CN107133691B - Topology optimization method for wind power plant power transmission network - Google Patents
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Abstract
The invention relates to a topology optimization method for a wind power plant power transmission network, which comprises the following steps: acquiring coordinates of all preset collection points; calculating the optimal path L from the ith preset collection point to the jth wind power plantijAnd obtaining the total path length S from the ith preset collection point to N wind power plantsi(ii) a Comparing the total path length S corresponding to each preset collection pointiObtaining the total length SiMinimum value of SminA target collection point and a target boosting station point; acquiring M optimal topological structures by taking an economic index as a target; and calculating the reliability indexes of the M optimal topological structures to obtain the topological structure with the optimal economic index under the condition of meeting the reliability. Aiming at the condition that a plurality of wind power plants exist in a regional offshore wind power plant, workers can obtain a wind power plant power transmission network topological structure with good economical efficiency under the condition of meeting the reliability by applying the topological optimization method for the wind power plant power transmission network.
Description
Technical Field
The invention relates to the technical field of wind power generation, in particular to a topological optimization method for a wind power plant power transmission network.
Background
With the rise of ocean economy, a regional wind power plant is constructed at sea and is connected with a high-voltage power transmission network on land, and the development direction is gradually changed. China's offshore wind power plant planning has a plurality of large blocks with capacity exceeding million kilowatts. Research into regional offshore power transmission networks constructed from multiple offshore wind farms is a real and urgent task. However, traditional offshore wind power research is limited to a single offshore wind farm project level, and topological research on regional offshore wind farm transmission networks is lacking.
Disclosure of Invention
Based on the above, it is necessary to provide a topology optimization method for a wind farm power transmission network aiming at the problem of lack of topology research of a regional offshore wind farm power transmission network, and provide a wind farm power transmission network topology which is good in economy under the condition of meeting the reliability condition aiming at the regional offshore wind farm power transmission network topology.
A topology optimization method for a wind farm power transmission network comprises the following steps:
in the collection area, obtaining the coordinates of all preset collection points;
according to the coordinates of the preset collection point, calculating the optimal path L from the ith preset collection point to the jth wind power plantijAnd obtaining the total length of the path from the ith preset collection point to the N wind power plants
Comparing the total path length S corresponding to each preset collection pointiObtaining the total length S of the pathiMinimum value of Smin＝min(S1，S2，S3，...，SO) And the minimum value S is setminSelecting the corresponding preset collection point as a target collection point, and taking the minimum value SminSelecting a connection point of the corresponding wind power plant as a target boosting station;
obtaining an initial topological structure of a power transmission network of a wind power plant according to a target collection point and a target boosting site, and optimizing the initial topological structure by taking a preset index as a target to obtain M optimal topological structures;
calculating the reliability indexes of the M optimal topological structures to obtain a topological structure with the optimal economic index under the condition of meeting the reliability;
wherein i is more than or equal to 1 and less than or equal to O, j is more than or equal to 1 and less than or equal to N, O is the number of the preset collection points, O is more than or equal to 2 and O is a natural number, N is the number of the wind power plant, N is more than or equal to 2 and N is a natural number.
According to the topology optimization method for the wind power plant power transmission network, aiming at a regional offshore wind power plant, firstly, an initial topology structure can be obtained by determining a target collection point and target boosting sites of a plurality of wind power plants. And secondly, optimizing the initial topological structure to obtain M optimal topological structures with good economical efficiency. And finally, calculating the reliability indexes of the M optimal topological structures, thereby obtaining the optimal wind power plant power transmission network topological structure under the condition of meeting the reliability. Therefore, under the condition that a plurality of wind power plants exist in a regional offshore wind farm, a worker can obtain a wind power plant power transmission network topological structure with good economical efficiency under the condition of meeting the reliability by applying the topological optimization method for the wind power plant power transmission network.
In one embodiment, the reliability index is a power grid output capacity blocking probability LOSP, the reliability condition is that LOSP is less than or equal to Nx eta, and eta is a screening constant; the specific process of calculating the reliability indexes of the M preferred topological structures is to calculate the reliability indexes of the M preferred topological structures by adopting a minimum cut-set algorithm. The method includes the steps that not only is economic indexes considered, but also the reliability of the topological structure needs to be considered when the regional wind power plant power transmission network topological structure is constructed. The minimal cut-set algorithm is a powerful tool for analyzing complex topologies. The minimum cut set algorithm can simplify any complex topological network structure into an equivalent series-parallel structure, so that the reliability index of the wind power plant power transmission network topological structure can be conveniently solved. According to the topology optimization method for the wind power plant power transmission network, the reliability indexes of the M optimal topology structures can be intuitively obtained by adopting a minimum cut-set algorithm, and then the optimal wind power plant power transmission network topology structure can be obtained through screening of reliability conditions.
