Wind turbine information processing device, wind turbine information processing method, and wind turbine information processing program

Abstract

To provide a windmill information processing device capable of achieving suitable windmill arrangement.SOLUTION: According to one embodiment, a wind turbine information processing device includes a simulation unit that separately simulates wind conditions in first and second regions and outputs simulation results of the wind conditions in the first and second regions. The apparatus further includes a model construction unit that constructs a prediction model of wind conditions in the first and second regions based on the simulation results of the first and second regions. The apparatus further includes a location determination unit that determines candidate locations for wind turbine locations in the first and second regions on the basis of the prediction models for the first and second regions. The apparatus further includes a data combining unit that combines the simulation result of the first region and the simulation result of the second region to display candidate points for wind turbine arrangement in the first and second regions on a display unit in a form in which the first region and the second region are combined.SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] An embodiment of the present invention relates to a wind turbine information processing device, a wind turbine information processing method, and a wind turbine information processing program. [Background technology]

[0002] When designing a wind power generation facility equipped with multiple wind turbines, it is desirable to select an optimal wind turbine layout for the wind turbines. For example, it is desirable to select a wind turbine layout that enables profitable wind power generation and sound operation of the wind turbines. An example of such a wind power generation facility is a wind farm. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Application Publication No. 2023-069910 Summary of the Invention [Problem to be solved by the invention]

[0004] For example, there is a known method for selecting an optimal wind turbine location using a tool that supports the selection of wind turbine locations. This method makes it possible to select a wind turbine location that maximizes the amount of power generated by the entire wind power generation facility while satisfying constraints on various wind condition parameters.

[0005] However, this method has several problems. For example, there are problems with how to determine the placement of wind turbines when determining the placement of wind turbines in multiple analysis domains, when determining the placement of wind turbines while taking into account the relationships between wind turbines, or when placing a new wind turbine while taking into account the impact of existing wind turbines.

[0006] Furthermore, when the analysis of wind conditions covers a wide area, such as when installing wind turbines offshore, performing the analysis all at once can take a long time. In some cases, the analysis values ​​may not converge, and the analysis may never be completed.

[0007] Therefore, the embodiments of the present invention provide a wind turbine information processing device, a wind turbine information processing method, and a wind turbine information processing program that can realize a suitable wind turbine arrangement. [Means for solving the problem]

[0008] According to one embodiment, a wind turbine information processing device includes a simulation unit that separately simulates wind conditions in a first and a second region and outputs simulation results of the wind conditions in the first and second regions. The device further includes a model construction unit that constructs a predictive model of wind conditions in the first and second regions based on the simulation results for the first and second regions. The device further includes a placement determination unit that determines candidate wind turbine placement sites for the first and second regions based on the predictive model for the first and second regions. The device further includes a data combination unit that combines the simulation results for the first region with the simulation results for the second region to display candidate wind turbine placement sites for the first and second regions on a display unit in a combined form for the first region and the second region. [Brief explanation of the drawings]

[0009] [Figure 1] 1 is a block diagram showing the configuration of a wind turbine information processing device of a first embodiment. FIG. [Figure 2] FIG. 2 is a plan view showing an example of a wind condition simulation model according to the first embodiment. [Figure 3] FIG. 3 is a diagram illustrating an example of a governing equation according to the first embodiment. [Figure 4] 1 is a side view showing an example of a wind turbine according to a first embodiment. FIG. [Figure 5] FIG. 2 is a plan view for explaining a method for determining the layout of wind turbines in the first embodiment. [Figure 6] FIG. 3 is a diagram for explaining a method for setting the inter-wind turbine separation distance in the first embodiment. [Figure 7] FIG. 4 is a diagram for explaining a method for combining analysis regions in the first embodiment. [Figure 8] 3 is a flowchart showing the flow of a wind turbine information processing method according to the first embodiment. DETAILED DESCRIPTION OF THE INVENTION

[0010] DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS Hereinafter, embodiments of the present invention will be described with reference to the drawings. In Figures 1 to 8, the same components are denoted by the same reference numerals, and redundant description will be omitted.

[0011] (First embodiment) FIG. 1 is a block diagram showing the configuration of a wind turbine information processing device 1 according to the first embodiment.

[0012] The wind turbine information processing device 1 includes an input unit 11, an information processing unit 12, and a display unit 13. The information processing unit 12 includes a memory unit 21, a calculation unit 22, and a determination unit 23. The memory unit 21 includes a wind condition data memory unit 21a, a wind turbine data memory unit 21b, a land data memory unit 21c, a social data memory unit 21d, a setting value memory unit 21e, a cost data memory unit 21f, and a distance memory unit 21g. The calculation unit 22 includes a simulation unit 22a, a model construction unit 22b, a placement determination unit 22c, and a data combination unit 22d.

[0013] The wind turbine information processing device 1 is a device that processes information related to wind turbines. The wind turbine information processing device 1 is used, for example, to optimize the placement of wind turbines when designing a wind power generation facility equipped with multiple wind turbines. An example of a wind power generation facility is a wind farm. The wind turbine information processing device 1 of this embodiment calculates and outputs the optimal placement of the multiple wind turbines based on the wind conditions, land conditions, social conditions, etc. of the wind power generation facility.

[0014] The wind conditions include, for example, various conditions related to wind. Examples of wind conditions include the annual energy production (AEP) obtained from the multiple wind turbines, the wind angle of the wind flowing into each wind turbine, and the wake effect that a wind turbine on the leeward side receives from a wind turbine on the windward side. Another example of a wind condition is the extreme wind speed V ref , V e50 The wind conditions may further include various constraints on the wind.

[0015] Land conditions include, for example, various constraints on the land (site) on which wind power generation facilities are to be installed. Examples of land conditions include distance constraints on the distance from buildings, roads, rivers, etc. to the wind turbine, and pollution constraints on light blocking, noise, etc. caused by the wind turbine. Other examples of land conditions include construction constraints on the slope, ground, etc. on which the wind turbine is to be installed, and rights constraints on land rights, etc.

[0016] Social conditions include, for example, various constraints imposed by society on the area in which wind power generation facilities are installed, such as constraints on flora and fauna protection areas, constraints on landscape protection areas, and constraints on cultural heritage protection areas.

[0017] The wind turbine information processing device 1 is, for example, a computer such as a PC (personal computer). The wind turbine information processing device 1 is realized, for example, by installing a computer program for processing information about wind turbines into the computer. The installation may be performed by inserting a recording medium on which the computer program is recorded into the computer, or by downloading the computer program from a server to the computer via a network.

[0018] The wind turbine information processing device 1 will be described in detail below with reference to Fig. 1. In this description, other figures besides Fig. 1 will also be referenced as appropriate.

[0019] The input unit 11 receives various input operations from an operator and outputs information corresponding to the input operations to the information processing unit 12. The input unit 11 is configured by input devices such as a mouse and a keyboard, for example.

