CN109871972B - Optimal arrangement method and system for fans in wind field - Google Patents
Optimal arrangement method and system for fans in wind field Download PDFInfo
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Abstract
The invention discloses an optimal arrangement method and system of fans in a wind field, wherein the method comprises the following steps: acquiring meteorological and geographical conditions of a wind field and fan parameters to be selected; determining at least one optimization objective and at least one optimization constraint; constructing a rectangular area according to the wind field area, dividing the rectangular area into a plurality of grids, and setting that the fan can only be positioned at the center of each grid; writing an effective position array matrix corresponding to the shape of the grid of the rectangular area and the wind field area, constructing a solution space by the design variables to be optimized in the effective position array matrix, searching an optimal fan arrangement scheme in the solution space according to a target function by applying a binary discrete particle swarm optimization algorithm, and obtaining the optimal fan arrangement scheme after optimization.
Description
The technical field is as follows:
the invention relates to the field of wind power, in particular to an optimal arrangement method and system for fans in a wind field.
Background art:
with the continuous increase of energy demand and the continuous temperature rise of wind power generation tides, the early planning and development of wind power plants are more and more emphasized. The optimal arrangement of the fans is a key link in the early planning of the wind power plant, and directly influences the utilization rate of wind resources, the utilization rate of land resources and the economic level of the wind power plant. For a wind power plant with a given total floor area, if the mutual influence of the wake flows of all the fans is not considered, the more the number of the arranged fans is, the larger the total power generation amount is, and the better the economy is. However, in practical situations, due to wake effect, the upstream fan will affect the downstream fan, that is, the wind speed at the hub is reduced and the turbulence is increased, so that the number of fans that can be installed in a wind field with a fixed area is limited, and an optimal value exists, so that the economy of the wind field is optimal. Therefore, according to the wind energy resource condition at the site of the wind power plant, after the model of the wind turbine is selected, the arrangement number and the position of the wind turbine are reasonably determined, and the method is very important for improving the economy of the wind power plant.
Commercial software is widely used in the engineering field to solve the problem of planning wind farms. The commercial software has the function of an automatic cloth machine scheme, namely after a designer inputs parameters such as installed capacity of a wind farm, the number of fans, the distance between fans and the like, the software can automatically generate the cloth machine scheme, and the designer often excessively depends on the function. The principle of the automatic power distribution scheme is that software firstly searches a first machine position with the best power generation amount in the wind power plant, after the machine position is searched and fixed, the software starts to search a second machine position with the largest power generation amount, after the machine position is searched and fixed, the software continues to search a third machine position … … and the like, and finally a layout scheme of the whole wind power plant is generated. However, the scheme is limited to the single-machine power generation amount of certain fans, and the comprehensive power generation amount of the whole wind field is not considered, so that the cloth machine method does not belong to an optimization method, and the scheme of the finally obtained cloth machine is not an optimal scheme.
In addition, the wind driven generator of the existing wind field is generally a single-impeller wind driven generator, the generating efficiency is low, and the generating efficiency can be only indirectly adjusted by adjusting the inclination angle of the blades and the size of the impeller. Therefore, it is urgently needed to provide a dual-impeller fan structure to select and adjust the impellers according to the wind speed, so as to indirectly improve the wind energy utilization rate, and a method for optimally arranging the fan with the dual-impeller structure is also a great problem.
The invention content is as follows:
the invention aims to provide an optimal arrangement method and system for fans in a wind farm, which automatically generate an optimal arrangement scheme with the minimum electricity consumption cost or the maximum total generated energy based on a binary discrete particle swarm optimization algorithm by considering multidimensional data influencing fan arrangement according to the wind energy resource condition at a site of the wind farm. Meanwhile, an improved double-impeller wind driven generator and an optimized arrangement method thereof are provided, and the wind energy utilization efficiency is greatly improved.
The invention is implemented by the following technical scheme:
in a first aspect, the present invention provides a method for optimizing arrangement of fans in a wind farm, including:
step S1, acquiring meteorological and geographical conditions of a wind field and fan parameters to be selected;
step S2, determining at least one optimization objective and at least one optimization constraint;
step S3, constructing a rectangular area according to the wind field area, dividing the rectangular area into a plurality of grids, and setting that the fan can only be positioned at the center of each grid;
step S4, writing an effective position array matrix corresponding to the grids of the rectangular area and the shape of the wind field area, wherein in the effective position array matrix, each element takes the value of 1 or 0, 1 represents the position where the fan can be arranged, 0 represents the position where the fan cannot be arranged, and the position where the fan cannot be arranged corresponds to the place where the geographical conditions in the wind field area are not suitable for arranging the fan and the place outside the wind field area;
and S5, constructing a solution space by using the design variables to be optimized, searching an optimal fan arrangement scheme in the solution space according to a target function by using a binary discrete particle swarm optimization algorithm, and obtaining the optimal fan arrangement scheme after optimization.
The invention provides an optimal arrangement method of fans in a wind field, which adopts the technical scheme that: acquiring meteorological and geographical conditions of a wind field and fan parameters to be selected; determining at least one optimization objective and at least one optimization constraint; constructing a rectangular area according to the wind field area, dividing the rectangular area into a plurality of grids, and setting that the fan can only be positioned at the center of each grid; writing an effective position array matrix corresponding to the grids of the rectangular area and the shape of the wind field area, wherein in the effective position array matrix, each element takes the value of '1' or '0', the '1' represents the position where a fan can be arranged, the '0' represents the position where the fan cannot be arranged, and the position where the fan cannot be arranged corresponds to the place where the geographical conditions in the wind field area are not suitable for arranging the fan and the place outside the wind field area; and constructing a solution space by using the design variables to be optimized, searching an optimal fan arrangement scheme in the solution space according to a target function by using a binary discrete particle swarm optimization algorithm, and optimizing to obtain the optimal fan arrangement scheme.
According to the optimal arrangement method of the fans in the wind farm, the optimal arrangement scheme with the minimum electricity consumption cost or the maximum total generated energy is automatically generated based on the binary discrete particle swarm optimization algorithm by considering the multidimensional data influencing the arrangement of the fans according to the wind energy resource condition at the site of the wind farm.
