Information processing system, information processing device, information processing method, and program

Abstract

To increase the accuracy of predicting turbulence intensity at a planned installation site of a wind turbine used in wind power generation.SOLUTION: A control unit 11 of a management server 10 performs control so that: a generation unit 103 generates second data by removing trend data of a predetermined time unit from first data indicating a lapse of time for each predetermined wind direction at a predetermined observation position; a calculation unit 104 calculates disturbance intensity at the observation position based on the second data; and a transmission control unit 105 performs control to transmit the disturbance intensity for each wind direction and each wind speed at the observation position to a user terminal.SELECTED DRAWING: Figure 3

Description

[Technical Field]

[0001] The present invention relates to an information processing system, an information processing device, an information processing method, and a program. [Background technology]

[0002] The wind blowing toward a wind turbine used in wind power generation is not constant and can create a fatigue load that generates fatigue stress. Therefore, when a wind turbine is installed, its fatigue strength, which is its ability to withstand fatigue loads, is confirmed in advance. The fatigue strength of a wind turbine is confirmed based on the results of an evaluation of turbulence intensity, which is the strength of wind speed fluctuations caused by the weather and topography of the planned installation site. Turbulence intensity is calculated based on observation data of wind conditions (wind speed, wind direction, etc.) observed at or near the planned installation site for more than one year, and related technology exists (for example, Patent Document 1). Observation data on wind conditions is obtained using a wind observation tower (mast) or a Doppler lidar, which observes wind conditions by shining lasers in multiple directions at particles moving in the atmosphere and utilizing the Doppler effect caused by the reflection of the lasers. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Publication No. 2023-072587 Summary of the Invention [Problem to be solved by the invention]

[0004] When a Doppler LIDAR observes wind conditions, turbulence intensity is typically calculated based on the average wind speed over a 10-minute period for each wind direction, measured every second or so, and the standard deviation of the wind speed, which indicates the variation from that average wind speed. Here, instantaneous changes in wind speed are difficult to absorb by the wind turbine's inherent control functions and are prone to become fatigue loads. In contrast, gradual, non-instantaneous changes in wind speed are not difficult to absorb by the wind turbine's inherent control functions, even if the wind speed before and after the change differs significantly, and are therefore less likely to become fatigue loads. For example, changes in wind speed over a second-by-second period are instantaneous changes in wind speed and are prone to become fatigue loads. In contrast, trends in wind speed over a 10-minute period are gradual, non-instantaneous changes in wind speed and are therefore less likely to become fatigue loads. However, conventional methods for calculating turbulence intensity include trends in wind speed over 10-minute intervals. Therefore, when wind speeds differ significantly before and after a 10-minute interval, excessive turbulence intensity is predicted, resulting in wind turbines being required to have fatigue strength that is higher than necessary. This can lead to increased procurement costs for wind turbines, partial shutdowns of wind turbines, or postponements of wind turbine installations.

[0005] An object of the present invention is to improve the accuracy of prediction of turbulence intensity at a proposed installation site of a wind turbine used for wind power generation. [Means for solving the problem]

[0006] The present invention, which was completed with this object in mind, is an information processing system characterized by having: a generation means for generating second data by removing trend components in a predetermined unit of time from first data representing the time course of wind speed for each predetermined wind direction at a predetermined observation position; a calculation means for calculating turbulence intensity at the observation position based on the second data; and an output means for outputting the turbulence intensity for each wind direction and each wind speed. Here, the generating means may extract the trend component by analyzing the relationship between time and wind speed in the first data, and generate the second data by removing the trend component from the first data. The generating means may also be characterized in that it generates the second data by removing the trend component in a second time unit longer than the first time from the first data including the wind speed for each wind direction at the observation position recorded every first hour. The generating means may generate the second data by removing the trend component in units of 10 minutes as the second time from the first data recorded every second or approximately every second as the first time. The calculation means may also be characterized in that it calculates the turbulence intensity based on a wind speed standard deviation that indicates the variation of the wind speed for each wind direction recorded every first hour from the average wind speed for the second hour unit. The calculation means may further calculate, based on the turbulence intensity at the observation position, a fatigue load of a first wind turbine installed at a location corresponding to the observation position. The calculation means may further calculate the turbulence intensity at the observation position caused by a wake generated by the positional relationship between the first wind turbine and a nearby n-th wind turbine (n is an integer value of 2 or greater), and the fatigue load of the first wind turbine. The generating means may generate the second data by removing the trend component from the first data observed by a wind observation device capable of measuring wind speed by utilizing the Doppler effect caused by reflection of a laser beam irradiated in multiple directions toward particles moving through the atmosphere, and the calculating means may calculate the turbulence intensity based on the first data, the second data, and third data representing the time course of wind speed for each wind direction at a predetermined observation position observed separately by a wind observation tower near the observation position. The calculation means may further calculate the turbulence intensity at the observation position based on the first data, and calculate the turbulence intensity based on the third data and a difference between the turbulence intensity based on the first data and the turbulence intensity based on the second data. The generating means may further generate fourth data by applying a reduction rate of the turbulence intensity of the second data relative to the first data to the third data, and the calculating means may further calculate the turbulence intensity based on the fourth data. The generating means may further generate fifth data by interpolating the fourth data based on the statistical value of the reduction rate and the third data, and the calculating means may further calculate the turbulence intensity based on the fifth data. The present invention also provides an information processing device comprising: a generating means for generating second data by removing trend components in a predetermined time unit from first data representing the time course of wind speed for each predetermined wind direction at a predetermined observation position; a calculating means for calculating turbulence intensity at the observation position based on the second data; and an output means for outputting the turbulence intensity for each wind direction and each wind speed. The present invention also provides an information processing method comprising the steps of: generating second data by removing trend components in predetermined time units from first data representing the time course of wind speed for each predetermined wind direction at a predetermined observation position; calculating turbulence intensity at the observation position based on the second data; and outputting the turbulence intensity for each wind direction and each wind speed. The present invention also provides a program for causing a computer to perform the following functions: generating second data by removing trend components in predetermined time units from first data representing the time course of wind speed for each predetermined wind direction at a predetermined observation position; calculating turbulence intensity at the observation position based on the second data; and outputting the turbulence intensity for each wind direction and each wind speed. [Effects of the Invention]

[0007] According to the present invention, it is possible to improve the accuracy of prediction of turbulence intensity at a planned installation site of a wind turbine used for wind power generation. [Brief explanation of the drawings]