[0020] The information processing unit 12 performs various information processes. The information processing unit 12 includes a memory unit 21 that stores various data and programs, a calculation unit 22 that performs various calculations, and a determination unit 23 that makes various determinations. The memory unit 21 is configured with memory and storage such as a read-only memory (ROM), a random access memory (RAM), a hard disk drive (HDD), or a solid state drive (SSD). On the other hand, the calculation unit 22 and the determination unit 23 are configured with processors such as a central processing unit (CPU) or an application specific integrated circuit (ASIC). The information processing unit 12 may further include a communication device that controls communication between the wind turbine information processing device 1 and other devices, and a memory port for attaching an external memory to the wind turbine information processing device 1.

[0021] The display unit 13 displays on its screen various pieces of information output from the information processing unit 12. The display unit 13 is configured, for example, with a display device such as a liquid crystal display. Note that the information from the information processing unit 12 may be displayed locally on the display unit 13 of the wind turbine information processing device 1, or may be remotely displayed on a display unit of a device other than the wind turbine information processing device 1.

[0022] The wind condition data storage unit 21a stores wind condition data showing the results of the wind condition simulation when the simulation unit 22a performs a wind condition simulation. The wind turbine data storage unit 21b stores data related to wind turbines, such as the wind conditions described above. The wind turbine data storage unit 21b also stores data related to existing wind turbines in a wind power generation facility when designing the addition of a new wind turbine to a wind power generation facility that already has existing wind turbines. The land data storage unit 21c stores data related to the land (site) on which the wind power generation facility will be installed, such as the land conditions described above. The social data storage unit 21d stores data related to the social aspects of the area where the wind power generation facility will be installed, such as the social conditions described above.

[0023] The setting value storage unit 21e stores various setting values ​​used when the information processing unit 12 processes information. The cost data storage unit 21f stores data related to the cost of the wind power generation facility. The distance storage unit 21g stores data related to the wind turbine separation distance described below.

[0024] In this embodiment, before the information processing unit 12 performs information processing, the operator inputs data and setting values ​​required for the information processing in advance from the input unit 11. The data and setting values ​​input from the input unit 11 are stored in the wind turbine data storage unit 21b, land data storage unit 21c, social data storage unit 21d, setting value storage unit 21e, cost data storage unit 21f, distance storage unit 21g, etc., as shown in Fig. 1. Note that such data and setting values ​​may be automatically acquired by the wind turbine information processing device 1 instead of being manually input into the wind turbine information processing device 1 by the operator.

[0025] Next, we will explain in detail functional blocks such as the simulation unit 22a, model construction unit 22b, placement determination unit 22c, data combination unit 22d, and judgment unit 23. These functional blocks are realized, for example, by a processor in the wind turbine information processing device 1 executing a computer program for processing information about wind turbines. This program is an example of a wind turbine information processing program.

[0026] [Simulation section 22a] The simulation unit 22a simulates wind conditions in the analysis domain and outputs the simulation results of the wind conditions in the analysis domain. The simulation unit 22a simulates the wind conditions in the analysis domain using wind condition analysis software such as MASCOT (registered trademark) or RIAM-COMPACT (registered trademark). The analysis domain is a calculated domain that corresponds to a real domain (study domain) where the placement of wind turbines is being considered. An example of a study domain is an area within the site of a wind power generation facility. The simulation unit 22a saves wind condition data indicating the simulation results of the wind conditions in the wind condition data storage unit 21a.

[0027] In this embodiment, the simulation unit 22a simulates wind conditions separately in multiple analysis domains and outputs simulation results of the wind conditions for each analysis domain. FIG. 7 shows domains R1 and R2 as examples of these analysis domains. In this case, the simulation unit 22a simulates wind conditions separately in domains R1 and R2 and outputs simulation results of the wind conditions for domain R1 and simulation results of the wind conditions for domain R2. Domain R1 is an example of a first domain, and domain R2 is an example of a second domain. Further details of FIG. 7 will be described later. The number of the multiple analysis domains may be three or more.

[0028] The simulation unit 22a samples the position coordinates of various wind turbine arrangements using random numbers (quasi-random numbers). The simulation unit 22a further samples, from the various wind turbine arrangements, a wind turbine arrangement that maximizes the total amount of power generated by all the wind turbines using a greedy algorithm or the like. Alternatively, the simulation unit 22a samples, from the various wind turbine arrangements, a wind turbine arrangement that has the smallest difference from the current wind turbine arrangement. When sampling various wind turbine arrangements, the type of each wind turbine may be changed to various types.

[0029] FIG. 2 is a plan view showing an example of the wind condition simulation model 2 according to the first embodiment.

[0030] 2, the wind condition simulation model 2 includes an analysis center 31, a minimum analysis grid range 32, a target region 33, an analysis region 34, an additional region 35, an upstream buffer region 41, a downstream buffer region 42, a side buffer region 43, and a side buffer region 44. FIG. 2 also shows the wind turbine position P and the inflow wind W. The simulation unit 22a of this embodiment simulates wind conditions using the wind condition simulation model 2 shown in FIG. 2.

[0031] 2 further shows the X, Y, and Z directions, which are perpendicular to each other. In this embodiment, the +Z direction is the upward direction, the -Z direction is the downward direction, and the XY plane is the horizontal plane. In this embodiment, the -Z direction coincides with the direction of gravity, but it may be tilted from the direction of gravity.

[0032] The wind turbine position P is the position where the wind turbine is located. The inflow wind W is the wind that flows into the wind turbine from the upwind side of the wind turbine. The analysis center 31 is the central position of the analysis of the wind condition simulation model 2. The minimum analysis grid range 32 is the area of ​​the smallest constituent unit of the target area 33 of the wind condition simulation. The analysis area 34 is an area that surrounds the target area 33 in a ring shape. The additional area 35 is an area that is located on the upwind side of the analysis area 34 and is added to the analysis area 34. Note that the above-mentioned "analysis area" in which the simulation unit 22a simulates wind conditions may be the same as the analysis area 34, or may be different from the analysis area 34.

[0033] The upstream buffer area 41 is an area adjacent to the additional area 35 on the upwind side of the additional area 35. The downstream buffer area 42 is an area adjacent to the analysis area 34 on the downwind side of the analysis area 34. The side buffer areas 43, 44 are areas adjacent to the side of the analysis area 34 and the additional area 35. In FIG. 2, the side buffer area 43 is located in the +Y direction of the analysis area 34 and the additional area 35, and the side buffer area 44 is located in the -Y direction of the analysis area 34 and the additional area 35.

[0034] In this embodiment, before starting the wind resource simulation, the operator uses the input unit 11 to specify the above-mentioned study area in advance. The operator also inputs topographical data for the area including the study area in advance from the input unit 11. The operator also inputs wind inflow conditions such as wind direction, wind speed, and turbulence intensity, as well as wind turbine information such as the shape of the wind turbines and the number of installed wind turbines, in advance from the input unit 11. The operator also inputs data such as other wind conditions, land conditions, and social conditions in advance from the input unit 11. The data input by the operator in this way is used in the wind resource simulation. For example, the specification of the study area and the contents of the topographical data are reflected in each area of ​​the wind resource simulation model 2.

[0035] FIG. 3 is a diagram illustrating an example of the governing equations of the first embodiment.

