CN110334391A

CN110334391A - A kind of various dimensions constraint wind power plant collection electric line automatic planning

Info

Publication number: CN110334391A
Application number: CN201910433214.0A
Authority: CN
Inventors: 朱敏
Original assignee: MingYang Smart Energy Group Co Ltd
Current assignee: MingYang Smart Energy Group Co Ltd
Priority date: 2019-05-23
Filing date: 2019-05-23
Publication date: 2019-10-15
Anticipated expiration: 2039-05-23
Also published as: CN110334391B

Abstract

The invention discloses a kind of various dimensions to constrain wind power plant collection electric line automatic planning, comprising steps of 1) Intelligent partition；2) Optimum cost is planned in region；3) the global Optimum cost of the trans-regional planning of route；4) T meets path optimization.The present invention solves the problems, such as that the computationally intensive times such as traditional NP algorithm " dimension calamity ", genetic algorithm, RRT fast search algorithm, ant group algorithm are long, by the wind power plant current collection layout of roads problem of extensive multi-quantity, carry out subregion, it defeats in detail by different level, solves the problems, such as high-intensitive, non-linear, the high time complexity of current collection layout of roads, so that the planning of wind power plant can considerable, gradual change progress, and it carries out reducing influence of the human factor to program results from optimizing under various dimensions constraint.

Description

A kind of various dimensions constraint wind power plant collection electric line automatic planning

Technical field

The present invention relates to the technical field of wind power plant planning, refer in particular to a kind of various dimensions constraint wind power plant collection electric line from Dynamic planing method.

Background technique

Current wind power plant planing method both at home and abroad is mostly to combine the actual needs of concrete engineering, is based on warp by designer Hand-designed is tested, current collection layout of roads needs Combining with terrain, roading, can accomplish Optimum cost, however be confined to design The experience of personnel, planning level is irregular, and current collection line cost deviation is huge.Thus obtained collector system electrical arrangement is difficult To obtain preferable economy.The experience level of designer to planning quality height play the role of it is conclusive, for large size Complicated wind power plant, the design cycle is longer, and is often difficult to obtain optimal current collection line topological mode.

The current collection layout of roads algorithm of each blower producer is often all merely able to planning apart from optimal topology, and can not plan Optimum cost topology out, does not fully consider the three-dimensional optimal path of complicated landform yet, or only considers that active area divides And region interaction is had ignored, program results are usually unable to reach the purpose of Optimum cost, and planning time is substantially all in half an hour More than.This algorithm uses for reference engineer's thought, and Comprehensive considers the various practical problems encountered in algorithm design process, wraps Quick sub-zone dividing is included, the three-dimensional path planning of complicated landform, Optimum cost topology, single double back path, subregion is interactive, into Multiple depression of order is gone, within ten minutes by planning time control.

Wind energy is got more and more extensive concerning of people as a kind of important renewable energy, and wind power plant cost is high, Middle current collection line construction cost is tall and big by 10% in entire Construction of Wind Power cost, wind power plant cross-section of cable difference and collector system The selection of topological structure is very heavy to collector system cost impact, and the Large Scale Wind Farm Integration for blower arrangement than comparatively dense, design Personnel generally require to do it is a large amount of calculate work, by comparing different topological structures, the cost of more each scheme, finally from Middle selection preferably scheme, this process is generally more very long, and calculating is also more complicated, and program results are heavily dependent on The experience level of designer, obtained result are often also not optimal.

Summary of the invention

The optimal difficulty of lower economy is constrained it is an object of the invention to solve engineer's time length, be difficult to various dimensions, A kind of various dimensions constraint wind power plant collection electric line automatic planning is proposed, solves traditional NP algorithm " dimension calamity ", heredity Long problem of the computationally intensive time such as algorithm, RRT fast search algorithm, ant group algorithm, by the wind power plant current collection of extensive multi-quantity Layout of roads problem carries out subregion, defeats in detail by different level, solve the high-intensitive, non-linear of current collection layout of roads, Gao Shi Between complexity the problem of so that the planning of wind power plant can considerable, gradual change progress, and carry out under various dimensions constraint from seeking It is excellent, reduce influence of the human factor to program results.

To achieve the above object, a kind of technical solution provided by the present invention are as follows: various dimensions constraint wind power plant collection electric line Automatic planning, comprising the following steps:

1) Intelligent partition

Region division is carried out, wind machine array in wind power plant is divided into multiple active areas centered on booster stations, and And each field capacity is limited, wind power plant current collection layout of roads is a various dimensions, nonlinear planning problem, in order to The complexity of planning is reduced, depression of order processing is grouped to the blower of the whole audience first, had not only met engineer's rule, but also energy in this way Enough play the role of dimensionality reduction, cumbersome classification work is manually carried out with computer classes substitution, in order to reach this purpose, using mould Paste clustering algorithm；

2) Optimum cost is planned in region

To each subregion structure figures G (V, E), layout of roads problem is converted into graph theoretic problem, with the mathematics of graph theory Method obtains the topology of the Optimum cost in each sub-regions；

3) the global Optimum cost of the trans-regional planning of route

Feasible path between subregion is explored in increase, and subregion Optimum cost tree is added in interregional feasible path In, dynamic optimization is carried out, final whole audience Optimum cost topology is obtained；

4) T meets path optimization

Optimum cost topology achieved above, does not consider the problems of that T connects crosspoint, and there are some in Practical Project T connects mode, needs the line topological progress T of acquisition connecing optimization thus, to obtain economical more preferably current collection connection topology.

In step 1), Intelligent region division is carried out using fuzzy clustering algorithm, concrete condition is as follows:

Clustering is that individual or object are divided classification by similarity degree or distance, so that in same class Similitude between element is more stronger than the element similitude of other classes, it is therefore intended that makes the homogeney maximization and class of element between class The heterogeneous of element maximizes between class, and main the sample gathered in the same data set should be approximate each other according to being, and Belonging to different groups of sample should be dissimilar, and essence is to carry out cumbersome classification work with computer classes substitution manual sort； Here, region division is carried out to whole audience blower using fuzzy clustering algorithm, during using fuzzy clustering algorithm, it is contemplated that Wind power plant Practical Project demand needs to carry out following two limitation:

A, based on the particularity of wind power plant, need to obtain the radial cluster result centered on booster stations, and not block Shape cluster；

B, it due to the limitation of current-carrying capacity of cable, needs to limit the capacity of every collection electric line, therefore the blower of every cluster is done Capacity check, if transfinite；

In fuzzy clustering algorithm, the number of clusters for determining cluster, the i.e. quantity of subregion are first had to, and cluster initial center point There is conclusive effect to the final result of cluster, consider that wind-powered electricity generation planning is practical, cluster the selection of initial point, to divide as far as possible Dissipate and be uniformly distributed, reasonable region division is obtained with fast and easy, thus after needing to improve traditional fuzzy clustering algorithm again into Row region division；

Wherein, the quantity determination of subregion is as follows:

For a wind power plant, the subregion quantity of collection electric line is unknown before connection type does not determine, but energy The enough minimum subregion quantity that division is calculated according to the limitation of the capacity and each subregion maximum capacity of wind field, therefore, first Subregion quantity is clustered according to minimum value subregion quantity, and reasonable iteration stopping condition is set, if being unable to complete Cluster then gradually increases subregion quantity, until clustering successfully；

Steps are as follows for improved fuzzy clustering algorithm:

Step 1: setting relevant parameter, including minimum subregion quantity min_group, most subregion quantity max_ Group, initial classes heart maximum cycle fuzzy_max, blower be not belonging to any class maximum cycle station_max, The cluster heart moves maximum number of iterations delt_max and tolerance tolerence；

Step 2: it is explored since subregion quantity group is minimal set subregion number min_group, at this time the number of the cluster heart Amount is group, and cluster is divided into each subregion, planned in each subregion the collection electric line of formation be known as a string or One collection electric line；

Step 3: judging whether subregion number group is more than maximum value max_group, if executing the following 4th without if Otherwise step illustrates cluster failure；