[0008] [Figure 1] 1 is a diagram illustrating an example of the overall configuration of an information processing system to which the present embodiment is applied. [Figure 2] 2 is a diagram illustrating an example of a hardware configuration of a management server that configures the information processing system of FIG. 1. FIG. [Figure 3] FIG. 2 illustrates an example of a functional configuration of a control unit of the management server. [Figure 4] 10 is a flowchart showing an example of the flow of processing up to calculation of the fatigue load of the first wind turbine, among the processing of the management server. [Figure 5] 10A to 10C are diagrams showing a specific example of a process for generating second data by removing a trend component from first data. [Figure 6] FIG. 10 is a diagram showing a specific example of calculated turbulence intensity. [Figure 7] 1. FIG. 4 is a diagram showing a specific example of a table listing turbulence intensity for each wind direction and wind speed, which is output to a user terminal constituting the information processing system of FIG. [Figure 8] 2 is a diagram showing specific examples of the sizes of a wind turbine, a mast, and a wind observation device that constitutes the information processing system of FIG. 1. FIG. [Figure 9] FIG. 10 is a diagram showing a specific example of a wake. [Figure 10] 1A is a diagram showing a specific example of a method for calculating turbulence intensity at an observation position from first to third data, and FIG. 1B is a diagram showing a specific example of wind speed standard deviation in second time units calculated by the calculation formula in FIG. [Figure 11] 10A and 10B are diagrams showing a specific example of interpolation of missing data from lidar observations. [Figure 12] FIG. 10 is a diagram showing a specific example of interpolation of missing LIDAR observation data. [Figure 13] 10 is a graph showing the verification results of the fourth data. [Figure 14] 10 is a graph showing the verification results of the fifth data. DETAILED DESCRIPTION OF THE INVENTION

[0009] Hereinafter, embodiments of the present invention will be described in detail with reference to the accompanying drawings. <Configuration of information processing system> FIG. 1 is a diagram showing an example of the overall configuration of an information processing system 1 to which the present embodiment is applied. The information processing system 1 is configured by connecting a management server 10, a wind condition observation device 30 which is a mobile device for observing wind conditions, a mast terminal 50 installed on a wind condition observation tower (hereinafter referred to as a "mast") which is a structure for observing wind conditions, and a user terminal 70 via a network 90. ​​The network 90 is, for example, a LAN (Local Area Network), the Internet, etc.

[0010] (Management Server 10) The management server 10 constituting the information processing system 1 is an information processing device that serves as a server for managing the entire information processing system 1. The management server 10 acquires observation data related to wind conditions observed by a wind condition observation device 30 installed at a location where wind condition observation is performed (hereinafter referred to as an "observation site"). The wind condition observation device 30 in this embodiment is a Doppler LIDAR that irradiates a laser in multiple directions toward particles moving in the atmosphere and observes wind conditions using the Doppler effect caused by the reflection of the laser. Hereinafter, observation data related to wind conditions observed by a Doppler LIDAR serving as the wind condition observation device 30 will be referred to as "LIDAR observation data."

[0011] The LIDAR observation data observed by the wind condition observation device 30 includes observation data on wind conditions in the vertical direction at the observation location. Specifically, the LIDAR observation data includes data (hereinafter referred to as "first data") that represents the time course of wind speed for each wind direction at each position (hereinafter referred to as "observation position") set at a predetermined altitude from the ground of the observation location into the sky. The first data includes wind speed for each wind direction at the observation location recorded every predetermined first hour. In this embodiment, the "first hour" is one second or approximately one second. Note that, since the observation location and the planned installation site of the wind turbine usually do not coincide, the LIDAR observation data is corrected according to the distance between the observation location and the planned installation site of the wind turbine, etc. The following description is based on the assumption that the LIDAR observation data is data that has been corrected to correct the discrepancy between the observation location and the planned installation site of the wind turbine.

[0012] The management server 10 generates data (hereinafter referred to as "second data") by removing trend components of a predetermined time unit from the first data included in the acquired LIDAR observation data. In this embodiment, the "predetermined time unit" refers to a time unit that is predetermined as a second time that is longer than the first time. In this embodiment, the first time is one second or approximately one second, which is an instantaneous time, whereas the second time is a non-instantaneous unit of 10 minutes. The method by which the management server 10 generates the second data will be described later.

[0013] Based on the generated second data, the management server 10 calculates the turbulence intensity, which is the strength of the disturbance in the wind speed at the observation location, for each wind direction and wind speed. The disturbance in the wind speed at the observation location can be caused by the weather, topography, etc. of the observation site, or by wake. "Wake" refers to the effect of attenuation of the wind speed and disturbance of the inflow wind caused by the rotation of the blades of the nth wind turbine (n is an integer greater than or equal to 2) that has already been installed or is planned to be installed near the observation site or a wind turbine (hereinafter referred to as the "first wind turbine") that is planned to be installed near the observation site. The method by which the management server 10 calculates the turbulence intensity at the observation location will be described later.

[0014] The management server 10 acquires information about the first wind turbine whose fatigue strength is to be confirmed (hereinafter referred to as "first wind turbine information") sent from the user terminal 70. The first wind turbine information includes information about the installation location of the first wind turbine, its specifications (e.g., height, blade size, etc.), and the surrounding environment. The management server 10 calculates the fatigue load of the first wind turbine based on the turbulence intensity at the observation position and the first wind turbine information.

[0015] Furthermore, management server 10 acquires information relating to the nth wind turbine (hereinafter referred to as "nth wind turbine information") transmitted from user terminal 70. The nth wind turbine information includes information such as the installation location, specifications (e.g., height, blade size, etc.) and surrounding environment of the nth wind turbine. Management server 10 calculates the fatigue load of the first wind turbine based on the turbulence intensity at the observation position caused by the weather, topography, etc. of the observation site, and the turbulence intensity at the observation position caused by the wake generated by the positional relationship between the first wind turbine and the nth wind turbine. The positional relationship between the first wind turbine and the nth wind turbine is identified from the first wind turbine information and the nth wind turbine information.

[0016] The management server 10 also acquires data on wind conditions observed by a mast near the observation location (hereinafter referred to as "mast observation data") via the mast terminal 50. The mast observation data includes observation data on wind conditions in the vertical direction at the observation location where the mast is installed. Specifically, the mast observation data includes data (hereinafter referred to as "third data") that represents the time course of wind speed for each wind direction at each observation location set at predetermined altitudes from the ground above the observation location. The third data includes data on wind speed for each wind direction in units of a second hour (10 minutes). Note that since the observation location where the mast is installed and the planned installation location of the first wind turbine usually do not match, in practice the mast observation data is corrected depending on factors such as the distance between the observation location where the mast is installed and the planned installation location of the first wind turbine. The following description is based on the assumption that the mast observation data is data that has been corrected to correct any mismatch between the observation location where the mast is installed and the planned installation location of the first wind turbine.

[0017] The management server 10, which has acquired the third data, can calculate the turbulence intensity at the observation position based on the first to third data. In this case, the management server 10 calculates the turbulence intensity at the observation position based on the difference between the turbulence intensity calculated based on the first data and the turbulence intensity calculated based on the second data, and the third data. Specifically, for example, the management server 10 calculates the turbulence intensity by generating fourth data by applying the reduction rate of the turbulence intensity of the second data relative to the first data (hereinafter referred to as the "second data reduction rate") to the third data.

[0018] LIDAR observation data is prone to missing measurements due to weather conditions such as rain or fog. Therefore, when there are missing measurements in the LIDAR observation data, the management server 10 calculates turbulence intensity by generating data (hereinafter referred to as "fifth data") that interpolates the fourth data based on the statistical value of the second data reduction rate and the third data. Examples of the statistical value of the second data reduction rate include the median and average value of the second data reduction rate. The configuration and processing of the management server 10 will be described in detail below.

