CN116611961A - Micro site selection and fan selection collaborative optimization method for offshore wind farm - Google Patents

Micro site selection and fan selection collaborative optimization method for offshore wind farm Download PDF

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CN116611961A
CN116611961A CN202310900122.5A CN202310900122A CN116611961A CN 116611961 A CN116611961 A CN 116611961A CN 202310900122 A CN202310900122 A CN 202310900122A CN 116611961 A CN116611961 A CN 116611961A
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戚远航
侯鹏
金荣森
罗浩宇
杨之乐
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University of Electronic Science and Technology of China Zhongshan Institute
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Abstract

The invention relates to a fan locating technology in a wind farm, in particular to a microscopic locating and fan type-selecting collaborative optimization method of an offshore wind farm, which comprises the following steps of 1, establishing a wind farm boundary and planning the total capacity of the wind farm; step 2, determining the total number of fans in the whole field when different fans are selected; step 3, establishing a model of fan position space expression; and 4, optimizing the position space based on a particle swarm algorithm. According to the invention, through integrating the total capacity in the wind field and the fan information, considering the characteristics of wake effect, and determining the position of each fan in the wind field through iterative analysis, the invention optimizes the unit type selection and the unit position layout, and provides a layout method with unequal row-column intervals on the layout.

Description

Micro site selection and fan selection collaborative optimization method for offshore wind farm
Technical Field
The invention relates to a fan site selection technology in a wind farm, in particular to a microscopic site selection and fan type selection collaborative optimization method for an offshore wind farm.
Background
The key point of the microscopic site selection of the wind farm is how to arrange fans by considering the influence of wake effect so as to reduce wake loss, the theoretical calculation of the deficiency of wind speed after the wind speed passes through blades is a non-convex nonlinear model, the commonly used model is a Jensen model, the calculation of the deficiency of wind speed by considering the influence of wake among a plurality of fans also needs to judge the mutual influence degree among fans under different wind speeds, and half of the fans adopt a root mean square method, so that the complexity of the whole model is very high. Solving such problems is typically based on heuristic or intelligent algorithms. From the finally formed optimization scheme of microscopic site selection, the fan optimization arrangement is divided into two types: regularized arrangement and discretized arrangement. The regular arrangement generally refers to arrangement of fans in rows and columns, and the discretized arrangement refers to arrangement scheme of fans in a built area without certain rule.
The current regular fan arrangement scheme generally follows the regular arrangement scheme with the same row-to-row spacing and the same column-to-column spacing, and the non-uniform regular arrangement scheme provided by the patent does not appear yet, and is opposite to the arrangement mode with equal distance between rows and columns.
In the current wind field site selection technology, the regular arrangement of the positions and the technology of increasing the energy utilization rate by utilizing wake flow cannot be considered.
Disclosure of Invention
Therefore, the invention provides a cooperative optimization method for microscopic site selection and fan type selection of an offshore wind farm, which is used for solving the problem that the technology of regularly arranging positions and increasing the energy utilization rate by utilizing wake flow cannot be considered in the current wind farm site selection technology in the prior art.
In order to achieve the aim, the invention provides a method for collaborative optimization of microscopic site selection and fan selection of an offshore wind farm, which comprises the following steps,
step S1, determining a wind power plant boundary, and determining the total capacity of the wind power plant according to the wind power plant boundary;
step S2, inputting fan information of a plurality of alternative fans, and determining the total number of fans in the whole wind farm when different fans are selected according to the total capacity of the wind farm and the input fan information;
step S3, establishing a model of position space expression of the corresponding fans according to different types of the selected fans;
and S4, optimizing the position space based on a particle swarm algorithm, and determining the final fan model selection and address selection.