[0036] The simulation unit 22a of this embodiment generates a wind condition simulation model 2 for calculating predetermined physical quantities based on a governing equation. The governing equation of this embodiment is the Navier-Stokes equation, as shown in Fig. 3. In the wind condition simulation of this embodiment, the Navier-Stokes equation is adopted as the governing equation that expresses the physical laws in the mesh model as mathematical equations.

[0037] In FIG. 3, ρ represents the density of the fluid, μ represents the viscosity of the fluid, and ν represents the dynamic viscosity coefficient of the fluid. In this embodiment, the fluid is air. The Navier-Stokes equations shown in FIG. 3 include a time term, a pressure term, an advection term, and a viscosity term, as well as an external force term resulting from the external force F from the wind turbine rotor. When the Navier-Stokes equations are expressed as three equations in the X, Y, and Z directions, the external force term is the X component F of the external force F. X , Y component F Y , and the Z component F Z It is a vector quantity expressed as:

[0038] FIG. 4 is a side view showing an example of the wind turbine 3 of the first embodiment.

[0039] The wind turbine 3 shown in FIG. 4 includes a tower 51, a nacelle 52, a hub 53, and a plurality of blades 54.

[0040] The tower 51 extends in the Z direction from its lower end on the ground side to its upper end on the nacelle 52 side. The nacelle 52 is attached to the upper end of the tower 51 and houses a generator (not shown). A hub 53 is attached to the rotor of the generator. Each blade 54 is attached to the hub 53.

[0041] 4, when the plurality of blades 54 are rotated by wind power, the rotation of these blades 54 is transmitted to the generator via the hub 53 and the rotating shaft. As a result, the generator is driven by wind power to generate AC voltage.

[0042] Hereinafter, the description of the wind turbine information processing device 1 will resume with reference to Fig. 1 again. In this description, other figures besides Fig. 1 will also be referenced as appropriate.

[0043] [Model Construction Section 22b] The model construction unit 22b constructs a prediction model of the wind conditions in the analysis area based on the simulation results of the wind conditions in the analysis area. The model construction unit 22b constructs the prediction model using a regression analysis method such as Gaussian process regression. The model construction unit 22b may construct the prediction model using a neural network, a random forest, or support vector regression.

[0044] The wind condition prediction model of this embodiment is a model that uses the position coordinates of one wind turbine as explanatory variables. Examples of wind conditions handled by the prediction model include the AEP, upflow angle, wake effect, and extreme wind speed V ref , V e50, an index of turbulence state I, and an index of wind speed change during a storm. The wind conditions handled by the prediction model are called wind condition parameters. The wake effect prediction model is a model that uses the position coordinates of the downwind wind turbine and the distance and angle between the downwind wind turbine and its nearest neighbor as explanatory variables. Model construction unit 22b constructs a prediction model using, for example, wind condition data stored in wind condition data storage unit 21a and wind conditions stored in wind turbine data storage unit 21b.

[0045] In this embodiment, the model construction unit 22b constructs a prediction model of wind conditions for a plurality of analysis regions based on the simulation results of wind conditions for the plurality of analysis regions. For example, the model construction unit 22b constructs a prediction model of wind conditions for region R1 based on the simulation results of wind conditions for region R1, and constructs a prediction model of wind conditions for region R2 based on the simulation results of wind conditions for region R2 (see FIG. 7 for regions R1 and R2). In this embodiment, for example, the prediction models of wind conditions for the plurality of analysis regions are combined by the data combination unit 22d, which will be described later, and then the optimal placement of the wind turbines is determined by the placement determination unit 22c.

[0046] [Placement determining unit 22c] The placement determination unit 22c determines candidate locations for wind turbine placement in the analysis area based on a prediction model of wind conditions in the analysis area. This process of "determining candidate locations for wind turbine placement in the analysis area" is different from the process in which the data combination unit 22d combines the prediction models of wind conditions for multiple analysis areas (e.g., areas R1 and R2) and then makes a final decision on the optimal placement of wind turbines in the entire area (e.g., the entire area R3) obtained by combining them. In other words, this process of "determining candidate locations for wind turbine placement in the analysis area" is a process of extracting locations in each of the multiple analysis areas where wind turbines can be placed. The difference between "determining candidate locations for wind turbine placement" and "final decision on wind turbine placement" will be described in detail later.

[0047] The placement determination unit 22c discretizes the position coordinates of candidate points for wind turbine placement using random numbers (quasi-random numbers) or multiple grid points (or multiple points lined up along the contour lines of the terrain). Furthermore, the placement determination unit 22c determines the position coordinates of multiple wind turbines by formulating the determination of the position coordinates of candidate points for wind turbine placement as an integer linear programming problem in the discretized space. In this case, the placement determination unit 22c uses a design variable that indicates whether or not a wind turbine exists at a certain position coordinate. If a wind turbine is not to be placed at that position coordinate, the value of the design variable for that position coordinate is "0". If a wind turbine is to be placed at that position coordinate, the value of the design variable for that position coordinate is "1". At position coordinates (candidate points) that are candidates for wind turbine placement, a prediction model is used to determine the AEP and extreme wind speed V ref , V e50 This makes it possible to select a larger number of candidate points than the number of wind turbine coordinates used in the simulation, and to determine candidate points (candidate positions) for wind turbine placement that will produce the largest amount of power generation with fewer simulations.

[0048] The placement determination unit 22c may use a greedy algorithm to find the position coordinates of candidate locations for wind turbine placement. In this case, the placement determination unit 22c selects one wind turbine from the placement of multiple wind turbines, and changes only the position coordinates of the selected wind turbine so that the total AEP increases, without changing the position coordinates of the other wind turbines. The placement determination unit 22c repeats this search sequentially for each wind turbine until the wind turbine placement no longer needs to be updated.

[0049] The placement determination unit 22c may determine the position coordinates of candidate locations for wind turbine placement using a gradient method without using discretization. For example, since the AEP prediction model can determine the gradient (the amount of change in AEP when the position coordinates of the wind turbine move slightly), it is possible to determine the wind turbine placement that maximizes AEP using a gradient method that uses this gradient. Although the gradient method cannot be used in simulations because it is not possible to determine the gradient, it can be used by using a prediction model. This makes it possible to determine the placement of wind turbines that will generate the most power more quickly.

[0050] The placement determination unit 22c determines candidate locations for wind turbine placement in the analysis area based on, for example, the wind conditions of the analysis area and the land and / or social conditions of the analysis area. In one example, candidate locations for wind turbine placement are determined taking into account not only wind conditions but also land conditions, such as placing wind turbines at locations sufficiently far from fishing ports. In this case, the land conditions stored in the land data storage unit 21c are used for the placement determination. In another example, candidate locations for wind turbine placement are determined taking into account not only wind conditions but also social conditions related to flora and fauna protection areas. In this case, the social conditions stored in the social data storage unit 21d are used, for example, for the placement determination of candidate locations for wind turbine placement.