[0019] (Wind Condition Observation Device 30) The wind observation device 30 constituting the information processing system 1 is a Doppler LIDAR. The LIDAR observation data observed by the wind observation device 30 includes first data. As described above, the first data includes wind speeds for each wind direction at the observation location recorded every first hour. Here, the "wind direction" refers to, for example, wind directions obtained by dividing the 360-degree azimuth of the observation location into 16 directions (hereinafter referred to as "16 wind directions"). The 16 wind directions are N (north), NNE (north-northeast), NE (northeast), ENE (east-northeast), E (east), ESE (east-southeast), SE (southeast), SSE (south-southeast), S (south), SSW (south-southwest), SW (southwest), WSW (west-southwest), W (west), WNW (west-northwest), NW (northwest), and NNW (north-northwest).

[0020] The wind condition observation device 30 transmits the LIDAR observation data for each observation position, including the first data, to the management server 10. The timing at which the wind condition observation device 30 transmits the LIDAR observation data to the management server 10 is not particularly limited. For example, the LIDAR observation data may be transmitted in real time, or may be transmitted every predetermined time (second, minute, day, week, month, year, etc.). Furthermore, the LIDAR observation data may be transmitted in response to an inquiry from the management server 10 regarding the provision of LIDAR observation data.

[0021] (Mast terminal 50) The mast terminals 50 constituting the information processing system 1 are information processing devices installed on each mast, and acquire mast observation data observed by the mast and transmit it to the management server 10. The mast observation data includes observation data on wind conditions in units of second hours (10 minutes) measured by each of a plurality of sensors installed on the mast. The sensors installed on the mast include a wind speed sensor, a wind direction sensor, a temperature sensor, a humidity sensor, and a barometric pressure sensor, and the measurement results of wind speed, wind direction, temperature, humidity, barometric pressure, etc. measured by these sensors are acquired as mast observation data. The mast observation data includes third data.

[0022] There is no particular limitation on the timing at which the mast terminal 50 transmits the mast observation data to the management server 10. For example, the mast observation data may be transmitted in real time, or may be transmitted at predetermined intervals (seconds, minutes, days, weeks, months, years, etc.). Furthermore, the mast observation data may be transmitted in response to an inquiry from the management server 10 regarding the provision of mast observation data.

[0023] (user terminal 70) The user terminal 70 constituting the information processing system 1 is an information processing device such as a personal computer, smartphone, or tablet terminal operated by a user who uses the information processing system 1. The user terminal 70 transmits information input by the user to the management server 10. Examples of information input by the user and transmitted to the management server 10 include query information input to inquire of the management server 10 about data such as the second data, the turbulence intensity at the observation position, and the fatigue load of the first wind turbine.

[0024] Furthermore, the user terminal 70 acquires various types of information transmitted from the management server 10 and displays it on a display or the like. For example, the user terminal 70 acquires and displays data such as the second data, the turbulence intensity at the observation position, and the fatigue load of the first wind turbine transmitted from the management server 10 based on the inquiry information.

[0025] The above-described configuration of the information processing system 1 is merely an example, and it is sufficient that the information processing system 1 as a whole has the functions to realize the above-described processes. Therefore, some or all of the functions to realize the above-described processes may be shared or cooperated among the devices in the information processing system 1. For example, some or all of the functions of the management server 10 may be functions of any of the wind condition observation devices 30, the mast terminal 50, and the user terminal 70. Furthermore, some or all of the functions of the management server 10, the wind condition observation devices 30, the mast terminal 50, and the user terminal 70 that constitute the information processing system 1 may be transferred to another server (not shown). This facilitates processing by the information processing system 1 as a whole and also enables the processes to complement each other.

[0026] <Hardware configuration of management server 10> FIG. 2 is a diagram illustrating an example of a hardware configuration of the management server 10 that constitutes the information processing system 1 of FIG. The management server 10 has a control unit 11, a memory 12, a storage unit 13, a communication unit 14, an operation unit 15, and a display unit 16. These units are connected by a data bus, an address bus, a PCI (Peripheral Component Interconnect) bus, etc.

[0027] The control unit 11 is a processor that controls the functions of the management server 10 through the execution of various software such as an OS (operating system) and application software. The control unit 11 is configured, for example, by a CPU (Central Processing Unit). The memory 12 is a storage area that stores various software and data used for executing the software, and is used as a working area for calculations. The memory 12 is configured, for example, by a RAM (Random Access Memory).

[0028] The storage unit 13 is a storage area that stores input data for various software programs and output data from various software programs. The storage unit 13 is composed of, for example, a hard disk drive (HDD), a solid state drive (SSD), a semiconductor memory, etc. that are used to store programs and various setting data. The storage unit 13 is provided with databases that store various types of data. For example, the storage unit 13 stores a lidar DB 131 that stores lidar observation data, a second DB 132 that stores second data, a mast DB 133 that stores mast observation data, an evaluation DB 134 that stores data used to evaluate turbulence intensity, and a wind turbine DB 135 that stores first wind turbine information and n-th wind turbine information.

[0029] The communication unit 14 transmits and receives data to and from the wind condition observation device 30, the mast terminal 50, the user terminal 70, and external devices via the network 90. ​​The operation unit 15 is configured with, for example, a keyboard, a mouse, mechanical buttons, and switches, and accepts input operations. The operation unit 15 also includes a touch sensor that forms a touch panel integrally with the display unit 16. The display unit 16 is configured with, for example, a liquid crystal display or an organic EL (Electro Luminescence) display used to display information, and displays images, text data, and the like.

[0030] <Hardware configuration of the wind condition observation device 30, the mast terminal 50, and the user terminal 70> The wind condition observation device 30, the mast terminal 50, and the user terminal 70 may all have the same hardware configuration as the management server 10 shown in Fig. 2. That is, the wind condition observation device 30, the mast terminal 50, and the user terminal 70 may each have a control unit, memory, storage unit, communication unit, operation unit, and display unit similar to the control unit 11, memory 12, storage unit 13, communication unit 14, operation unit 15, and display unit 16 of the management server 10 shown in Fig. 2. For this reason, illustrations and descriptions of the hardware configurations of the wind condition observation device 30, the mast terminal 50, and the user terminal 70 will be omitted.

[0031] <Functional Configuration of the Control Unit 11 of the Management Server 10> FIG. 3 is a diagram illustrating an example of the functional configuration of the control unit 11 of the management server 10. As shown in FIG. In the control unit 11 of the management server 10, a management unit 101, an acquisition unit 102, a generation unit 103 as a generation means, a calculation unit 104 as a calculation means, and a transmission control unit 105 as an output means function.