In one embodiment, the process of obtaining the coordinates of the preset rendezvous point specifically includes the following steps: dividing the collection region into a plurality of first regions; and selecting the middle points of all side lengths of the first area as the preset collection points. According to the topology optimization method for the wind power plant power transmission network, the collection area is subjected to grid division, and the side length middle point of the first area obtained through division is selected as the preset collection point. The acquisition of the coordinates of the preset collection points is beneficial to calculating and obtaining the optimal path of each preset collection point and each wind power plant by taking the number of the preset collection points as cycle times.
In one embodiment, the process of calculating the optimal path L from each preset collection point to each wind farm specifically includes the following steps: dividing each wind farm into a plurality of second regions; selecting the middle point of the side length of the second area as a preset boosting station; and selecting the minimum value of the distances between the preset collection point and all the preset boosting stations of each wind power plant as the optimal path L according to the coordinates of the preset collection point and the preset boosting stations. According to the topology optimization method for the wind power plant power transmission network, the wind power plant is subjected to grid division, and the middle point of the side length of the second region obtained through division is selected as the preset boosting station. Under the condition that the coordinates of the preset collection point and the preset boosting site are known, the topological optimization method for the wind power plant power transmission network can quickly calculate the path distance between the preset collection point and the preset boosting site, so that the optimal path can be conveniently screened subsequently.
In one embodiment, the step of obtaining an initial topology structure of a power transmission network of a wind farm according to a target collection point and a target booster station, and optimizing the initial topology structure with an economic index as a target specifically includes the following steps: numbering the target collection points and the target boosting stations; constructing chromosome codes of a genetic algorithm according to the numbers of the target collection points and the target boosting stations, wherein the chromosome codes represent topological structures between the target collection points and the target boosting stations, and one chromosome represents one topological structure; constructing an initial population of a genetic algorithm according to the chromosome codes; constructing a fitness function, wherein the fitness function is the reciprocal of the total path length of the topological structure; selecting chromosome individuals in the initial population, performing one or more operations of crossing, mutation and evolution reversion, obtaining a new generation population by taking fitness function value increase as an optimization target, inserting the new generation population into the initial population, and continuing to perform cycle optimization until the cycle times reach a preset genetic algebra. According to the topology optimization method for the wind power plant power transmission network, the initial topology structure is optimized by adopting a genetic algorithm, and the fitness function is set to be the reciprocal of the total path length of the topology structure, so that the economy of the topology structure is better and better.
In one embodiment, the specific process of constructing the chromosome code of the genetic algorithm is as follows: generating a connection matrix according to the serial numbers of the target collection point and the target boosting station; the connection matrix comprises a first column, a second column and a third column; the first column represents the number of the target rendezvous point or the target booster station; the second column represents a number of the target booster station; when the value of the third column is "1", there is a line connection between the target sink point represented by the first column or the target booster station and the target booster station represented by the second column; when the value of the third column is "0", there is no line connection between the target sink point represented by the first column or the target booster station and the target booster station represented by the second column; the third column is selected to be chromosome coded. Thus, the chromosome coding is binary coding. Binary coding facilitates crossover, mutation, and/or inversion operations in genetic algorithms.
In one embodiment, the specific process of constructing the chromosome code of the genetic algorithm further comprises: in a topological structure between the target sink point and the target booster station point represented by the chromosome code, the connectivity of the topological structure is constrained not to exceed N + 2. And the connectivity is a line connected between the target collection point and the target boosting station or between the target boosting stations. According to the topology optimization method for the wind power plant power transmission network, the connectivity is constrained, an unreasonable topological structure with excessive connectivity is avoided, and therefore the calculation efficiency of the genetic algorithm is improved.
In one embodiment, the specific process of performing the selection operation on the chromosome individuals in the initial population is as follows: calculating the adaptive value of each chromosome individual according to the fitness function; determining the selection probability of the chromosome individual by adopting a roulette selection method. Roulette selection is a selection algorithm that selects chromosomes from an initial population. Wherein the probability of the chromosome being selected is proportional to its fitness function value. Therefore, the larger the fitness function value of the chromosome is, the higher the probability of selection is, and the population evolution direction is towards the direction of better economy.