[0051] The placement determination unit 22c may determine candidate locations for wind turbine placement in the analysis area based on the construction costs and / or operation costs of the wind turbines in the analysis area. This makes it possible to obtain, for example, a wind turbine placement that reduces costs while increasing the AEP. An example of the cost of a wind turbine is the cable cost of the wind turbine. In this case, the "cost-related data" stored in the cost data storage unit 21f may be used for placement determination. Furthermore, the placement determination unit 22c may obtain the AEP from the wind turbine data storage unit 21b or based on a prediction model.

[0052] The placement determination unit 22c may determine candidate locations for wind turbine placement in the analysis area based on the wind turbine separation distance according to the wind direction in the analysis area. In this case, the "data related to the wind turbine separation distance" stored in the distance storage unit 21g may be used for placement determination. Details of the wind turbine separation distance will be described later with reference to FIG. 6.

[0053] The placement determination unit 22c may determine candidate locations for wind turbine placement in the analysis domain based on the influence of the wake from the wind turbine in the analysis domain. For example, candidate locations for wind turbine placement may be determined taking into consideration the influence of the wake from an existing wind turbine on the upwind side on a new wind turbine on the downwind side. This makes it possible to obtain a wind turbine placement that is less influenced by the wake from the wind turbine, for example. The placement determination unit 22c may obtain data on the influence of the wake from the wind turbine data storage unit 21b, or may obtain it based on a prediction model.

[0054] In this embodiment, the placement determination unit 22c determines candidate locations for wind turbine placement in multiple analysis regions based on prediction models of wind conditions for the multiple analysis regions. For example, the placement determination unit 22c determines candidate locations for wind turbine placement in region R1 based on the prediction model of wind conditions for region R1, and determines candidate locations for wind turbine placement in region R2 based on the prediction model of wind conditions for region R2 (see FIG. 7 for regions R1 and R2).

[0055] The placement determination unit 22c may determine candidate locations for wind turbine placement in these analysis areas based on interactions between these analysis areas. For example, the placement determination unit 22c may determine candidate locations for wind turbine placement in areas R1 and R2 based on interactions between areas R1 and R2. An example of such interactions is the inflow of wind from area R1 to area R2.

[0056] [Judgment section 23] The determination unit 23 determines whether candidate locations for wind turbine placement in the analysis area satisfy predetermined conditions. This makes it possible to determine whether the wind turbine placement is appropriate. Examples of predetermined conditions include constraints on wind conditions, constraints on land conditions, and constraints on social conditions. For example, it determines whether the AEP for the wind turbine placement is greater than a predetermined value. In this case, it may also determine whether each wind turbine is located more than a predetermined distance from a fishing port and / or whether each wind turbine satisfies constraints related to flora and fauna protection areas.

[0057] In this embodiment, the determination unit 23 determines whether the candidate sites for wind turbine placement in multiple analysis regions satisfy predetermined conditions. For example, the determination unit 23 determines whether the candidate sites for wind turbine placement in region R1 satisfy predetermined conditions, and also determines whether the candidate sites for wind turbine placement in region R2 satisfy predetermined conditions (see FIG. 7 for regions R1 and R2). In this embodiment, the predetermined conditions for region R1 and the predetermined conditions for region R2 are the same, but they may be different from each other.

[0058] [Data connection section 22d] When it is determined that the candidate locations for wind turbine placement in multiple analysis domains satisfy predetermined conditions, the data combining unit 22d combines the simulation results of the wind conditions for the multiple analysis domains. For example, when it is determined that the candidate locations for wind turbine placement in domain R1 satisfy predetermined conditions and when it is determined that the candidate locations for wind turbine placement in domain R2 also satisfy predetermined conditions, the data combining unit 22d combines the simulation results of the wind conditions for domain R1 and the simulation results of the wind conditions for domain R2. Figure 7 shows domain R3 obtained by combining domains R1 and R2. Domain R3 shown in the center of Figure 7 includes the simulation results of the wind conditions for domain R1 and the simulation results of the wind conditions for domain R2 as the simulation results of the wind conditions for domain R3.

[0059] The data combining unit 22d combines the simulation results of wind conditions for multiple analysis regions, thereby displaying candidate locations for wind turbine placement for the multiple analysis regions on the display unit 13 in a combined form for the multiple analysis regions. For example, the data combining unit 22d combines the simulation results of wind conditions for regions R1 and R2, thereby displaying candidate locations for wind turbine placement for regions R1 and R2 on the display unit 13 in a combined form for regions R1 and R2. Region R3 shown on the right side of FIG. 7 illustrates an example of candidate locations for wind turbine placement displayed on the display unit 13 in a combined form for regions R1 and R2. Region R3 shown on the right side of FIG. 7 includes candidate locations for wind turbine placement for region R1 and candidate locations for wind turbine placement for region R2 as candidate locations for wind turbine placement for region R3. FIG. 7 illustrates candidate locations for wind turbine placement, where four wind turbines, surrounded by dashed lines, are to be placed at the locations shown in FIG. 7. FIG. 7 further shows the prediction results of the prediction model for region R1 and the prediction results of the prediction model for region R2 in region R3.

[0060] In this way, instead of directly calculating the simulation results of the wind conditions in region R3, the wind turbine information processing device 1 of this embodiment calculates the simulation results of the wind conditions in region R1 and region R2, and then combines the simulation results of the wind conditions in region R1 and region R2 to calculate the simulation results of the wind conditions in region R3. Therefore, the wind turbine information processing device 1 of this embodiment determines candidate wind turbine locations for each of regions R1 and R2, and then combines the simulation results of regions R1 and R2 to calculate the simulation results for region R3. This combining process is performed by the data combining unit 22d, as described above. The reason for adopting this method in this embodiment will be explained below.

[0061] In general, in mesoscale analysis carried out when calculating wind parameters, topography is considered to be the dominant factor that influences flow fields such as wind speed.

[0062] Therefore, in this embodiment, after constructing a prediction model, prediction is not performed all at once for the entire target area (e.g., area R3), but rather, prediction is performed after dividing the area so that the influence of each topography is reflected in one of the divided areas (e.g., areas R1 and R2). This makes it possible to prevent problems such as predicted values ​​not converging in a single prediction, while more effectively reflecting the influence of each topography on the target area.

[0063] Furthermore, in this embodiment, a prediction model is constructed for each region obtained by division (e.g., each of regions R1 and R2), candidate locations for wind turbine placement are determined for each region, and then the simulation results for the divided regions are combined. This makes it possible for the placement determination unit 22c to perform discretization processing of the wind turbine positions before combining the data to be combined. As a result, after determining candidate locations for optimal placement of wind turbines, when performing an analysis to finally determine the optimal placement of wind turbines for the entire target region (e.g., region R3), it is possible to reduce the number of candidate locations for wind turbines to be analyzed compared to when discretization processing is not performed.

[0064] The data combining unit 22d displays one or more candidate locations for wind turbine placement in region R3, either sequentially or simultaneously, on the display unit 13. This allows the operator to select a suitable wind turbine placement (determine the optimal placement location for the wind turbine) by selecting one candidate location from these candidate locations. The selection of a suitable wind turbine placement may be performed automatically by the wind turbine information processing device 1, instead of being performed manually by the operator. In this case, for example, the placement determination unit 22c performs an optimal placement calculation on the simulation results of wind conditions for multiple analysis domains combined by the data combining unit 22d (prediction models of wind conditions for multiple analysis domains), and determines the optimal placement location for at least one or more wind turbines (a group of optimal placement locations in the case of multiple locations).