Further, in the step S1, point location information of a plurality of wind farm edge positions is obtained, and sequentially connected clockwise, an area formed by wrapping each point location information is determined as a wind farm boundary, and the determined total capacity of the wind farm is recorded as P WF 。
Further, in the step S2, the types of the candidate fans are numbered and respectively recorded as a first type of candidate fan, a second type of candidate fan, an nth type of candidate fan, and for a jth type of candidate fan in the types of candidate fans, a fan information set { C is set t,j , C p,j , D j ,P j }, wherein C t,j Representing the power coefficient of the j-th class of alternative fans, C p,j Represents the thrust coefficient of the j-th class of alternative fans, D j Representing the diameter of the wind wheel of the blade of the j-th type alternative fan, P j Indicating the rated power of the j-th class of alternative fans.
Further, in the step S2, the total number of fans is determined according to the selected type of the alternative fans, and is set,
，
wherein ,and the number of fans which need to be installed in the whole wind farm when the j-th type of alternative fans are selected is represented.
Further, the step S3 includes,
step S3-1: optionally selecting one point O (xo, yo) as the center of the initial concentric circle in the construction area, and selecting the length R as the initial lengthThe radius of the initial concentric circle is used for generating an initial concentric circle, and two initial straight lines passing through the centers of the initial concentric circles are established and respectively marked as a first initial straight line f 1 A second initial straight line f 2 Wherein a first initial straight line f 1 An included angle with the horizontal direction isA first initial straight line f 1 With a second initial straight line f 2 The included angle is->A first initial straight line f 1 The intersection points with the initial concentric circles are a and a', and a pointer d=1 is set; two straight line equations are set up as +.>And->；
Step S3-2: selecting length Lj, taking O (xo, yo) as a circle center along the Oa direction, taking R+Lj as a radius, generating a concentric circle of an initial concentric circle, and recording the concentric circle and f 1 The intersection points of a1 and a1' and d=d+1;
step S3-3: repeating step 3-2 until all the points of the construction boundary fall in the generated concentric circles, f 1 Intersection points with the concentric circles are sequentially denoted as a1, a2, a3, & gt, an, and a1', a2', a3, & gt, an 'along the Oa' direction, and a plurality of and straight lines f are generated by passing the intersection points a1, a2, a3., an and a1', a2', a3', & gt, an', respectively 2 Straight lines with the same slope;
step S3-4: in a first initial straight line f 1 Generating a plurality of tangent lines at the intersection point of each concentric circle, and solving the sum straight line f and each tangent line 2 And taking each intersection point as a fan site selection point.
Further, in step S3-3, it is determined whether all the points of the construction boundary fall within the generated concentric circles by the convexwell function.
Further, the step S4 includes,
step S4-1: setting the population quantity of particles, and setting the value range of the positions and the speeds of the particles;
step S4-2: initializing the position of the particles and randomly generating the speed of the particles;
step S4-3: calculating the fitness of all particles in the population i;
step S4-4: determining fitness function values at different particle positions, and comparing to obtain global optimal particles Qg and local optimal particles Qi;
step S4-5: entering a particle optimization module after the S4-2 is completed, and gradually finding an optimized solution through information interaction in the process of simulating biological foraging; the particle swarm algorithm belongs to one of intelligent algorithms, and gradient information of functions does not need to be known;
step S4-6: updating the speed and position of the particles;
step S4-7: calculating the fitness of all particles again, comparing with the historical data, and determining an optimal solution;
step S4-8: if the adaptability of the new particles is better than that of the global optimal particles, updating the global optimal particles;
step S4-9: repeating the processes from the step 4-3 to the step 4-7 until the maximum iteration number Nmax is reached; and finally outputting a result.
