix, and constructing the artificial fish schools.
The artificial fish shoal is constructed according to the power distribution network data, the line parameters and the number of the artificial fish shoals, and therefore the power grid line planning problem is solved based on an improved artificial fish shoal algorithm (FA-AFSA).
And analyzing the central position of the artificial fish school according to the number of the artificial fish school, and executing the clustering behavior and the rear-end collision behavior of the improved artificial fish school algorithm according to the central position of the fish school to obtain a first fish school state.
Comparing the first fish school state with the state of the current bulletin board, updating the state of the current bulletin board to the first fish school state and corresponding position information when the first fish school state is more optimal than the state of the current bulletin board, executing the foraging behavior of the improved artificial fish school algorithm when the state of the current bulletin board is more optimal than the first fish school state, and outputting a second fish school state; comparing the second fish school state with the state of the current bulletin board, updating the state of the current bulletin board to the second fish school state and corresponding position information when the second fish school state is more optimal than the state of the current bulletin board, executing the random behavior of the improved artificial fish school algorithm when the state of the current bulletin board is more optimal than the second fish school state, outputting a third fish school state, and updating the state of the current bulletin board to the third fish school state and corresponding position information; and when the current iteration times are smaller than the maximum iteration times, returning to the step of analyzing the central position of the artificial fish school according to the number of the artificial fish school, executing the clustering behavior and the rear-end collision behavior of the improved artificial fish school algorithm according to the central position of the fish school, obtaining a first fish school state, stopping iteration until the current iteration times are equal to the maximum iteration times, 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 the 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 more optimal, the bulletin board is updated to be changed into the state of the current artificial fish. When the iteration is stopped, the state and the corresponding position information of the artificial fish recorded on the current bulletin board are optimal, therefore, the state and the corresponding position information of the artificial fish recorded on the current bulletin board correspond to the power grid line to be optimized to obtain a power grid line planning result, and according to the power grid line planning result, an objective function of a power transmission grid line extension planning mathematical model of investment operation, load cost and network loss of the power system is solved, so that an objective function value can be optimal.
The objective function is:
where minG is the minimum objective function value, n is the number of allowed overhead lines, CiCost of the i-th line per unit length, BiIs the length, x, of the ith line to be selectediThe number of the coil on the line, A is the power price of the power grid line loss, n0For the number of lines originally present in the grid, eiFor the number of already existing lines on line I, IiThe magnitude of the current passing through the line, riT is the effective running time in the system, which is the size 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 SiThe number of the artificial fishes in the search field by taking the visual field as the radius is NfWhen N isfNot equal to 0, searching the central position S of the artificial fishcCombining a firefly optimization algorithm, and moving the artificial fish to the central position according to a swarm moving step length formula through mutual attraction and movement of fluorescence degrees among the fireflies; when N is presentfWhen the value is 0, foraging is performed.
Wherein, the group moving step formula is as follows:
wherein mu is the step length of the movement, omega is the disturbance factor, rand is the random factor subject to uniform distribution,
as disturbance term, SiFor the current position state of the artificial fish, SjRandomly selecting a position state S within the visual field perception range of the artificial fishinextThe position of the artificial fish at the next moment. The group moving step formula is combined with a firefly optimization algorithm, and the firefly individuals move according to mutual attraction of fluorescence, so that the aim of reducing the trapping of the group into a local optimal solution is fulfilled.
In one embodiment, the rear-end collision behavior of the improved artificial fish swarm algorithm comprises the following steps: the current position state of the artificial fish is recorded as SiFinding another position state S in the artificial fish visual field perceptionmaxkCalculating the food concentration K of the other positionkWhen the food concentration K at another locationkFood concentration K greater than current locationiAnd another position state SmaxkCongestion factor delta, number of artificial fish in search field NfAnd the total number n of artificial fish satisfies
The artificial fish moves to another position state SmaxkMoving a step length in the direction, wherein the step length is a self-adaptive step length and is expressed by a rear-end moving self-adaptive step length formula, and the rear-end moving self-adaptive step length formula is as follows:
wherein S isinextThe position of the artificial fish at the next moment, omega is a disturbance factor, SiFor the current position state of the artificial fish, SmaxkFor artificial fish visual field perceptionAnd searching for another position state, rand being a random factor subject to uniform distribution, and step being the step size of the previous movement.