[0065] Even when it is determined that the candidate sites for wind turbine installation in at least one of the regions R1 and R2 do not satisfy the predetermined conditions, the data combining unit 22d may display the candidate sites for wind turbine installation in the regions R1 and R2 on the display unit 13. In this case, information that the candidate sites for wind turbine installation do not satisfy the predetermined conditions may also be displayed on the display unit 13 along with the candidate sites for wind turbine installation.

[0066] The data combining unit 22d may further display candidate locations for wind turbine placement in one analysis domain on the display unit 13 without combining them with other analysis domains. In other words, the candidate locations for wind turbine placement may be displayed without combining the analysis domains by the data combining unit 22d. In this case, the candidate locations for wind turbine placement may be displayed on the display unit 13 by the determining unit 23 or the model constructing unit 22b instead of by the data combining unit 22.

[0067] 7, the data combining unit 22d combines two analysis areas (areas R1 and R2), but it may combine three or more analysis areas. In this case, the data combining unit 22d displays the candidate wind turbine locations for the three or more analysis areas on the display unit 13 in a form in which the three or more analysis areas are combined.

[0068] Furthermore, if the placement determination unit 22c determines at least one or more optimum placement positions (group) for wind turbines after the combination by the data combination unit 22d, the placement determination unit 22c (or the data combination unit 22d) may display the optimum placement position (group) on the display unit 13. In this case, the placement determination unit 22c may display the candidate locations for wind turbine placement and the optimum placement position (group) together on the display unit 13 in a manner that distinguishes the candidate locations for wind turbine placement from the optimum placement position (group).

[0069] As described above, the wind turbine information processing device 1 of this embodiment performs wind condition simulations for each individual analysis domain, and displays candidate locations for wind turbine placement in a combined form across multiple analysis domains. This makes it possible to present candidate locations for wind turbine placement across the entire area (or a wide area) of the wind turbine, even in cases where the scale of the wind power generation facility is large, such as a wind farm, and the site of the wind power generation facility cannot be represented in a single analysis domain, in a form displayed within a single combined domain. For example, it is possible to present candidate locations for wind turbine placement that satisfy predetermined constraints for the entire wind power generation facility and maximize the AEP of the entire wind power generation facility. It is also possible to present candidate locations for wind turbine placement that maximize the AEP of the entire wind power generation facility and minimize the cable cost of the entire wind power generation facility.

[0070] Furthermore, according to this embodiment, by dividing the wind conditions (wind conditions) of an analysis target into multiple analysis targets, determining the wind conditions (wind conditions) and candidate locations for wind turbine placement, and combining these to obtain a prediction model for the wind conditions (wind conditions) of the entire analysis target, it becomes possible to solve problems such as analysis results not converging even when wind turbines are placed over a wide area, such as when wind turbines are placed offshore instead of on land.In addition, by first determining candidate locations for wind turbine placement for each divided analysis target, it becomes possible to reduce the calculation load when determining the optimal placement location(s) of the wind turbines.

[0071] Various operational examples of the wind turbine information processing device 1 will be described below, continuing to refer to Fig. 1. In this description, other figures besides Fig. 1 will also be referred to as appropriate.

[0072] (1) AEP maximization method The AEP maximization method described here is a method for maximizing the AEP within one analysis domain.

[0073] First, the simulation unit 22a samples the position coordinates (wind turbine coordinates) of various wind turbine arrangements within the analysis domain to create initial data. Then, the AEP and various wind condition parameters for each wind turbine arrangement are calculated based on the wind condition simulation within the analysis domain.

[0074] Next, the model construction unit 22b uses the initial data to construct a prediction model with the wind turbine coordinates as explanatory variables and the AEP and wind parameters as objective variables. The prediction model is constructed using, for example, Gaussian process regression. However, the Jensen model is used to construct the prediction model for wake effects.

[0075] Next, the location determination unit 22c determines the wind turbine location that maximizes the AEP on the prediction model from among multiple wind turbine locations that satisfy various constraints. This wind turbine location is output to the determination unit 23 as the aforementioned "candidate site for wind turbine location." Examples of constraints include constraints on wind condition parameters, constraints on the distance between wind turbines, constraints on possible locations for wind turbine construction, and constraints on the distance between wind turbines and buildings.

[0076] If the wake effects of wind turbines are not taken into account, the determination of wind turbine placement can be viewed as an integer linear programming problem. In this case, a mathematical programming solver can be used to obtain an exact solution for wind turbine placement.

[0077] When considering the wake effects of wind turbines, a sequential search is performed using the optimal wind turbine layout when the wake effects of wind turbines are not considered as the initial solution. Specifically, one wind turbine is selected from the layout of multiple wind turbines, and while the position coordinates of the other wind turbines remain unchanged, the position coordinates of only the selected wind turbine are changed so that the total AEP of these wind turbines (AEP of the analysis domain) increases. This type of search is repeated sequentially for each wind turbine until the wind turbine layout no longer needs to be updated.

[0078] FIG. 5 is a plan view for explaining a method for determining the wind turbine layout in the first embodiment.

[0079] 5(a) and 5(b) show how wind turbine 3d is added to analysis domain R that includes wind turbines 3a to 3c, and the position of wind turbine 3d is changed while the positions of wind turbines 3a to 3c are fixed. The position of wind turbine 3d is determined, for example, so as to maximize the total AEP of wind turbines 3a to 3d.

[0080] (2) Two-objective optimization method The bi-objective optimization method described here is a method for optimizing two variables within a single analysis domain. Examples of the two variables are the AEP and cable cost within the analysis domain.

[0081] First, the simulation unit 22a samples position coordinates (wind turbine coordinates) of various wind turbine layouts of multiple wind turbines within the analysis domain using, for example, NSGA-II (Non-dominated Sorting Genetic Algorithms II). For example, 100 wind turbine layouts of n wind turbines (n is an integer of 2 or more) are sampled. Note that each wind turbine coordinate satisfies constraints on wind condition parameters, constraints on possible locations for wind turbine construction, and constraints on the distance between the wind turbine and the building.

[0082] This bi-objective optimization method calculates the AEP and cable cost for each wind turbine layout. The AEP is calculated using, for example, Gaussian process regression. The cable cost is calculated after determining how to connect the cables for each wind turbine layout using, for example, the Esau-Williams method. It also determines whether each wind turbine layout satisfies the distance constraints between wind turbines.

[0083] Next, the placement determination unit 22c inputs the AEP and cable cost of each wind turbine placement into NSGA-II. This makes it possible to determine whether each wind turbine placement is suitable from the perspective of bi-objective optimization. The placement determination unit 22c repeats the process of inputting the AEP and cable cost of each wind turbine placement into NSGA-II until a wind turbine placement that satisfies predetermined conditions is obtained. The wind turbine placement that satisfies the predetermined conditions is output to the determination unit 23 as the aforementioned "candidate wind turbine placement site."