[0032] The management unit 101 stores and manages various types of data in various databases provided in the storage unit 13 (see FIG. 2 ). For example, the management unit 101 stores and manages LIDAR observation data including the first data in a LIDAR DB 131. The management unit 101 also stores and manages the second data in a second DB 132. The management unit 101 also stores and manages mast observation data including the third data in a mast DB 133. The management unit 101 also stores and manages data used for evaluating turbulence intensity, such as the fourth and fifth data, in an evaluation DB 134. The management unit 101 also stores and manages the first wind turbine information and the nth wind turbine information in a wind turbine DB 135.

[0033] The acquisition unit 102 acquires various types of information transmitted to the management server 10. For example, the acquisition unit 102 acquires LIDAR observation data transmitted from the wind condition observation device 30. The first data of the LIDAR observation data includes wind speeds for each wind direction at the observation position recorded every first hour.

[0034] The acquiring unit 102 also acquires mast observation data transmitted from the mast terminal 50. The third data of the mast observation data includes wind speeds for each wind direction at the observation position recorded every second hour. The acquiring unit 102 also acquires first wind turbine information transmitted from the user terminal 70. The acquiring unit 102 also acquires nth wind turbine information transmitted from the user terminal 70. The acquiring unit 102 also acquires various types of inquiry information transmitted from the user terminal 70.

[0035] The generation unit 103 generates various types of information. For example, the generation unit 103 generates second data by removing a trend component in a second time unit from first data included in the lidar observation data managed by the management unit 101. The method for removing the trend component from the first data to generate the second data is not particularly limited. For example, the trend component may be extracted by analyzing the relationship between time and wind speed in the first data, and the trend component may be removed from the first data to generate the second data. Note that the analysis method used to extract the trend component from the first data is not particularly limited, and any analysis method, such as linear regression analysis, may be used.

[0036] The calculation unit 104 calculates the turbulence intensity for each wind direction and each wind speed at the observation position based on the second data generated by the generation unit 103. Specifically, the calculation unit 104 calculates the turbulence intensity at the observation position based on the wind speed standard deviation indicating the variation of the wind speed for each wind direction recorded every first hour relative to the average wind speed in every second hour.

[0037] Furthermore, the calculation unit 104 calculates the turbulence intensity at the observation position based on the first data managed by the management unit 101. Then, the calculation unit 104 calculates the turbulence intensity at the observation position based on the difference between the turbulence intensity calculated based on the first data and the turbulence intensity calculated based on the second data, and based on the third data managed by the management unit 101. Specifically, the calculation unit 104 calculates the second data reduction rate, and calculates the turbulence intensity at the observation position based on the second data reduction rate and the third data.

[0038] Furthermore, when there are missing data in the LIDAR observation data, the calculation unit 104 calculates the turbulence intensity at the observation position by interpolating the missing data in the LIDAR observation data based on the statistical value of the second data reduction rate and the third data. For example, when interpolating the missing data in the LIDAR observation data, the calculation unit 104 uses the median, which is an example of the statistical value of the second data reduction rate, thereby preventing the calculated turbulence intensity from being too low and the fatigue load from being underestimated.

[0039] Furthermore, calculation unit 104 calculates the fatigue load of the first wind turbine based on the calculated turbulence intensity at the observation position and the first wind turbine information. Furthermore, if an nth wind turbine is present, calculation unit 104 calculates the turbulence intensity at the observation position and the fatigue load of the first wind turbine, taking into consideration the second data generated by generation unit 103 and the wake that may occur due to the positional relationship between the first wind turbine and the nth wind turbine identified from the first wind turbine information and the nth wind turbine information. The "positional relationship between the first wind turbine and the nth wind turbine" is defined by, for example, the distance between the first wind turbine and the nth wind turbine, the number of the nth wind turbine, the direction of the nth wind turbine as seen from the first wind turbine, etc.

[0040] The transmission control unit 105 controls the transmission of various types of information via the communication unit 14 (see FIG. 2). For example, the transmission control unit 105 controls the transmission of the second data, fourth data, fifth data, etc. generated by the generation unit 103 to the user terminal 70. The transmission control unit 105 also controls the transmission of information calculated by the calculation unit 104, such as the turbulence intensity at the observation position and the fatigue load of the first wind turbine, to the user terminal 70.

[0041] <Management Server Processing Flow> Fig. 4 is a flowchart showing an example of the flow of processing up to the calculation of the fatigue load of the first wind turbine, among the processing performed by the management server 10. In the example shown in Fig. 4, it is assumed that the use of mast observation data is required to calculate the turbulence intensity at the observation position. When management server 10 receives LIDAR observation data including the first data from wind condition observation device 30 (YES in step 401), it acquires the received LIDAR observation data (step 402) and stores and manages the acquired LIDAR observation data in a database (for example, LIDAR DB 131 in FIG. 2) (step 403). On the other hand, if LIDAR observation data has not been received from wind condition observation device 30 (NO in step 401), management server 10 repeats step 401 until LIDAR observation data is received from wind condition observation device 30.

[0042] The management server 10 generates second data by removing the trend component in units of the second time from the first data included in the acquired lidar observation data (step 404). Specifically, the management server 10 generates second data by removing the trend component in units of 10 minutes from the first data.

[0043] When the first wind turbine information is transmitted from the user terminal 70 (YES in step 405), the management server 10 acquires the transmitted first wind turbine information (step 406) and stores and manages the acquired first wind turbine information in a database (for example, the wind turbine DB 135 in FIG. 2) (step 407). On the other hand, if the first wind turbine information has not been transmitted from the user terminal 70 (NO in step 405), the management server 10 repeats step 405 until the first wind turbine information is transmitted from the user terminal 70.

[0044] When the management server 10 receives mast observation data including the third data from the mast terminal 50 (YES in step 408), it acquires the received mast observation data (step 409) and stores and manages the acquired mast observation data in a database (e.g., mast DB 133 in FIG. 2) (step 410). On the other hand, if no mast observation data has been received from the mast terminal 50 (NO in step 408), the management server 10 repeats step 408 until mast observation data is received from the mast terminal 50.

[0045] When the nth wind turbine information is transmitted from the user terminal 70 (YES in step 411), the management server 10 acquires the transmitted nth wind turbine information (step 412) and stores and manages the acquired nth wind turbine information in a database (for example, the wind turbine DB 135 in FIG. 2) (step 413). On the other hand, if the nth wind turbine information has not been transmitted from the user terminal 70 (NO in step 411), the management server 10 proceeds to step 414, assuming that there is no nth wind turbine that can cause a wake.

[0046] The management server 10 calculates the turbulence intensity at the observation position (step 414). Specifically, the management server 10 calculates the second data reduction rate, and calculates the turbulence intensity at the observation position by interpolating missing second data based on the statistical value of the calculated second data reduction rate and the third data. Furthermore, if an nth wind turbine is present, the management server 10 further calculates the turbulence intensity at the observation position caused by a wake generated by the positional relationship between the first wind turbine and the nth wind turbine.

[0047] The management server 10 calculates the fatigue load of the first wind turbine (step 415). Specifically, the management server 10 calculates the fatigue load of the first wind turbine based on the turbulence intensity at the observation position caused by the weather, topography, etc. of the observation site, and the first wind turbine information. Furthermore, if an nth wind turbine is present, the management server 10 calculates the fatigue load of the first wind turbine based on the turbulence intensity at the observation position caused by the weather, topography, etc. of the observation site, the turbulence intensity at the observation position caused by the wake, the first wind turbine information, and the nth wind turbine information.