In one embodiment, the method further comprises the following steps: and calculating to obtain the total path length of the topological structure by adopting a Dijkstra shortest path algorithm. The Dijkstra algorithm expands outward layer by layer with a target collection point as a center until all target boosting stations are expanded. The Dijkstra algorithm is used for calculating the shortest path from the target collection point to the target boosting site, so that the optimal path from the target collection point to the target boosting site is obtained, and the power transmission network topological structure with good economy is obtained.
In one embodiment, the method further comprises the following steps: obtaining an optimal topological structure according to a genetic algorithm, and changing the connection position of more than one path in the optimal topological structure; and judging whether the changed topological structures are communicated or not, and if so, calculating and storing the total path length of the changed topological structures. After the optimal topological structure is obtained, the connection position of more than one path in the optimal topological structure is changed, so that a plurality of optimal topological structures are obtained, the situation that other optimal topological structures meet the reliability condition after the optimal topological structure is screened out when reliability screening is subsequently carried out is avoided, and the optimal topological structure with the best economy under the condition that the reliability condition is met is obtained.
Drawings
FIG. 1 is a flow chart of a topology optimization method for a wind farm power transmission network of the present invention;
FIG. 2 is a schematic diagram of a collection area in an embodiment of the invention;
FIG. 3 is a schematic diagram of a connection matrix in an embodiment of the invention;
FIG. 4 is a schematic diagram of a crossover operation in an embodiment of the present invention;
FIG. 5 is a schematic diagram illustrating a variant operation according to an embodiment of the present invention;
FIG. 6 is an arrangement diagram of a preferred topology in an embodiment of the present invention;
fig. 7 is a diagram illustrating an arrangement of reliability indicators of a preferred topology according to an embodiment of the present invention.
100. Collection region, 101, first region, 201, first column, 202, second column, 203, third column.
Detailed Description
To facilitate an understanding of the invention, the invention will now be described more fully with reference to the accompanying drawings. Preferred embodiments of the present invention are shown in the drawings. This invention may, however, be embodied in many different forms and should not be construed as limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete.
As shown in fig. 1, a topology optimization method for a wind farm power transmission network includes the following steps:
s10: and in the collection area, obtaining the coordinates of all preset collection points. And the power transmission lines of the wind power plants are connected into a collection area and then are connected with an external power grid in a grid mode. And a plurality of preset collection points which can be used for collecting a plurality of wind power plant transmission lines are arranged in the collection area. The regional wind power transmission grid system can be a regional offshore wind power transmission grid system and can also be a regional land wind power transmission grid system.
S20: according to the coordinates of the preset collection points, calculating the optimal path L from the ith preset collection point to the jth wind power plantijAnd obtaining the total length of the path from the ith preset collection point to N wind power plants
The optimal path is a path having the shortest distance. The optimal path from the ith preset collection point to the jth wind power plant is Lij. Because there are N wind power plants, the ith preset collection point corresponds to N optimal paths, respectively using Li1，Li2，Li3，……LiNAnd (4) showing. The total path length of the N optimal paths corresponding to the ith preset sink point is Si。
S30: comparing the total path length S corresponding to each preset sink pointiObtaining the total path length SiMinimum value of Smin＝min(S1，S2，S3，...，SO) And the minimum value SminSelecting the corresponding preset collection point as a target collection point, and taking the minimum value SminAnd selecting the connecting point of the corresponding wind power plant as a target boosting station. Therefore, the target collection point and each target boosting site are determined by the topology optimization method for the wind power plant power transmission network, and nodes are provided for the subsequent construction of the topology structure of the wind power plant power transmission network. Compared with other preset collection points, the sum of the distances from the target collection point to each target boosting station is the minimum value, so that the total length of the lines of the subsequently constructed topological structure of the wind power plant power transmission network is as small as possible, the use amount of cables can be reduced, and the economy of the topological structure is improved.
S40: and according to the target collection point and the target boosting site, obtaining an initial topological structure of the power transmission network of the wind power plant, optimizing the initial topological structure by taking an economic index as a target and the economic index as the total path length of the topological structure, and obtaining M optimal topological structures. Wherein M is more than or equal to 5. Specifically, the initial topological structure is optimized by a genetic algorithm, a particle swarm optimization method, an immune optimization method, a hill climbing method or a neural network algorithm. The M optimal topological structures are obtained in order to ensure that at least one optimal topological structure meets the reliability condition when reliability screening is carried out subsequently, so that the topological structures which meet the reliability condition and are good in economy can be obtained.