[0084] The simulation unit 22a may employ other bi-objective optimization methods or multi-objective optimization methods instead of NSGA-II. Examples of bi-objective optimization methods or multi-objective optimization methods include genetic algorithms, evolutionary computing, and Bayesian optimization. The simulation unit 22a may employ a branch-and-bound algorithm or Prim's algorithm instead of the Esau-Williams algorithm.

[0085] (3) Simultaneous optimization of multiple analysis domains The method described here uses simulation results from multiple analysis domains to optimize wind turbine placement across multiple analysis domains, which may correspond to the entire area (or a wide area) within a wind power generation facility, for example.

[0086] First, the simulation unit 22a samples the position coordinates (wind turbine coordinates) of various wind turbine locations for one or more wind turbines within each analysis domain. Next, the model construction unit 22b constructs prediction models for AEP, upflow angle, extreme wind speed, power exponent, turbulence intensity, etc. for each analysis domain. Next, the location determination unit 22c determines candidate locations for wind turbine location for each analysis domain. Specifically, predicted values ​​of the position coordinates of candidate locations for wind turbine location are calculated for each analysis domain.

[0087] Next, the data combining unit 22d combines the simulation results and wind turbine placement data for the multiple analysis domains. The data obtained by combining is called combined data. The data combining unit 22d determines the placement of the multiple wind turbines included in the multiple analysis domains using a mathematical programming solver, sequential search, NSGA-II, etc. on the combined data. This allows the wind turbine placement determined by the placement determination unit 22c to be revised into a new wind turbine placement. At this time, it is also possible to accommodate constraints such as the number of wind turbines to be placed in each analysis domain. The wind turbine placement determined by the data combining unit 22d is displayed on the display unit 13 as the aforementioned "candidate wind turbine placement sites."

[0088] The determination unit 23 makes the above-mentioned determination between the processing by the placement determination unit 22c and the processing by the data combination unit 22d.

[0089] The AEP maximization method and the bi-objective optimization method described above may be performed by the data combination unit 22d for multiple combined analysis domains, instead of by the placement determination unit 22c for each analysis domain. For example, the data combination unit 22d can obtain a wind turbine placement that increases the total AEP for multiple analysis domains while reducing the total cable cost for the multiple analysis domains.

[0090] (4) Wind turbine placement optimization method taking into account the distance between wind turbines The method described here optimizes wind turbine placement using a threshold value for the wind turbine separation distance set for each direction. This method may be performed by the placement determination unit 22c for each analysis domain, or by the data combination unit 22d for multiple combined analysis domains. Details of the wind turbine separation distance will be described later with reference to FIG. 6.

[0091] This method determines the layout of wind turbines without considering the constraints on the separation distance between the wind turbines, calculates the distance and orientation between any two wind turbines for the determined layout, and then modifies the layout of wind turbines based on the distance and orientation between the two wind turbines so that the constraints on the separation distance between the wind turbines are satisfied.

[0092] For example, when determining the position coordinates of multiple wind turbines by formulating an integer linear programming problem, the design variable x indicates whether a wind turbine exists at a certain position coordinate i. i If no wind turbine is located at the location coordinate i, the design variable x i The value of is "0". When a wind turbine is placed at position coordinate i, the design variable x i The value of design variable x is "1". i is a binary variable (0-1 variable) that takes on the value "0" or "1". The constraint on the wind turbine separation distance between location coordinate i and location coordinate j is expressed as in equation (1).

number

[0093] On the other hand, it is also possible to speed up the modification of wind turbine placement by adding redundant constraints to find the position coordinates of multiple wind turbines. In calculations using a mathematical programming solver, it is thought that a relaxed problem in which the explanatory variables are relaxed from 0-1 variables to continuous-valued variables is first solved. However, the solution to the relaxed problem contains many elements with values ​​other than 0 or 1, and it is thought that it will take a long time to find an integer solution after solving the relaxed problem. Therefore, a redundant constraint expressed in equation (2) is adopted so that the solution to the relaxed problem contains many elements with the value "0".

number

[0094] As mentioned above, this method may be performed by the placement determination unit 22c for each analysis domain, or by the data combination unit 22d for multiple combined analysis domains. In the latter case, it becomes possible to take into account the distance and direction between two wind turbines in the same analysis domain and the distance and direction between two wind turbines in different analysis domains.

[0095] FIG. 6 is a diagram for explaining a method for setting the inter-wind turbine separation distance in the first embodiment.

[0096] FIG. 6 shows the distance and direction from a wind turbine located at wind turbine position P (hereinafter referred to as "first wind turbine"). FIG. 6 also shows the incoming wind W flowing into the first wind turbine. In FIG. 6, the incoming wind W flows into the first wind turbine from a direction near 0°. A direction near 0° is called the incoming direction (or wind direction) of the incoming wind W. The incoming direction of the incoming wind W tends to be a constant direction depending on the topography near wind turbine position P. For example, if wind turbine position P is located near the coast, the incoming wind W often blows from land toward the sea, or from the sea toward land. In this case, the incoming direction of the incoming wind W often faces the landward or seaward direction of wind turbine position P.

[0097] Figure 6 also uses dotted hatching to indicate a prohibited area where the placement of another wind turbine (hereafter referred to as the "second wind turbine") is prohibited. The shape of the prohibited area is roughly a circle with a radius of 800 m. However, at azimuths near 0° and 180°, two sectors are added to this circle. If a second wind turbine were placed within the sector, the relationship between the first and second wind turbines would become that of an upwind turbine and a leeward turbine, and the wake from one turbine would have a negative impact on the other. Therefore, at azimuths near 0° and 180°, the two sectors create a wider prohibited area.

[0098] According to this embodiment, by using the inter-wind turbine separation distance setting in Fig. 6 when determining the wind turbine placement, it is possible to ensure an appropriate distance between the wind turbines. In this embodiment, for example, the inter-wind turbine separation distance setting in Fig. 6 is stored in the distance memory unit 21g and used when determining the wind turbine placement. Note that the prohibited areas in Fig. 6 are set using 16 different directions (16 wind directions) obtained by dividing 360° into 16 parts.

[0099] 7 and 8, further details of the information processing performed by the wind turbine information processing device 1 will be described below. In this description, the symbols shown in FIG. 1 will also be used as appropriate.

[0100] FIG. 7 is a diagram for explaining a method for combining analysis regions in the first embodiment.

[0101] 7 shows regions R1 and R2 as examples of two analysis regions. The simulation unit 22a separately simulates wind conditions in regions R1 and R2, and outputs a simulation result of the wind conditions in region R1 and a simulation result of the wind conditions in region R2.

[0102] The data combining unit 22d combines the simulation results of wind conditions in region R1 with the simulation results of wind conditions in region R2, and displays candidate wind turbine locations for regions R1 and R2 in a combined form on the display unit 13. Figure 7 shows region R3 obtained by combining regions R1 and R2.