[0048] <Example> 5A to 5C are diagrams showing a specific example of a process for generating second data by removing a trend component from first data. A specific example of the first data is shown in Fig. 5(A). The first data shown in Fig. 5(A) is data that represents the passage of time (Time(s)) of wind speed (Wind speed (m / s)) for a certain wind direction at a certain observation location for each first hour (1 second or approximately 1 second) in units of a second hour (10 minutes (600 seconds)). The solid line L1 shows the change in wind speed for each first hour. In other words, the solid line L1 shows the instantaneous change in wind speed. The solid line L2 shows the average wind speed. The dashed lines L3 and L4 show the wind speed standard deviation.

[0049] Figure 5(B) shows a specific example of the trend component in the first hourly unit extracted from the first data in Figure 5(A). Solid line L5 is the trend component in the second hourly unit extracted by linear regression analysis of the relationship between time (Time(s)) and wind speed (Wind speed (m / s)) in the first data in Figure 5(A). Solid line L5 shows that the wind speed (Wind speed (m / s)) is approximately 12 (m / s) when time (Time(s)) is 0 (s) and approximately 20 (m / s) when time (Time(s)) is 600 (s) (10 minutes).

[0050] That is, the solid line L5 indicates that the wind speed (m / s) changed gradually by approximately 8 m / s over a non-instantaneous period of 10 minutes (600 seconds). As described above, non-instantaneous, gradual changes in wind speed are unlikely to result in fatigue loads on the wind turbine, even if the wind speed before and after the change is significantly different, because these changes can be absorbed by the wind turbine's inherent control functions. The wind speed trend in 10-minute (600-second) increments shown in Figure 5(B) is a non-instantaneous, gradual change in wind speed, and is unlikely to result in fatigue loads. For this reason, the second data shown in Figure 5(B) is generated by removing the trend component shown in Figure 5(B) from the first data shown in Figure 5(A).

[0051] FIG. 5(C) shows a specific example of the generated second data. The second data shown in FIG. 5(C) is obtained by removing the trend component shown in FIG. 5(B) from the first data shown in FIG. 5(A). That is, removing the trend component, solid line L5, shown in FIG. 5(B), from the solid line L1 in FIG. 5(A) results in the solid line L6 shown in FIG. 5(C). The dashed lines L7 and L8 in FIG. 5(C) represent the wind speed standard deviation. Comparing the wind speed standard deviation (dashed lines L3 and L4) shown in FIG. 5(A) with the wind speed standard deviation (dashed lines L7 and L8) shown in FIG. 5(C), the removal of the trend component reduces the range of variation. Removing the trend component, solid line L5, prevents excessive prediction of turbulence intensity.

[0052] FIG. 6 is a diagram showing a specific example of the calculated turbulence intensity. Figure 6 shows a specific example of data representing the relationship between wind speed, wind speed standard deviation, wind direction, and turbulence intensity at a certain observation location in units of 2 hours (10 minutes). Turbulence intensity is the value obtained by dividing the wind speed standard deviation by the wind speed. For example, at 0:00 AM on January 1, 20XX (20XX / 1 / 1 0:00), the wind speed is 10.5 m / s, the wind speed standard deviation is 1.5 m / s, the wind direction is 343°, and the turbulence intensity is 14.3%. The relationships between wind speed, wind speed standard deviation, wind direction, and turbulence intensity 10 minutes, 20 minutes, and 30 minutes after 0:00 AM on January 1, 20XX (20XX / 1 / 1 0:00) are shown in Figure 6.

[0053] FIG. 7 is a diagram showing a specific example of a table listing turbulence intensity for each wind direction and wind speed, which is output to the user terminal 70 constituting the information processing system 1 of FIG. Figure 7 shows a specific example of a table listing turbulence intensity at a certain observation location for wind directions in 30° increments, which divides the 360° azimuth into 12 sections, and for wind speeds in 1 m / s increments. For example, when the wind direction is 0° and the wind speed is 1 m / s, the turbulence intensity is 22.02%, and when the wind direction is 30° and the wind speed is 2 m / s, the turbulence intensity is 15.11%. Specific examples of turbulence intensity for other wind speeds are shown in Figure 7.

[0054] The table shown in Figure 7 shows turbulence intensity for each wind direction divided into 12 sections of 360 degrees, and is primarily used to evaluate turbulence intensity when the observation site has flat terrain, such as on plains or offshore. In contrast, when the observation site has complex terrain, such as in a mountainous region, an evaluation with more detailed divisions of wind direction is required. For this reason, although not shown, tables are generated that show turbulence intensity for each wind direction divided into 16 sections of 360 degrees, or for each wind direction divided into 32 sections of 360 degrees.

[0055] FIG. 8 is a diagram showing specific examples of the sizes of the wind turbine 200, the mast 500, and the wind condition observation device 30 that constitutes the information processing system 1 of FIG. In the mast 500, the results of observation of wind conditions by various sensors installed at a height h1 facing upward in the vertical direction are acquired as mast observation data. The typical height h1 of the observation position of the mast 500 is 58 meters. However, because wind turbines 200 have tended to become larger in recent years, the mast observation data at height h1 is often insufficient as observation data for confirming the fatigue strength of the wind turbine 200. For this reason, the fatigue strength of the wind turbine 200 is confirmed by also using LIDAR observation data from the wind condition observation device 30, which allows observation from an observation position higher than height h1.

[0056] For example, there may be cases where observation data required to confirm the fatigue strength of wind turbine 200 is an observation data from an observation position at a height of two-thirds or more of the height h2 of hub 241 of wind turbine 200. In this case, if height h2 of hub 241 exceeds height h4, which is 1.5 times the height h1 of the observation position of mast 500, then the mast observation data will be insufficient. Specifically, if height h1 is 58 meters, height h4 will be 87 meters. For this reason, mast observation data will be insufficient as observation data to confirm the fatigue strength of wind turbine 200 where height h2 of hub 241 exceeds 87 meters.

[0057] In this case, observation data (i.e., the fourth and fifth data described above) that also utilizes LIDAR observation data from wind condition observation device 30, which enables observation of wind conditions at observation positions above height h4, is used to confirm the fatigue strength of wind turbine 200. Wind condition observation device 30 emits laser 301 in multiple directions toward the sky above the observation location, making it possible to acquire LIDAR observation data from the observation position at height h3, where the tips of wind turbine 200's blades 242 are at their highest. Furthermore, unlike LIDAR observation data, mast observation data does not normally retain data on instantaneous changes in wind conditions. For this reason, LIDAR observation data is essential data for confirming the fatigue strength of wind turbine 200.