Step 4: being divided into group sub-regions for wind field extension set, be still considered as a sub-regions for double loop, due to poly- Class inner blower quantity, that is, each subregion capacity is not limited in class process, therefore after the completion of cluster, it needs to height Current-carrying capacity is detected in region, when being more than capacity limit, needs to re-start cluster, is embedded in a fuzzy circulation thus, For re-circulation cluster in such cases, fuzzy is initialized as 0, maximum number of iterations fuzzy_max；

Step 5: judging whether fuzzy is less than fuzzy_max, if so then execute following step 6, otherwise group=group + 1 increases planning subregion quantity and updates, and group is subregion quantity, executes step 3 above；

Step 6: since the distance of blower to the class heart uses polar form, vector and boosting when booster stations to blower Stand to the class heart vector angle be greater than 90 ° when, then blower is not belonging to such, if blower is not belonging to any class, reselect just The beginning class heart is clustered, therefore is embedded in a station circulation, and for cluster again in this case, station is initial 0 is turned to, maximum number of iterations station_max；

Step 7: judging whether station is less than station_max, if so then execute following step 8, otherwise cycle accumulor Fuzzy=fuzzy+1 updates cycle-index, and fuzzy is cycle-index, executes step 5 above；

Step 8: the initial classes heart of cluster is obtained using roulette algorithm, in the selection course of the initial classes heart, in order to obtain Reasonable Clustering Effect is obtained, the group point of mutual distance as far as possible is selected, the iteration step of algorithm can not only be reduced in this way Suddenly and classification results are enabled to more evenly；It has main steps that: it is initial as first to randomly choose a blower o'clock first Then class cluster central point selects that o'clock farthest apart from the point as second initial classes cluster central point, then selects distance Central point of the maximum point of the distance of the first two point as third initial classes cluster, and so on, it is initial until selecting group Class cluster central point；After the initial classes heart determines, i.e., all class heart points have all determined that then cluster result has determined, meeting completely So that cluster result lack of diversity, in some instances it may even be possible to cause cluster to fail, introduce wheel disc algorithm thus, the more remote then point of distance is selected The probability selected is higher, rather than the point of 100% selection lie farthest away, both ensure that being uniformly distributed for initial point in this way, and had been simultaneously Cluster provides a variety of cluster possible outcomes；

Step 9: calculate each blower point to each class heart distance d_ic, i ∈ (1, n_node), c ∈ (1, n_c), i are indicated I-th Fans, n_node indicate the quantity of blower, and c indicates that the class heart, n_c indicate the quantity of the class heart, d_icIndicate that the i-th Fans arrive The distance of c-th of class heart, and distance is normalized, be converted into each blower to each class heart subordinated-degree matrix, According to degree of membership by blower node division into different clusters；Any class is not belonging to if there is blower, i.e. blower node is to all The distance of the class heart is all positive infinity, illustrates that the class heart of initial selected is unable to complete cluster, needs to reselect the initial classes heart, Station++ returns to step 7 above, reselects the initial classes heart and is calculated, otherwise continues to execute following step 10；Blower section Point to class heart distance calculating method is: in order to obtain radial cluster result, using blower to booster stations and class heart line Vertical range is distance of the blower to the class heart, without the use of blower to the linear distance of the class heart；Wherein, station++ is indicated The each cycle accumulor one of station, for counting；

When booster stations to class heart phasorWith booster stations to blower phasorWhen angle is less than or equal to 90 °, blower to the class heart Distance be blower to phasorVertical range d_ic=d, if angle is greater than 90 °,WithAngle be greater than 90, then The distance of blower to the class heart is set as d_jc=∞；

The degree of membership of fuzzy clustering calculates as follows:

Work as d_ic=∞, then being subordinate to angle value is 0；

d_ic--- it is distance of i-th Fans to c-th of class heart；

N_c --- indicate the quantity of the class heart；

M --- it is Weighted Index；

member_ic--- it is degree of membership of i-th Fans to c-th of class heart；

For Mr. Yu's Fans, by blower node division into the maximum cluster of degree of membership, if this Fans is to all The distance of the class heart is all positive infinity, then the angle value that is subordinate to of this Fans to the class heart is all 0, is not belonging to any class, then exits this Secondary cluster reselects the initial classes heart, re-starts cluster calculation；

Step 10: carrying out the division of cluster class to blower, recalculate the cluster heart, and update degree of membership, until the cluster heart no longer changes, Following step 11 is executed, in an iterative process, can not be divided into any cluster if there is blower, then executes step 9 above, weight The new selection initial classes heart, clusters again；If the iteration delt_max class heart still changes, following step 11 is executed；

Step 11: can obtain preliminary cluster result by step 10, but there is no to every string wind in cluster process Machine capacity is limited, cluster result the problem of there may be overloads, therefore needs to carry out overload detection to cluster result, is calculated every The capacity of string blower if do not overloaded, clusters success, returns if it exceeds double back threshold limit value, then return to step 6 above Return final sub-zone dividing result.

In step 2), to each sub-regions procurement cost optimal tree, concrete condition is as follows:

To each subregion, layout of roads problem is converted to graph theoretic problem, with the mathematics of graph theory by structure figures G (V, E) Method obtains the topology of the Optimum cost in each sub-regions；

Optimum cost topology in subregion, is obtained by following steps:

2.1) it constructs and does not intersect in region, the shortest effective connected graph in path；

2.2) the optimal three-dimensional path between blower active path is searched for, and using three-dimensional distance between blower as the power on side Value；

2.3) it is formed in subregion with the most short link topology for target of cable length；

2.4) topological cable length achieved above is minimum, is not that the cost of investment of cable is minimum, result still need into One step is enhanced, and proposes a kind of innovatory algorithm that dynamic adjusts and optimizes repeatedly thus, solves in practice because that cannot advise The problem of being distributed selection conductor cross-section according to trend before drawing is advised by the optimization that dynamic tree algorithm obtains subregion the lowest cost Draw topological structure；Cable cost is f*l between two blowers, and f is every kilometer of monovalent cost of cable, and unit is ten thousand/km, with electricity The current-carrying capacity (i.e. the section of cable) of cable is related, and cable institute band blower is more, and the selected cross-section of cable is thicker, and cable cost is higher；l For the length of cable, unit km；Therefore representative cost ∑ f*l is not optimal for cable length ∑ l minimum；

2.5) above 2.1) -2.4 are repeated) step, obtain each subregion Optimum cost topology.

In step 2.1), the method for constructing effectively connected graph in subregion is as follows:

For wind power plant collection electric line, collection electric line can not intersect, and in order to obtain the connected graph of wind power plant, compare Following 3 kinds of rules obtain connected graph:

A, it is complete trails connected graph, i.e., connects all blowers two-by-two, there are a large amount of crossedpaths for such connected graph, and Many unreasonable paths are also attached, and later period planning can be made extremely complex, do not consider such Path Connection；

It b, is to use the n Fans nearest with certain Fans, as feasible path, the feasible path that such mode obtains, Connected graph can be simplified, but still have the problem of intersecting, and filtered out part feasible path；

It c, is the feasible path obtained using Delaunay Triangulation, such connection type, there is no asking for intersection Topic, and the path cooked up is reasonable, therefore Delaunay Triangulation is selected to obtain connected graph, and Delaunay Triangulation is by point The convex polygon that collection is formed is split into a series of triangles, so as to guarantee not intersect between all sides；

By Delaunnay tessellation, reasonable blower active path connected graph is obtained.