[0103] Region R3 shown in the center of Figure 7 includes the simulation results of wind conditions in region R1 and the simulation results of wind conditions in region R2 as the simulation results of wind conditions in region R3. Region R3 shown on the right side of Figure 7 includes the candidate sites for wind turbine placement in region R1 and the candidate sites for wind turbine placement in region R2 as the candidate sites for wind turbine placement in region R3. Figure 7 shows the candidate sites for wind turbine placement, where four wind turbines, surrounded by dashed lines, are to be placed at the positions shown in Figure 7. Figure 7 also shows the prediction results of the prediction model for region R1 and the prediction results of the prediction model for region R2 in region R3.

[0104] As described above, the data combining unit 22d may correct the wind turbine placement determined by the placement determining unit 22c into a new wind turbine placement. For example, the data combining unit 22d may fine-tune the wind turbine placement determined by the placement determining unit 22c. In this case, the wind turbine placement obtained by the correction by the data combining unit 22d is displayed on the display unit 13 as the above-mentioned "candidate site for wind turbine placement."

[0105] Fig. 8 is a flowchart showing the flow of the wind turbine information processing method of the first embodiment. The wind turbine information processing method shown in Fig. 8 is performed by the wind turbine information processing device 1 of this embodiment.

[0106] First, the simulation unit 22a sets various conditions (step S1) based on information input by the operator via the input unit 11. The operator inputs, for example, information about the area to be considered for the placement of wind turbines, and the various pieces of information stored in the storage unit 21.

[0107] The subsequent processing of steps S2 to S13 is performed for each of the plurality of analysis regions. Note that the "analysis region" that appears in the description of steps S2 to S13 refers to each of the plurality of analysis regions.

[0108] Next, the simulation unit 22a executes a simulation of wind conditions in the analysis domain and outputs the simulation results of wind conditions in the analysis domain (step S2). As a result, wind condition data indicating the simulation results of wind conditions is stored in the wind condition data storage unit 21a. The simulation by the simulation unit 22a is performed using the conditions set in step S1.

[0109] Next, the model construction unit 22b constructs a prediction model of the wind conditions of the analysis area based on the simulation results of the wind conditions of the analysis area (step S3). The prediction model is constructed using, for example, wind condition data stored in the wind condition data storage unit 21a.

[0110] Next, the placement determination unit 22c outputs candidate locations for wind turbine placement in the analysis domain based on a prediction model of wind conditions in the analysis domain (step S4). As a result, candidate locations for each wind turbine are determined taking into account the wind conditions. For example, a wind turbine placement that makes the AEP of the analysis domain greater than a preset lower limit is output as a candidate location for wind turbine placement. In step S4, the placement determination unit 22c outputs one or more candidates for wind turbine placement in the analysis domain.

[0111] Next, the determination unit 23 determines whether a predetermined candidate exists among these candidates (step S5). The predetermined candidate is the candidate that has the largest AEP among these candidates, the smallest cost among these candidates, and wind conditions that satisfy predetermined set values. An example of a candidate that satisfies predetermined set values ​​for wind conditions is a candidate whose extreme wind speed is smaller than the threshold set in step S1. If the determination result in step S5 is No, the process returns to step S4. If the determination result in step S5 is Yes, the process proceeds to step S11.

[0112] In step S11, the placement determination unit 22c calculates the degree of influence of land conditions and social conditions on a predetermined candidate for wind turbine placement. For example, the placement determination unit 22c stores in advance, in the land data storage unit 21c and the social data storage unit 21d, information in which the details of land conditions and social conditions have been quantified in advance. The placement determination unit 22c calculates the degree of influence by applying this information to the predetermined candidate for wind turbine placement.

[0113] Next, the placement determination unit 22c outputs candidate locations for wind turbine placement in the analysis area based on the land conditions and social conditions of the analysis area (step S12). For example, the placement determination unit 22c generates a revised wind turbine placement by modifying the predetermined candidate wind turbine placement based on the influence of the land conditions and social conditions. In this case, the revised wind turbine placement is output as the "candidate location for wind turbine placement" in step S12. Alternatively, multiple candidates may be determined in advance as predetermined candidates for wind turbine placement in step S5, and a candidate for which the influence of the land conditions and social conditions is smaller than a threshold value may be output as the "candidate location for wind turbine placement" in step S12.

[0114] Next, the determination unit 23 determines whether the candidate site for wind turbine placement output in step S12 satisfies predetermined land conditions and social conditions (step S13). If the determination result in step S13 is No, the process returns to step S12. If the determination result in step S13 is Yes, the process proceeds to step S21.

[0115] The processing of steps S2 to S13 continues until the number of times the wind condition simulation is executed reaches the number of analysis domains corresponding to the study domain (step S21). For example, if there are three analysis domains, the wind condition simulation is executed three times.

[0116] In step S22, the data combining unit 22d combines the simulation results of the wind conditions for the multiple analysis domains, and displays candidate locations for wind turbine placement for the multiple analysis domains in a combined form for the multiple analysis domains on the display unit 13. At this time, the data combining unit 22d may modify the wind turbine placements output in step S13 and display the modified wind turbine placements. Examples of such modifications will be described later.

[0117] The wind turbine information processing device 1 of this embodiment may display not only the wind turbine locations in analysis areas that satisfy the specified land conditions and social conditions, but also the wind turbine locations in analysis areas that do not satisfy the specified land conditions or social conditions. In this case, it is desirable for the wind turbine information processing device 1 to display the wind turbine locations in a manner that makes it possible to distinguish between analysis areas that satisfy the specified land conditions and social conditions and analysis areas that do not satisfy the specified land conditions and social conditions.

[0118] Furthermore, the wind turbine information processing device 1 of this embodiment may perform the wind turbine information processing method using a flow different from the flow shown in Fig. 8. For example, the wind turbine information processing device 1 may perform steps S5 and S11 not only after step S4 but also after step S3. In this case, the wind turbine information processing device 1 repeats steps S2, S3, S5, and S11 each time the process returns from step S21 to step 2. Thereafter, when the determination in step S21 becomes Yes, the wind turbine information processing device 1 performs step S22.

[0119] Alternatively, the wind turbine information processing device 1 may perform steps S1, S2, S3, and S11, but not steps S4, S5, S12, and S13, and then perform step S21. In this case, if the determination result of step S21 is No, the wind turbine information processing device 1 returns to step S2 and performs steps S2, S3, S11, and S21 again. On the other hand, if the determination result of step S21 is Yes, the wind turbine information processing device 1 may create combined data and display candidate sites for wind turbine placement by taking wind conditions, land conditions, and social conditions into account on the combined data (step S22).

[0120] As described above, the wind turbine information processing device 1 of this embodiment performs a simulation of wind conditions for each individual analysis domain, and displays candidate wind turbine placement sites in a combined form across multiple analysis domains. This makes it possible to present candidate wind turbine placement sites across the entire area (or a wide area) of the wind turbine, even in cases where the wind power generation facility is large, such as a wind farm, and the entire site cannot be represented in a single analysis domain, within a single combined domain. This processing makes it possible to achieve optimal wind turbine placement. This processing also makes it possible to prevent problems such as predictions not converging after a single prediction, while also allowing the influence of each terrain to be more effectively reflected in the target area. This processing also makes it possible for the placement determination unit 22c to perform discretization processing on wind turbine positions before combining data to be combined. This makes it possible to reduce the number of candidate wind turbine locations to be analyzed compared to when discretization processing is not performed.