Further, the position of the particles is an optimized variable, and the optimized variable is xo, yo, R, lj,、/>Q, each optimized variable is a continuous variable, S WT 、D WT Is an integer variable, where q is a scaling factor for adjusting the values of R and Lj, S WT Representing the type of the optimally selected fans for the integer variable, D WT For the length type of the blade of the selected fan type, the range of values of R and Li [4Dj, 10Dj]。
Further, the step S4-3 comprises,
step S4-3-1: optimization according to step 4-1 initializationVariable S WT 、D WT Determining information of a currently selected fan and determining { C (current fan) t,j , C p,j , D j }；
Step S4-3-2: according to xo, yo, R, li,、/>Takes the value of (2) as input, and repeatedly performs step S3;
step S4-3-3: solving each fan coordinate through the wake function model and wind speed distribution data measured by the current wind field, and considering annual capacity of the fan of wake effect;
step S4-3-4: the value of the fitness function is determined.
Further, in the step 3-6, the velocity and position formula of the updated particles is set as follows:
，
wherein The speed of the ith particle at time t; />The historical optimal position of the current particle; />Historical optimal positions, namely global optimal values, of all particles; s is(s) 1 and s2 Is [0, 1]Random numbers in between. Wherein w is inertial weight, l 1 Is a local learning factor, l 2 Is a global learning factor.
Compared with the prior art, the method has the beneficial effects that the method integrates the total capacity and the fan information in the wind field, considers the characteristics of wake effect, determines the positions of the fans in the wind field through iterative analysis, increases the rationality of fan arrangement in the wind field, and enhances the utilization of energy. The invention can optimize the unit selection and unit position layout at the same time, and provides an arrangement method with unequal row-column spacing on the layout, and compared with the existing regularized fan arrangement mode, the two points can improve the productivity of the wind power plant.
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FIG. 1 is a schematic flow chart of a method for collaborative optimization of micro site selection and fan type selection of an offshore wind farm in an embodiment;
FIG. 2 is a plot of fan position after addressing using the collaborative optimization method described in the examples.
Detailed Description
In order that the objects and advantages of the invention will become more apparent, the invention will be further described with reference to the following examples; it should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the invention.
Preferred embodiments of the present invention are described below with reference to the accompanying drawings. It should be understood by those skilled in the art that these embodiments are merely for explaining the technical principles of the present invention, and are not intended to limit the scope of the present invention.
It should be noted that, in the description of the present invention, terms such as "upper," "lower," "left," "right," "inner," "outer," and the like indicate directions or positional relationships based on the directions or positional relationships shown in the drawings, which are merely for convenience of description, and do not indicate or imply that the apparatus or elements must have a specific orientation, be constructed and operated in a specific orientation, and thus should not be construed as limiting the present invention.
Furthermore, it should be noted that, in the description of the present invention, unless explicitly specified and limited otherwise, the terms "mounted," "connected," and "connected" are to be construed broadly, and may be either fixedly connected, detachably connected, or integrally connected, for example; can be mechanically or electrically connected; can be directly connected or indirectly connected through an intermediate medium, and can be communication between two elements. The specific meaning of the above terms in the present invention can be understood by those skilled in the art according to the specific circumstances.
Referring to fig. 1 and 2, fig. 1 is a schematic flow chart of a method for collaborative optimization of micro site selection and fan selection of an offshore wind farm in an embodiment; FIG. 2 is a plot of fan position after addressing using the collaborative optimization method described in the examples.
The invention provides a method for cooperatively optimizing microscopic site selection and fan selection of an offshore wind farm, which comprises the following steps,
step S1, determining a wind power plant boundary, and determining the total capacity of the wind power plant according to the wind power plant boundary;
step S2, inputting fan information of a plurality of alternative fans, and determining the total number of fans in the whole wind farm when different fans are selected according to the total capacity of the wind farm and the input fan information;
step S3, establishing a model of position space expression of the corresponding fans according to different types of the selected fans;
and S4, optimizing the position space based on a particle swarm algorithm, and determining the final fan model selection and address selection.
Further, in the step S1, point location information of a plurality of wind farm edge positions is obtained, and sequentially connected clockwise, an area formed by wrapping each point location information is determined as a wind farm boundary, and the determined total capacity of the wind farm is recorded as P WF In this embodiment, the number of point location information is seven, and the boundary point of the wind field is A, B, C, D, E, F, G.