In step 2.2), the optimal three-dimensional path between blower active path is searched for, i.e. search connected graph active path Optimal three-dimensional path, it is specific as follows:

The distance between plains region blower can be calculated by linear distance, but for mountainous region area or It is with a varied topography, point-to-point transmission linear distance cannot be directly used, in order to obtain more accurate cost model, needing to obtain can walking along the street Three-dimensional path between diameter, i.e. blower and blower or between booster stations and blower, and using this three-dimensional distance value as the weight on side, The algorithm for obtaining three-dimensional path has ant colony and RRT fast search tree algorithm, and wherein ant group algorithm planning time is long, and RRT algorithm is deposited The problem of possibly can not plan outbound path, Astar algorithm is a kind of flat in figure as one of heuristic search algorithm On face, there is the path of multiple nodes, finds out the minimum algorithm by cost；

Therefore, the three-dimensional distance value of feasible path is obtained using Astar algorithm, with reading wind electric field blower first figurate number According to terrain data then being carried out grid dividing, each mesh point has corresponding X, Y, then Astar can be used in Z value Algorithm, which obtains three-dimensional distance, when exploring the surroundings nodes of some node, can introduce the gradient in Astar algorithm calculating process Coefficient and landform make cost calculation more accurate；For wind power plant with a varied topography, the distance between blower cannot letter Single is calculated using the linear distance between two o'clock, needs to consider landform, the gradient problem of wind field, using Astar algorithm energy It is enough to obtain more accurate three-dimensional distance.

In step 2.3), formed in subregion with the most short link topology for target of cable length, specific as follows:

By step 2.1) and 2.2), the connected graph of wind power plant scatterplot and the distance power of each communication path are had been obtained for Value, using blower scatterplot as point, weight by the distance between blower as side can structural map G (V, E)；

The topological line optimization problem of wind power plant collector system can be stated are as follows: in wind power plant comprising booster stations and The position of several typhoon power generators, booster stations and wind-driven generator is selected, need to obtain meet engineering requirements and The best wind power plant collector system Topology connection scheme of economy, Topology connection optimization problem is a mathematics optimization problem；

Optimum cost target is that asking for figure G (V, the E) for meeting known restrictive condition is found in mathematics graph theory in subregion It inscribes, V is the set on all vertex figure G in G (V, E), represents the position of wind power plant wind power generating set and booster stations herein；E is Scheme the set on all sides G, indicates the cable connection feelings between booster stations and wind-driven generator, wind-driven generator and wind-driven generator Condition；Scheming G (V, E) simultaneously is a weighted graph, and the weight in each edge is the three-dimensional distance power between blower representated by the side Value；

Prim algorithm is the minimal spanning tree algorithm for seeking weighting connected graph, the side that Prim minimum tree algorithm search arrives It not only include all vertex in connected graph, and the weights sum on all sides is also minimum in the tree that subset is constituted；Therefore it adopts It can be obtained with Prim algorithm with the most short minimum tree for target of distance, this minimum tree includes all blower nodes, and It not will form ring.

In step 2.4), subregion the lowest cost is asked to plan topologies, specific as follows:

The weight on the side of Prim minimum tree is set as distance, and it is not cable that resulting minimum tree, which is that cable length is minimum, Totle drilling cost minimum tree, result still need to further be enhanced, and in order to obtain Optimum cost tree, propose a kind of dynamic adjustment and repeatedly In practice because that cannot be distributed selection conductor cross-section according to trend before planning, i.e., the innovatory algorithm of optimization solves the problems, such as When on the basis of Prim is apart from minimum tree by increasing while and deleting, new tree is formed, and whether more to detect new tree cost Excellent mode obtains cost minimization tree；

The basic principle of innovatory algorithm is as follows:

In G (V, E) connected graph, include vertex V={ A, B, C, D, E, F, G }, side E=AB, BC, CD, DE, EF, FA, AG, BG, CG, EG, FG, CE }, the weight on side be the distance between two o'clock weight=12,10,3,4,8,14,16,7,6,2,9, 5 }, then according to Prim algorithm, obtain Prim minimum tree, this minimum tree includes all vertex, be not present ring, and whole tree away from From weight minimum；The cost for calculating Prim minimum tree is W (k)；

On the basis of this Prim minimum tree carry out dynamic adjustment, need first by outside all trees all sides take out BC, CG, CE, FG, AG, AF }, and be ranked up according to the sequence of weight from small to large, formed side queue QV=CE, CG, FG, BC, AF, AG }, the head of the queue CE of QV is taken out, is added in tree；

A ring is formed at this time, and { CD, ED } is taken out, according to from small to large in remaining side in ring by ring={ CE, ED, CD } Sequence be ranked up to form queue QE={ CD, ED }, by QE head of the queue CD edge contract, form a new tree；Calculate this tree Totle drilling cost, be denoted as W (n), the costs of tree and two adjusted trees more originally are set more if cost is more excellent Newly, if cost does not reduce, other sides in QE are continued checking, until QE is sky, remaining side in re-inspection QV, until QV is It is empty；

Optimum cost calculation method is as follows in subregion:

Step 1: Prim algorithm is used, minimum spanning tree is asked；

Step 2: calculating the cost of present tree T (k) tree, and the method for cost accounting is as follows:

First according to subregion inner connection mode, the current-carrying capacity of each node cables is obtained, is inquired and is corresponded to according to current-carrying capacity Cost coefficient, adding up last calculates whole audience cost；

The method of cost accounting of the m articles collection electric line is as follows:

f_i--- in this collection electric line, the cost coefficient of i-th of route, depending on the current-carrying capacity of cable, unit is ten thousand/ km；

l_i--- in this collection electric line, the length of i-th of route, unit km；

Node --- in this collection electric line, the quantity of route；

Cost_m--- the cost of this collection electric line；

Wind power plant whole audience cost:

Cost_m--- the cost of the m articles collection electric line；

N --- the item number of collection electric line, double-circuit line are considered as a collection electric line；

f_c--- switchgear cost, unit are ten thousand/screen；

The total number of c --- switchgear；

A --- wind power plant collection electric line totle drilling cost；

Step 3: taking out all sides of T (k) outside, they are put into QV queue according to the sequence of power from small to large；

Step 4: it is added in T (k) tree from head of the queue is taken out in queue QV, and other sides in loop resulting from is pressed The sequence of weight from small to large is put into queue QE.

Step 5: taking out head of the queue from the head queue QE and delete from loop, to constitute a new tree T (n)；

Step 6: copy step 2 calculate tree T (n) total cost be denoted as W (n), if W (n) < W (k), then it represents that new tree at This more has, and tree is updated to the tree that cost more has:

W (k)=W (n), T (k)=T (n)

Then queue QV and QE are emptied and returns to step 3；

If W (n) >=W (k), judge whether queue QE is empty, if otherwise returning to step 5, if judging QV whether Sky terminates to plan and exit if empty, and T (k) is exactly optimum programming as a result, corresponding minimum total cost is W (k), if queue QV is not empty, then returns to step 4.

In step 3), feasible path is explored between subregion, specific as follows:

For fuzzy clustering, some blower can both belong to such, it is also possible to belong to other classes, only belong to different clusters Probability it is different, there is following possibilities during physical planning: by this region inner blower point, being divided into other subregions After interior, and it is lower to carry out Topology connection its cost, so exploring between feasible path string；

Step 1: the feasible path connected graph of the whole audience is obtained using Delaunay algorithm first；

Step 2: it filters out the path inside Delaunay connected graph subregion and there is the path intersected, then residual paths For the feasible path between string, feasible path in subregion is added in Optimum cost tree, new connected graph is obtained；

Step 3: the minimum tree algorithm of dynamic is called, obtains final whole audience Optimum cost tree, if overload, this tree cannot As optimal tree.

In step 4), T connects optimization and every trail electric line is individually handled as follows, and steps are as follows:

Step 1: obtaining blower farthest apart from booster stations in string, based on this node, inquires main line, until boosting It stands:

Step 2: handling main line, since booster stations beginning, detects this node and a upper node and next The angle of a node, if do vertical line if it is acute angle for acute angle, then continue to inquire if it is obtuse angle, until detecting most Latter Fans；

Step 3: handling branch, and whether the angle of detection branch and main line is acute angle, if it is acute angle, does Otherwise vertical line continues to inquire, until branch inquiry finishes；

T is obtained as a result, connects path.