Further, the meteorological and geographical conditions of the wind field to be optimized comprise the size and shape of the wind field, the surface roughness z0A wind rose plot including summer season wind resource data and winter season wind resource data, and a wind speed wind frequency distribution.
Further, at least one optimization objective is determined, specifically:
calculating by using a cost model, establishing a function of the total construction cost of the wind power plant and the number of fans, and calculating the total power generation of the wind power plant based on a Jensen wake flow model;
calculating to obtain a target function according to the total construction cost of the wind power plant and the total power generation amount of the wind power plant;
and optimizing the objective function by adopting a genetic algorithm, randomly generating an initial population consisting of 800 individuals in an initial state, sequentially carrying out individual fitness evaluation, selection operation, cross operation and variation operation to obtain 20 optimal offspring, and carrying out recycling calculation until the objective function converges to obtain an optimal solution, thus obtaining the minimum value of the objective function, and taking the lowest cost of unit power generation as an optimization target.
Further, the calculation of the total power generation amount of the wind power plant based on the Jensen wake flow model specifically comprises the following steps:
obtaining the kinetic energy loss of a wake area by using a certain fan in the wind area, wherein the fan is influenced by the wake of a plurality of peripheral units, and the kinetic energy loss of the wake area is obtained by the superposition of the kinetic energy loss caused by the upstream to the fan;
and calculating to obtain the total generated energy of the wind power plant according to the number of the fans installed in the wind power plant.
Further, the fan in the wind field is a double-impeller wind driven generator, and the double-impeller wind driven generator comprises:
the first-stage impeller, the second-stage impeller and the impeller rotating speed combining mechanism; the impeller rotating speed merging mechanism is provided with a first input shaft, a second input shaft, a first output shaft and a second output shaft, the first-stage impeller is in driving connection with the first input shaft, the second-stage impeller is in driving connection with the second input shaft, the first output shaft is in driving connection with the input shaft of the first generator through a first clutch, and the second output shaft is in driving connection with the input shaft of the second generator through a second clutch;
the first-stage impeller is coaxially connected with the second-stage impeller, the length of blades of the first-stage impeller is greater than that of blades of the second-stage impeller, the rotating directions of the first-stage impeller and the second-stage impeller are opposite during working, and the first-stage impeller is positioned in front of the second-stage impeller;
the impeller rotating speed combining mechanism comprises a sun gear, a gear ring and a planet carrier which are coaxially arranged, a plurality of planet gears are arranged on the planet carrier, the gear ring is provided with inner teeth and outer teeth, the planet gears are meshed between the inner teeth of the gear ring and the sun gear, a driving gear is arranged on the first input shaft and is meshed with the outer teeth of the gear ring, the second input shaft is connected with a rotating shaft of the sun gear, the rotating shaft of the planet carrier is in driving connection with an output shaft through an intermediate shaft, one end of the output shaft forms the first output shaft, and the other end of the output shaft forms the second output shaft;
when the wind speed is less than a first threshold value, blades of the first-stage impeller and blades of the second-stage impeller are subjected to pitch variation, the first-stage impeller stops generating electricity, the second-stage impeller is in a rotating electricity generation state, the first clutch is in a meshing state, and the second clutch is in a separation state;
when the wind speed is not less than the first threshold value and not more than the second threshold value, blades of the first-stage impeller and the second-stage impeller are subjected to pitch variation, so that the first-stage impeller and the second-stage impeller are both in a rotating power generation state, the first clutch is in a meshing state, and the second clutch is in a separating state;
when the wind speed is greater than the second threshold value, the first-stage impeller and the second-stage impeller are both in a rotating power generation state, and the first clutch and the second clutch are both in a meshing state.
In a second aspect, the present invention provides an optimized arrangement system of fans in a wind farm, including:
the multi-dimensional data acquisition module is used for acquiring meteorological and geographical conditions of a wind field and fan parameters to be selected;
an optimization condition generation module for determining at least one optimization objective and at least one optimization constraint;
the rectangular area generating module is used for constructing a rectangular area according to the wind field area, dividing the rectangular area into a plurality of grids, and setting that the fan can only be positioned in the middle of each grid;
the fan position arrangement module is used for compiling an effective position array matrix corresponding to the grids of the rectangular area and the shape of the wind field area, wherein in the effective position array matrix, each element takes the value of 1 or 0, 1 represents the position where a fan can be arranged, 0 represents the position where the fan cannot be arranged, and the position where the fan cannot be arranged corresponds to the place where the geographical conditions in the wind field area are not suitable for arranging the fan and the place outside the wind field area;
and the fan arrangement scheme generation module is used for constructing a solution space according to the design variables to be optimized, searching an optimal fan arrangement scheme in the solution space according to a target function by using a binary discrete particle swarm optimization algorithm, and obtaining the optimal fan arrangement scheme after optimization.
The invention provides an optimized arrangement system of fans in a wind field, which adopts the technical scheme that: acquiring meteorological and geographical conditions of a wind field and fan parameters to be selected through a multi-dimensional data acquisition module; determining at least one optimization objective and at least one optimization constraint through an optimization condition generation module; through a rectangular area generation module, a rectangular area is constructed according to a wind field area, the rectangular area is divided into a plurality of grids, and a fan is set to be positioned only in the center of each grid; writing an effective position array matrix corresponding to the grids of the rectangular area and the shape of the wind field area through a fan position arrangement module, wherein in the effective position array matrix, each element takes the value of 1 or 0, the 1 represents the position where a fan can be arranged, the 0 represents the position where the fan cannot be arranged, and the position where the fan cannot be arranged corresponds to the place where the geographical conditions in the wind field area are not suitable for arranging the fan and the place outside the wind field area; and constructing a solution space by using the design variables to be optimized through a fan arrangement scheme generation module, searching an optimal fan arrangement scheme in the solution space according to a target function by using a binary discrete particle swarm optimization algorithm, and obtaining the optimal fan arrangement scheme after optimization.
According to the optimal arrangement system of the fans in the wind farm, the optimal arrangement scheme with the minimum electricity consumption cost or the maximum total generated energy is automatically generated based on the binary discrete particle swarm optimization algorithm by considering the multidimensional data influencing the arrangement of the fans according to the wind energy resource condition at the site of the wind farm.