Further, in the step S2, the types of the candidate fans are numbered and respectively recorded as a first type of candidate fan, a second type of candidate fan, an nth type of candidate fan, and for a jth type of candidate fan in the types of candidate fans, a fan information set { C is set t,j , C p,j , D j ,P j }, wherein C t,j Representing the power coefficient of the j-th class of alternative fans, C p,j Represents the thrust coefficient of the j-th class of alternative fans, D j Representing the diameter of the wind wheel of the blade of the j-th type alternative fan, P j Indicating the rated power of the j-th class of alternative fans.
Further, in the step S2, the total number of fans is determined according to the selected type of the alternative fans, and is set,
，
wherein ,and the number of fans which need to be installed in the whole wind farm when the j-th type of alternative fans are selected is represented.
Further, the step S3 includes,
step S3-1: step S3-1: randomly selecting one point O (xo, yo) as the center of an initial concentric circle in a construction area, selecting the length R as the radius of the initial concentric circle, generating the initial concentric circle, establishing two initial straight lines passing through the center of the initial concentric circle and respectively marking the initial straight lines as first initial straight lines f 1 A second initial straight line f 2 Wherein a first initial straight line f 1 An included angle with the horizontal direction isA first initial straight line f 1 With a second initial straight line f 2 The included angle is->A first initial straight line f 1 The intersection points with the initial concentric circles are a and a', and a pointer d=1 is set; two straight line equations are set up as +.>And->；
Step S3-2: selecting length Lj, taking O (xo, yo) as a circle center along the Oa direction, taking R+Lj as a radius, generating a concentric circle of an initial concentric circle, and recording the concentric circle and f 1 The intersection points of a1 and a1' and d=d+1;
step S3-3: repeating step 3-2 until all the points of the construction boundary fall in the generated concentric circles, f 1 The intersections with the concentric circles are denoted as a1, a2, a3, a.and an in order along the Oa direction, and in order along the Oa' directionDenoted as a1', a2', a3', an ' are generated by crossing points a1, a2, a3., an and a1', a2', a3' 2 Straight lines with the same slope;
step S3-4: in a first initial straight line f 1 Generating a plurality of tangent lines at the intersection point of each concentric circle, and solving the sum straight line f and each tangent line 2 And taking each intersection point as a fan site selection point.
Specifically, in step S3-3, it is determined whether all the points of the construction boundary fall within the generated concentric circles by the convexhall function.
Specifically, the step S4 includes,
step S4-1: setting the population quantity of particles, and setting the value range of the positions and the speeds of the particles;
step S4-2: initializing the position of the particles and randomly generating the speed of the particles;
step S4-3: calculating the fitness of all particles in the population i;
step S4-4: determining fitness function values at different particle positions, and comparing to obtain global optimal particles Qg and local optimal particles Qi;
step S4-5: entering a particle optimization module after the S4-2 is completed, and gradually finding an optimized solution through information interaction in the process of simulating biological foraging;
step S4-6: updating the speed and position of the particles;
step S4-7: calculating the fitness of all particles again, comparing with the historical data, and determining an optimal solution;
step S4-8: if the adaptability of the new particles is better than that of the global optimal particles, updating the global optimal particles;
step S4-9: repeating the processes from the step 4-3 to the step 4-7 until the maximum iteration number Nmax is reached; and finally outputting a result.
Specifically, the position of the particles is an optimized variable, and the optimized variable is xo, yo, R, lj,、/>Q, each optimized variable is a continuous variable, S WT 、D WT Is an integer variable, where q is a scaling factor for adjusting the values of R and Lj, S WT Representing the type of the optimally selected fans for the integer variable, D WT For the length type of the blade of the selected fan type, the range of values of R and Li [4Dj, 10Dj]。
Specifically, the step S4-3 comprises,
step S4-3-1: optimization variable S initialized according to step 4-1 WT 、D WT Determining information of a currently selected fan and determining { C (current fan) t,j , C p,j , D j }；
Step S4-3-2: according to xo, yo, R, li,、/>Takes the value of (2) as input, and repeatedly performs step S3;
step S4-3-3: solving each fan coordinate through the wake function model and wind speed distribution data measured by the current wind field, and considering annual capacity of the fan of wake effect;
step S4-3-4: the value of the fitness function is determined.