Compared with prior art, the present invention have the following advantages that with the utility model has the advantages that

For the present invention by being combined to many algorithms, calculating process is reasonable, meets engineer's thinking, will be extensive The planning problem of multi-quantity, carry out subregion, defeat in detail by different level, solve current collection layout of roads it is high-intensitive, non-linear, The problem of high time complexity, user need to only input the terrain data and booster stations and blower point of wind power plant to be planned The automatic planning to wind power plant collection electric line can be completed, test cases, which shows this method, may finally complete collection electric line certainly Dynamic design, and the current collection line cost obtained is lower, and runing time is short, and operation result is reliable.

In short, the method for the present invention only needs to input the terrain data of wind power plant and blower point can be in several points of minutes Interior acquisition wind power plant current collection connections connection type；Further, it enables wind power plant planning can considerable, gradual change progress, subtract Few influence of the human factor to program results, can greatly shorten the current collection layout of roads time, save plenty of time cost and people Work cost.

Detailed description of the invention

Fig. 1 is the flow diagram of the method for the present invention.

Fig. 2 is for blower to the class heart apart from schematic diagram.

Fig. 3 is improved fuzzy clustering algorithm flow chart.

Fig. 4 a is complete trails connected graph.

Fig. 4 b is the connected graph for connecting nearest n Fans.

Fig. 4 c is Delaunay connected graph.

Fig. 5 a is G (V, E) connected graph.

Fig. 5 b is Prim minimum tree graph.

Fig. 5 c is addition QV head of the queue figure.

Fig. 5 d is to delete QE head of the queue figure.

Fig. 6 is that dynamic minimum tree obtains Optimum cost tree optimization flow chart.

Two-by-two connected mode schematic diagram of Fig. 7 a between blower.

T-type mode of connection schematic diagram of Fig. 7 b between blower.

Fig. 8 a is some case wind power plant area schematic (reading machine site information and geography information).

Fig. 8 b is that fuzzy clustering carries out radial grouping schematic diagram.

Fig. 8 c is that Delaunay Triangulation algorithm obtains connected graph.

Fig. 8 d is that Astar algorithm calculates three-dimensional path and path length result figure.

Fig. 8 e is that Prim algorithm is obtained apart from minimum tree schematic diagram.

Fig. 8 f is Optimum cost tree schematic diagram in the minimum tree algorithm acquisition group of dynamic.

Fig. 8 g is Optimum cost topological diagram in whole audience all subregion.

Fig. 8 h is whole audience triangulation topological diagram.

Fig. 8 i is feasible path figure between search subregion.

Fig. 8 j is to call the minimum tree algorithm of dynamic, obtains whole audience Optimum cost tree schematic diagram.

Fig. 8 k is that retrieval T connects, and carries out T and connects the schematic diagram after optimization.

Specific embodiment

The present invention is further explained in the light of specific embodiments.

As shown in Figure 1, the constraint wind power plant collection electric line automatic planning of various dimensions provided by the present embodiment, including with Lower step:

1) Intelligent partition

Region division is carried out, the wind machine array of wind power plant is divided into multiple active areas centered on booster stations, and And each field capacity is limited, wind power plant current collection layout of roads is a various dimensions, nonlinear planning problem, in order to The complexity of planning is reduced, depression of order processing is grouped to the blower of the whole audience first.Engineer's rule, but also energy were not only met in this way Enough play the role of dimensionality reduction, manually carries out cumbersome classification work with computer classes substitution.In order to reach this purpose, we are adopted With improved fuzzy clustering algorithm.

2) Optimum cost is planned in region

To each subregion structure figures G (V, E), layout of roads problem is converted into graph theoretic problem, with the mathematics of graph theory Method obtains the topology of the Optimum cost in each sub-regions.

3) the global Optimum cost of the trans-regional planning of route

Feasible path between subregion is explored in increase, and subregion Optimum cost tree is added in interregional feasible path In, dynamic optimization is carried out, final whole audience Optimum cost topology is obtained.

4) T meets path optimization

Optimum cost topology achieved above, does not consider the problems of that T connects crosspoint, and exists in Practical Project certain T connect mode, need the line topological of acquisition carrying out T thus to connect optimization, be opened up with obtaining economical more preferably current collection connection It flutters.

Clustering is that individual or object are divided classification by similarity degree (or distance), so that in same class Element between similitude it is more stronger than the element similitude of other classes.Purpose be to make the homogeney of element between class maximize and The heterogeneous of element maximizes between class and class.Its main foundation is that the sample gathered in the same data set should be approximate each other, And the sample for belonging to different groups should be sufficiently dissimilar, essence is to carry out cumbersome classification with computer classes substitution manual sort Work.This method carries out region division to whole audience blower using fuzzy clustering algorithm, during using fuzzy clustering algorithm, In view of wind power plant Practical Project demand, need to carry out following two restrictive condition:

B, it due to the limitation of current-carrying capacity of cable, needs to limit the capacity of every collection electric line, therefore the blower of every cluster is done Capacity check, if transfinite.

In fuzzy clustering algorithm, the number of clusters (i.e. the quantity of subregion) for determining cluster is first had to, and cluster initial center Point has conclusive effect to the final result of cluster, considers that wind-powered electricity generation planning is practical, clusters the selection of initial point, be as far as possible Be uniformly dispersed distribution, obtains reasonable region division with fast and easy, therefore after needing to improve traditional fuzzy clustering algorithm again Carry out region division.

Wherein, the quantity determination of subregion is as follows:

For a wind power plant, the subregion quantity of collection electric line is unknown before connection type does not determine, but one As be easy to be limited according to the capacity and each subregion maximum capacity of wind field the minimum subregion quantity of division be calculated.Cause This first clusters subregion quantity according to minimum value subregion quantity, and reasonable iteration stopping condition is arranged, if can not Cluster is completed, then gradually increases subregion quantity, until clustering successfully.

As shown in figure 3, steps are as follows for improved fuzzy clustering algorithm:

Step 1: setting relevant parameter, including minimum subregion quantity min_group, most subregion quantity max_ Group, initial classes heart maximum cycle fuzzy_max, blower be not belonging to any class maximum cycle station_max, The cluster heart moves maximum number of iterations delt_max and tolerance tolerence.

Step 2: it is explored since subregion quantity group is minimal set subregion number min_group, at this time the number of the cluster heart Amount is group, and cluster is divided into each subregion, planned in each subregion the collection electric line of formation be known as a string or One collection electric line.

Step 3: judging whether subregion number group is more than maximum value max_group, if executing the following 4th without if Otherwise step illustrates cluster failure.

Step 4: being divided into group sub-regions for wind field extension set, be still considered as a sub-regions for double loop, due to poly- Class inner blower quantity, that is, each subregion capacity is not limited in class process, therefore after the completion of cluster, it needs to height Current-carrying capacity is detected in region, when being more than capacity limit, needs to re-start cluster, is embedded in a fuzzy circulation thus, For re-circulation cluster in such cases, fuzzy is initialized as 0, maximum number of iterations fuzzy_max.

Step 5: judging whether fuzzy is less than fuzzy_max, if so then execute following step 6, otherwise group=group + 1 increases planning subregion quantity and updates, and group is subregion quantity, executes step 3 above.

Step 6: since the distance of blower to the class heart uses polar form, vector and boosting when booster stations to blower Stand to the class heart vector angle be greater than 90 ° when, then blower is not belonging to such, if blower is not belonging to any class, reselect just The beginning class heart is clustered, therefore is embedded in a station circulation, and for cluster again in this case, station is initial 0 is turned to, maximum number of iterations station_max.

Step 7: judging whether station is less than station_max, if so then execute following step 8, otherwise cycle accumulor Fuzzy=fuzzy+1 updates cycle-index, and fuzzy is cycle-index, executes step 5 above.