S50: and calculating the reliability indexes of the M optimal topological structures to obtain the topological structure with the optimal economic index under the condition of meeting the reliability. And screening the M preferred topological structures according to the reliability condition. And sequencing the rest optimized topological structures meeting the reliability condition according to the economic indexes, and selecting the topological structure with the best economic efficiency.
Wherein i is more than or equal to 1 and less than or equal to O, j is more than or equal to 1 and less than or equal to N, O is the number of preset collection points, O is more than or equal to 2 and is a natural number, N is the number of wind power plants, N is more than or equal to 2 and is a natural number. For example, if the number of wind farms is 6, N is 6. If the number of the predetermined collection points is 100, the value of O is 100. When i is 3 and j is 4, L3，4Representing the optimal path from the 3 rd preset collection point to the 4 th wind farm.
And the total path length of 6 optimal paths from the 3 rd preset collection point to the 6 wind power plants is represented.
According to the topology optimization method for the wind power plant power transmission network, aiming at a regional offshore wind power plant, firstly, an initial topology structure can be obtained by determining a target collection point and target boosting sites of a plurality of wind power plants. And secondly, optimizing the initial topological structure to obtain M optimal topological structures with good economical efficiency. And finally, calculating the reliability indexes of the M optimal topological structures, thereby obtaining the optimal wind power plant power transmission network topological structure under the condition of meeting the reliability. Therefore, under the condition that a plurality of wind power plants exist in a regional offshore wind farm, workers use the topology optimization method for the wind power plant power transmission network to obtain a wind power plant power transmission network topology structure which is good in economy under the condition that reliability is met.
Further, as shown in fig. 2, the process of obtaining the coordinates of the preset rendezvous point specifically includes the following steps: dividing the collection area 100 into a plurality of first areas 101; the middle points of all the side lengths of the first region 101 are selected as preset collection points. According to the topology optimization method for the wind power plant power transmission network, the collection area 100 is divided, and the side length middle point of the first area 101 obtained through division is selected as a preset collection point. Specifically, the side length of the first region 101 is 3% to 8% of the side length of the collection region 100. In particular, the side length of the first region 101 is 5% of the side length of the collection region 100. It is understood that the preset collection point may also be selected from a vertex of the first region 101, a trisection point of a side length of the first region 101, or a centroid of the first region 101. The collection area 100 may be divided into a mesh shape, and the shape of the first area 101 may be a triangle, a quadrangle, a pentagon, or a circle.
Further, the process of calculating the optimal path L from each preset collection point to each wind farm specifically includes the following steps: dividing each wind farm into a plurality of second regions; selecting the middle point of the side length of the second area as a preset boosting station; and selecting the minimum value of the distances between the preset collection point and all the preset boosting stations of each wind power plant as an optimal path L according to the coordinates of the preset collection point and the preset boosting stations. According to the topology optimization method for the wind power plant power transmission network, the wind power plant is divided, and the side length midpoint of the second area obtained through division is selected as the preset boosting site.
According to the coordinate conditions of the preset collection point and the preset boosting station, the topological optimization method for the wind power plant power transmission network can quickly calculate the path distance between the preset collection point and the preset boosting station, so that the optimal path can be conveniently screened subsequently. Therefore, step S20 may specifically be that, taking the number of the preset collection points as the number of cycles, sequentially calculating to obtain the optimal path L from one preset collection point to N wind farms, and the total distance path length S of the N optimal paths.
Specifically, the side length of the second area is 3% -8% of the side length of the wind power plant area. In particular, the side length of the second region is 5% of the side length of the wind farm region. It is understood that the preset boosting station may also select a vertex of the second region, a trisection point of the side length of the second region, or a centroid of the second region. The wind farm area may be divided into a grid shape and the second area may be triangular, quadrangular, pentagonal or circular in shape.