[0121] After performing the process of step S22, the wind turbine information processing device 1 of this embodiment may perform a process to finally determine the optimal placement of wind turbines in the entire target area (for example, area R3). This process corresponds to the above-mentioned example of "correction." The process to finally determine the optimal placement of wind turbines will be described below.

[0122] In this process, if wind turbine wake (places where wind speed decreases or wind turbulence increases) is not taken into consideration, the final decision process is treated as an integer linear programming problem, and an exact solution is obtained using a general-purpose mathematical programming solver.

[0123] On the other hand, when wind turbine wake is taken into consideration, the exact solution explained when the effects of wind turbine wake are not taken into consideration is used as the initial solution, and a sequential search is performed based on this initial solution to finally determine the optimal placement of the wind turbines. Specifically, a search is performed to increase the AEP of the entire wind turbine group by changing the coordinates of the candidate location of any one wind turbine from among the candidate locations of the wind turbine group across the entire target area. This search is repeated sequentially until the AEP of the entire wind turbine group is maximized.

[0124] In this embodiment, as an example of the process of combining the simulation results of the first region and the simulation results of the second region, the process of combining the simulation results of the region R1 and the simulation results of the region R2 has been described. Here, in the process of combining the simulation results of the first region and the simulation results of the second region, only the simulation results of the first and second regions may be combined, or the simulation results of three or more regions including the first and second regions may be combined.

[0125] Although several embodiments have been described above, these embodiments are presented only as examples and are not intended to limit the scope of the invention. The novel devices, methods, and programs described herein may be embodied in various other forms. Furthermore, various omissions, substitutions, and modifications may be made to the forms of the devices, methods, and programs described herein without departing from the spirit of the invention. The appended claims and their equivalents are intended to cover such forms and modifications that fall within the scope and spirit of the invention. [Explanation of symbols]

[0126] 1: Wind turbine information processing device, 2: Wind condition simulation model, 3: windmill, 3a: windmill, 3b: windmill, 3c: windmill, 3d: windmill, 11: input unit, 12: information processing unit, 13: display unit, 21: Storage unit, 21a: Wind condition data storage unit, 21b: Wind turbine data storage unit, 21c: Land data storage unit, 21d: Social data storage unit, 21e: setting value storage unit, 21f: cost data storage unit, 21g: distance storage unit, 22: Calculation unit, 22a: Simulation unit, 22b: Model construction unit, 22c: placement determination unit, 22d: data combination unit, 23: determination unit, 31: Analysis center, 32: Minimum analysis grid range, 33: Target area, 34: Analysis area, 35: Additional area, 41: Upstream buffer area, 42: Downstream buffer area, 43: Side buffer area, 44: Side buffer area, 51: Tower, 52: Nacelle, 53: Hub, 54: Blade

Claims

1. a simulation unit that separately simulates wind conditions in the first and second regions and outputs simulation results of the wind conditions in the first and second regions; a model constructing unit that constructs a prediction model of wind conditions in the first and second regions based on the simulation results of the first and second regions; a placement determination unit that determines candidate locations for wind turbine placement in the first and second areas based on the prediction models for the first and second areas; a data combining unit that combines the simulation results of the first area and the simulation results of the second area to display candidate wind turbine locations for the first and second areas on a display unit in a form in which the first area and the second area are combined; A wind turbine information processing device comprising:

2. 2. The wind turbine information processing device according to claim 1, wherein the placement determination unit determines candidate sites for wind turbine placement in the first and second regions based on the wind conditions in the first and second regions and land conditions and / or social conditions in the first and second regions.

3. The wind turbine information processing device according to claim 1 , wherein the placement determination unit determines candidate sites for wind turbine placement in the first and second regions based on construction costs and / or operating costs of the wind turbines in the first and second regions.

4. The wind turbine information processing device according to claim 1 , wherein the arrangement determination unit determines candidate locations for wind turbine arrangement in the first and second regions based on a separation distance between the wind turbines according to wind directions in the first and second regions.

5. The wind turbine information processing device according to claim 1 , wherein the placement determination unit determines candidate points for wind turbine placement in the first and second regions based on an interaction between the first and second regions.

6. The wind turbine information processing device according to claim 1 , wherein the placement determination unit determines candidate locations for wind turbine placement in the first and second regions based on the influence of wakes from existing wind turbines in the first and second regions.

7. further comprising a determination unit that determines whether the wind turbine location candidates in the first and second regions satisfy predetermined conditions; 2. The wind turbine information processing device according to claim 1, wherein the data combining unit displays the candidate wind turbine locations in the first and second regions on the display unit when it is determined that the candidate wind turbine locations in the first and second regions satisfy the predetermined condition.

8. The wind turbine information processing device according to claim 7 , wherein the predetermined conditions include constraints on the wind conditions in the first and second regions.

9. The wind turbine information processing device according to claim 8 , wherein the predetermined conditions further include constraints related to land conditions and / or social conditions of the first and second areas.

10. The wind turbine information processing device according to claim 1 , wherein the simulation unit uses wind condition analysis software to simulate wind conditions in the first and second regions.

11. The wind turbine information processing device according to claim 1 , wherein the model construction unit constructs the prediction models for the first and second regions using a regression analysis method.

12. 2. The wind turbine information processing device according to claim 1, wherein the placement determination unit calculates the amounts of power generated by the wind turbines in the first and second areas based on the prediction models for the first and second areas, and determines candidate sites for wind turbine placement in the first and second areas based on the amounts of power generated by the wind turbines in the first and second areas.

13. 13. The wind turbine information processing device according to claim 12, wherein the placement determination unit determines candidate sites for wind turbine placement in the first and second regions based on the amounts of power generated by the wind turbines in the first and second regions and the cable costs of the wind turbines in the first and second regions.

14. Simulating wind conditions in the first and second regions separately and outputting simulation results of the wind conditions in the first and second regions; constructing a predictive model of wind conditions for the first and second regions based on the simulation results for the first and second regions; determining candidate locations for wind turbine placement in the first and second regions based on the prediction models for the first and second regions; by combining the simulation results of the first area and the simulation results of the second area, the candidate wind turbine locations for the first and second areas are displayed on a display unit in a form in which the first area and the second area are combined. A wind turbine information processing method comprising:

15. Simulating wind conditions in the first and second regions separately and outputting simulation results of the wind conditions in the first and second regions; constructing a predictive model of wind conditions for the first and second regions based on the simulation results for the first and second regions; determining candidate locations for wind turbine placement in the first and second regions based on the prediction models for the first and second regions; by combining the simulation results of the first area and the simulation results of the second area, the candidate wind turbine locations for the first and second areas are displayed on a display unit in a form in which the first area and the second area are combined. A wind turbine information processing program that causes a computer to execute a wind turbine information processing method including the steps of:

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