Step 8: the initial classes heart of cluster is obtained using roulette algorithm, in the selection course of the initial classes heart, in order to obtain Reasonable Clustering Effect is obtained, the group point of mutual distance as far as possible is selected, the iteration step of algorithm can not only be reduced in this way Suddenly and classification results are enabled to more evenly；It has main steps that: it is initial as first to randomly choose a blower o'clock first Then class cluster central point selects that o'clock farthest apart from the point as second initial classes cluster central point, then selects distance Central point of the maximum point of the distance of the first two point as third initial classes cluster, and so on, it is initial until selecting group Class cluster central point；After the initial classes heart determines, i.e., all class heart points have all determined that then cluster result has determined, meeting completely So that cluster result lack of diversity, in some instances it may even be possible to cause cluster to fail, introduce wheel disc algorithm thus, the more remote then point of distance is selected The probability selected is higher, rather than the point of 100% selection lie farthest away, both ensure that being uniformly distributed for initial point in this way, and had been simultaneously Cluster provides a variety of cluster possible outcomes.

Step 9: calculate each blower point to each class heart distance d_ic, i ∈ (1, n_node), c ∈ (1, n_c), i are indicated I-th Fans, n_node indicate the quantity of blower, and c indicates that the class heart, n_c indicate the quantity of the class heart, d_icIndicate that the i-th Fans arrive The distance of c-th of class heart, and distance is normalized, be converted into each blower to each class heart subordinated-degree matrix, According to degree of membership by blower node division into different clusters；Any class is not belonging to if there is blower, i.e. blower node is to all The distance of the class heart is all positive infinity, illustrates that the class heart of initial selected is unable to complete cluster, needs to reselect the initial classes heart, Station++ (station++ indicates each cycle accumulor one of station, for counting) returns to step 7 above, reselects The initial classes heart is calculated, and following step 10 is otherwise continued to execute；Blower node is to class heart distance calculating method: in order to obtain Radial cluster result uses distance of the blower to the vertical range of booster stations and class heart line for blower to the class heart, without It is shown in Figure 2 using blower to the linear distance of the class heart.

The degree of membership of fuzzy clustering calculates as follows:

Work as d_ic=∞, then being subordinate to angle value is 0；

d_ic--- it is distance of i-th Fans to c-th of class heart；

N_c --- indicate the quantity of the class heart；

M --- it is Weighted Index；

member_ic--- it is degree of membership of i-th Fans to c-th of class heart；

For Mr. Yu's Fans, by blower node division into the maximum cluster of degree of membership, if this Fans is to all The distance of the class heart is all positive infinity, then the angle value that is subordinate to of this Fans to the class heart is all 0, is not belonging to any class, then exits this Secondary cluster reselects the initial classes heart, re-starts cluster calculation.

Step 10: carrying out the division of cluster class to blower, recalculate the cluster heart, and update degree of membership, until the cluster heart no longer changes, Following step 11 is executed, in an iterative process, can not be divided into any cluster if there is blower, then executes step 9 above, weight The new selection initial classes heart, clusters again；If the iteration delt_max class heart still changes, following step 11 is executed.

To each subregion, layout of roads problem is converted to graph theoretic problem, with the mathematics of graph theory by structure figures G (V, E) Method obtains the topology of the Optimum cost in each sub-regions.

Optimum cost topology in subregion, is obtained by following steps:

2.4) topological cable length achieved above is minimum, is not that the cost of investment of cable is minimum, result still need into One step is enhanced, and proposes a kind of innovatory algorithm that dynamic adjusts and optimizes repeatedly thus, solves in practice because that cannot advise The problem of being distributed selection conductor cross-section according to trend before drawing is advised by the optimization that dynamic tree algorithm obtains subregion the lowest cost Draw topological structure.Cable cost is f*l between two blowers, and f is every kilometer of monovalent cost of cable, and unit is ten thousand/km, with electricity The current-carrying capacity (i.e. the section of cable) of cable is related, and cable institute band blower is more, and the selected cross-section of cable is thicker, and cable cost is higher；l For the length of cable, unit km；Therefore representative cost ∑ f*l is not optimal for cable length ∑ l minimum.

2.5) above step 2.1 is repeated) -2.4), obtain each subregion Optimum cost topology.

For wind power plant collection electric line, the general electric line that collects can not intersect, in order to obtain the connected graph of wind power plant, Referring to fig. 4 shown in a to Fig. 4 c, it is compared as follows 3 kinds of rules and obtains connected graph:

It b, is to use the n Fans nearest with certain Fans, as feasible path, the feasible path that such mode obtains, It can simplify connected graph, but still have the problem of intersecting, and filtered out part feasible path；

It c, is the feasible path obtained using Delaunay Triangulation, such connection type, there is no asking for intersection Topic, and the path cooked up is relatively reasonable, therefore Delaunay Triangulation is selected to obtain connected graph, Delaunay Triangulation The convex polygon that point set is formed is split into a series of triangles, so as to guarantee not intersect between all sides；

By Delaunnay tessellation, available reasonable blower active path connected graph.

The distance between plains region blower can be calculated by linear distance, but for mountainous region area or It is with a varied topography, point-to-point transmission linear distance cannot be directly used, in order to obtain more accurate cost model, it would be desirable to obtain feasible Path (between blower and blower or between booster stations and blower three-dimensional path), and using this three-dimensional distance value as the power on side Value, obtaining the more common algorithm of three-dimensional path has ant colony, and RRT fast search tree algorithm, wherein ant group algorithm planning time is too long, RRT algorithm there are problems that possibly not planning that outbound path, Astar algorithm are one as one of heuristic search algorithm Kind has the path of multiple nodes, finds out the minimum algorithm by cost on graphics plane.

Therefore, the three-dimensional distance value of feasible path is obtained using Astar algorithm, with reading wind electric field blower first figurate number According to terrain data then being carried out grid dividing, each mesh point has corresponding X, Y, then Astar can be used in Z value Algorithm, which obtains three-dimensional distance, when exploring the surroundings nodes of some node, can introduce the gradient in Astar algorithm calculating process Coefficient, landform (such as different landform meadows, if land acquisition, cost coefficients are waited) make cost calculation more accurate. For wind power plant with a varied topography, the distance between blower cannot be counted simply using the linear distance between two o'clock It calculates, needs to consider the landform of wind field, the problems such as the gradient, more accurate three-dimensional distance can be obtained using Astar algorithm.

By step 2.1) and 2.2), we have been obtained for wind power plant scatterplot connected graph and each communication path away from From weight, we are using blower scatterplot as point, and weight by the distance between blower as side can structural map G (V, E).

The topological line optimization problem of wind power plant collector system can be stated are as follows: in wind power plant comprising booster stations and The position of several typhoon power generators, booster stations and wind-driven generator is selected, need to obtain meet engineering requirements and The best wind power plant collector system Topology connection scheme of economy, Topology connection optimization problem is a mathematics optimization problem.

Optimum cost target is that asking for figure G (V, the E) for meeting known restrictive condition is found in mathematics graph theory in subregion It inscribes, V is the set on all vertex figure G in G (V, E), and the position of wind power plant wind power generating set and booster stations is represented in this algorithm It sets；E is the set on all sides figure G, indicates the cable between booster stations and wind-driven generator, wind-driven generator and wind-driven generator Connection.Scheming G (V, E) simultaneously is a weighted graph, and the weight in each edge is the three-dimensional between blower representated by the side Apart from weight.

Prim algorithm is the minimal spanning tree algorithm for seeking weighting connected graph, the side subset that minimum tree algorithm search arrives It not only include all vertex in connected graph, and the weights sum on all sides is also minimum in the tree constituted.Therefore it uses Prim algorithm can be obtained with the most short minimum tree for target of distance, this minimum tree includes all blower nodes, and not It will form ring.

The weight on the side of Prim minimum tree is set as distance, and it is not cable that resulting minimum tree, which is that cable length is minimum, Totle drilling cost minimum tree, result still can be further enhanced, and in order to obtain Optimum cost tree, propose a kind of dynamic adjustment and repeatedly In practice because that cannot be distributed selection conductor cross-section according to trend before planning, i.e., the innovatory algorithm of optimization solves the problems, such as When on the basis of Prim is apart from minimum tree by increasing while and deleting, new tree is formed, and whether more to detect new tree cost Excellent mode obtains cost minimization tree.