Specifically, in the step 3-6, the velocity and position formula of the updated particles is set as follows:
，
wherein The speed of the ith particle at time t; />The historical optimal position of the current particle; />Historical optimal positions, namely global optimal values, of all particles; s is(s) 1 and s2 Is [0, 1]Random numbers in between. Wherein w is inertial weight, l 1 Is a local learning factor, l 2 Is a global learning factor.
Compared with the prior art, the method has the beneficial effects that the method integrates the total capacity and the fan information in the wind field, considers the characteristics of wake effect, determines the positions of the fans in the wind field through iterative analysis, increases the rationality of fan arrangement in the wind field, and enhances the utilization of energy.
Thus far, the technical solution of the present invention has been described in connection with the preferred embodiments shown in the drawings, but it is easily understood by those skilled in the art that the scope of protection of the present invention is not limited to these specific embodiments. Equivalent modifications and substitutions for related technical features may be made by those skilled in the art without departing from the principles of the present invention, and such modifications and substitutions will be within the scope of the present invention.
The foregoing description is only of the preferred embodiments of the invention and is not intended to limit the invention; various modifications and variations of the present invention will be apparent to those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims (9)

1. A method for cooperatively optimizing microscopic site selection and fan selection of an offshore wind farm is characterized by comprising the steps of,
step S1, determining a wind power plant boundary, and determining the total capacity of the wind power plant according to the wind power plant boundary;
step S2, inputting fan information of a plurality of alternative fans, and determining the total number of fans in the whole wind farm when different fans are selected according to the total capacity of the wind farm and the input fan information;
step S3, establishing a model of position space expression of the corresponding fans according to different types of the selected fans;
step S4, optimizing the position space based on a particle swarm algorithm, and determining the final fan model selection and address selection;
the step S3 of this method comprises the steps of,
step S3-1: randomly selecting one point O (xo, yo) as the center of an initial concentric circle in a construction area, selecting the length R as the radius of the initial concentric circle, generating the initial concentric circle, establishing two initial straight lines passing through the center of the initial concentric circle and respectively marking the initial straight lines as first initial straight lines f 1 A second initial straight line f 2 Wherein a first initial straight line f 1 An included angle with the horizontal direction isA first initial straight line f 1 With a second initial straight line f 2 The included angle is->A first initial straight line f 1 The intersection points with the initial concentric circles are a and a', and a pointer d=1 is set; two straight line equations are set up as +.>And->；
Step S3-2: selecting length Lj, taking O (xo, yo) as a circle center along the Oa direction, taking R+Lj as a radius, generating a concentric circle of an initial concentric circle, and recording the concentric circle and f 1 The intersection points of a1 and a1' and d=d+1;
step S3-3: repeating step 3-2 until all the points of the construction boundary fall in the generated concentric circles, f 1 Intersection points with the concentric circles are sequentially denoted as a1, a2, a3, & gt, an, and a1', a2', a3, & gt, an 'along the Oa' direction, and a plurality of and straight lines f are generated by passing the intersection points a1, a2, a3., an and a1', a2', a3', & gt, an', respectively 2 Straight lines with the same slope;
step S3-4: in a first initial straight line f 1 Generating a plurality of tangent lines at the intersection point of each concentric circle, and solving the sum straight line f and each tangent line 2 And taking each intersection point as a fan site selection point.