Further, in the multidimensional data acquisition module, the meteorological and geographic conditions of the wind field to be optimized comprise the size and shape of the wind field and the surface roughness z0A wind rose plot including summer season wind resource data and winter season wind resource data, and a wind speed wind frequency distribution.
Further, the optimization condition generation module is specifically configured to determine at least one optimization objective:
calculating by using a cost model, establishing a function of the total construction cost of the wind power plant and the number of fans, and calculating the total power generation of the wind power plant based on a Jensen wake flow model;
calculating to obtain a target function according to the total construction cost of the wind power plant and the total power generation amount of the wind power plant;
and optimizing the objective function by adopting a genetic algorithm, randomly generating an initial population consisting of 800 individuals in an initial state, sequentially carrying out individual fitness evaluation, selection operation, cross operation and variation operation to obtain 20 optimal offspring, and carrying out recycling calculation until the objective function converges to obtain an optimal solution, thus obtaining the minimum value of the objective function, and taking the lowest cost of unit power generation as an optimization target.
Further, the optimization condition generation module is specifically configured to calculate the total power generation amount of the wind farm based on a Jensen wake model:
obtaining the kinetic energy loss of a wake area by using a certain fan in the wind area, wherein the fan is influenced by the wake of a plurality of peripheral units, and the kinetic energy loss of the wake area is obtained by the superposition of the kinetic energy loss caused by the upstream to the fan;
and calculating to obtain the total generated energy of the wind power plant according to the number of the fans installed in the wind power plant.
The invention has the advantages that:
according to the wind energy resource condition at the site of the wind power plant, the multi-dimensional data influencing the arrangement of the fans are considered, and the optimal arrangement scheme with the minimum electricity consumption cost or the maximum total electricity generation amount is automatically generated based on the binary discrete particle swarm optimization algorithm. Meanwhile, the wind energy utilization efficiency is improved by improving the double-impeller fan, and an optimal configuration method suitable for a wind field of the double-impeller fan is correspondingly provided.
Description of the drawings:
in order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
Fig. 1 is a flowchart of a method for optimizing a fan layout based on multidimensional wind field data according to an embodiment of the present invention;
FIG. 2 is a schematic diagram of a system for optimizing a fan layout based on multi-dimensional wind field data according to an embodiment of the present invention;
fig. 3 is a schematic structural diagram of a dual-impeller wind turbine provided in the third embodiment;
fig. 4 is a schematic diagram of an impeller rotation speed combining mechanism provided in the third embodiment.
The specific implementation mode is as follows:
the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Example one
In a first aspect, fig. 1 shows a flowchart of a method for optimizing a fan layout based on multidimensional wind field data according to an embodiment of the present invention; as shown in fig. 1, an embodiment provides a method for optimizing a fan layout based on multidimensional wind field data, including:
step S1, acquiring meteorological and geographical conditions of a wind field and fan parameters to be selected;
step S2, determining at least one optimization objective and at least one optimization constraint;
step S3, constructing a rectangular area according to the wind field area, dividing the rectangular area into a plurality of grids, and setting that the fan can only be positioned at the center of each grid;
step S4, writing an effective position array matrix corresponding to the shape of the grid of the rectangular area and the wind field area, wherein in the effective position array matrix, each element takes the value of '1' or '0', the '1' represents the position where the fan can be arranged, the '0' represents the position where the fan cannot be arranged, and the position where the fan cannot be arranged corresponds to the place where the geographical conditions are not suitable for arranging the fan in the wind field area and the place outside the wind field area;
and S5, constructing a solution space by using the design variables to be optimized, searching an optimal fan arrangement scheme in the solution space according to a target function by using a binary discrete particle swarm optimization algorithm, and obtaining the optimal fan arrangement scheme after optimization.
The invention provides a fan arrangement optimization method based on multi-dimensional wind field data, which has the technical scheme that: acquiring meteorological and geographical conditions of a wind field and fan parameters to be selected; determining at least one optimization objective and at least one optimization constraint; constructing a rectangular area according to the wind field area, dividing the rectangular area into a plurality of grids, and setting that the fan can only be positioned at the center of each grid; writing an effective position array matrix corresponding to the grids of the rectangular area and the shape of the wind field area, wherein in the effective position array matrix, each element takes the value of '1' or '0', the '1' represents the position where the fan can be arranged, the '0' represents the position where the fan cannot be arranged, and the position where the fan cannot be arranged corresponds to the place where the geographical conditions are not suitable for arranging the fan in the wind field area and the place outside the wind field area; and constructing a solution space by using the design variables to be optimized, searching an optimal fan arrangement scheme in the solution space according to a target function by using a binary discrete particle swarm optimization algorithm, and optimizing to obtain the optimal fan arrangement scheme.
According to the method for optimizing the fan arrangement based on the multi-dimensional wind field data, the multi-dimensional data influencing the fan arrangement is considered according to the wind energy resource condition at the site of the wind power field, and the optimal arrangement scheme with the minimum electricity consumption cost or the maximum total generated energy is automatically generated based on the binary discrete particle swarm optimization algorithm.
The rectangular area is constructed by taking the maximum north-south distance and the maximum east-west distance of the wind field area as side lengths.
As a preferred embodiment of the invention, the meteorological and geographical conditions of the wind farm to be optimized include the size and shape of the wind farm, the surface roughness z0Wind rose diagram and wind speed wind frequency distribution, and includes summer season wind resource data and winter season wind resource data from the wind rose diagram.
In consideration of the characteristic that the main wind energy directions in summer and winter are basically vertical, wake flow values obtained by utilizing the summer-season wind resource data and the winter-season wind resource data are compared, the position of a fan with the difference exceeding a first threshold value and the single-machine wake flow obtained by utilizing the summer-season wind resource calculation exceeding a second threshold value is re-optimized, the interval of the fans in the main wind energy directions in the vertical winter is mainly properly increased, and the interval of the fans in the main wind energy directions in the parallel winter is properly reduced.
Wherein the fan parameter to be selected comprises the hub diameter D0Hub height h and power-speed curve.
The at least one optimization constraint is a minimum fan distance, the minimum fan distance is determined according to the terrain of a wind field, the soil bearing capacity and/or the dynamic load borne by the fan, the minimum fan distance can also be simplified to be m times of the diameter of a fan wind wheel, and the value of m is between 3 and 5.
Wherein, the length of side of net equals the fan minimum distance.