On the basis of the foregoing embodiment, an initial topology structure of a power transmission network of a wind farm is obtained according to a target collection point and a target boost station, and a process of optimizing the initial topology structure with an economic index as a target specifically includes the following steps: numbering the target collection points and the target boosting stations; constructing chromosome codes of a genetic algorithm according to the numbers of the target collection points and the target boosting sites, wherein the chromosome codes represent topological structures between the target collection points and the target boosting sites, and one chromosome represents one topological structure; constructing an initial population of a genetic algorithm according to the chromosome codes; constructing a fitness function, wherein the fitness function is the reciprocal of the total path length of the topological structure; selecting chromosome individuals in the initial population, performing one or more operations of crossing, mutation and evolution reversion, obtaining a new generation population by taking fitness function value increase as an optimization target, inserting the new generation population into the initial population, and continuing to perform cycle optimization until the cycle times reach a preset genetic algebra.
The scale of the initial population is determined according to the number N of the wind power plants. For example, when N is 6, the chromosome individuals in the initial population range from 50 to 100.
Specifically, the fitness function is as follows:
in the formula, D1iAnd (3) representing the path distance from the target collection point to the ith target boosting station, wherein i is more than or equal to 1 and less than or equal to N. It should be noted that the lines between the wind farms are not shared. For example, if the path from the third target booster station to the target collection point is that the third target booster station is connected with the target collection point through the second target booster station, then D13Equal to the distance between the third target booster station and the second target booster station plus the distance between the second target booster station and the target collection point.
According to the topology optimization method for the wind power plant power transmission network, the initial topology structure is optimized through the genetic algorithm, the fitness function is set to be the reciprocal of the total path length of the topology structure, and therefore the topology structure with better and better economy is obtained.
Specifically, as shown in fig. 3, the specific process of constructing chromosome codes of the genetic algorithm is as follows: generating a connection matrix according to the numbers of the target collection points and the target boosting stations; the connection matrix comprises a first column 201, a second column 202 and a third column 203; the first column 201 represents the number of target rendezvous points or target booster stations; the second column 202 represents the number of target booster stations; when the value of the third column 203 is "1", there is a line connection between the target sink point or target booster station represented by the first column 201 and the target booster station represented by the second column 202; when the value of the third column 203 is "0", there is no line connection between the target sink point or target booster station represented by the first column 201 and the target booster station represented by the second column 202; the third column 203 selects chromosome codes. Referring to fig. 3, the number of the target collection point is 1, the number of the target boost station of the first wind farm is 2, the number of the target boost station of the second wind farm is 3, and so on, and the number of the target boost station of the nth wind farm is N + 1. The first number in the third column 203 is 1, indicating that there is a line connection between the target collection point and the target boost site of the first wind farm. Thus, the chromosome coding is binary coding. Binary coding facilitates crossover, mutation, and/or inversion operations in genetic algorithms.
Specifically, the specific process of constructing the chromosomal code of the genetic algorithm further comprises: in the topological structure between the target sink point and the target boosting station point represented by the chromosome coding, the connectivity of the constrained topological structure is not more than N + 2. And the connectivity is a line connected between the target collection point and the target boosting station or between the target boosting stations. When the connectivity is excessive, the number of lines connected between the target collection point and the target boosting station or between the target boosting stations is excessive, so that the total path length of the lines is greatly increased, and the economic principle is not met. Therefore, a topology with too much connectivity is not a preferred topology. According to the topology optimization method for the wind power plant power transmission network, the connectivity is constrained, an unreasonable topological structure with excessive connectivity is avoided, and therefore the calculation efficiency of the genetic algorithm is improved.
Specifically, the specific process of performing the selection operation on the chromosome individuals in the initial population is as follows: calculating the adaptive value of each chromosome individual according to the fitness function; the selection probability of the individual chromosome is determined by a roulette selection method. Roulette selection is a selection algorithm that selects chromosomes from an initial population. Wherein the probability of the chromosome being selected is proportional to its fitness function value. The selection probability is the fitness function value of the chromosome/sum of fitness function values of all chromosomes. Therefore, the bigger the fitness function value of the chromosome is, the higher the probability of selection is, so that the population evolution direction is towards the direction of better and better economy, and the genetic algorithm is converged more quickly.
For the initialized population, the fitness function value of each chromosome is calculated, then the selected probability of each chromosome is calculated, the fitness function value and the selected probability are compared, the chromosome with the minimum selected probability is eliminated, the chromosome with the maximum selected probability is copied, and the copied chromosome replaces the position of the eliminated chromosome, so that the operation of selecting the population is completed.
Specifically, the specific process of performing crossover operation on chromosome individuals in the population is as follows: assuming a chromosome length of 10, the parent of the crossover operation was determined using partial mapping hybridization, and the parent samples were grouped pairwise, with the following procedure repeated for each group.