Wherein another position state SmaxkCongestion factor delta, number of artificial fish in search field NfAnd the total number n of artificial fish satisfies
Indicating that the degree of congestion is low.
In one embodiment, the random behavior of the improved artificial fish school algorithm is to randomly select a position within the field of view and move in that direction.
Where random behavior is a default to foraging behavior.
In one embodiment, the foraging behavior of the modified artificial fish swarm algorithm comprises:
the current position state of the artificial fish is recorded as SiRandomly selecting a position state S within the visual field perception rangejRespectively calculating the food concentration of the current position and the food concentration of a randomly selected position, and recording the calculated food concentration as KiAnd KjComparing the food concentration of the current location with a randomly selected one of the locations, if Kj>KiAnd randomly selecting a position state S by taking the previous moving step length of the artificial fish as the current moving step lengthjThe direction movement and the foraging movement step length formula are as follows:
wherein S isinextThe position of the artificial fish at the next moment is obtained; rand () is a random number within 0 to 1 subject to uniform distribution;
if K isj≤KiAnd executing random behavior and continuously searching for a new position.
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 schools according to the power grid line to be optimized; according to the power distribution network data, the line parameters and the number of the artificial fish schools, each artificial fish represents one line, whether the lines represented by the artificial fish are communicated or not is judged by using the node incidence matrix, and the artificial fish schools are constructed; and analyzing the central position of the artificial fish school according to the number of the artificial fish school, iteratively executing an improved artificial fish school algorithm according to the central position of the fish school, 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 for planning the power grid line, so that the convergence speed is increased, and the problems that the power grid line is easy to fall into local extreme values and the like are solved; and the optimization of multiple constraint conditions is also met, and the purposes of safety and economic coordination are also met.
The improved artificial fish swarm algorithm is used for carrying out simulation calculation on the 18-node power grid line planning 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:
by using the 18-node power distribution system shown in fig. 2 to perform simulation calculation, the original 10 nodes and 9 lines of the power distribution system are increased to 18 nodes and 27 optional lines along with the increase of the power load. And planning the power grid line of the power distribution system by adopting an improved artificial fish swarm algorithm to obtain the optimized path of the power distribution system shown in the figure 3.
In order to verify that the improved artificial fish swarm algorithm is suitable for solving the current power grid line planning problem and has better stability, the operation results of the FA-AFSA, GA and PSO algorithms which are operated for 20 times are taken for comparative analysis, and the convergence characteristic of the algorithm is shown in FIG. 4. Comparing and analyzing the operation results of the FA-AFSA, GA and PSO algorithms which are operated for 20 times respectively, and seeing that the optimization rate of the improved artificial fish swarm algorithm (FA-AFSA) is the highest, and the time consumption is relatively short secondly, as shown in Table 1.
TABLE 1 comparison of FA-AFSA planning method with AFSA, GA, PSO planning method
It should be understood that, although the steps in the flowchart of fig. 1 are shown in order as indicated by the arrows, the steps are not necessarily performed in order as indicated by the arrows. The steps are not performed in the exact order shown and described, and may be performed in other orders, unless explicitly stated otherwise. Moreover, at least a portion of the steps in fig. 1 may include multiple sub-steps or multiple stages that are not necessarily performed at the same time, but may be performed at different times, and the order of performance of the sub-steps or stages is not necessarily sequential, but may be performed in turn or alternately with other steps or at least a portion of the sub-steps or stages of other steps.
The technical features of the above embodiments can be arbitrarily combined, and for the sake of brevity, all possible combinations of the technical features in the above embodiments are not described, but should be considered as the scope of the present specification as long as there is no contradiction between the combinations of the technical features.
The above-mentioned embodiments only express several embodiments of the present application, and the description thereof is more specific and detailed, but not construed as limiting the scope of the invention. It should be noted that, for a person skilled in the art, several variations and modifications can be made without departing from the concept of the present application, which falls within the scope of protection of the present application. Therefore, the protection scope of the present patent shall be subject to the appended claims.