2. The method for collaborative optimization of microscopic site selection and fan type selection of an offshore wind farm according to claim 1, wherein in the step S1, point location information of a plurality of wind farm edge positions is obtained and sequentially connected clockwise, a region formed by wrapping each point location information is determined as a wind farm boundary, and the determined total capacity of the wind farm is recorded as P WF 。
3. The method according to claim 2, wherein in the step S2, the types of the candidate fans are numbered and respectively denoted as a first type of candidate fan and a second type of candidate fan, and a third N type of candidate fan, and for the j type of candidate fan, a fan information set { C t,j , C p,j , D j , P j }, wherein C t,j Representing the power coefficient of the j-th class of alternative fans, C p,j Represents the thrust coefficient of the j-th class of alternative fans, D j Representing the diameter of the wind wheel of the blade of the j-th type alternative fan, P j Indicating the rated power of the j-th class of alternative fans.
4. A method for collaborative optimization of microscopic site selection and wind farm selection according to claim 3, wherein in step S2, the total number of wind turbines is determined according to the selected candidate wind turbine types, and is set,
，
wherein ,and the number of fans which need to be installed in the whole wind farm when the j-th type of alternative fans are selected is represented.
5. The collaborative optimization method for micro-site selection and fan selection of an offshore wind farm according to claim 4, wherein in step S3-3, it is determined whether all the points of the construction boundary fall within the generated concentric circles by means of a convexwell function.
6. The method for collaborative optimization of micro-site selection and fan selection of an offshore wind farm according to claim 5, wherein step S4 comprises,
step S4-1: setting the population quantity of particles, and setting the value range of the positions and the speeds of the particles;
step S4-2: initializing the position of the particles and randomly generating the speed of the particles;
step S4-3: calculating the fitness of all particles in the population i;
step S4-4: determining fitness function values at different particle positions, and comparing to obtain global optimal particles Qg and local optimal particles Qi;
step S4-5: entering a particle optimization module after the S4-2 is completed, and gradually finding an optimized solution through information interaction in the process of simulating biological foraging;
step S4-6: updating the speed and position of the particles;
step S4-7: calculating the fitness of all particles again, comparing with the historical data, and determining an optimal solution;
step S4-8: if the adaptability of the new particles is better than that of the global optimal particles, updating the global optimal particles;
step S4-9: repeating the processes from the step 4-3 to the step 4-7 until the maximum iteration number Nmax is reached; and finally outputting a result.
7. The collaborative optimization method for microscopic site selection and fan selection of an offshore wind farm according to claim 6, wherein the position of the particles is an optimized variable, and the optimized variable is xo, yo, R, lj,、/>Q, each optimized variable is a continuous variable, S WT 、D WT Is an integer variable, where q is a scaling factor for adjusting the values of R and Lj, S WT Representing the type of the optimally selected fans for the integer variable, D WT For the length type of the blade of the selected fan type, the range of values of R and Li [4Dj, 10Dj]。
8. The method for collaborative optimization of micro-site selection and fan selection of an offshore wind farm according to claim 7, wherein step S4-3 comprises,
step S4-3-1: optimization variable S initialized according to step 4-1 WT 、D WT Determining information of a currently selected fan and determining { C (current fan) t,j , C p,j , D j }；
Step S4-3-2: according to xo, yo, R, li,、/>Takes the value of (2) as input, and repeatedly performs step S3;
step S4-3-3: solving each fan coordinate through the wake function model and wind speed distribution data measured by the current wind field, and considering annual capacity of the fan of wake effect;
step S4-3-4: the value of the fitness function is determined.
9. The method for collaborative optimization of micro-site selection and fan selection of an offshore wind farm according to claim 8, wherein in the steps 3-6, the velocity and position formula of the updated particles is set as follows:
，
wherein The speed of the ith particle at time t; />The historical optimal position of the current particle; />Historical optimal positions, namely global optimal values, of all particles; s is(s) 1 and s2 Is [0, 1]Random numbers in between; wherein w is inertial weight, l 1 Is a local learning factor, l 2 Is a global learning factor.
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