Referring to fig. 4, first, two random integers r1 and r2 are generated in the interval [1, 10], two positions are determined, and intermediate data of the two positions are crossed, such as r1 is 4 and r1 is 7. Then, the crossed chromosomes are examined. If the topology represented by the crossed chromosomes is not connected, the crossed chromosomes are reduced.
Specifically, the specific process of performing mutation operation on chromosome individuals in the population is to randomly select two points in the chromosome individuals and exchange the positions of the two points. Referring to fig. 5, assume that the chromosome length is 10. Two integers r1 and r2 are randomly generated in the interval [1, 10], such as r 1-4 and r 1-7, and codes at corresponding positions of r1 and r2 are exchanged.
Specifically, the optimization of the initial topology by using the genetic algorithm further comprises the following steps: and calculating to obtain the total path length of the topological structure by adopting a Dijkstra shortest path algorithm (Dijkstra algorithm). The Dijkstra algorithm expands outward layer by layer with a target collection point as a center until all target boosting stations are expanded. The Dijkstra algorithm is used for calculating the shortest path from the target collection point to the target boosting site, so that the optimal path from the target collection point to the target boosting site is obtained, and the power transmission network topological structure with good economy is obtained.
Specifically, the optimization of the initial topology by using the genetic algorithm further comprises the following steps: obtaining an optimal topological structure according to a genetic algorithm, and changing the connection position of more than one path in the optimal topological structure; and judging whether the changed topological structures are communicated or not, and if so, calculating and storing the total path length of the changed topological structures. After the optimal topological structure is obtained, the connection position of more than one path in the optimal topological structure is changed, so that a plurality of optimal topological structures are obtained, the situation that other optimal topological structures meet the reliability condition after the optimal topological structure is screened out when reliability screening is subsequently carried out is avoided, and the optimal topological structure with the best economy under the condition that the reliability condition is met is obtained. Furthermore, by deforming the optimal topology, it is also an effective way to obtain the preferred topology.
On the basis of the embodiment, the reliability index is the blocking probability LOSP of the output capacity of the power grid, the reliability condition is that the LOSP is less than or equal to Nxeta, and eta is a screening constant; the specific process of calculating the reliability indexes of the M optimal topological structures is to calculate the reliability indexes of the M optimal topological structures by adopting a minimum cut-set algorithm. The method includes the steps that not only is economic indexes considered, but also the reliability of the topological structure needs to be considered when the regional wind power plant power transmission network topological structure is constructed. The minimal cut-set algorithm is a powerful tool for analyzing complex topologies. The minimum cut set algorithm can simplify any complex topological network structure into an equivalent series-parallel structure, so that the reliability index of the wind power plant power transmission network topological structure can be conveniently solved. According to the topology optimization method for the wind power plant power transmission network, the reliability indexes of the M optimal topology structures can be intuitively obtained by adopting a minimum cut-set algorithm, and then the optimal wind power plant power transmission network topology structure can be obtained through screening of reliability conditions.
Specifically, referring to fig. 6, assuming that the number of wind farms is 6, the number of preferred topologies is 10. In fig. 6, the ordering is by total length of the lines of the topology. As shown in fig. 6, reliability indexes of 10 preferred topologies are calculated, and if the value of the screening constant η is 0.01, the reliability condition is that LOSP is less than or equal to 6 × 0.01, which is 0.06. Therefore, in fig. 7, in addition to the first topology, other topologies meet the reliability condition, and in consideration of the economic principle, the second topology scheme in fig. 6 is obtained according to the above-mentioned topology optimization method for the wind farm power transmission network.
It can be understood that the topology optimization method for the wind power plant power transmission network can also adopt a minimum path method or an equivalence method to calculate the reliability index.
The technical features of the embodiments described above may be arbitrarily combined, and for the sake of brevity, all possible combinations of the technical features in the embodiments described above are not described, but should be considered as being within 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 invention, 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 inventive concept, which falls within the scope of the present invention. Therefore, the protection scope of the present patent shall be subject to the appended claims.