The design variables to be optimized comprise the total number of installed fans in the wind farm and the specific arrangement position of each fan.
Wherein the objective function is that the electricity cost is lowest or the total electricity generation is largest.
As a preferred embodiment of the present invention, at least one optimization objective is determined, specifically:
calculating by using a cost model, establishing a function of the total construction cost of the wind power plant and the number of fans, and calculating the total power generation of the wind power plant based on a Jensen wake flow model;
calculating to obtain a target function according to the total construction cost of the wind power plant and the total power generation amount of the wind power plant;
and optimizing the objective function by adopting a genetic algorithm, randomly generating an initial population consisting of 800 individuals in an initial state, sequentially carrying out individual fitness evaluation, selection operation, cross operation and variation operation to obtain 20 optimal offspring, and carrying out recycling calculation until the objective function converges to obtain an optimal solution to obtain the minimum value of the objective function, wherein the minimum value is used as an optimization target with the lowest unit generating capacity cost.
As a preferred embodiment of the present invention, the calculation of the total power generation amount of the wind farm based on the Jensen wake model specifically comprises:
obtaining the kinetic energy loss of a wake zone by using a certain fan in a wind field area, wherein the fan is influenced by the wake of a plurality of peripheral units, and the kinetic energy loss of the wake zone is obtained by the superposition of the kinetic energy loss of the upstream to the fans;
and calculating to obtain the total generated energy of the wind power plant according to the number of the fans installed in the wind power plant.
Wherein, the kinetic energy loss of the wake zone is calculated by the following formula:
when N fans are installed in the wind power plant, the total generated energy of the whole wind power plant is as follows:
as a preferred embodiment of the present invention, the total construction cost of the wind farm specifically is:
acquiring the investment cost of a single fan, namely tempering, and the proportion of the cost of installing a large number of fans in the wind power plant to the total investment of the wind power plant;
and calculating to obtain the total construction cost of the wind power plant according to the number of the fans installed in the wind power plant and by combining the toughening of the investment cost of a single fan and the proportion of the cost of installing a large number of fans in the wind power plant to the total investment of the wind power plant.
Assuming that the total investment cost of the wind power plant is only a function related to the number of arranged wind turbines, if the investment cost of a single wind turbine is quantified as unit 1, the single-machine investment cost can be reduced to 2/3 at most when a large number of wind turbines are arranged, and the investment cost of the wind power plant can be expressed by the following formula:
wherein f iscFor investment costs of wind farms, NtIs the total number of the fans.
Wherein, in step S5, the binary discrete particle swarm optimization algorithm comprises the following sub-steps,
(a) a plurality of particles are arranged in the population, and each particle represents a fan arrangement scheme; each particle has a position vector and a velocity vector and comprises a plurality of dimensions, the dimension number is the same as the number of all elements in the effective position array matrix, the value of each dimension is 1 or 0, and the particles respectively correspond to positions where a fan can be placed or not placed;
(b) randomly generating the initial position and the initial speed of each particle in the population, limiting the maximum speed of the particle at any moment, and setting an iteration termination condition;
(c) recording the current iteration step number, calculating the objective function value of each particle under the current iteration step number, updating the historical optimal value of each particle, and updating the global optimal value under the current iteration step number;
(d) sequentially judging whether each element of the effective position array matrix is 0: if the value is 0, forcibly updating the value of the particle dimension corresponding to the element to be 0; if not, the velocity of the particle is updated according to equation 1, and the position of the particle is updated according to equation 2:
wherein: v. ofnew、xnewThe updated particle velocity and position; v. ofold、xoldThe speed and the position of the particles in the last iteration step are obtained; i is a particle number; d is the particle dimension; w is the inertial weight; c1, c2 and c3 are learning factors; r1, r2 and rho are random numbers between 0 and 1; p is a radical ofidThe historical best value of the d d dimension for the particle numbered i; p is a radical ofgdFor the current iterationOptimal value of d dimension of all particles under step number;
at each iteration, the inertia weight w value in equation (4) is dynamically adjusted according to equation (7):
wherein: w is amax、wminThe maximum and minimum inertia weight limit values are obtained; n is a radical ofcurIs the current iteration step number; n is the maximum number of iteration steps;
(e) re-evaluating the objective function value of each particle, and updating the historical optimal value of each particle and the global optimal value of the particle swarm according to the objective function value;
(f) and (d) judging whether the program is converged, finishing the algorithm iteration when the maximum iteration step number is reached or the convergence condition is met, and otherwise, turning to the step (d).
Wherein the maximum velocity V of the particles at any timemaxIs 20 to 40 percent of the feasible solution area.
Wherein the iteration termination condition is that the iteration times reach the maximum iteration times.
Considering the calculation speed and accuracy together, it is preferable to use analytic form wake models for the single-fan wake to describe the wake effect of the fan, such as PARK wake model, modified PARK wake model, etc. In the PARK model and the modified PARK model, the influence of turbulence effect is not considered in the fluid calculation characteristics in the near wake region, the wind speed is considered to be uniformly distributed along the section after the wind wheel, and the wake influence region linearly expands along with the distance from the plane of the wind wheel. For the case that the straight line distance of the upstream and downstream fans is more than 4 times the diameter of the wind wheel:
D(x)＝D0+2KX(8)
where D (x) is the diameter of the wake effect area downstream of the fan at x, U (x) is the wind speed at the hub of the fan downstream of x, U0 is the incoming flow velocity, Ct is the thrust coefficient, D0 is the rotor diameter, k is the wake expansion coefficient, h is the hub height, zo is the surface roughness, and A is an empirical constant (typically 0.5).
For the case that the downstream fan is positioned in a plurality of upstream fan wake areas simultaneously, the invention proposes to adopt a speed loss square linear superposition mode to process the speed calculation problem of the wake flow overlapping area. The inflow wind speed of the downstream fan is calculated using equation (11):
wherein: u shape0For free incoming flow wind speed, UiAnd U is the wind speed at the hub of the downstream fan.