[0058] FIG. 9 is a diagram showing a specific example of a wake. As described above, when the nth wind turbine is installed near the first wind turbine, the disturbance in wind speed caused by the wake is taken into account when calculating the turbulence intensity at the observation position and the fatigue load of the first wind turbine. For example, Fig. 9 shows wind turbines 201 to 203 installed in that order from upwind to downwind of the incoming wind. In the example of Fig. 9, wind turbines 201 and 202 are the nth wind turbines, and wind turbine 203 is the first wind turbine.

[0059] 9, the incoming wind that flows into wind turbine 201 generates a wake due to the rotation of blade 211 of wind turbine 201, and the incoming wind including the wake flows into wind turbine 202. Furthermore, the incoming wind that flows into wind turbine 202 generates a wake due to the rotation of blade 221 of wind turbine 202, and the incoming wind including the wake flows into wind turbine 203.

[0060] That is, the incoming wind flowing into wind turbine 202, the nth wind turbine, includes a wake generated in the region between wind turbine 201 and wind turbine 202. Similarly, the incoming wind flowing into wind turbine 203, the first wind turbine, includes a wake generated in the region between wind turbine 202 and wind turbine 203. Therefore, when calculating the turbulence intensity at the observation position and the fatigue load of wind turbine 203, the wake generated in the region between wind turbine 202 and wind turbine 203 is taken into consideration. This makes it possible to calculate turbulence intensity and fatigue load that are more realistic. If the calculated fatigue load of wind turbine 203 is large, the fatigue load of wind turbine 203 can be reduced by increasing the distance between wind turbine 202 and wind turbine 202 to reduce the effect of the wake.

[0061] FIG. 10A is a diagram showing a specific example of a method for calculating the turbulence intensity at the observation position from the first to third data. FIG. 10A shows an example of a calculation formula for calculating the turbulence intensity at the observation position from the first to third data. In the calculation formula shown in FIG. 10A, "σ VL,det " is the wind speed standard deviation of the second data in the second time unit. VL" is the wind speed standard deviation of the first data in the second hourly unit. mast " is the standard deviation of the wind speed in the second hour unit of the first data. In other words, "σ mast,det " is "σ VL,det ", "σ VL ", and "σ mast " can be calculated from

[0062] FIG. 10(B) is a diagram showing a specific example of the wind speed standard deviation in the second time unit calculated by the calculation formula of FIG. 10(A). 10(B) shows the wind speed standard deviation of the first data included in the lidar observation data, the wind speed standard deviation of the second data, and the second data reduction rate. Also shown are the wind speed standard deviation of the third data included in the mast observation data, and the wind speed standard deviation of the fourth data obtained by multiplying the wind speed standard deviation of the third data by the second data reduction rate, in units of a second time (10 minutes).

[0063] For example, at 0:00 AM on July 24, 20XX (20XX / 7 / 24 0:00), the wind speed standard deviation of the first data is 0.36 (m / s), the wind speed standard deviation of the second data is 0.354 (m / s), and the second data reduction rate is 0.983 (%). The wind speed standard deviation of the third data is 0.282 (m / s), and the wind speed standard deviation of the fourth data is 0.277 (m / s). Specific examples of the wind speed standard deviation of the first data, the wind speed standard deviation of the second data, the second data reduction rate, the wind speed standard deviation of the third data, and the wind speed standard deviation of the fourth data for other time periods are shown in FIG. 10(B).

[0064] 11(A) and (B) and FIG. 12 are diagrams showing specific examples of interpolation of missing data from lidar observations. 11(A) shows data representing the third data, including wind speed, wind speed bin, wind direction, wind direction bin, and wind speed standard deviation, and the second data reduction rate, expressed in units of a second time (10 minutes). Here, a "wind speed bin" is a section of wind speed, and a "wind direction bin" is a division of wind direction. For example, at 12:10 AM on July 24, 20XX (20XX / 7 / 24 0:10), the third data wind speed is "1.921" (m / s), the wind speed bin is "0" (m / s), the wind direction is "120.85" (°), the wind direction bin is "ESE" (east-southeast), the wind speed standard deviation is "0.282" (m / s), and the second data reduction rate is "0.931" (%). Furthermore, 10 minutes later at 0:20 AM (20XX / 7 / 24 0:20), the wind speed of the third data is "2.096" (m / s), the wind speed bin is "2.5" (m / s), the wind direction is "118.25" (°), the wind direction bin is "ESE" (east-southeast), the wind speed standard deviation is "0.326" (m / s), and the second data reduction rate is "0.841" (%).

[0065] Fig. 11(B) shows a table listing the median values ​​of the second data reduction rates shown in Fig. 11(A) for each wind speed bin and each wind direction bin. In the table shown in Fig. 11(B), the wind speed bins (wind speeds) are 2.5 (m / s), 3.5 (m / s), 4.5 (m / s), 5.5 (m / s), 6.5 (m / s), 7.5 (m / s), 8.5 (m / s), 9.5 (m / s), 10.5 (m / s), 11.5 (m / s), 13 (m / s), 15 (m / s), 17 (m / s), 19 (m / s), 21 (m / s), 23 (m / s), and 25 (m / s), and there are 16 wind direction bins (wind directions).

[0066] 11(B), for example, when the wind speed bin is "2.5" (m / s) and the wind direction bin is "N" (north), the median value of the second data reduction rate is "0.945" (%). Also, when the wind speed bin is "2.5" (m / s) and the wind direction bin is "NNE" (north-northeast), the median value of the second data reduction rate is "0.933" (%).

[0067] As described above, when the second data reduction rate cannot be calculated due to missing LIDAR observation data, the missing data is interpolated. Specifically, for example, the median of the second data reduction rate at which the wind speed bin and wind direction bin of the mast observation data corresponding to the missing data match the wind speed bin and wind direction bin in the table shown in Figure 11(B) is substituted for the second data. In this way, the missing data is interpolated.

[0068] FIG. 12 shows a conceptual diagram of missing data interpolation. In the data shown in FIG. 12, the white areas represent observation data D1 with no missing data, and the hatched areas represent missing data Dm with missing data and interpolated data D2 for interpolating the missing data. As shown in FIG. 12, when comparing the third data included in the mast observation data with the first data included in the lidar observation data, there is more missing data Dm in the first data than in the third data. This is because, as mentioned above, the first data is easily affected by weather conditions such as rain and fog. Because the second data is the first data from which the trend component has been removed, the number of observation data D1 and missing data Dm is the same as the first data.

[0069] The fourth data is obtained by applying the turbulence intensity reduction rate based on the second data to the third data. The fourth data is generated based on the first to third data. Therefore, as shown in FIG. 12, the fourth data includes all missing data Dm of the first to third data. The interpolation method shown in FIGS. 11(A) and 11(B) above is used for the fourth data. As a result, fifth data is generated by interpolating the missing data Dm of the first and second data out of the missing data Dm of the fourth data.

[0070] <Verification> FIG. 13 is a graph showing the verification results of the fourth data. The graph shown in FIG. 13 has wind speed (m / s) on the horizontal axis and turbulence intensity on the vertical axis. The solid line L11 is special mast observation data observed at the mast every first hour (every second or approximately every second) as positive data for verification. The dashed line L12 is fourth data calculated by the method according to this embodiment. Point P1 is third data observed in units of 10 minutes, which is the second hour.