Claims (8)
1. A topology optimization method for a wind farm power transmission network is characterized by comprising the following steps:
dividing a collection area into a plurality of first areas, selecting middle points of all side lengths of the first areas as preset collection points, and obtaining coordinates of all the preset collection points;
according to the coordinates of the preset collection point, calculating the optimal path L from the ith preset collection point to the jth wind power plantij′Dividing each wind power plant into a plurality of second areas, selecting the middle point of the side length of each second area as a preset boosting station, and selecting the minimum value of the distance between the ith preset collection point and all preset boosting stations of the jth wind power plant as the optimal path L according to the coordinates of the preset collection points and the preset boosting stationsij′And obtaining the total length of the path from the ith preset collection point to the N wind power plants
Comparing the total path length S corresponding to each preset collection pointiObtaining the total length S of the pathiMinimum value of Smin＝min(S1，S2，S3，...，SO) And the minimum value S is setminSelecting the corresponding preset collection point as a target collection point, and taking the minimum value SminSelecting a connection point of the corresponding wind power plant as a target boosting station;
obtaining an initial topological structure of a power transmission network of a wind power plant according to a target collection point and a target boosting site, optimizing the initial topological structure by taking an economic index as a target, wherein the economic index is the total path length of the topological structure, and obtaining M optimal topological structures;
calculating the reliability indexes of the M optimal topological structures to obtain a topological structure with the optimal economic index under the condition of meeting the reliability;
wherein i is more than or equal to 1 and less than or equal to O, j is more than or equal to 1 and less than or equal to N, O is the number of the preset collection points, O is more than or equal to 2 and O is a natural number, N is the number of the wind power plant, N is more than or equal to 2 and N is a natural number.
2. The topology optimization method for a wind farm power transmission network according to claim 1, wherein the reliability index is a grid output capacity blocked probability, LOSP, the reliability condition is that LOSP is less than or equal to nxη, η is a screening constant; the specific process of calculating the reliability indexes of the M preferred topological structures is to calculate the reliability indexes of the M preferred topological structures by adopting a minimum cut-set algorithm.
3. The topology optimization method for the wind farm power transmission network according to claim 1 or 2, wherein the process of obtaining the initial topology of the wind farm power transmission network according to the target collection point and the target boosting site, and optimizing the initial topology with the economic index as a target specifically comprises the following steps:
numbering the target collection points and the target boosting stations;
constructing chromosome codes of a genetic algorithm according to the numbers of the target collection points and the target boosting stations, wherein the chromosome codes represent topological structures between the target collection points and the target boosting stations, and one chromosome represents one topological structure;
constructing an initial population of a genetic algorithm according to the chromosome codes;
constructing a fitness function, wherein the fitness function is the reciprocal of the total path length of the topological structure;
selecting chromosome individuals in the initial population, performing one or more of crossing, mutation and evolution reversion operation to obtain a new generation population by taking fitness function value increase as an optimization target, inserting the new generation population into the initial population, and continuing to perform cycle optimization until the cycle times reach a preset genetic algebra.
4. The topology optimization method for wind farm power transmission network according to claim 3, characterized in that the specific process of constructing chromosome coding of genetic algorithm is as follows:
generating a connection matrix according to the serial numbers of the target collection point and the target boosting station; the connection matrix comprises a first column, a second column and a third column; the first column represents the number of the target rendezvous point or the target booster station; the second column represents a number of the target booster station; when the value of the third column is "1", there is a line connection between the target sink point represented by the first column or the target booster station and the target booster station represented by the second column; when the value of the third column is "0", there is no line connection between the target sink point represented by the first column or the target booster station and the target booster station represented by the second column; the third column is selected to be chromosome coded.
5. The method for topology optimization of wind farm power transmission networks according to claim 3, wherein said specific process of constructing chromosome coding of genetic algorithm further comprises: in a topological structure between the target sink point and the target booster station point represented by the chromosome code, the connectivity of the topological structure is constrained not to exceed N + 2.
6. The topology optimization method for the wind farm power transmission network according to claim 3, wherein the specific process of the selection operation of the chromosome individuals in the initial population is as follows:
calculating the adaptive value of each chromosome individual according to the fitness function;
determining the selection probability of the chromosome individual by adopting a roulette selection method.
7. The topology optimization method for wind farm power transmission network according to claim 3, further comprising the steps of:
and calculating to obtain the total path length of the topological structure by adopting a Dijkstra shortest path algorithm.
8. The topology optimization method for wind farm power transmission network according to claim 3, further comprising the steps of:
obtaining an optimal topological structure according to a genetic algorithm, and changing the connection position of more than one path in the optimal topological structure;
and judging whether the changed topological structures are communicated or not, and if so, calculating and storing the total path length of the changed topological structures.
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