Referring to fig. 2, in a second aspect, the present invention provides a system 100 for optimizing a fan layout based on multi-dimensional wind farm data, comprising:
the multi-dimensional data acquisition module 101 is used for acquiring meteorological and geographical conditions of a wind field and fan parameters to be selected;
an optimization condition generation module 102, configured to determine at least one optimization objective and at least one optimization constraint;
the rectangular area generating module 103 is used for constructing a rectangular area according to the wind field area, dividing the rectangular area into a plurality of grids, and setting that the fan can only be positioned in the center of each grid;
the fan position arrangement module 104 is used for compiling an effective position array matrix corresponding to the grids of the rectangular area and the shape of the wind field area, wherein in the effective position array matrix, each element takes the value of 1 or 0, 1 represents the position where a fan can be arranged, 0 represents the position where the fan cannot be arranged, and the position where the fan cannot be arranged corresponds to the place where the geographical conditions are not suitable for arranging the fan in the wind field area and the place outside the wind field area;
and the fan arrangement scheme generation module 105 is used for constructing a solution space according to the design variables to be optimized, searching an optimal fan arrangement scheme in the solution space according to a target function by using a binary discrete particle swarm optimization algorithm, and obtaining the optimal fan arrangement scheme after optimization.
The invention provides a fan arrangement optimization system 100 based on multi-dimensional wind field data, which adopts the technical scheme that: acquiring meteorological and geographical conditions of a wind field and fan parameters to be selected through a multidimensional data acquisition module 101; determining at least one optimization objective and at least one optimization constraint through an optimization condition generation module 102; through a rectangular area generation module 103, a rectangular area is constructed according to a wind field area, the rectangular area is divided into a plurality of grids, and a fan is set to be positioned only in the center of each grid; writing an effective position array matrix corresponding to the grids of the rectangular region and the shape of the wind field region through a fan position arrangement module 104, wherein in the effective position array matrix, each element takes the value of 1 or 0, 1 represents the position where a fan can be arranged, 0 represents the position where the fan cannot be arranged, and the position where the fan cannot be arranged corresponds to the place where the geographical conditions are not suitable for arranging the fan in the wind field region and the place outside the wind field region; the fan arrangement scheme generation module 105 constructs a solution space according to the design variables to be optimized, an optimal fan arrangement scheme is searched in the solution space according to a target function by using a binary discrete particle swarm optimization algorithm, and the optimal fan arrangement scheme is obtained after optimization.
According to the system for optimizing the fan arrangement based on the multi-dimensional wind field data, the multi-dimensional data influencing the fan arrangement is considered according to the wind energy resource condition at the site of the wind power field, and the optimal arrangement scheme with the minimum electricity consumption cost or the maximum total generated energy is automatically generated based on the binary discrete particle swarm optimization algorithm.
In the multidimensional data acquisition module 101, the meteorological and geographic conditions of the wind field to be optimized include the size and shape of the wind field, the surface roughness z0Wind rose diagram and wind speed wind frequency distribution, and includes summer season wind resource data and winter season wind resource data from the wind rose diagram.
As a preferred embodiment of the present invention, the optimization condition generating module 102 is specifically configured to determine at least one optimization objective:
calculating by using a cost model, establishing a function of the total construction cost of the wind power plant and the number of fans, and calculating the total power generation of the wind power plant based on a Jensen wake flow model;
calculating to obtain a target function according to the total construction cost of the wind power plant and the total power generation amount of the wind power plant;
and optimizing the objective function by adopting a genetic algorithm, randomly generating an initial population consisting of 800 individuals in an initial state, sequentially carrying out individual fitness evaluation, selection operation, cross operation and variation operation to obtain 20 optimal offspring, and carrying out recycling calculation until the objective function converges to obtain an optimal solution to obtain the minimum value of the objective function, wherein the minimum value is used as an optimization target with the lowest unit generating capacity cost.
As a preferred embodiment of the present invention, the optimization condition generating module 102 is specifically configured to calculate the total power generation amount of the wind farm based on a Jensen wake model:
obtaining the kinetic energy loss of a wake zone by using a certain fan in a wind field area, wherein the fan is influenced by the wake of a plurality of peripheral units, and the kinetic energy loss of the wake zone is obtained by the superposition of the kinetic energy loss of the upstream to the fans;
and calculating to obtain the total generated energy of the wind power plant according to the number of the fans installed in the wind power plant.
As a preferred embodiment of the present invention, the optimization condition generation module is specifically configured to calculate the total construction cost of the wind farm:
acquiring the investment cost of a single fan, namely tempering, and the proportion of the cost of installing a large number of fans in the wind power plant to the total investment of the wind power plant;
and calculating to obtain the total construction cost of the wind power plant according to the number of the fans installed in the wind power plant and by combining the toughening of the investment cost of a single fan and the proportion of the cost of installing a large number of fans in the wind power plant to the total investment of the wind power plant.
Compared with the prior art, the invention has the beneficial effects that:
according to the wind energy resource condition at the site of the wind power plant, the multi-dimensional data influencing the arrangement of the fans are considered, and the optimal arrangement scheme with the minimum electricity consumption cost or the maximum total electricity generation amount is automatically generated based on the binary discrete particle swarm optimization algorithm.
Example two
As a preferred embodiment of the present invention, wherein the at least one optimization objective is selected from the group consisting of a minimum electricity cost, a maximum total electricity production, a maximum capacity factor, a minimum wake loss, a maximum internal rate of return, a maximum developer cost, a maximum net present value, and combinations thereof.
Based on Cost consideration, an optimization target is determined, and because the fan optimization problem focuses on the optimization of the fan arrangement scheme (namely the optimization of the number and the position), only the investment Cost (about 2/3 of the total investment of a wind field) related to fan equipment is considered, so that the Cost model can be assumed to be related to the number and the number of the fans only, and the total investment Cost is determinedtotalAs shown in equation (12):
wherein: costtotalCost for the total Cost of investmenttCost per fan, NtAnd arranging the number of the wind power plants.
If only one model is involved in the optimization, Cost can be reducedtAs unit 1, formula (12) is further abbreviated as formula (13):
in this embodiment, the optimization objective adopts the minimum electricity consumption cost as the optimization objective, because the electricity consumption cost directly reflects the economy of the wind farm.
When minimizing the power cost is the optimization objective, an objective function can be defined as shown in equation (14):
where, fitness is an objective function value, Ptotal represents the total power generation amount of a known arrangement, and Cost represents the power generation Cost (given by equation 12 or 13).