Claims (6)
1. A power grid line planning method based on an improved artificial fish swarm algorithm is characterized by comprising 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 schools according to the power grid line to be optimized;
according to the power distribution network data, the line parameters and the number of the artificial fish schools, each artificial fish represents one line, whether the lines represented by the artificial fish are communicated or not is judged by using a node incidence matrix, and the artificial fish schools are constructed;
analyzing the central position of the artificial fish school according to the number of the artificial fish school, and executing the clustering behavior and the rear-end collision behavior of the improved artificial fish school algorithm according to the central position of the fish school to obtain a first fish school state;
comparing the first fish swarm state with the state of the current bulletin board, updating the state of the current bulletin board to a first fish swarm state and corresponding position information when the first fish swarm state is more optimal than the state of the current bulletin board, executing foraging behavior of the improved artificial fish swarm algorithm when the state of the current bulletin board is more optimal 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, updating the state of the current bulletin board to the second fish swarm state and corresponding position information when the second fish swarm state is more optimal than the state of the current bulletin board, executing the random behavior of the improved artificial fish swarm algorithm when the state of the current bulletin board is more optimal than the second fish swarm state, outputting a third fish swarm state, and updating the state of the current bulletin board to the third fish swarm state and corresponding position information;
and when the current iteration times are less than the maximum iteration times, returning to the step of analyzing the central position of the artificial fish school according to the number of the artificial fish school, executing the clustering behavior and the rear-end collision behavior of the improved artificial fish school algorithm according to the central position of the fish school, obtaining a first fish school state, stopping iteration until the current iteration times are less than or equal to the maximum iteration times, and outputting a power grid line planning result according to the state of the current bulletin board.
2. The method of claim 1, wherein the clustering behavior of the modified artificial fish swarm algorithm comprises:
artificial operationThe current position state of the fish is recorded as SiThe number of the artificial fishes in the search field by taking the visual field as the radius is NfWhen N isfNot equal to 0, searching the central position S of the artificial fishcCombining a firefly optimization algorithm, and moving the artificial fish to the central position according to a swarm moving step length formula through mutual attraction and movement of fluorescence degrees among the fireflies;
when N is presentfWhen the value is 0, foraging is performed.
3. The method of claim 2, wherein the cluster moving step size is formulated as:
wherein mu is the step length of the movement, omega is the disturbance factor, rand is the random factor subject to uniform distribution,
as disturbance term, SiFor the current position state of the artificial fish, SjRandomly selecting a position state S within the visual field perception range of the artificial fishinextThe position of the artificial fish at the next moment.
4. The method of claim 1, wherein the tailgating behavior of the modified artificial fish swarm algorithm comprises:
the current position state of the artificial fish is recorded as SiFinding another position state S in the artificial fish visual field perceptionmaxkCalculating the food concentration K of the other positionkWhen the food concentration K at said another locationkFood concentration K greater than current locationiAnd another position state SmaxkCongestion factor delta, number of artificial fish in search field NfAnd the total number m of the artificial fishes satisfies
The artificial fish moves to another position state SmaxkMoving a step length in the direction, wherein the step length is a self-adaptive step length and is expressed by a rear-end moving self-adaptive step length formula, and the rear-end moving self-adaptive step length formula is as follows:
wherein S isinextThe position of the artificial fish at the next moment, omega is a disturbance factor, SiFor the current position state of the artificial fish, SmaxkFor finding another position state in the artificial fish visual field perception, rand is a random factor subject to uniform distribution, and step is the step length of the previous movement.
5. The method of claim 1, wherein the random behavior of the modified artificial fish school algorithm is to randomly select a position within the field of view and move in that direction.
6. 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 SiRandomly selecting a position state S within the visual field perception rangejRespectively calculating the food concentration of the current position and the food concentration of a randomly selected position, and recording the calculated food concentration as KiAnd KjComparing the food concentration of the current location with a randomly selected one of the locations, if Kj＞KiAnd randomly selecting a position state S by taking the previous moving step length of the artificial fish as the current moving step lengthjThe direction movement and the foraging movement step length formula are as follows:
wherein S isinextFor the next time of artificial fishEngraving positions; rand () is a random number within 0 to 1 subject to uniform distribution;
if K isj≤KiAnd executing random behavior and continuously searching for a new position.
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| CN114124783A (en) * | 2022-01-26 | 2022-03-01 | 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 |
| CN116488250A (en) * | 2023-03-17 | 2023-07-25 | 长电新能有限责任公司 | Capacity optimization configuration method for hybrid energy storage system |
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