The basic principle of innovatory algorithm is as follows:

Fig. 5 a be G (V, E) connected graph, include vertex V={ A, B, C, D, E, F, G }, side E=AB, BC, CD, DE, EF, FA, AG, BG, CG, EG, FG, CE }, the weight on side be the distance between two o'clock weight=12,10,3,4,8,14,16,7,6,2, 9,5 }, then minimum tree shown in Fig. 5 b can get according to Prim algorithm, this minimum tree includes all vertex, ring is not present, and Whole tree apart from weight minimum；The cost for calculating Prim minimum tree is W (k).

Dynamic adjustment is carried out on the basis of this Prim minimum tree.We first by outside all trees all sides take out BC, CG, CE, FG, AG, AF }, and be ranked up according to the sequence of weight from small to large, formed side queue QV=CE, CG, FG, BC, AF, AG }, the head of the queue CE of QV is taken out, is added in tree, referring to shown in Fig. 5 c.

A ring is formed at this time, and { CD, ED } is taken out, according to from small to large in remaining side in ring by ring={ CE, ED, CD } Sequence be ranked up to form queue QE={ CD, ED }, by QE head of the queue CD edge contract, form a new tree, as fig 5d, The totle drilling cost for calculating this tree is denoted as W (n), the cost of tree and two adjusted trees more originally, if cost is more excellent The update set continues checking other sides in QE if cost does not reduce, until QE is empty, remaining in re-inspection QV Side, until QV is sky.

Optimum cost calculation method is as follows in subregion:

Step 1: Prim algorithm is used, minimum spanning tree is asked；

Node --- in this collection electric line, the quantity of route；

Cost_m--- the cost of this collection electric line；

Wind power plant whole audience cost:

Cost_m--- the cost of the m articles collection electric line；

N --- the item number of collection electric line (double-circuit line is considered as a collection electric line)

f_c--- switchgear cost, unit are ten thousand/screen；

The total number of c --- switchgear；

A --- wind power plant collection electric line totle drilling cost；

W (k)=W (n), T (k)=T (n)

Then queue QV and QE are emptied and returns to step 3；

It is as shown in Figure 6 that dynamic minimum tree obtains Optimum cost tree optimization process.

In step 3), feasible path is explored between subregion, specific as follows:

For fuzzy clustering, some blower can both belong to such, it is also possible to belong to other classes, only belong to different clusters Probability it is different, there is possibilities during physical planning: this region inner blower point is divided into other subregions Afterwards, and to carry out Topology connection its cost lower, so exploring between feasible path string；

In step 4), by above several steps, wind power plant can be obtained and connect wires the topological diagram on road, between blower Connection type is to connect two-by-two, but in general Practical Project, there is also the T-type mode of connection, this mode can more supernumerary segment Cost-saving, as shown in Fig. 7 a, 7b.

Every trail electric line is individually handled as follows, steps are as follows:

T can be obtained as a result, and connect path.

We are tested by taking some wind power plant as an example below；It is as shown in Figure 8 a wind-powered electricity generation field areas and blower point Bitmap reads wind power plant geography information and blower point information, wherein No. 0 point is expressed as booster stations point, No. 1-50 expression Blower point, the blower number of units of single time collection electric line are up to 6, and the blower number of units of double back collection electric line is up to 12, this Case Riming time of algorithm be 2 points 05 second.

Fuzzy clustering group result is as shown in Figure 8 b.

It is analyzed by taking certain subregion as an example, using Delaunay Triangulation algorithm, obtains connected graph, and call Astar algorithm calculates weight of the distance between the blower as side, as shown in Fig. 8 c, 8d.

Using Prim minimum tree algorithm, obtain with the most short minimum tree for target of distance, as figure 8 e shows.

The minimum tree algorithm of dynamic is called, is obtained using Optimum cost as the Optimum cost tree of target, as illustrated in fig. 8f.

To Optimum cost tree in remaining subregion planning subregion, whole audience all subregion Optimum cost tree is obtained, such as Fig. 8 g It is shown.

Triangulation obtains whole audience blower connection type, as shown in Fig. 8 h.

Connection and crossedpath, path between being gone here and there in string are filtered out, and calls Astar algorithm, three-dimensional path between being gone here and there Weight, as illustrated in fig. 8i.

The minimum tree algorithm of dynamic is called, subregion interaction, whole audience Optimum cost tree after being optimized, such as Fig. 8 j institute are carried out Show.

It carries out T and connects retrieval, to the branch that can be carried out T and connect, progress T connects processing, obtains final whole audience current collection line topological Figure, as shown in Fig. 8 k.

Embodiment described above is only the preferred embodiments of the invention, and but not intended to limit the scope of the present invention, therefore All shapes according to the present invention change made by principle, should all be included within the scope of protection of the present invention.

Claims

1. a kind of various dimensions constrain wind power plant collection electric line automatic planning, which comprises the following steps:

1) Intelligent partition

Region division is carried out, wind machine array in wind power plant is divided into multiple active areas centered on booster stations, and right Each field capacity is limited, and wind power plant current collection layout of roads is a various dimensions, nonlinear planning problem, in order to reduce The complexity of planning is grouped depression of order processing to the blower of the whole audience first, has not only met engineer's rule in this way, but also can rise It is acted on to dimensionality reduction, manually carries out cumbersome classification work with computer classes substitution, in order to reach this purpose, using fuzzy poly- Class algorithm；

2) Optimum cost is planned in region

To each subregion structure figures G (V, E), layout of roads problem is converted into graph theoretic problem, with the mathematical method of graph theory Obtain the Optimum cost topology in each sub-regions；

3) the global Optimum cost of the trans-regional planning of route

Feasible path between subregion is explored in increase, and interregional feasible path is added in subregion Optimum cost tree, Dynamic optimization is carried out, final whole audience Optimum cost topology is obtained；

4) T meets path optimization

Optimum cost topology achieved above, does not consider the problems of that T connects crosspoint, and there are some T to connect in Practical Project Mode needs the line topological progress T of acquisition connecing optimization, thus to obtain economical more preferably current collection connection topology.

2. a kind of various dimensions according to claim 1 constrain wind power plant collection electric line automatic planning, it is characterised in that: In step 1), Intelligent region division is carried out using fuzzy clustering algorithm, concrete condition is as follows:

Clustering is that individual or object are divided classification by similarity degree or distance, so that the element in same class Between similitude it is more stronger than the element similitude of other classes, it is therefore intended that maximize the homogeney of element between class and class and class Between the heterogeneous of element maximize, it is main according to being that the sample gathered in the same data set should be approximate each other, and belongs to The sample of difference group should be dissimilar, and essence is to carry out cumbersome classification work with computer classes substitution manual sort；Here, Region division is carried out to whole audience blower using fuzzy clustering algorithm, during using fuzzy clustering algorithm, it is contemplated that wind-powered electricity generation Field Practical Project demand, needs to carry out following two restrictive condition:

A, based on the particularity of wind power plant, need to obtain the radial cluster result centered on booster stations, and it is not blocky poly- Class；

B, it due to the limitation of current-carrying capacity of cable, needs to limit the capacity of every collection electric line, therefore capacity is done to the blower of every cluster Detection, if transfinite；

In fuzzy clustering algorithm, the number of clusters for determining cluster, the i.e. quantity of subregion are first had to, and clusters initial center point to poly- The final result of class has conclusive effect, considers that wind-powered electricity generation planning is practical, clusters the selection of initial point, to disperse as far as possible Even distribution obtains reasonable region division with fast and easy, therefore needs to carry out area again after improving traditional fuzzy clustering algorithm Domain divides；

Wherein, the quantity determination of subregion is as follows:

For a wind power plant, the subregion quantity of collection electric line is unknown before connection type does not determine, but being capable of root It therefore first will be sub according to the minimum subregion quantity that division is calculated in capacity and each subregion the maximum capacity limitation of wind field Region quantity is clustered according to minimum value subregion quantity, and reasonable iteration stopping condition is arranged, if being unable to complete cluster, Then gradually increase subregion quantity, until clustering successfully；

Steps are as follows for improved fuzzy clustering algorithm:

Step 1: setting relevant parameter, including minimum subregion quantity min_group, most subregion quantity max_group, just It is mobile that beginning class heart maximum cycle fuzzy_max, blower are not belonging to any class maximum cycle station_max, the cluster heart Maximum number of iterations delt_max and tolerance tolerence；

Step 2: it is explored since subregion quantity group is minimal set subregion number min_group, the quantity of the cluster heart is at this time Group, it is divided into cluster in each subregion, plans that the collection electric line of formation is known as a string or one in each subregion Collect electric line；

Step 3: judging whether subregion number group is more than maximum value max_group, no if executing following step 4 without if Then illustrate cluster failure；

Step 4: wind field extension set is divided into group sub-regions, is still considered as a sub-regions for double loop, due to clustering Class inner blower quantity, that is, each subregion capacity is not limited in journey, therefore after the completion of cluster, it needs to sub-regions Interior current-carrying capacity is detected, and when being more than capacity limit, needs to re-start cluster, is embedded in a fuzzy circulation thus, is used for Re-circulation cluster in such cases, fuzzy are initialized as 0, maximum number of iterations fuzzy_max；

Step 5: judging whether fuzzy is less than fuzzy_max, and if so then execute following step 6, otherwise group=group+1 increases Add planning subregion quantity and update, group is subregion quantity, executes step 3 above；

Step 6: since the distance of blower to the class heart uses polar form, when vector and the booster stations of booster stations to blower arrive When the vector angle of the class heart is greater than 90 °, then blower is not belonging to such, if blower is not belonging to any class, reselects initial classes The heart is clustered, therefore is embedded in a station circulation, and for cluster again in this case, station is initialized as 0, maximum number of iterations station_max；

Step 8: the initial classes heart of cluster is obtained using roulette algorithm, in the selection course of the initial classes heart, in order to be closed The Clustering Effect of reason selects mutual distance group point as far as possible, can not only reduce the iterative step of algorithm in this way and And enable to classification results more evenly；It has main steps that: randomly choosing a blower o'clock first as first initial classes cluster Then central point selects that o'clock farthest apart from the point as second initial classes cluster central point, then selects apart from preceding two Central point of the maximum point of the distance of a point as third initial classes cluster, and so on, until selecting group initial classes cluster Central point；After the initial classes heart determines, i.e., all class heart points have all determined that then cluster result has determined completely, can make Cluster result lack of diversity, in some instances it may even be possible to cause cluster to fail, introduce wheel disc algorithm thus, the distance the remote, what point was selected Probability is higher, rather than the point of 100% selection lie farthest away, both ensure that being uniformly distributed for initial point in this way, while being cluster A variety of cluster possible outcomes are provided；

Step 9: calculate each blower point to each class heart distance d_ic, i ∈ (1, n_node), c ∈ (1, n_c), i indicate i-th Fans, n_node indicate the quantity of blower, and c indicates that the class heart, n_c indicate the quantity of the class heart, d_icIndicate the i-th Fans to c The distance of a class heart, and distance is normalized, be converted into each blower to each class heart subordinated-degree matrix, according to Degree of membership is by blower node division into different clusters；Any class, i.e. blower node to all class hearts are not belonging to if there is blower Distance be all positive infinity, illustrate that the class heart of initial selected is unable to complete cluster, need to reselect the initial classes heart, Station++ returns to step 7 above, reselects the initial classes heart and is calculated, otherwise continues to execute following step 10；Blower section Point to class heart distance calculating method is: in order to obtain radial cluster result, using blower to booster stations and class heart line Vertical range is distance of the blower to the class heart, without the use of blower to the linear distance of the class heart；Wherein, station++ is indicated The each cycle accumulor one of station, for counting；

When booster stations to class heart phasorWith booster stations to blower phasorAngle be less than or equal to 90 ° when, blower to the class heart away from From for blower to phasorVertical range d_ic=d, if angle is greater than 90 °,WithAngle be greater than 90, then blower Distance to the class heart is set as d_jc=∞；

The degree of membership of fuzzy clustering calculates as follows:

Work as d_ic=∞, then being subordinate to angle value is 0；

d_ic--- it is distance of i-th Fans to c-th of class heart；

N_c --- indicate the quantity of the class heart；

M --- it is Weighted Index；

member_ic--- it is degree of membership of i-th Fans to c-th of class heart；

For Mr. Yu's Fans, by blower node division into the maximum cluster of degree of membership, if this Fans is to all class hearts Distance be all positive infinity, then the angle value that is subordinate to of this Fans to the class heart is all 0, is not belonging to any class, then exits this time poly- Class reselects the initial classes heart, re-starts cluster calculation；

Step 10: carrying out the division of cluster class to blower, recalculate the cluster heart, and update degree of membership, until the cluster heart no longer changes, executes Step 11 below can not be divided into any cluster if there is blower in an iterative process, then execute step 9 above, select again The initial classes heart is selected, is clustered again；If the iteration delt_max class heart still changes, following step 11 is executed；

Step 11: can obtain preliminary cluster result by step 10, but there is no hold to every string blower in cluster process Amount limited, cluster result there may be overload the problem of, therefore need to cluster result carry out overload detection, calculate every string wind The capacity of machine if do not overloaded, clusters success, returns most if it exceeds double back threshold limit value, then return to step 6 above Whole sub-zone dividing result.

3. a kind of various dimensions according to claim 1 constrain wind power plant collection electric line automatic planning, it is characterised in that: In step 2), to each sub-regions procurement cost optimal tree, concrete condition is as follows:

To each subregion, layout of roads problem is converted to graph theoretic problem, with the mathematical method of graph theory by structure figures G (V, E) Obtain the Optimum cost topology in each sub-regions；

Optimum cost topology in subregion, is obtained by following steps:

2.2) the optimal three-dimensional path between blower active path is searched for, and using three-dimensional distance between blower as the weight on side；

2.4) topological cable length achieved above is minimum, is not that the cost of investment of cable is minimum, result still needs to further It is enhanced, proposes the innovatory algorithm that a kind of dynamic adjusts and optimizes repeatedly thus, solve in practice because cannot be before planning The problem of being distributed selection conductor cross-section according to trend, is opened up by the optimization planning that dynamic tree algorithm obtains subregion the lowest cost Flutter structure；Cable cost is f*l between two blowers, and f is every kilometer of monovalent cost of cable, and unit is ten thousand/km, with cable Current-carrying capacity, that is, cable section is related, and cable institute band blower is more, and the selected cross-section of cable is thicker, and cable cost is higher；L is cable Length, unit km；Therefore representative cost ∑ f*l is not optimal for cable length ∑ l minimum；

4. a kind of various dimensions according to claim 3 constrain wind power plant collection electric line automatic planning, it is characterised in that: In step 2.1), the method for constructing effectively connected graph in subregion is as follows:

For wind power plant collection electric line, collection electric line can not intersect, and in order to obtain the connected graph of wind power plant, be compared as follows 3 kinds of rules obtain connected graph:

A, it is complete trails connected graph, i.e., connects all blowers two-by-two, there are a large amount of crossedpath for such connected graph, and very much Unreasonable path is also attached, and later period planning can be made extremely complex, do not consider such Path Connection；

It b, is using the n Fans nearest with certain Fans, as feasible path, the feasible path that such mode obtains can Simplify connected graph, but still has the problem of intersecting, and filtered out part feasible path；

It c, is the problem of there is no intersections using the feasible path of Delaunay Triangulation acquisition, such connection type, and And the path cooked up is reasonable, therefore Delaunay Triangulation is selected to obtain connected graph, Delaunay Triangulation is by point set shape At convex polygon be split into a series of triangles, so as to guarantee not intersect between all sides；