When the arrangement is known, the total power generation Ptotal is calculated as follows:
1) sequencing the fans in the known arrangement from upstream to downstream according to the incoming wind direction;
2) calculating the wind speed U of each fan hub in turn by the formulas 2 and 4i；
3) Calculating the wind speed U of each fan by the interpolation of the power curve of the faniLower fan power P (U)i)；
4) Calculating the total power generation P of all fans in each wind directiondirSee formula (15):
wherein f (U) is a wind speed probability distribution function, UinAnd UoutRespectively cutting in wind speed and cutting out wind speed of the fan;
5) after the total power generation amount of each wind direction is calculated according to the steps, the total power generation amount under each wind condition can be calculated:
in the formula (f)dir(j) For the frequency of the respective wind direction (given by the wind rose), the number 16 indicates that 360 ° is equally divided into 16 directions.
EXAMPLE III
Because the target function is that the electricity consumption cost is lowest or the total electricity generation amount is largest, the influence of the wake flow model on the yield of the wind power plant is larger, if the wake flow model is not considered, the theoretical result obtained by calculating the electricity generation amount of the wind power plant is larger than the actual yield of the wind power plant, the influence of the wake flow model must be considered when the wind power plant is arranged, and the electricity generation amount is calculated by frequently adopting a Jensen wake flow model; then, the existing wind turbine configuration is optimized, and in this embodiment, based on the fan configuration optimization method in the above embodiment, one-step improvement is performed, and the specific scheme is as follows:
a. calculating a wind resource map;
and calculating a map of wind resources, and optimally arranging the wind field to obtain the maximum output. The wind resource map displays wind resources in a given area, and is generally used for optimal arrangement of wind farms.
b. Displaying a wind resource map on a background map;
c. defining fan area attributes for optimization calculation;
the optimization calculation is to calculate the optimal arrangement of the wind farm according to different parameters (such as the number of fans, the fan pitch, and the influence of wake effect on the wind farm arrangement) in one or more set fan areas, such as the rectangular area in the above embodiments.
d. Optimizing the arrangement of the positions of the wind power plant;
after the steps are carried out, the arrangement of the wind turbine generator is optimized through two methods of rapid optimization and complete optimization in WindPRO software. Each optimization places a series of fans at the optimal point, with the arrangement complying with the spacing requirements and taking into account the wake attenuation model. The complete optimization time is long, but the optimized result is larger than the random model yield, and the fan efficiency is high.
Based on the method in the embodiment, the position relation between the wind resource map and the background map is considered, the overall consideration is carried out on the fan arrangement from the space aspect, and the arrangement result is more preferable.
Example four
In this embodiment, the fan suitable for the method and the system for optimizing the fan arrangement based on the multi-dimensional wind field data in the first embodiment may be a dual-impeller wind turbine with a specific structure, and the wind turbine with the structure significantly improves the wind energy utilization rate.
Referring to fig. 3 and 4, fig. 3 is a schematic structural diagram of a dual-impeller wind turbine generator provided in this embodiment, and fig. 4 is a schematic diagram of an impeller rotation speed combining mechanism provided in this embodiment.
In order to effectively utilize wind energy and increase the generated power, the present embodiment provides the following wind power generator, including: the first-stage impeller 1, the second-stage impeller 2 and the impeller rotating speed combining mechanism; the impeller rotating speed combining mechanism is provided with a first input shaft 31, a second input shaft 32, a first output shaft 41 and a second output shaft 42, the primary impeller 1 is in driving connection with the first input shaft 31, the secondary impeller 2 is in driving connection with the second input shaft 32, the first output shaft 41 is in driving connection with an input shaft of a first generator through a first clutch, and the second output shaft 42 is in driving connection with an input shaft of a second generator through a second clutch.
The first-stage impeller 1 is coaxially connected with the second-stage impeller 2, the length of the blade of the first-stage impeller 1 is larger than that of the blade of the second-stage impeller 2, the rotating directions of the first-stage impeller 1 during working are opposite, and the first-stage impeller 1 is located in front of the second-stage impeller 2.
When the wind turbine works, airflow firstly passes through the first-stage impeller 1 and then passes through the second-stage impeller 2, and the diameter of the second-stage impeller 2 is smaller than that of the first-stage impeller 1, so that the lowest wind speed required by the work of the second-stage impeller 2 is also smaller than that of the first-stage impeller 1. In order to increase the stability of the machine head during operation, the primary impeller 1 and the secondary impeller 2 rotate in opposite directions, so that the torque is offset.
The impeller rotating speed combining mechanism can combine the rotating speeds of the first-stage impeller 1 and the second-stage impeller 2, so that a larger output rotating speed is obtained, the generator is driven to work, the residual wind energy is effectively utilized, and the generating efficiency is improved.
Impeller rotational speed merges mechanism includes sun gear 51, ring gear 52 and the planet carrier 53 of coaxial setting, be equipped with a plurality of planet wheels 54 on the planet carrier 53, ring gear 52 is equipped with internal tooth and external tooth, planet wheel 54 meshes the internal tooth of ring gear 52 with between the sun gear 51, first input shaft 31 is equipped with drive gear 55, drive gear 55 with the external tooth meshing of ring gear 52, second input shaft 32 with sun gear 51's pivot is connected, the pivot of planet carrier 53 is passed through jackshaft 6 and is connected with the output shaft drive, the one end of output shaft forms first output shaft 41, the other end forms second output shaft 42.
For example, the rotation speed of the sun gear 51 is n1, the rotation speed of the ring gear 52 is n2, the rotation speed of the carrier 53 is n3, the tooth number ratio of the internal teeth of the ring gear 52 to the sun gear 51 is a, and n3 is (n1+ a × n2)/(1+ a). Thereby realizing the superposition of the rotating speed and the moment.
In one example, the blade length of the primary impeller is 75m and the blade length of the secondary impeller is 35 m. Under the condition that one generator works, when only one primary impeller works, the starting wind speed of the fan is 4m/s, the rated wind speed is 15m/s, the safe wind speed is 25m/s and the rated power is 3MW, and when only one secondary impeller works, the starting wind speed of the fan is 3m/s, the rated wind speed is 10m/s, the safe wind speed is 25m/s and the rated power is 1.5 MW.