[0071] 13, the mast observation data (solid line L11) observed every first hour for verification purposes and the fourth data (dashed line L12) calculated by the method according to this embodiment are approximately the same. However, as mentioned above, since LIDAR observation data is prone to missing data, the fifth data is generated by interpolating the missing data.

[0072] FIG. 14 is a graph showing the verification results of the fifth data. The graph shown in Figure 14 is obtained by removing point P1, which represents the third data, from the graph shown in Figure 13 and adding a dashed-dotted line L13, which represents the fifth data. As mentioned above, the fifth data is calculated using the median of the second data reduction rate. This prevents the fatigue load from being underestimated. As a result, as shown in Figure 14, the fifth data (dashed-dotted line L13), in which missing data have been interpolated, is located slightly above the mast observation data (solid line L11), which was observed approximately every second for verification purposes. In other words, as shown in Figures 13 and 14, there is no particular safety issue in using the fifth data when evaluating the turbulence intensity at the observation position.

[0073] In summary, the information processing system 1 (see FIG. 1) according to this embodiment only needs to have the following configuration, and can take on a variety of different embodiments. That is, the information processing system 1 is an information processing system characterized by having a generation unit 103 (see Figure 3) of the management server 10 as a generation means for generating second data by removing the trend component in the first time unit from first data representing the time progression of wind speed for each predetermined wind direction at a predetermined observation position, a calculation unit 104 (see Figure 3) of the management server 10 as a calculation means for calculating the turbulence intensity at the observation position based on the second data, and a transmission control unit 105 (see Figure 3) of the management server 10 as an output means for outputting the turbulence intensity at the observation position for each predetermined wind direction and wind speed.

[0074] This allows the turbulence intensity at the observation position to be calculated based on the second data obtained by removing the trend component from the first data, and output for each wind direction and wind speed. As a result, excessive turbulence intensity is prevented from being calculated, improving the accuracy of predictions of turbulence intensity at the planned installation site of the first wind turbine used for wind power generation.

[0075] Here, the generation unit 103 may be characterized in that it extracts a trend component in the first time unit by analyzing the relationship between time and wind speed in the first data, and generates the second data by removing the trend component in the first time unit from the first data. In this way, a trend component in the first time unit is extracted by analyzing the relationship between time and wind speed in the first data, and the second data is generated by removing the extracted trend component from the first data. As a result, excessive turbulence intensity is prevented from being calculated, thereby improving the accuracy of prediction of turbulence intensity at a planned installation site for a first wind turbine used for wind power generation.

[0076] The generating unit 103 may also be characterized in that it generates second data by removing trend components in units of a second time period longer than the first time period from first data including wind speeds for each predetermined wind direction at an observation position recorded every first time period. This generates second data by removing trend components in units of a second time period longer than the first time period from first data including wind speeds for each wind direction at the observation location recorded every first hour. As a result, calculation of excessive turbulence intensity is suppressed, improving the accuracy of predictions of turbulence intensity at the planned installation site of the first wind turbine used for wind power generation.

[0077] The generating unit 103 may also be characterized in that it generates second data by removing trend components in units of 10 minutes as the second time from the first data recorded every second or approximately every second as the first time. This generates second data by removing trend components in units of 10 minutes as the second time from the first data recorded every second or approximately every second as the first time. As a result, calculation of excessive turbulence intensity is suppressed, thereby improving the accuracy of prediction of turbulence intensity at the planned installation site of the first wind turbine to be used for wind power generation.

[0078] The calculation unit 104 may also be characterized in that it calculates the turbulence intensity at the observation position based on the wind speed standard deviation, which indicates the variation of the wind speed for each predetermined wind direction recorded every first hour from the average wind speed in the second hour unit. This allows the turbulence intensity at the observation position to be calculated based on the wind speed standard deviation, which indicates the variation of the wind speed for each wind direction recorded every first hour relative to the average wind speed over the second hour. As a result, excessive turbulence intensity is prevented from being calculated, improving the accuracy of predictions of turbulence intensity at the planned installation site of the first wind turbine to be used for wind power generation.

[0079] The calculation unit 104 may further calculate, based on the turbulence intensity at the observation position, a fatigue load of the first wind turbine installed at a location corresponding to the observation position. This allows the fatigue load of the first wind turbine installed at a location corresponding to the observation position to be calculated based on the turbulence intensity at the observation position, which prevents excessive fatigue loads from being calculated, improving the accuracy of fatigue load prediction for the first wind turbine used in wind power generation.

[0080] The calculation unit 104 may further calculate the turbulence intensity at the observation position caused by a wake that occurs due to the positional relationship between the first wind turbine and a nearby n-th wind turbine (n is an integer value of 2 or more), and the fatigue load of the first wind turbine. This allows the turbulence intensity at the observation position due to the wake and the fatigue load on the first wind turbine to be calculated, making it possible to calculate turbulence intensity and fatigue load that are in line with the actual situation.

[0081] The generating unit 103 may also generate second data by removing a trend component in a second time unit from first data observed by a wind observation device 30 capable of measuring wind speed by utilizing the Doppler effect caused by the reflection of a laser beam irradiated in multiple directions toward particles moving through the atmosphere, and the calculating unit 104 may calculate the turbulence intensity at the observation position based on the first data, the second data, and third data representing the time course of wind speed for each predetermined wind direction at a predetermined observation position, which is separately observed by a mast near the observation position. This allows the turbulence intensity at the observation position to be calculated based on the first data included in the lidar observation data, the second data obtained by removing the trend component in second-hour units from the first data, and the third data observed separately by the mast.As a result, even if confirmation of the fatigue strength of the first wind turbine using only the lidar observation data is not permitted under the law, it becomes possible to confirm the fatigue strength of the first wind turbine using the lidar observation data and the mast observation data.

[0082] The calculation unit 104 may further calculate the turbulence intensity at the observation position based on the first data, and calculate the turbulence intensity at the observation position based on the difference between the turbulence intensity at the observation position based on the first data and the turbulence intensity at the observation position based on the second data, and the third data. This allows the trend component to be extracted from the third data based on the difference between the turbulence intensity based on the first data and the turbulence intensity based on the second data, making it possible to calculate the turbulence intensity based on the lidar observation data and mast observation data from which the trend component has been removed.

[0083] The generation unit 103 may further generate fourth data by applying a second data reduction rate, which is a reduction rate of the turbulence intensity of the second data relative to the first data, to the third data, and the calculation unit 104 may further calculate the turbulence intensity based on the fourth data. As a result, fourth data is generated by extracting the trend component from the third data using the second data reduction rate, and turbulence intensity is calculated based on the fourth data. As a result, it becomes possible to calculate turbulence intensity based on the lidar observation data and mast observation data from which the trend component has been removed.

[0084] The generating unit 103 may further generate fifth data by interpolating the fourth data based on the statistical value of the second data reduction rate and the third data, and the calculating unit 104 may further calculate the turbulence intensity based on the fifth data. This generates fifth data by interpolating the fourth data based on the statistical value of the second data reduction rate and the third data. As a result, even if there are gaps in the lidar observation data due to weather conditions such as rain or fog, the gaps can be interpolated using the mast observation data.