5. a kind of various dimensions according to claim 3 constrain wind power plant collection electric line automatic planning, it is characterised in that: In step 2.2), the optimal three-dimensional path between blower active path is searched for, that is, searches for optimal the three of connected graph active path Path is tieed up, specific as follows:

The distance between plains region blower can be calculated by linear distance, but for mountainous region area or landform Complexity directly cannot use point-to-point transmission linear distance to need to obtain feasible path, i.e., to obtain more accurate cost model Three-dimensional path between blower and blower or between booster stations and blower, and using this three-dimensional distance value as the weight on side, it obtains The algorithm of three-dimensional path has ant colony and RRT fast search tree algorithm, and wherein ant group algorithm planning time is long, and RRT algorithm exists can The problem of capable of can not planning outbound path, Astar algorithm are one kind in graphics plane as one of heuristic search algorithm On, there is the path of multiple nodes, finds out the minimum algorithm by cost；

Therefore, the three-dimensional distance value of feasible path is obtained using Astar algorithm, first reading wind electric field blower terrain data, so Terrain data is subjected to grid dividing afterwards, each mesh point has corresponding X, Y, then Z value Astar algorithm can be used to obtain Three-dimensional distance, in Astar algorithm calculating process, when exploring the surroundings nodes of some node, can introduce gradient coefficient and Landform makes cost calculation more accurate；For wind power plant with a varied topography, the distance between blower cannot simply make It is calculated with the linear distance between two o'clock, needs to consider landform, the gradient problem of wind field, can be obtained using Astar algorithm More accurate three-dimensional distance.

6. a kind of various dimensions according to claim 3 constrain wind power plant collection electric line automatic planning, it is characterised in that: In step 2.3), formed in subregion with the most short link topology for target of cable length, specific as follows:

By step 2.1) and 2.2), have been obtained for wind power plant scatterplot connected graph and each communication path apart from weight, Using blower scatterplot as point, weight by the distance between blower as side can structural map G (V, E)；

The topological line optimization problem of wind power plant collector system can be stated are as follows: comprising booster stations and several in wind power plant The position of typhoon power generator, booster stations and wind-driven generator is selected, needs to obtain and meets engineering requirements and economy The best wind power plant collector system Topology connection scheme of property, Topology connection optimization problem is a mathematics optimization problem；

Optimum cost target is G the problem of finding figure G (V, the E) for meeting known restrictive condition in mathematics graph theory in subregion V is the set on all vertex figure G in (V, E), represents the position of wind power plant wind power generating set and booster stations herein；E is figure G institute There is the set on side, indicates the cable connection situation between booster stations and wind-driven generator, wind-driven generator and wind-driven generator；Together When figure G (V, E) be a weighted graph, the weight in each edge is the three-dimensional distance weight representated by the side between blower；

Prim algorithm is the minimal spanning tree algorithm for seeking weighting connected graph, the side subset institute structure that minimum tree algorithm search arrives At tree in, not only include all vertex in connected graph, and the weights sum on all sides is also minimum；Therefore Prim is used to calculate Method can be obtained with the most short minimum tree for target of distance, this minimum tree includes all blower nodes, and not will form Ring.

7. a kind of various dimensions according to claim 3 constrain wind power plant collection electric line automatic planning, it is characterised in that: In step 2.4), subregion the lowest cost is asked to plan topologies, specific as follows:

The weight on the side of Prim minimum tree is set as distance, and it is not cable assembly that resulting minimum tree, which is that cable length is minimum, This minimum tree, result still need to further be enhanced, and in order to obtain Optimum cost tree, propose that a kind of dynamic adjusts and optimizes repeatedly Innovatory algorithm, solve the problems, such as in practice because cannot before planning according to trend be distributed selection conductor cross-section, that is, exist Prim, by increasing while with deleting, forms new tree, and whether detect new tree cost more excellent on the basis of minimum tree Mode obtain cost minimization tree；

The basic principle of innovatory algorithm is as follows:

In G (V, E) connected graph, include vertex V={ A, B, C, D, E, F, G }, side E=AB, BC, CD, DE, EF, FA, AG, BG, CG, EG, FG, CE }, the weight on side is the distance between two o'clock weight={ 12,10,3,4,8,14,16,7,6,2,9,5 }, Then according to Prim algorithm, Prim minimum tree is obtained, this minimum tree includes all vertex, and ring, and the distance of whole tree is not present Weight is minimum；The cost for calculating Prim minimum tree is W (k)；

A ring is formed at this time, and { CD, ED } is taken out in remaining side in ring by ring={ CE, ED, CD }, suitable according to from small to large Sequence is ranked up to form queue QE={ CD, ED }, by QE head of the queue CD edge contract, forms a new tree；Calculate the total of this tree Cost, is denoted as W (n), the costs of tree and two adjusted trees more originally, the update set if cost is more excellent, If cost does not reduce, other sides in QE are continued checking, until QE is sky, remaining side in re-inspection QV, until QV is sky；

Optimum cost calculation method is as follows in subregion:

Step 1: Prim algorithm is used, minimum spanning tree is asked；

First according to subregion inner connection mode, obtain the current-carrying capacity of each node cables, according to current-carrying capacity inquiry it is corresponding at This coefficient, add up last calculating whole audience cost；

f_i--- in this collection electric line, the cost coefficient of i-th of route, depending on the current-carrying capacity of cable, unit is ten thousand/km；

Node --- in this collection electric line, the quantity of route；

Cost_m--- the cost of this collection electric line；

Wind power plant whole audience cost:

Cost_m--- the cost of the m articles collection electric line；

f_c--- switchgear cost, unit are ten thousand/screen；

The total number of c --- switchgear；

A --- wind power plant collection electric line totle drilling cost；

Step 4: it is added in T (k) tree from head of the queue is taken out in queue QV, and weight is pressed on other sides in loop resulting from Sequence from small to large is put into queue QE.

Step 6: the total cost for copying step 2 to calculate tree T (n) is denoted as W (n), if W (n) < W (k), then it represents that new tree cost is more Have, tree be updated to the tree that cost more has:

W (k)=W (n), T (k)=T (n)

Then queue QV and QE are emptied and returns to step 3；

If W (n) >=W (k), judge whether queue QE is empty, if otherwise returning to step 5, if judging whether QV is empty, if Empty then to terminate to plan and exit, T (k) is exactly optimum programming as a result, corresponding minimum total cost is W (k), if queue QV is not Sky then returns to step 4.

8. a kind of various dimensions according to claim 1 constrain wind power plant collection electric line automatic planning, it is characterised in that: In step 3), feasible path is explored between subregion, specific as follows:

For fuzzy clustering, some blower can both belong to such, it is also possible to belong to other classes, only belong to the general of different clusters Rate is different, and there is following possibilities during physical planning: this region inner blower point is divided into other subregions Afterwards, and to carry out Topology connection its cost lower, so exploring to feasible path；

Step 2: it filters out the path inside Delaunay connected graph subregion and there is the path intersected, then residual paths are string Between feasible path, will in subregion feasible path be added Optimum cost tree in, obtain new connected graph；

Step 3: the minimum tree algorithm of dynamic is called, final whole audience Optimum cost tree is obtained, if overload, this tree cannot function as Optimal tree.

9. a kind of various dimensions according to claim 1 constrain wind power plant collection electric line automatic planning, it is characterised in that: In step 4), T connects optimization and every trail electric line is individually handled as follows, and steps are as follows:

Step 1: obtaining blower farthest apart from booster stations in string, based on this node, inquires main line, until booster stations:

Step 2: handling main line, since booster stations beginning, detects this node and a upper node and next section The angle of point, if do vertical line if it is acute angle for acute angle, then continue to inquire if it is obtuse angle, until detecting last Fans；

Step 3: handling branch, and whether the angle of detection branch and main line is acute angle, if it is acute angle, does vertical Otherwise line continues to inquire, until branch inquiry finishes；

T is obtained as a result, connects path.