Since the energy loss is large and the power is low in the operation of the first impeller in the low wind speed operation, two thresholds are involved in the control of the wind turbine, the first threshold being 6m/s and the second threshold being 10m/s, in order to enable the wind turbine to adapt to wind speeds in a wide range and to effectively utilize wind resources.
The specific control method comprises the following steps: and obtaining the wind speed, and when the wind speed is less than a first threshold value, changing the pitch of the blades of the first-stage impeller 1 and the second-stage impeller 2 to enable the first-stage impeller 1 to stop generating power, enabling the second-stage impeller 2 to be in a rotating power generation state, enabling the first clutch to be in a meshing state and enabling the second clutch to be in a separating state. Therefore, the fan is started to generate power at low wind speed, the internal consumption of the fan in the power generation process is reduced, and the power generation efficiency is improved.
When the wind speed is not less than the first threshold value and not more than the second threshold value, blades of the first-stage impeller 1 and the second-stage impeller 2 are changed into the pitch, so that the first-stage impeller 1 and the second-stage impeller 2 are both in a rotating power generation state, the first clutch is in a meshing state, and the second clutch is in a separating state. Thereby, high power generation is performed through the first impeller, and the surplus wind energy is effectively utilized through the second impeller. In this mode, the maximum power of the fan can reach 4 MW.
When the wind speed is greater than the second threshold value, the first-stage impeller 1 and the second-stage impeller 2 are both in a rotating power generation state, and the first clutch and the second clutch are both in an engaged state. When the wind speed greatly exceeds the rated wind speed 10m/s required by a single generator, the two generators are used for generating power simultaneously, the maximum generating power can reach 8MW, wind energy can be effectively utilized, the generating power is improved, the diameter of an impeller cannot be increased, the blades are prevented from being too long, and the manufacturing, transporting and installing and maintaining costs are increased.
Because the diameter of the primary impeller 1 is large, when the wind power is smaller than a first threshold value, the primary impeller 1 cannot be driven to rotate, the blades of the primary impeller 1 are adjusted, the windward area is reduced, airflow passes through the primary impeller 1, the secondary impeller 2 is directly driven to rotate, and in order to reduce the starting wind speed, the second clutch is in a separation state, and only the first generator works.
When the wind power is increased to a first threshold value and a second threshold value, the first impeller also starts to rotate, the second impeller effectively utilizes the residual wind energy, and the rotating speeds of the first impeller and the second impeller are superposed by the impeller rotating speed combining mechanism to drive the first generator to work.
When the wind power is continuously increased to be larger than the second threshold value, the rotating speed of the impeller cannot be infinitely increased, but the driving force is increased, the second clutch is engaged, and the first generator and the second generator are driven to generate electricity at the same time, so that the generating efficiency is improved.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents, improvements and the like that fall within the spirit and principle of the present invention are intended to be included therein.
Claims (6)
1. An optimal arrangement method of fans in a wind field is characterized in that:
the fan in the wind field is bilobed wheel aerogenerator, and it includes:
the first-stage impeller, the second-stage impeller and the impeller rotating speed combining mechanism; the impeller rotating speed merging mechanism is provided with a first input shaft, a second input shaft, a first output shaft and a second output shaft, the first-stage impeller is in driving connection with the first input shaft, the second-stage impeller is in driving connection with the second input shaft, the first output shaft is in driving connection with the input shaft of the first generator through a first clutch, and the second output shaft is in driving connection with the input shaft of the second generator through a second clutch;
the first-stage impeller is coaxially connected with the second-stage impeller, the length of blades of the first-stage impeller is greater than that of blades of the second-stage impeller, the rotating directions of the first-stage impeller and the second-stage impeller are opposite during working, and the first-stage impeller is positioned in front of the second-stage impeller;
the impeller rotating speed combining mechanism comprises a sun gear, a gear ring and a planet carrier which are coaxially arranged, a plurality of planet gears are arranged on the planet carrier, the gear ring is provided with inner teeth and outer teeth, the planet gears are meshed between the inner teeth of the gear ring and the sun gear, a driving gear is arranged on the first input shaft and is meshed with the outer teeth of the gear ring, the second input shaft is connected with a rotating shaft of the sun gear, the rotating shaft of the planet carrier is in driving connection with an output shaft through an intermediate shaft, one end of the output shaft forms the first output shaft, and the other end of the output shaft forms the second output shaft;
when the wind speed is less than a first threshold value, blades of the first-stage impeller and blades of the second-stage impeller are subjected to pitch variation, the first-stage impeller stops generating electricity, the second-stage impeller is in a rotating electricity generation state, the first clutch is in a meshing state, and the second clutch is in a separation state;
when the wind speed is not less than the first threshold value and not more than the second threshold value, blades of the first-stage impeller and the second-stage impeller are subjected to pitch variation, so that the first-stage impeller and the second-stage impeller are both in a rotating power generation state, the first clutch is in a meshing state, and the second clutch is in a separating state;
when the wind speed is greater than a second threshold value, the first-stage impeller and the second-stage impeller are in a rotating power generation state, and the first clutch and the second clutch are in a meshing state;
the optimal arrangement method comprises the following steps:
step S1, acquiring meteorological and geographical conditions of a wind field and fan parameters to be selected;
step S2, determining at least one optimization objective and at least one optimization constraint, wherein the determining at least one optimization objective includes: calculating by using a cost model, establishing a function of the total construction cost of the wind power plant and the number of fans, and calculating the total power generation of the wind power plant based on a Jensen wake flow model; calculating to obtain a target function according to the total construction cost of the wind power plant and the total power generation amount of the wind power plant; optimizing the objective function by adopting a genetic algorithm, randomly generating an initial population consisting of 800 individuals in an initial state, sequentially carrying out individual fitness evaluation, selection operation, cross operation and variation operation to obtain 20 optimal offspring, and carrying out recycling calculation until the objective function converges to obtain an optimal solution, thereby obtaining the minimum value of the objective function, wherein the minimum value is used as an optimization target with the lowest unit generating capacity cost;
step S3, constructing a rectangular area according to the wind field area, dividing the rectangular area into a plurality of grids, and setting that the fan can only be positioned at the center of each grid;
step S4, writing an effective position array matrix corresponding to the grids of the rectangular area and the shape of the wind field area, wherein in the effective position array matrix, each element takes the value of 1 or 0, 1 represents the position where the fan can be arranged, 0 represents the position where the fan cannot be arranged, and the position where the fan cannot be arranged corresponds to the place where the geographical conditions in the wind field area are not suitable for arranging the fan and the place outside the wind field area;
and S5, constructing a solution space by using the design variables to be optimized, searching an optimal fan arrangement scheme in the solution space according to a target function by using a binary discrete particle swarm optimization algorithm, and obtaining the optimal fan arrangement scheme after optimization.