[0085] Furthermore, the management server 10 (see FIG. 3) as the information processing device according to this embodiment only needs to have the following configuration, and can take on a variety of different embodiments. That is, the management server 10 is an information processing device characterized by having a generation unit 103 as a generation means for generating second data by removing the trend component in the first time unit from first data representing the time course of wind speed for each predetermined wind direction at a predetermined observation position, a calculation unit 104 as a calculation means for calculating the turbulence intensity at the observation position based on the second data, and a transmission control unit 105 as an output means for outputting the turbulence intensity at the observation position for each predetermined wind direction and wind speed.

[0086] Furthermore, the information processing method according to this embodiment only needs to have the following configuration, and can take on a variety of different embodiments. That is, the information processing method includes the steps of: generating second data by removing the trend component in the first time unit from first data representing the time course of wind speed for each predetermined wind direction at a predetermined observation position; calculating the turbulence intensity at the observation position based on the second data; and outputting the turbulence intensity for each predetermined wind direction and wind speed.

[0087] The program according to this embodiment may have the following configuration, and may take various forms. In other words, it is a program that enables the management server 10 as a computer to realize the following functions: generating second data by removing the trend component in the first time unit from first data that represents the time progression of wind speed for each predetermined wind direction at a predetermined observation position; calculating the turbulence intensity at the observation position based on the second data; and outputting the turbulence intensity for each predetermined wind direction and wind speed.

[0088] <Other embodiments> Although the present embodiment has been described above, the present invention is not limited to the above-described embodiment. Furthermore, the effects of the present invention are not limited to those described in the above-described embodiment. For example, the configuration of the information processing system 1 shown in FIG. 1, the hardware configuration of the management server 10 shown in FIG. 2, and the functional configuration of the control unit 11 of the management server 10 shown in FIG. 3 are merely examples for achieving the object of the present invention and are not particularly limited. It is sufficient that the information processing system 1 of FIG. 1 is provided with a function that can execute the above-described processing as a whole, and the hardware configuration and functional configuration used to realize this function are not limited to the above-described examples.

[0089] Furthermore, the order of the steps of the processing of the management server 10 shown in Fig. 4 is merely an example and is not particularly limited. The processing is not limited to being performed in chronological order according to the order of the steps shown in the figure, and may be performed in parallel or individually, without necessarily being performed in chronological order. Furthermore, the specific examples shown in Figs. 5 to 12 are also merely examples and are not particularly limited.

[0090] Furthermore, in the above-described embodiment, a Doppler LIDAR is used as the wind condition observation device 30, but this is not limiting. Any device capable of observing the wind conditions at the observation location every first hour will suffice, and the device is not particularly limited to a Doppler LIDAR, and may be another device capable of observation using other techniques.

[0091] In the above embodiment, the first time period is one second or approximately one second, and the second time period is ten minutes, but the first and second times are not particularly limited. For example, the first time period may be a time period that can be considered instantaneous, less than one second or more than one second. The second time period may be a time period that can be considered non-instantaneous, less than ten minutes or more than ten minutes. [Explanation of symbols]

[0092] 1...information processing system, 10...management server, 11...control unit, 12...memory, 13...storage unit, 14...communication unit, 15...operation unit, 16...display unit, 30...wind condition observation device, 50...mast terminal, 70...user terminal, 101...management unit, 102...acquisition unit, 103...generation unit, 104...calculation unit, 105...transmission control unit, 90...network

Claims

1. a generating means for generating second data by removing a trend component in a predetermined time unit from first data representing the time course of wind speed for each predetermined wind direction at a predetermined observation position; a calculation means for calculating turbulence intensity at the observation position based on the second data; an output means for outputting the turbulence intensity for each of the wind directions and each of the wind speeds; An information processing system comprising:

2. the generating means extracts the trend component by analyzing the relationship between time and wind speed in the first data, and generates the second data by removing the trend component from the first data. The information processing system according to claim 1 .

3. the generating means generates the second data by removing the trend component in a second time unit longer than the first time from the first data including the wind speed for each wind direction at the observation position recorded every first time. The information processing system according to claim 1 .

4. the generating means generates the second data by removing the trend component in units of 10 minutes as the second time from the first data recorded every second or approximately every second as the first time. The information processing system according to claim 3 .

5. the calculation means calculates the turbulence intensity based on a wind speed standard deviation indicating a variation of the wind speed for each wind direction recorded for each first hour from an average wind speed for each second hour. The information processing system according to claim 3 .

6. the calculation means further calculates, based on the turbulence intensity at the observation position, a fatigue load of a first wind turbine installed at a location corresponding to the observation position. The information processing system according to claim 1 .

7. the calculation means further calculates the turbulence intensity at the observation position caused by a wake generated by a positional relationship between the first wind turbine and a nearby n-th wind turbine (n is an integer value of 2 or more), and a fatigue load of the first wind turbine. The information processing system according to claim 6.

8. the generating means generates the second data by removing the trend component from the first data observed by a wind observation device capable of measuring wind speed by utilizing the Doppler effect caused by reflection of a laser beam irradiated in multiple directions toward particles moving in the atmosphere; the calculation means calculates the turbulence intensity based on the first data, the second data, and third data that indicates the time course of wind speed for each wind direction at a predetermined observation position, the data being separately observed by a wind observation tower located near the observation position. The information processing system according to claim 1 .

9. the calculation means further calculates the turbulence intensity at the observation position based on the first data, and calculates the turbulence intensity based on the third data and a difference between the turbulence intensity based on the first data and the turbulence intensity based on the second data. The information processing system according to claim 8 .

10. the generating means further generates fourth data by applying a reduction rate of the turbulence intensity of the second data with respect to the first data to the third data; The calculation means further calculates the turbulence intensity based on the fourth data. The information processing system according to claim 9 .

11. the generating means further generates fifth data by interpolating the fourth data based on the statistical value of the reduction rate and the third data; The calculation means further calculates the turbulence intensity based on the fifth data. The information processing system according to claim 10.

12. a generating means for generating second data by removing a trend component in a predetermined time unit from first data representing the time course of wind speed for each predetermined wind direction at a predetermined observation position; a calculation means for calculating turbulence intensity at the observation position based on the second data; an output means for outputting the turbulence intensity for each of the wind directions and each of the wind speeds; An information processing device comprising:

13. generating second data by removing trend components in a predetermined time unit from first data representing the time course of wind speed for each predetermined wind direction at a predetermined observation position; calculating turbulence intensity at the observation position based on the second data; outputting the turbulence intensity for each of the wind directions and each of the wind speeds; An information processing method comprising:

14. On the computer, a function of generating second data by removing trend components in a predetermined time unit from first data that represent the time course of wind speed for each predetermined wind direction at a predetermined observation position; a function of calculating turbulence intensity at the observation position based on the second data; a function of outputting the turbulence intensity for each wind direction and each wind speed; A program to achieve this.

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