2. The method for optimizing the arrangement of fans in a wind farm according to claim 1, wherein,
the meteorological and geographical conditions of the wind farm to be optimized include the size and shape of the wind farm, the surface roughness z0, the wind rose plot including summer and winter wind resource data, and the wind speed wind frequency distribution.
3. The method for optimizing the arrangement of fans in a wind farm according to claim 1,
the calculation of the total power generation amount of the wind power plant based on the Jensen wake flow model specifically comprises the following steps:
obtaining the kinetic energy loss of a wake area by using a certain fan in the wind area, wherein the fan is influenced by the wake of a plurality of peripheral units, and the kinetic energy loss of the wake area is obtained by the superposition of the kinetic energy loss caused by the upstream to the fan;
and calculating to obtain the total generated energy of the wind power plant according to the number of the fans installed in the wind power plant.
4. An optimal arrangement system of fans in a wind field is characterized in that:
the fan in the wind field is bilobed wheel aerogenerator, and it includes:
the first-stage impeller, the second-stage impeller and the impeller rotating speed combining mechanism; the impeller rotating speed merging mechanism is provided with a first input shaft, a second input shaft, a first output shaft and a second output shaft, the first-stage impeller is in driving connection with the first input shaft, the second-stage impeller is in driving connection with the second input shaft, the first output shaft is in driving connection with the input shaft of the first generator through a first clutch, and the second output shaft is in driving connection with the input shaft of the second generator through a second clutch;
the first-stage impeller is coaxially connected with the second-stage impeller, the length of blades of the first-stage impeller is greater than that of blades of the second-stage impeller, the rotating directions of the first-stage impeller and the second-stage impeller are opposite during working, and the first-stage impeller is positioned in front of the second-stage impeller;
the impeller rotating speed combining mechanism comprises a sun gear, a gear ring and a planet carrier which are coaxially arranged, a plurality of planet gears are arranged on the planet carrier, the gear ring is provided with inner teeth and outer teeth, the planet gears are meshed between the inner teeth of the gear ring and the sun gear, a driving gear is arranged on the first input shaft and is meshed with the outer teeth of the gear ring, the second input shaft is connected with a rotating shaft of the sun gear, the rotating shaft of the planet carrier is in driving connection with an output shaft through an intermediate shaft, one end of the output shaft forms the first output shaft, and the other end of the output shaft forms the second output shaft;
when the wind speed is less than a first threshold value, blades of the first-stage impeller and blades of the second-stage impeller are subjected to pitch variation, the first-stage impeller stops generating electricity, the second-stage impeller is in a rotating electricity generation state, the first clutch is in a meshing state, and the second clutch is in a separation state;
when the wind speed is not less than the first threshold value and not more than the second threshold value, blades of the first-stage impeller and the second-stage impeller are subjected to pitch variation, so that the first-stage impeller and the second-stage impeller are both in a rotating power generation state, the first clutch is in a meshing state, and the second clutch is in a separating state;
when the wind speed is greater than a second threshold value, the first-stage impeller and the second-stage impeller are in a rotating power generation state, and the first clutch and the second clutch are in a meshing state;
the system comprises:
the multi-dimensional data acquisition module is used for acquiring meteorological and geographical conditions of a wind field and fan parameters to be selected;
the optimization condition generation module is used for calculating a cost model, establishing a function of the total construction cost of the wind power plant and the number of the fans, and calculating the total power generation amount of the wind power plant based on the Jensen wake flow model; calculating to obtain a target function according to the total construction cost of the wind power plant and the total power generation amount of the wind power plant; optimizing the objective function by adopting a genetic algorithm, randomly generating an initial population consisting of 800 individuals in an initial state, sequentially carrying out individual fitness evaluation, selection operation, cross operation and variation operation to obtain 20 optimal offspring, and carrying out recycling calculation until the objective function converges to obtain an optimal solution, thereby obtaining the minimum value of the objective function, wherein the minimum value is used as an optimization target with the lowest unit generating capacity cost; and determining at least one optimization constraint;
the rectangular area generating module is used for constructing a rectangular area according to the wind field area, dividing the rectangular area into a plurality of grids, and setting that the fan can only be positioned in the middle of each grid;
the fan position arrangement module is used for compiling an effective position array matrix corresponding to the grids of the rectangular area and the shape of the wind field area, wherein in the effective position array matrix, each element takes the value of 1 or 0, 1 represents the position where a fan can be arranged, 0 represents the position where the fan cannot be arranged, and the position where the fan cannot be arranged corresponds to the place where the geographical conditions in the wind field area are not suitable for arranging the fan and the place outside the wind field area;
and the fan arrangement scheme generation module is used for constructing a solution space according to the design variables to be optimized, searching an optimal fan arrangement scheme in the solution space according to a target function by using a binary discrete particle swarm optimization algorithm, and obtaining the optimal fan arrangement scheme after optimization.
5. The optimal arrangement system of the fans in the wind farm according to claim 4, wherein,
in the multi-dimensional data acquisition module, the meteorological and geographic conditions of the wind field to be optimized comprise the size and shape of the wind field, the surface roughness z0, a wind rose diagram and wind speed and wind frequency distribution, wherein the wind rose diagram comprises summer wind resource data and winter wind resource data.
6. The optimal arrangement system of fans in a wind farm according to claim 4,
the optimization condition generation module is specifically used for calculating the total power generation amount of the wind power plant based on a Jensen wake model:
obtaining the kinetic energy loss of a wake area by using a certain fan in the wind area, wherein the fan is influenced by the wake of a plurality of peripheral units, and the kinetic energy loss of the wake area is obtained by the superposition of the kinetic energy loss caused by the upstream to the fan;
and calculating to obtain the total generated energy of the wind power plant according to the number of the fans installed in the wind power plant.
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